Transcript
Page 1: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

ACTUAL CONDITION AND CHARACTERISTICS OF SLOPE

FAILURE IN EAST TIMOR BY MULTIVARIATE

STATISTICAL ANALYSIS

By:

05KC051

Lourenco Soares

A thesis submitted to Department of Civil and Environmental

Engineering, Saitama University, Japan for the Requirements of

Master’s Degree

August 2007

Supervisor

Professor Hidehiko KAZAMA

Department of Civil and Environmental Engineering

Graduate School of Science and Engineering

Saitama University, JAPAN

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ACKNOWLEDGEMENT

At the outset, it is my duty to acknowledge with gratitude the generous help that I

have received from my advisor, Professor Hidehiko KAZAMA. He is responsible for

involving me in this master’s course in the first place. He taught me how to ask questions and

express my ideas. He showed me different ways to approach a research problem and the need

to be persistent to accomplish any goal. I also thank Mrs. Yumiko SHIRO and Mr. Masato

IWAMA for their strong support in making me acquainted with Japanese life style in the past

two years. I expresses with my deepest, heart felt gratitude to Mr. Kobayashi for being

helpful person.

Besides my advisor, I would like to thank the rest of my thesis committee: Professor

Kunio Watanabe and Assoc. Professor M. Osada, who asked me good questions, gave

insightful comments and reviewed my work on a very short notice. And most of all I would

like to express my heart felt thanks to all staffs in Geosphere Research Institute of Saitama

University (GRIS) which have direct and indirect value for finalizing this thesis.

I would like to pass my great respect and Special thanks to Japan International

Cooperation Agency (JICA) and Japan International Cooperation Center (JICE) for their

support in funding my tuition and living expenses through out my stay in Japan to pursue the

Master program in Saitama University, Japan smoothly. Especially, I would like to express

my heart felt thanks to Mr. Mizuki MATSUZAKI, Ms. Yuri OSAWA, Mrs. WATANABE

and Ms. Sayaka OSHIMI for their strong support, advice, suggestions, encouragement and

their kindness cooperation for helping me in every aspect of my study and my life in Japan.

Last, but not least, I thank my family (Amain sayang , Maun Du, Ina Noi, Alin Eqi),

the late my father”† Salvador Soares” and my mother “ Andreza Soares, for giving me life in

the first place, for educating me with aspects from both arts and sciences, for unconditional

support and encouragement to pursue my interests, even when the interests went beyond

boundaries of language, field and geography. My brothers (Maun Domingos, Enty, Rito,

Abes and Aje) and friends: for sharing experience of life and dissertation to me, for listening

to my complaints and frustrations, and for believing in me, most of all supporting.

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CONTENTS

Acknowledgement

Contents

List of figures

List of tables

Abstract

CHAPTER I

Introduction

1.1 Background of study ………………………………………………….. 1

1.2 Propose and scope of the study ……………………………………….. 2

1.3 Data collection and methodology of research ………………………... 4

CHAPTER II

Study Site Description and Literature review

2.1 Study site description ………………………………………………… 9

2.1.1 Geographical condition, location, and boundaries of study site.. 9

2.1.2 Topography ……………………………………………………. 13

2.1.3 Geology, landforms and soil ………………………………….. 14

2.1.4 Climate ………………………………………………………… 19

2.1.5 Vegetation ……… …………………………………………….. 24

2.2 Literature review ……………………………………………………… 31

CHAPTER III

Actual Condition, Characteristics and Distribution of Slope Failure in East Timor

3.1 Introduction ……………………………………………………………. 36

3.2 Characteristics and distribution of slope failure in East Timor ……….. 40

3.2.1 Lithology ……………………………………………………… 41

3.2.2 Vegetation …….. ……………………………………………… 44

3.2.3 Inclination angle of slope ………………………………………. 46

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3.2.4 Direction of slope ……………………………………………… 49

3.2.5 Landscape topography ………………………………………… 51

3.2.6 Elevation ………………………………………………………. 53

3.2.7 Slope width………. ……………………………………………. 54

3.2.8 Slope length …………………………………………………… 58

CHAPTER IV

Analyzing Method

4.1 Logistic regression analysis …………………………………………… 63

4.2 Independent variables and sampling …………………………………. 67

4.3 GIS application for slope failure mapping …………………………….. 70

CHAPTER V

Analysis Result

5.1 Introduction ……………………………………………………………. 72

5.2 All study site analysis result …………………………………………… 72

5.3 Specific site Analysis ………………………………………………….. 82

5.3.1 Bobonaro site …………………………………………………. 82

5.3.2 Cailaco site ……………………………………………………. 92

5.3.3 Zumalai site …………………………………………………… 100

CHAPTER

Conclussion and Future Subject

6.1 Conclusions…….…………………………………………………………. 109

6.2 Future Subject ……………………………………………………………... 110

References ……………………………………………………………………. 111

APPENDIX A: Physical Data of Slope Failure and Unfailure slope

APPENDIX B: Logistic Regression Analysis

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LIST OF FIGURES

FIGURE DESCRIPTION PAGE

1.1 Flow chart of research methodology 6

1.2 Slope failure location with aerial photograph 7

2.1 Boundary of study site 10

2.2 Study site and slope failure 11

2.3 Study site and unfailure slope 12

2.4 Topography of East Timor 14

2.5 East Timor geological map 17

2.6 physical types of East Timor 18

2.7 Areas prone of landslide and flooding in East Timor 18

2.8 Altitude and mean temperature correlation 20

2.9 Monthly distribution of rainfall in East Timor (based on data

from Ferreira 1965) 20

2.10 The amount of daily rainfall from July – December 2006

in Dare station 22

2.11 The amount of daily rainfall from July – December 2006

in Aileu station 23

2.12 The amount of daily rainfall from July – December 2006

in Betano station 23

2.13 Climate 24

2.14 Natural distribution of forest in East Timor 26

2.15 Actual forest covers 27

2.16 Firewood cut by community as o source of income and

used for cooking 28

2.17 Cutting and burning the forest by community 29

2.18 Sifting agriculture (slashes and burn agriculture) 29

2.19 Category of land cover in East Timor 31

3.1 Older landslide topography in East Timor 37

3.2 Older landslide topography in East Timor 37

3.3 Recent landslide topography in East Timor 38

3.4 Recent landslide occurred on cut slope alongside road

in East Timor 39

3.5 Surface failure on hill slopes of mountainous in East Timor 39

3.6 Surface failure on hill slopes of mountainous in East Timor 40

3.7 Lithology 44

3.8 Distribution of vegetation 46

3.9 Distribution of inclination angle of slopes failure 48

3.10 Distribution of direction of slope 50

3.11 Landscape topography 52

3.12 Distribution of elevation 54

3.13 Width of landslide 56

3.14 Width of surface failure 57

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3.15 Width of surface failure and landslide 58

3.16 Length of landslide 60

3.17 Length of surface failure 51

3.18 Length of surface failure and landslide mix 59

4.1 Flow chart of logistic regression analysis 69

4.2 Flow chart of Production of probabilities of slope failure maps

based on GIS techniques 71

5.1 Ranking of the top ten significant item and category based on

influence ratio in all study site 76

5.2 The top ten ranking of interaction term when combined with

other variable based on influence ratio in all study site 76

5.3 Observed groups and predicted probabilities of slope failure

by logistic regression analysis 80

5.4 Histogram of p redicting for probabilities of slope failure 81

5.5 Map of relative slope failure susceptibility 81

5.6 Ranking of the top ten significant item and category based on

influence ratio in Bobonaro site 85

5.7 The top ten ranking of interaction term when combined with

other variable based on influence ratio in Bobonaro site 86

5.8 Observed groups and predicted probabilities of slope failure

by logistic regression analysis 89

5.9 Histogram of predicting for probabilities of slope failure

in Bobonaro site 90

5.10 Ranking of the top ten significant item and category based on

influence ratio in Cailaco site 95

5.11 The top ten ranking of interaction term when combined with

other variable based on influence ratio in Cailaco site 96

5.12 Observed groups and predicted probabilities of slope failure

by logistic regression analysis 99

5.13 Histogram of predicting for probabilities of slope failure

in Cailaco site 100

5.14 Ranking of the top ten significant item and category based on

influence ratio in Zumalai site 103

5.15 The top ten ranking of interaction term when combined with

other variable based on influence ratio in Zumalai site 104

5.16 Observed groups and predicted probabilities of slope failure

by logistic regression analysis 107

5.17 Predicting for probabilities of slope failure in Zumalai site 108

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LIST OF TABLES

TABLE DESCRIPTION PAGE

2.1 Land use in East Timor, Indonesia government estimation 30

2.2 Land use, Alternative estimation, Saldanha 30

3.1 Density of slope failure in study site 41

3.2 Description of geological structures in each study area 39

3.3 Lithology 43

3.4 Distribution of vegetation 45

3.5 Distribution of inclination angle of slope failure 48

3.6 Distribution of direction of slope 50

3.7 Landscape topography 52

3.8 Distribution of elevation 53

3.9 Width of landslide 55

3.10 Width of surface failure 56

3.11 Width of the mix of surface failure and landslide 57

3.12 Length of landslide 59

3.13 Length of surface failure 60

3.14 Length of surface failure and landslide mix 51

4.1 Classification of predicted the probabilities of slope failure

from the logistic regression analysis 67

4.2 Categories of the independent variables 68

5.1 Classification table of cut value 0.50 73

5.2 Coefficient values and influence ratio of logistic regression

of each item and category in all study site 73

5.3 Cofficient values and influence ratio of logistic regression

of interaction term with other item and category in all study site 74

5.4 Classification of predicted the probabilities of slope failure

from the logistic regression analysis 79

5.5 Predicting for probability of slope failure 80

5.6 Classification table of cut value 0.50 in Bobonaro site 82

5.7 Coefficient values and influence ratio of logistic regression

of each item and category in Bobonaro site 82

5.8 Cofficient values and influence ratio of logistic regression

of interaction term with other item and category in Bobonaro

site 83

5.9 Predicting for probability of slope failure in Bobonaro site 90

5.10 Classification table of cut value 0.50 in Cailaco site 92

5.11 Coefficient values and influence ratio of logistic regression

of each item and category in Cailaco site 92

5.12 Cofficient values and influence ratio of logistic regression

of interaction term with other item and category in Cailaco site 93

5.13 Predicting for probability of slope failure in Cailaco site 99

5.14 Classification table of cut value 0.50 in Zumalai site 100

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5.15 Coefficient values and influence ratio of logistic regression

of each item and category in Zumalai site 101

5.16 Cofficient values and influence ratio of logistic regression

of interaction term with other item and category in Zumalai site 102

5.17 Predicting for probability of slope failure in Zumalai site 107

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ABSTRACT

East Timor has risk number of natural hazards. Each year, heavy seasonal rain falling on

steep slopes causes frequent flash flooding and slope failure, which are considered to be the

two major natural hazards in the country. Apart from their potential to cause casualties and

damage to rural communities, these events cause major disruption to the fragile road network,

isolating communities and even whole districts for a long duration. Slope failures (i.e.,

landslide and surface failure) in mountainous terrain often occur as a result of heavy rainfall,

resulting in the loss of life and damage to the natural environment. In this regard, slope

failure hazard assessment as well as identify the characteristics and distribution of slope

failure can provide much mitigation through proper project planning and implementation.

Propose of this study are to know actual condition and characteristics of slope failure and

to determine clearly the factors influencing of the slope failure occurrence in East Timor. The

factors that influent to the slope failure in study area may be categories in the intrinsic

variables that contribute for slope failure, such as geology, inclination angle of the slope,

vegetation, elevation, direction and landscape topography of slope.

Logistic regression analysis is a multivariate technique that considers several physical

parameters that may affect probability. This modeling is intended to describe the likelihood

of slope failure on a regional scale, and is very suitable for the assessment of slope failure

actual condition and its characteristics because the observed data consist of item and category

with a value of 0(absence of slope failure) or 1(presence of slope failure). The predicting and

assess of slope failure occurrence for the training samples in this analysis. If we have a model

that successfully distinguishes the two groups based on a classification cutoff value of 0.5.

Result analysis shown that the model produced a concordance rate of 90 % with the use

of 0.5 as a classification cutoff value. By examining this result to predict that’s factors

influencing slope failure, we can see what a different classification rule should be adopted

when applying the model analysis to each factor in the study area and obtain regression

model composed of significant variables. The influence of the interaction among factors

contributing for slope failure occurrence was examined. When the interaction term were

introduce, the proportion of the observed all items and category predicted as high influence

ratio increased by 1 to 4 times of individual category, which indicated a better prediction.

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The comparison of the results from the analysis including the interaction terms among

category and the individual category, the interaction term indicate that interactions among the

variables of category were found to be significant for predicting probability of slope failure.

From the result, slope failure would most possibly occur in area where cover by bare land

and grassland and the elevation ranges from 200m to 800m, the surface slopes is steep and

thin sedimentary rocks.

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CONTENTS OF APPENDIX

Appendix A : Physical data of slope failure and un-failure

A.1 Physical data of slope failure

A.1.1 Bobonaro site …………………………………………………. 117

A.1.2 Cailaco site ……………………………………………………. 122

A.1.3 Zumalai site …………………………………………………… 126

A.1.4 Atsabe site ……………………………………………………. 128

A.1.5 Maliana site …………………………………………………… 130

A.1.6 Ainaro site ……………………………………………………. 131

A.1.7 Hatolia site …………………………………………………… 132

A.1.8 Hatobuilico site ……………………………………………… 133

A.2 Physical data of unfailure slope

A.2.1 Bobonaro site …………………………………………………. 134

A.2.2 Cailaco site ……………………………………………………. 139

A.2.3 Zumalai site …………………………………………………… 143

A.2.4 Atsabe site ……………………………………………………. 145

A.2.5 Maliana site …………………………………………………… 147

A.2.6 Ainaro site ……………………………………………………. 148

A.2.7 Hatolia site …………………………………………………… 149

A.2.8 Hatobuilico site ……………………………………………… 150

Appendix B : Logistic regression analysis result

B.1 All study site ………………………………………………………. 151

B.2 Specific site

B.2.1 Bobonaro site ………………………………………………. 165

B.2.2 Cailaco site ………………………………………………… 178

B.2.3 Zumalai site ……………………………………………….. 189

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CHAPTER I

INTRODUCTION

1.1 Background of Study

East Timor is a rugged island with a narrow or non existent coastal plain along its

northern coast and a southern coastal plain that varies from less than a kilometers wide in

some areas to as much as 20 km in others. Highest mountain with a height of 2,963 meters is

the Tatamailau or Ramelau in the Ainaro district. Slopes are steep, with as much as 44% of

the country having a slope of 40% or more. Slopes this steep may need a zigzag path to climb.

The soils are limestone-dominated. Such soils are prone to erosion, particularly on steep

slopes and where vegetation cover has been degraded by poor agricultural practices or

deforestation. This is the case in many parts of East Timor where the natural vegetation has

been modified by human activity over centuries leaving sparse savannah woodland or

grassland in most areas. East Timor is dryer than most equatorial islands, receiving most of

its rainfall during the northwestern monsoon, which occurs from December to March.

Southern slopes receive additional rain during the shorter southeast trade winds period

between May and July.

East Timor has risk number of natural hazards. Each year, heavy seasonal rain falling on

steep slopes causes frequent flash flooding and slope failure, which are considered to be the

two major natural hazards in the country. Apart from their potential to cause casualties and

damage to rural communities, these events cause major disruption to the fragile road network,

isolating communities and even whole districts for a long duration..

East Timor has two climate seasons are wet and dry season. From November to April, the

country is risk of tropical cyclones and lesser tropical storms, which can cause coastal

flooding and wave damage. In the dry season, drought conditions exist in large parts of East

Timor. A delay in the onset of seasonal rains can become disastrous as fires can get quickly

out of control.

East Timor has a very fragile environment. It is particularly dried compared with other

parts of the region, and is prone to regular droughts. Deforestation combined with steep

slopes, thin soils and heavy seasonal rains have resulted in erosion and soil loss.

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The situation has been exacerbated by deforestation, which has become more substantial

during the last three decades. One of the country’s most valued forest resources is

sandalwood which has now been reduced to just a few stands due to of over-exploitation.

Another problem is that many rural communities rely on selling wood for fuel as source of

family income and as a result, have contributed to deforestation.

Geological hazards also threaten East Timor. Areas to the north of the island have

experienced earthquakes of up to 6.9 on the Richter scale within the last 10 years. These can

cause local tsunamis. A four-meter-high tsunami, caused by a major earthquake, struck the

north coast of Timor in 1995. In addition, other hazards exist, including major transport

accidents; urban fires and agricultural hazards. These risks are likely to increase as the nation

develops unless necessary precautions are made and regulations put in place.

Slope failures (i.e., landslide and surface failure) in mountainous terrain often occur as a

result of heavy rainfall, resulting in the loss of life and damage to the natural environment. In

this regard, slope failure hazard assessment as well as identify the characteristics and

distribution of slope failure can provide much mitigation through proper project planning and

implementation.

1.2 Propose and Scope of the Study

It is difficult to examine the natural hazard as well as slope failure hazard in East Timor

because of the lack of consistent data, however little data has been collected to provide this

study. The primary aims of this initial study are to identify the major influence factors for

slope failure in East Timor. Logistic regression analysis is a multivariate statistical analysis

has been used extensively at most of previously researcher to predict the factors influence to

the slope failures occurrences. The purpose of this study is to present a method that utilizes

and employs statistical analysis to define the physical parameters contributing to the

occurrence of landslides. This method allows a series of statistically meaningful and

independent variables to be included in the assessment of the analysis model. The procedure

is based on the actual slope failure cases and is therefore representative of failure conditions

and relatively objective. Logistic regression analysis describing in this study is to:

• To know the actual condition and characteristics of slope failure in East Timor

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• Identify clearly the factors that are related to slope failures,

• Estimate the relative contribution of factors causing slope failures, and

• Establish a relation between the factors and slope failures.

The scope of activities with developing and applying the logistic regression analysis in

this study consist of five main steps:

• Pre-selection of variables based on a slope failure distribution analysis;

• Selection of statistically significant variables by a P-value significance test;

• Logistic regression modeling with those variables that passed the significance test;

• Logistic regression modeling with significant variables including the interaction

terms; and

• Evaluation of the model results.

In the first step, a slope failure characteristics analysis is used to pre-select the variables

that are relevant for the regression. This analysis involves overlaying the variables of

category of slope failure occurrences and the variables of category of a factor (such as

lithology), then calculating the percentage of coverage of the slope failure occurrence on

each class for each input factor, such as slope inclination angle within elevation factor. By

comparing the slope failure distributions, a preliminary ranking of the variables can be

developed. Important variables will be considered in the following significance tests.

In the second step, the significance p-value of 0.05 is specified as the cut-off value to

choose the variable for further analyses and 0.10 is chosen as the value for elimination of

insignificant variables. The variables that passed the significance test can be entered into the

logistic regression modeling in the next step. After the steps of pre-selection and significance

test, we can know the total of the independent variables were selected for the regression

analyzing.

In the third step, the model is checked for its goodness of fit by entering a variable or

removing a variable. Following the SPSS procedures, 20 iterations are preferred to obtain

optimal models of analysis. The final suitable logistic regression analysis is based on the

variables presented in the final step of the statistical calculation in the SPSS program, and the

regression coefficients are obtained.

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In the fourth step, the interaction terms representing the interactions among variables are

entered into the logistic regression analysis. In particular, the interactions among variables

from six category factors (i.e., lithology, vegetation cover, slope aspect, elevation inclination

angle and landscape topography) are selected to form the interaction terms for the

regression. The interactions among two, three, and four variables at one time were tested.

Only significant interaction terms are retained for analyzing. When interaction terms are

introduced into the model, the ranking of the significance of some of the variables will

change. Some of the variables showing significance in the previous step may become

insignificant, and some of the interaction terms showing significance are added into the

model. After many tests with the interaction terms, the model that produces the best

prediction result is adopted as the final optimal model.

In the fifth step, the models obtained from above and the factors influence to the slope

failure occurrences generated from the models are evaluated. Slope failure probability values

between 0 and 1 at each unique-condition unit are obtained from the final regression.

1.3 Data Collection and Methodology of Research

Slope failure often occurs at specific locations under certain topographic and geologic

conditions. Therefore it is important to utilize existing data (history of the problem and data

review) in order to understand the topography, geology, and properties of similar slope

failure. It is also important to understand their relationship with meteorologist factors,

chronology of topographic change or erosion by rivers, earthquakes, and other factors which

may have a relationship with the slope deformation surrounding the study site prior to the

detailed investigation.

In this study, data collections to provide this research are:

• Aerial photograph with magnitude scale 1:13,000

• Topography map with magnitude scale 1:15,000

• geology map with magnitude scale 1:350,000

• Rainfall Data (July 2004 – December 2006)

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Investigations of Aerial photographs are used to understand the chronologic and

topographic changes over the country. Furthermore, in order to be able to effectively

interpret the phenomena related to slope failure. By utilizing aerial photographs, it is possible

to interpret landslide phenomena and warning signs, geology structure, topography and

distribution of vegetation type.

Topographic investigation is necessary to identify any changes in the site topography.

That can be accomplished by recognizing; 1) the overall topographic feature of the site;

2) understanding the topographic characteristics of the site slopes; and 3) estimating the

regional geologic structure of the site. Such methods include comparing the aerial

photographs of the site and vicinity taken prior to and after the sliding, and interpreting the

topographic maps and aerial photographs.

Geological map is necessary to investigate geologic structure, however to identify the

bedrock distribution, rocks types and rock mass engineering properties in the surrounding study

site.

Based on aerial photograph and topographic map in the study area, there are 506 number

of slope failures from the inventory. For each slope failures inventory, it includes information

such as location, slope geometry (slope inclination angle, direction, width and length),

geology factor (rocks types), vegetation cover (high tree, low tree, grassland and no

vegetation), landscape topography (valley, ridge and flat) and slope aspect (direction) are

used for actual condition and characteristics of slope failures analysis.

Considering the regional variations identified and data availability in the above, six

factors were considered in this study: geology factor, vegetation cover, slope gradient (i.e.,

slope inclination angle), elevation, landscape topography and slope aspect (i.e., direction).

Detail research methodology in this study has shown in Figure 1.1.

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Figure.1.1 Flow chart of research methodology

Start

Select Study Area and Detection of Slope Failure

- Topographic Map - Geological Map

- Aerial Photograph - Rainfall Data

Map of Locations Representing of the Selecting Area of Slope Failure and

Unfailure Slope by Random

- Lothology - Vegetation Cover - Elevation

- Slope Gradient - Slope Aspect - Landscape Topography

Extraction of Independent Variables for points representing of Slope Failure and

Unfailure

Multivariate Statistical Analysis by Logistic Regression Analysis

Stepwise of logistic Regression Analysis

Development of Logistic regression Analysis

Result of Analysis and Discussion

Verification of the probabilities and Susceptibilities of Slope failure mapping

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Figure 1.2 Slope failure locations with aerial photograph.

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A key assumption using this approach is that the potential (occurrence possibility) of

slope failure will be comparable to the actual frequency of slope failure. After the study area

was selected, slope failure areas were detected in the study area by investigation of Aerial

photograph. The maps of aerial photograph used were these from January 2000 (Figure 1.2),

after slope failure. This air photograph, in combination with logistic regression analysis result

and GIS was used to evaluate and predicted the probability of slope failure in the study area.

A GIS database has been developed using ArcGIS version 3.3 software. The slope failure

in the study area and the factors contributing for slope failure have been recorded and saved

as separate layers in the database. All the data layers were in vector format, transformed in

grids with cell size 30x30 meters.

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CHAPTER II

STUDY SITE DESCRIPTION AND LITERATURE

REVIEW

2.1 Study Site Description

2.1 .1 Geographical Condition , Location, and Boundaries, of Study Site

East Timor is approximately the eastern half of the island of Timor, and part of the Lesser

Sunda Island chain, distant from Australia by only 500 km. It is between longitudes 1270 22

and 1320 25” and latitude 80 17” and 100 22” with a general orientation of southwest to

northeast. The area of East Timor as a whole is only about 15,007 km2 and the coastline is

706 km. Timor’s boundaries are as follows:

• In the north, the boundary of Wetar Strait with Ombai Strait.

• In the east, the boundary with the Maluku Strait.

• In the south, the boundary with the Timor Sea.

• In the west, the boundary with Nusa Tenggara Timor, the eastern region of Indonesia.

In this study, the study site at the western part of East Timor. There are lies between

latitude 080 52’’ 30’’ and 09

0 15’’00’’ to South and longitude 125

0 15’’ 30’’and 126

0

15’’00’’to East, and has area about 1448 km2 with elevations ranging from 200m to 2100m.

The study area is mountainous area, which is also landslide prone, and is quite flat in the

south. The underlying bedrock is limestone, siltstone, sandstone, shale and conglomerate.

Most folds are developed in the western mountainous area and a thrust fault extends from

north to south of the study area. The study site are covering eight sub district in western part

of East Timor, there are Bobonaro, Cailaco, Zumalai, Atsabe, Maliana, Ainaro, Hatolia and

Hatobuilico (Figure 2.1, Figure 2.2 , and Figure 2.3 ).

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Boundary of study site

Atsabe

Cailaco

Maliana

Bobonaro

Hatolia

Zumalai

Hatobuilico

Ainaro

Firuge 2.1 Boundary of study site N

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Cailaco

Maliana

Atsabe

Bobonaro

Hatolia

Zumalai

Ainaro

Hatobuilico

Figure 2.2 Study site and slope failure

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Atsabe

Cailaco

Maliana Bobonaro

Zumalai

Ainaro

Hatolia

Hatobuilico

Figure 2.3 Study site and unfailure slope

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2.1.2 Topography

A mountain range runs from the east to the west of East Timor. The mountainous terrain

results in many watersheds and streams, making transportation very difficult. The land is

made up of limestone, coral, thick clayey soil, sand and a small amount of volcanic origin. In

East Timor there are seven mountains with heights over 2000m as seen in the following table.

The highest mountain with a height of 2,963 metres is the Tatamailau peak of the Ramelau

Range in the Ainaro district. Name of District Height Mountain Above Sea Level

1.Tatamailau Ainaro 2,963 metres 2.Sabiria Aileu 2,495 metres 3.Usululi Baucau 2,620

metres 4.Harupai Ermera 2,293 metres 5.Cablake Manufahi 2,495 metres 6.Laklo Manatuto

2,050 metres 7.Matebian Baucau 2,373 metres As a broad outline, the watersheds of East

Timor can be divided into two areas; northern and southern. Of the many rivers in East

Timor, the following rivers flow all year round; the Laklo river in the district of Manatuto,

the Seical river in Baucau district, the Bulobo, Marobo, Malibaka and Nunura rivers in

Bobonaro district, Gleno river in Ermera district, Karau Ulun in Manufahi district, the rivers

of Dilor, Uca, Uwetoko, Bebui and Irabere in Viqueque district, the Loes river in Liquica,

and the Tono river in Oecussi.

Overall the climate in East Timor is classified as tropical. The minimum temperature

range is 18-21ºC while the maximum temperature range is 26-32ºC. In the north (as far east

as Baucau) the rainy season begins in November and is usually accompanied by a westerly

monsoon; the months of May and October are months of change from dry to wet season. In

the east and the south the situation is different - the rainy season is at its height in April. The

dry season occurs during May, and the rainy season returns at the beginning of June until

August. When it is winter in Australia (August to October), sometimes the temperature in

East Timor can be as low as 18ºc. This is also true of the opposite scenario. When it is

summer in Australia, the temperature is high on the coast of East Timor, even in the rainy

season.

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Figure 2.4 Topography of East Timor (Source from Internet)

2.1.3 Geology, land forms, and soil

Timor is a continental fragment, not a volcanic island. The foundation is largely made up

of limestone and other sedimentary deposits. This differentiates it from its neighbors to the

north and west in the Sunda chain which are volcanic. It is theorized that Timor, in fact, is a

piece of the Australian geological plate which, separated from the mainland, has been pushed

into the Indonesian plate. (Monk et al. 1997:23) That it has been repeatedly uplifted and

submerged over the millennia accounts for the presence of coral layers in the soil at heights

of up to 2,000 meters above sea level. The erosion of these rocks is considerable.

The topography of East Timor is dominated by a massive central backbone of up to

3,000 meters, the Ramelau mountain range, which is dissected by deep valleys prone to flash

floods. Toward the northern side, the mountains extend almost to the coast without extensive

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plains. To the south, on the other hand, mountains taper off some distance from the sea

leaving a wide littoral plain, more propitious for agriculture.

The plain is 20 km and even 30 km wide running almost the length of East Timor and

widens at the eastern end. There are more perennial streams flowing to the southern coast

which allow for more agriculture and irrigation.

The Fuiloro plateau, in the far East, descends in altitude southwards, from 700 meters to

1500 meters. The slope is almost unnoticeable due to the large area, which may have been

the primitive lagoon of a big fossil atoll. Three other main planaltic formations surround it:

Nári in the north, Lospalos to the center-west and Rare to the south. Nestled in the mountain

range near the border with West Timor lies the low plateau of Maliana in what was once a

gulf. This area is better suited to irrigated agriculture than the rest of East Timor. As much as

44 percent of East Timor may have a slope of land of more than 40 percent. (Monk et al.

1997:52; Dick 1991) A slope of 40 percent is difficult to descend and may need a zigzag path.

Bierenbroodspot (1986 in Monk et al. 1997:107) suggested the following erodibilty

classification and appropriate uses for sloping land on Timor:

• Land with less than 17 percent slope tends to be suitable for cultivation provided

that any incipient soil erosion is controlled;

• Land between 17 percent and 30 percent is best used for grazing as soil erosion

cannot be controlled on such steep slopes under permanent or shifting cultivation;

• Land over 30 percent suffering from soil erosion is unsuitable for sustainable

agriculture and can require reforestation or conversion to suitable tree or perennial

cover crops.

Soils are ultimately the combination of base rock, topography, climate, vegetation and, to

some extent, the fauna which is present in any one place. Topography influences the

weathering, depth, erodibility, infiltration, and leaching of a soil. The major limitations to

plant production, and therefore to agriculture, are steep slopes and shallow soils. The outer-

arc islands, dominated by limestone, generally have lower, rounded hills with relatively

infertile, alkaline soils. Often the better soils are only on the alluvial deposits along the coasts

and in depressions such as lake or lacustrine basins surrounded by steeper, eroded land. Such

a lacustrine basin occurs in north central Timor (Maliana).

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Climate is perhaps the most important factor affecting the development of tropical soils

(Mohr et al. 1972). The most important climatic factor affecting tropical soil fertility and

structure is temperature. Up to 20°C, humus forms faster than it is broken down, enriching

the soils with nutrients and improving its structure (Chambers 1983). Above 20°C, and

particularly in hot, arid Conditions, bacteria decompose dead vegetation faster than it

accumulates, with the result that humus and fertility levels diminish. Thus, many tropical

soils have a low organic content and inherent low fertility. Tropical soils can maintain natural

fertility where climatic conditions favor the accumulation of humus. This occurs in

continuously moist soils found in wetter regions or higher altitudes; or when nutrients are

resupplied from outside the system, such as when a volcanic eruption spreads mineral-rich

ash deposits over the land. A second important climatic factor affecting fertility and structure

is the soil moisture regime, that is, the relationship between the length of the dry season and

total rainfall. Most of the area experiences a seasonal climate.

Prolonged droughts are followed by total annual precipitation which falls within a few

months or even days. This strongly affects the movement of salts and minerals through the

soil. Soils may bake hard and crack during a prolonged dry season. These conditions are

intensified in savannas, because the annual fires remove the supply of new organic matter

and, at the end of the rainy season with ground cover at a minimum, heavy rainfall may result

in surface runoff with potential for rill and gully erosion. The soils of the outer-arc islands

tend to have less clay and, as a result, lower water holding capacity (WHC) than the inner

volcanic arc islands (Carson 1989). Shallow, calcareous soils on raised coral reefs on islands

such as Timor have a limited WHC; Timor's soils are 20-30 cm deep over the island

(Mahadeva and Laksono 1976), except where there are lake deposits. The area with steep

slopes and thin soils is naturally biased toward high rates of erosion. Some local farmers have

an understanding for the fragility of the soil and have developed a sophisticated indigenous

method of soil conservation. In other areas, however, soil is being lost at high rates through

inappropriate land management. In particular, high losses of organic matter occur during and

shortly after clearing, and before establishment of suitable cover crops. Under such

conditions, intense bombardment of the soil surface by rain can quickly break down soil-

organo aggregates, thus permitting high erosion losses. In addition, surface temperatures

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increase on cleared land, thus increasing oxidation and loss of organic matter. As it is

difficult to restore organic matter, conservation measures such as early planting of cover

crops, incorporation of plant residues and erosion control should be strictly followed

(FENCO 1981).

Figure 2.5: East Timor geological map (Instituto Superior Tecnico, Portugal, 2000)

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Figure 2.6 Physical types of East Timor Source: Monk et al. 1997: Figure 2.10, originally from RePPProT 1989b

The physical types present in East Timor are 2 - tidal swamps; 4 - meander belts; 7 - Fan and lahars; 8 - terraces; 9 -

undulating rolling and hillocky plains; 10 - hills; and 11 - mountains. (Monk, et al. 1997:50; original RePPProT). A

revised draft map is in preparation for East Timor by the Geological Research and Development Centre, Bandung -GRDC.

The geology of East Timor was mapped previously by Audley-Charles (1968).

Fig.2.7 Areas prone of landslide and flooding in East Timor

(Source: Monk et al. 1997: Figure 2.13originally RePPProt 1989a.

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2.1.4 Climate

Knowledge of climatic conditions is of great importance for environmental management.

Climatic maps showing the amount of rainfall, including dry or drought periods indicate what

crops that will grow on an island or in a particular valley, or what pests may migrate into the

area if particular crops are cultivated. Much historical data exists for both temperature and

rainfall from the Portuguese colonial period. East Timor continues to have more stations for

measuring these and other factors than do the neighboring areas in Indonesia. Climate is a

function of the latitude, wind patterns bringing rain, rainfall volume, seasonality, and

intensity, soils, and the altitude above sea level. There is a clear correlation for East Timor

between altitude and average temperature and seasonal variations as shown by Felgas (Figure

2.8, reproduced in Monk 1997).

While the general climate in East Timor can be classified as hot (average temperature 210

C) and humid (70-80 percent), the geographic position and the topography is such that

climatic conditions differ substantially between mountainous regions and lower altitudes.

Even regions of the same altitude have very different climates when separated by high

mountains which act like a wall. Therefore, since topography is not equal to climate, a

system that separates lowlands, mountains, and plains is a useful first step to classifying

climactic conditions.

On the southern coast rainfall is high, with volumes of 2,000 mm or more per year spread

over a longer period of months. On the northern coast, at the same altitudes, rainfall could be

as little as 500-1,000 mm per year and concentrated in a shorter period of months. The

Indonesian government, (RePPProT) used the Schmidt and Ferguson method of counting and

comparing months with more than or less than 100 mm rainfall each and the Fontanel and

Chantefort method of combining this with temperature data.

The result is that the northern coast is basically seasonally dry except on the coast which

is permanently dry. The southern coast is permanently moist (Monk et al. 1997:75-77). A

permanently moist climate might allow for the growing of two annual harvests of crops, such

as rice. However, for the purpose of land use planning, a more detailed discrimination of

climate is necessary. (See sections on rainfall, vegetative cover, and agriculture, below.)

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Figure 2.8: Altitude and mean temperature correlation

Source: Monk et al. 1997: Figure 2.19, originally from Felgas 1956

Figure 2.9 Monthly distribution of rainfall in Timor Leste (based on data from Ferreira 1965).

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It is difficult to examine present climate risk in East Timor because of the lack of

consistent climate data. During the Portuguese period several stations measured

rainfall/climate data for varying periods from 1914 to 1975, but many of these records are

incomplete. It is unclear how much data was recorded during the period of Indonesian

control from 1975 to 1999. Since 1999 there have been no meteorological or hydrological

services available in East Timor. In November 2000, 50 rain gauges were distributed around

the country by the Department of Agriculture and funded by AusAID, however little data has

been collected from these gauges (Ongoing monitoring and educational activities are

important to establish continuity in such programs). Automatic weather stations have been

installed at the main airports (Dili, Baucau and Suai) by the Australian Bureau of

Meteorology (Darwin). Weather or seasonal climate forecasts have only been used

sporadically by the National Disaster Management Office, and these were based on

information available from the internet. Furthermore, there are currently no means to

communicate this information to the users that require it. The Australian Bureau of

Meteorology will be providing weather forecasts for East Timor for as long as Australian

forces are present in the territory (see http://www.bom.gov.au/reguser/by_prod/aviation/).

As well as this lack of temperature and rainfall data, there is a lack of consistent data on a

range of climate-related processes like river runoff, tides, floods, and groundwater levels.

This lack of data makes it difficult to assess whether climate is changing in East Timor.

There is also insufficient data on which to base scenarios of future climate changes and its

impact on environmental and social systems. Nevertheless, some broad conclusions about

climate change in East Timor can be drawn and these will be discussed in the following

pages. East Timor is predominately influenced by the monsoon climate. There are two

distinct rainfall patterns: the Northern Monomodal Rainfall Pattern produces a 4-6 month wet

season beginning in December which affects most of the northern side of the country and

tapers to the East; and the Southern Bimodal Rainfall Pattern which produces a longer (7-9

month) wet season with two rainfall peaks starting in December and again in May which

affects the southern side of the country (Keefer 2000: 11). Rainfall can be broadly described

as being low to very low along the northern coast of East Timor (<1000mm/annum), low to

moderate throughout the central and elevated areas (1500-2000mm/annum), and relatively

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high (>2500mm/annum) in high altitude areas which are mostly in the west. In common with

most tropical locations, extremely heavy rainfalls occasionally occur over East Timor during

relatively short time intervals (Figure 2.9).

The general climatic conditions define two zones: northern areas and southern areas,

divided by mountains into:

• The northern area characterized by one rainfall peak within four to six months in the

wet season. The northern coastal areas have an average yearly rainfall from 500 to

1500 mm, while higher altitudes above 500 m receive abundant rainfall from 1 500 to

3 000 mm, an average of monthly rainfall from 50mm to 150mm (Figure 2.10).

• The southern areas characterized by two rainfall peaks that appear within seven to

nine months in the wet season. The first peak appears between December and

February and the second peak appears between May and June. The southern coastal

areas have an average annual rainfall from 1 500 to 2000 mm. The areas above 500 m

receive more abundant rainfall from 1 700 to 3 500 mm, an average of monthly

rainfall from 70 mm to 150mm (Figure 2.11 and 2.12).

The amount of daily rainfall in Dare station

0

20

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Intensity rainfall (mm/day)

Days

July-Dec. 2004 2005 2006

Figure 2.10 The amount of daily rainfall from July 2004 – December 2006

in Dare station

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The amount of daily ranifall in Aileu Station

0

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Intensity rainfall(mm/day)

Days

July -Dec. 2004 2005 2006

Figure 2.11 The amount of daily rainfall from July 2004 – December 2006

In Aileu station

The amount of daily rainfall in Betano station

0

20

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July-Dec. 2004 2005 2006

Figure 2.12 The amount of daily rainfall from July 2004 – December 2006

In Betano station

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Figure 2.13: Climate

Source: Monk et al. 1997: Figure 2.17, originally from RePPProT 1989a

2.1.5 Vegetation

The present vegetation cover is a combination of what could be there given the climate

and the particularities of each area, and anthropic actions of settlements, clearings,

agriculture, grazing, plantations, etc. This section speculates what the natural distribution of

forests and grasslands in East Timor would have been. It also assesses what is known of the

historical distribution of vegetation cover. It was noted previously that East Timor suffers

from an exceptionally dry climate, especially in the northern half. This condition directly

affects the likely historical distribution of forest. Monk suggests that classification of forests

in this area is particularly difficult because of the extreme influence of altitude and rainfall

patterns on forest types. These vary widely in small areas and along steep slopes. Not enough

work has been done on classification specifically for East Nusa Tenggara, Maluku and East

Timor. Figure 2.14 shows the types of forest which would be naturally occurring in eastern

Indonesia based on the number of dry months and annual rainfall.

According to the classification utilized in Monk et al. (1997), the natural vegetation for

East Timor would be various kinds of forest from evergreen in the mountains, especially the

southern slopes, to thorn forest along the northern coasts. Because of the influence of the

mountains on rainfall in the southern part of East Timor, by the 1950s rainforest originally

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occurring on the south escarpment of the Fuiloro limestone plateau had been extensively

replaced by secondary forest (Felgas 1956; van Steenis, un-publicized. in Monk et al.

1997:234). All land would be covered by different types of forest. Savanna and grassland are

assumed to be secondary vegetation (Monk et al. 1997:197).

This vegetation distribution would be before the indigenous people or the Portuguese

began to occupy the land. Monsoon forest, one of the most sensitive and vulnerable of the

tropical forest formations, is easily lost. The original monsoon forests of the dry regions have

been extensively replaced by savanna and grassland. Generations have repeatedly burnt the

dry forests for hunting and to accommodate shifting cultivation. (Monk et al. 1997:202)

When these forest types are disturbed, principally by burning, then secondary vegetation,

savanna or grasslands emerge. Figure 2.15 indicates there are very few areas of forest left.

Deforestation is not a phenomenon confined to the eastern part of the island. When Crippen

International carried out a detailed survey of forests in West Timor, it found that the majority

of this part of the island was also covered with savannas and grasslands (Crippen

International 1980 vol.14 - Forestry). It is also worth noting that when RePPProT used

Landsat images from 1972 to 1986 to update aerial photos and coverage estimates, there

were no aerial photos available for East Timor.

Official numbers exist for the location and distribution of forest types on East Timor but

these are of uncertain accuracy because of both their source and their age. Up-to date

information gathered from remote sensing satellites or aerial photography, and actual in-the-

field observations will be of critical importance. Monk et al. (1997: 211) concludes: "The

accuracy of historical data available for East Timor is even more difficult to assess as no

official survey seems to exist.” Felgas (1956) quotes estimates by Ruy Cinatti, the head of

the Portuguese Timor Agricultural 17 and Veterinary Technical Department indicating that

there were 74 km2

of mangroves; 2149 km2 of primary forest and 2646 km2 of savanna and

grassland.

This suggests that closed forest cover in East Timor rose from 16 percent in the 1950s to

29 percent in the 1980s. It is, however, not likely that such extensive reforestation occurred

either naturally or through human activity. This casts doubt on any forestcover figures for

East Timor. Scrub forest, savannas, and grasslands areas now make up as much as three

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fourths of the land. Various grasses, xerophytic shrubs in the driest areas, and other shrubs

are present including evergreens, small trees, and vines interspersed with stands of casuarina,

eucalyptus, bamboo, acacia, or even palms. (Metzner 1977:104-114) Although much

anecdotal information on the savannas exists, detailed quantitative descriptions are lacking.

There are three ecological descriptions including two prepared by consultancy companies on

West Timor (ACIL Australia Pty. 1986m; Crippen International 1980F).

Figure 2.14: Natural distribution of forest in East Timor Note: A = Evergreen rain forest; B= Semi-evergreen rain forest; C= Moist

deciduous forest; D= Dry deciduous forest;E= Thorn forest

Source: Monk et al 1997: Figure 4.4

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Figure 2.15 Actual forest Source: Monk et al. 1997: Figure 4.5 Based on data and maps from Collins et al.

1991 with permission from N.M.Collins of World Conservation Monitoring Centre;

The National Forestry Inventory Project, from the Directorate General of Forest

Inventory and Land Use Planning and Information System Development Project for

the Management of Tropical Forests; RePPProT 190b; K.A. Monk pers. obs.

The main consequences of deforestation are loss of genetic resources and increased risk

of erosion and flash floods resulting from bare hillsides. Even before the era of Portuguese

colonization, the original forest area of East Timor was shrinking as agriculture expanded

through plantations or household production. Particularly in a landscape not endowed with

fertile soils and regular and bountiful rainfall, the productivity of newly cleared lands quickly

falls and farmers are forced to burn and clear new lands.

Particularly in a landscape not endowed with fertile soils and regular and bountiful

rainfall, the productivity of newly cleared lands quickly falls and farmers are forced to burn

and clear new lands. If this occurs before the soil is entirely exhausted, the area will quickly

return to a secondary forest lacking the species and complexities of the primary forest.

In 1994, the GOI estimated actual land use (Table 2.1). The term “light forest lands” is

used for much of the shrub or savanna. Saldanha, (1999) describes a forest component

distinct from the majority shrubs (Table 2.2). As many as 70,000 hectares of forest were

burned in the last decade by official estimates but some analysts believe that the real number

is higher (Gomes 1999; 65).

There is not adequate information on the actual extent and conditions of the various

forests and forest types given the deforestation that has occurred in recent years. From the

time of the first settlers on the island there has been shifting cultivation with negative but not

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disastrous consequences. However, in recent years with the high increase in population in

certain areas, there is increased pressure on the land. Many Timorese have been displaced to

more marginal lands and their former lands occupied by migrant farmers whose practices

may not be adapted to Timorese conditions. The situation has been exacerbated by

deforestation, which has become more substantial during the last three decades. One of the

country’s most valued forest resources is sandalwood which has now been reduced to just a

few stands due to of over-exploitation. Another problem is that many rural communities rely

on selling wood for fuel as source of family income and as a result, have contributed to

deforestation (Figures 2.14, Figure 2.15 and Figure 2.16).

Figure 2.16 Firewood cut by community as a source of income and used for cooking.

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Figure 2.17 Cutting and burning the forest

Figure 2.18 Sifting agriculture (slashes & burn agriculture)

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Tabel 2.1 Land use in East Timor, 1994, Indonesia government estimated

Land use %

Human settlement 1

Irrigated rice field 3

Non-irrigated rice field 3

Plantation 3

Mixed framing 2

Light forest 76

Bush land 9

Lakes, ponds, swamps 0

Critical land 0

Others 1

East Timor is a comparatively small but mountainous territory, extending roughly 300 km in

length and 100 km at its widest point. Estimates of the extent of forest cover over East Timor are

notoriously variable. One respected study using LandSat imagery established a figure of 41 per cent

for the eastern half of the island, with just 29 per cent as closed forest; this figure was adopted by the

Indonesian government, which recorded forest cover as 40.6 per cent.3 These totals cover a wide range

of forest types, including predominantly open and mixed savanna along the drier northern coast and

hinterland, extensive eucalyptus and moist upland forests in the central highlands and semi deciduous

monsoon and tropical lowland forest blocks along the southern coast and hinterland (Figure 2.19)

Source: Brahmana and Emmanuel, 1994

Table 2.2 Land Use, Alternative estimation

Land use % Village 1

Rice paddies 3

Rain fed paddies 4

Plantation rice paddies 1

Mix plantation 1

Homogeneous mix 8

Shrubs 81

Forest 1

Swamps, lakes 0

Roads, rivers 1

Source: Saldanha, 1999

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Figure 2.19 Category of land cover in East Timor

(Modified version of map produced by GIS unit, Ministry of Agriculture and Fisheries and Japan International

Cooperation Agency [JICA], Dili, East Timor,2001)

2.2 Literature Review

A slope failure (i.e., landslide, surface failure, debris flow, rockfall and erosion) is define

by Cruden (1991) for the working party on world slope failure inventory, as “a movement of

a mass of rock, earth or debris down a slope”. Varnes (1978) indicated that slope movement

would be a better comprehensive term as it does not infer process. His definition is “a

downward and outward movement of slope forming materials under the influence of gravity”.

In both the mining and civil aspects of engineering, slope failures can take lives and negate

all the hard design and development processes involved in completion of a project. Slope

failures can occur at any time of the year and sometimes can happen without any obvious

warning signs. They can range from sinkholes to rockslides or avalanches. There are many

effective ways to prevent of slope failures but uncertainties about surrounding environmental

conditions that may cause a slope failure must be investigated to find the proper way to

handle the potential problem.

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From the aerial photo investigation in the study area, the slope failures were mainly

landslide and surface failure, and most landslide features is subdivides in recent and older

landslides. Cruden and Varnes, 1996 defined that Landslide described as “recent” have

distinct features, clearly define boundaries and have moved in the past several years. They

include active, suspended, and dormant earth flows and earth slides. Older landslides have

hummocky topography, muted features, and indistinct boundaries. This category includes

dormant, relic, and ancient earth flows and earth slides. Data on recent and older landslides

have been used to develop the landslide hazard analysis in this study. These landslides are

predominantly shallow failures with basal failure planes in the soil or weathered bedrock.

Although deeper earth and rock slides also occur, such deep landslides overlap areas with

shallow landslides.

Slope failures have caused large numbers of casualties and huge economic losses in

hilly and mountainous areas of the world. In tropical country like East Timor where heavy

rainfall occasionally occurred and high temperatures around the year, cause intense

weathering and formation of thick soil and weathered rock profile. With these set of climate

and geological condition, combined with other causative factors, slope failure is one of the

most destructive natural disasters in East Timor. Each year, a number of major slope failures

were reported in East Timor, involving fill and cut of natural slopes, which results in death of

people and have posed serious threats to settlements and structures that support transportation.

Most of these slope failure occurred on natural slope and cut slopes or embankments

alongside roads in mountainous areas.

Richard Dikau at al. (1996) stated as the probability of slope failure changes, due to

changing climate or increasing human activity it becomes more important to recognize the

potential event as well as geomorphologies and geology and these can be catalogued,

classified and mapped. A primary task, therefore, is to develop a manual of such indicators

and mapping techniques, providing a basic understanding to slope failure recognition.

However, potential sites that are slope failure-prone should therefore be identified in advance

to reduce such damage. In this regard, actual condition, distribution and characteristics of

slope failure will be known to provide much of the basic information essential for hazard

mitigation through proper project planning and implementation.

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Slope failure hazard was defined by Varnes (1984) as the probability of occurrence of a

potentially damaging slope failure phenomenon within a specified period of time and within

a given area. The factors that determine the slope failure hazard of an area may be grouped in

two categories: (1) the intrinsic variables that contribute to slope failure occurrence, such as

geology, slope inclination angle, slope aspect, elevation, soil geotechnical properties,

vegetation cover, and a long-term drainage patterns; and (2) the extrinsic variables that tend

to trigger slope failure occurrence, such as heavy rainfall, and earthquakes (Wu and Sidle

1995); Atkinson and Massari 1998). Obviously, the probability of slope failure occurrence

depends on both the intrinsic and extrinsic variables. However, the extrinsic variables may

change over a very short time span, and are thus very difficult to estimate. If extrinsic

variables are not taken into account, the term of “actual condition, characteristics and

distribution slope failure” could be employed to define the likelihood of occurrence of a

slope failure event. The spatial distribution of the intrinsic variables within a given area

determines the spatial distribution of relative slope failure occurrences in that region (Carrara

and others 1995). A variety of techniques, such as heuristic, statistical, and deterministic

approaches, has been developed to predicted probabilities of slope failure occurrences. In

heuristic approaches, expert opinions are used to estimate slope failure potential from data on

intrinsic variables. They are based on the assumption that the relationship between

probability of slope failure occurrences and the intrinsic variables are known and are

specified in the model of analysis. A set of variables are then entered into the analysis model

to estimate probability of slope failure occurrence (Niemann and Howes 1991; Anbalagan

1992; Pachauri and Pant 1992; Atkinson and Massari 1998). One problem with the heuristic

models is that they need long-term information on the slope failure and their causal factors

for a similar geo-environmental condition or for the same site, and these are, in most cases,

not available. Statistical analysis models involve the statistical determination of the

combinations of variables that have led to slope failure occurrence in the past. Quantitative or

semi-quantitative estimates are then made for areas currently free of slope failure, but where

similar conditions exist.

Logistic regression analysis is one of the multivariate statistical analysis models, is useful

for predicting presence or absence of a outcome based on values of a set of predictor

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34

variables.Klein-baum (1994), stated that the advantage of logistic regression analysis over

other multivariate statistical technique, including multiple regression analysis and

discriminant analysis, is that the dependent variable can have only two values an event

occurring or not occurring, and that predicted values can be interpreted as probability

because they are constrained to fall in the interval between 0 and 1.

Mark and Ellen (1995) used logistic regression to predict the sites of rainfall induced

shallow landslides that initiate debris flows in San Mateo County, California. In this study,

the dependent variable is a binary variable representing of the slope failure or un-failure of

slopes.

Recently, there were studies on slope failures hazard evaluation using GIS, and many of

these studies have applied probabilistic methods (Rowbotham and Dudycha 1998; Guzzetti et

al. 1999; Jibson et al. 2000; Luzi et al. 2000; Parise and Jibson 2000; Rautelal and Lakhera

2000; Baeza and Corominas 2001; Lee and Min 2001; Temesgen et al. 2001; Clerici et al.

2002; Donati and Turrini 2002; Lee et al. 2002a,b; Rece and Capolongo 2002; Zhou et al.

2002; Chung and Fabbri 2003; Remondo et al. 2003; Lee and Choi 2003c; Lee et al. 2004b).

The logistic regression method has also been applied to slope failure hazard mapping

(Atkinson and Massari 1998; Dai et al. 2001; Dai and Lee 2002; Ohlmacher and Davis 2003).

There are other methods for hazard mapping, such as the deterministic (or safety factor)

approach used by Gokceoglu et al. (2000); Romeo (2000); Carro et al. (2003); Shou and

Wang (2003), and Zhou et al. (2003). Fuzzy logic and artificial neural network methods have

also been applied in various case studies (Ercanoglu and Gokceoglu 2002; Pistocchi et al.

2002; Lee et al. 2003a, b; Lee et al. 2004a).

To represent the distinction quantitatively, logistic regression analysis were used. For this

analysis, the calculated and extracted factors were mapped to a 30-m-resolution grid. The

raster data were converted for the statistical program used. Then, using the logistic regression

analysis models, the spatial relationships between the slope failure location and each slope

failure-related factor, such as geology, vegetation cover, slope gradient (i.e., slope inclination

angle), elevation, landscape topography and slope aspect (i.e., direction), were analyzed in

the statistical program, and a formula of slope failure occurrence possibility was extracted

using the relationships. The formula was used for calculating the probability of slope failure

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35

occurrence, which was mapped to each grid cell. Finally, the susceptibility and probabilities

occurrence map was verified using known slope failure locations and success rates were

calculated (Chung and Fabbri 1999) for quantitative verification. In this study, GIS software,

ArcView 3.3 and statistical software, SPSS 10.0, were used as the basic analysis tools for

spatial management and data manipulation.

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36

CHAPTER III

ACTUAL CONDITION, CHARACTERISTICS AND

DISTRIBUTION OF SLOPE FAILURE IN EAST TIMOR

3.1 Introduction

According to aerial photograph investigation, the actual slope failure distribution will

established in this study are landslide, surface failure and mix of landslide and surface failure.

From the aerial photo investigation in the study area most landslide features is subdivides in

recent and older landslides (Figure 3.1, Figure 3.2, and Figure 3.3). Cruden and Varnes, 1996

defined that Landslide described as “recent” have distinct features, clearly define boundaries

and have moved in the past several years. They include active, suspended, and dormant earth

flows and earth slides. Older landslides have hummocky topography, muted features, and

indistinct boundaries. This category includes dormant, relic, and ancient earth flows and

earth slides. Data on recent and older landslides have been used to develop the landslide

hazard analysis in this study. These landslides are predominantly shallow failures with basal

failure planes in the soil or weathered bedrock. Although deeper earth and rock slides also

occur, such deep landslides overlap areas with shallow landslides.

In East Timor, slope failures are common in the mountainous areas and in many regions.

The high occasional rainfall, steep slopes, high weathering rates and slope material with a

low shear resistance or high clay content are often considered the main preconditions for

mass movement in East Timor, turning it in an inherent susceptible area of slope failure.

The main causal factors for slope failure in highlands, as found in international literature,

can be divided into preparatory and triggering causal factor (Glade and Crozier, 2004).

Preparatory causal factors, i.e. factors making slopes susceptible to movement over time

without actually initiating it, often reported for this region include the increasing population

pressure with slope disturbance and deforestation as a consequence and the reduction in

material strength by weathering. Triggering causal factors on the other hand can be seen as

external stimuli responsible for the actual initiation of mass movements. The triggering

causal factors in the region can be earthquakes, excessive rainfall events and human

disturbance such as slope excavation and terracing, inconsiderate irrigation and water leakage.

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37

Figure 3.1 Older landslide topography in East Timor (Source:Prof. H. Kazama documentation,August 2005)

Figure 3.2 Older landslide topography in East Timor (Source:Prof. H. Kazama documentation, August 2005)

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38

Figure 3.3 Recent landslide topography in East Timor (Source:Prof. H. Kazama documentation,August 2005)

In many regions of the East Timor highlands, a clear insight into the local causes for

mass movement is lacking. Therefore, the search for region-specific solutions is hampered.

In East Timor, slope failure i.e., landslides, surface falure, erosion, and rock fall are common

in the mountainous areas of all districts but so far no systematic scientific research has been

conducted on this topic. Western part of East Timor, situated on the southwestern foot slopes

of the mountainous of Tatamailau (Ainaro), Sabiria (Aileu), Harupai (Ermera), Atubuti

(Bobonaro) is the most sensitive area for slope failure in East Timor. As a broad outline, the

watersheds of East Timor can be divided into two areas; northern and southern. Of the many

rivers in this study site, the following rivers flow all year round; the Bulobo, Marobo,

Malibaka and Nunura rivers in Bobonaro , Gleno river in Ermera,ladibau in Hatolia, Aimera

in Cailaco, Belulik in Ainaro and Mola in Zumalai.

Mass movements associated with intense rainstorms are reported to have occurred

sporadically in mountainous since the twentieth century but the increase in fatalities and

losses as a consequence of the enormous population growth draws attention to the

phenomenon nowadays. By studying the causal factors for slope failure in these mountainous

areas of western part of East Timor, this study tries to contribute to the restricted knowledge

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39

on slope failure in East Timor. After a brief introduction of the study area and the spatial

distribution and characteristics of its landslides, the preconditions, preparatory and triggering

causal factors for mass movement affecting slope failure will be discussed with attention to

their spatial variation.

Figure 3.4 Recent landslide occurred on cut slopes alongside road in East Timor

(Source:Prof. H. Kazama documentation,August2005)

Figure 3.5 Surface failure on hill slopes of mountainous in East Timor (Source:Prof. H. Kazama documentation, August 2005)

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40

3.6 Surface failure on hill slopes of mountainous in East Timor

As state in above, According to aerial photograph investigation, the actual slope failure

distribution will established in this study are landslide, surface failure and mix of landslide

and slope failure. The distribution and characteristics of slope failure in East Timor has

shown in Table 3.1 to Table 3.13 and Figure 3.7 to Figure 3.18 .

3.2 Characteristics and Distribution of Slope Failure in East Timor

Two types of slope failures were identified based on aerial photograph and

topography map are surface failure and landslide. This study covers four of topographic

and air photograph sheets, and 506 number of slope failure and 506 number of unfailure

slope with area 1448 km2 are already mapped in the region. A significant number of these

slope failures were reactivations of old slope failures. There are density of the distribution

of slope failure in East Timor will describe in Table 3.1, and show that landslide and

slope failure are common in East Timor with highest density in Bobonaro , Cailaco and

Zumalai site, and moderately density in Hatolia and Atsabe site and the lowest density in

Maliana, Ainaro and Hatobuilico study site. Types of slope failure occurred in East

Timor dominantly by landslide 56% with density 0.28 Number/km2 ,surface failure are

37% with density 0.16 Number/km2 and mix of landslide and surface failure are 7% with

density 0.08 Number/km2.

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41

Table 3.1 Density of slope failure in study site

Area Type and density of slope failure Site

(Km2) Landslide Density

(N/km2)

Surface

failure

Density

(N/km2)

Mix Density

(N/km2)

Total

density

(N/km2)

Bobonaro 259 88 0.34 61 0.24 18 0.07 0.64

Cailaco 88 93 1.06 30 0.34 10 0.11 1.51

Zumalai 150 42 0.28 33 0.22 0 0 0.50

Atsabe 89 21 0.24 12 0.13 6 0.07 0.44

Maliana 75 16 0.21 15 0.20 0 0 0.41

Ainaro 385 5 0.01 18 0.05 0 0 0.06

Hatolia 255 13 0.05 7 0.03 0 0 0.08

Hatobuilico 147 5 0.03 13 0.09 0 0 0.12

Total 1448 283 0.28 189 0.16 34 0.08 0.35

3.2.1 Lithology

Lithology exerts a fundamental control on the geomorphology of a slope failure. The

nature and rate of geomorphological processes, including the slope failures process, is

partially on the lithology and weathering characteristics of the underlying materials. Based

on the East Timor geological map with 1:350,000- scale solid and superficial geological map

covering the study area were used to identify the geological groups, with each group

comprising units of broadly similar lithology. For analysis, the groups were further

reclassified into three categories of geological materials with similar engineering properties.

They are: sedimentary rocks with a few volcanic and igneous rocks, Sedimentary rocks and

littoral deposit and Sedimentary rocks and a few metamorphic rocks and volcanic rocks.

Detail of lithology analysis for this study are categories in five dominant lithology

based on geological map with attributes of geology in study area, namely: Sedimentary rocks

(Sr), Littoral deposit rocks (Ld), Metamorphic rocks (Mr) Igneous rocks (Ir) and volcanic

rock (Vr). Description of geological structures in each study area has shown in Table 3.2 and

Table 3.3, and Figure 3.7. It can be seen that many number of slope failure relatively highest

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42

density in sedimentary rocks and littoral deposit rocks and lowest in igneous rocks,

metamorphic rocks and volcanic rocks. The Tatamailau, Sabiria , Harupai , Atubuti

mountains hills located in the study area are several hundreds to two thousands meters high

in elevation is the most sensitive area for slope failure. There are composed of Miocene to

Pliocene sedimentary rocks such as sandstones, limestones and siltstones, in part associated

with a small amount of Mesozoic volcanic rocks. These sedimentary rocks and associated

volcanics make up the Bobonaro and Lolotoe formation that are arranged chronological in

other. Like many of the mountain ridges in the western region, these mountains hills

correspond to folded structures of anticlines and synclines, and are elongated toward the

north – northeast direction following the fold axes.

Table 3.2 Description of geological structures in each study area Study Area Age Lithology General Lithological

Description Bobonaro

Tertiary Pliocene

and

Miocene

Bobonaro

Complex

and

Lolotoe

Formation

Sedimentary

Rocks and a few

of Volcanic and

Igneous rocks

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock ; doleritic lava, volcanic

breccia, tuff, green sandstone,

metagabro a,d metadiorite

Cailaco Tertiary Pliocene

and

Miocene

Bobonaro

Complex

and

Viqueque

Formation

Sedimentary

rocks and littoral

deposit.

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock; alternating

conglomerate, conglomerate

sandstone, sandstone, a lot of

foraminifera in marl and sandstone

Zumalai Palaozoic

and

Mesozoic

Miocene

And

Permian

Bobonaro

Complex,

Cablaci

Limestone

and Cribas

Formation

Sedimentary

rocks and littoral

deposit

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock ;contains marine

foraminifera, Clastic limestone,

crustaline, fine coarse grained,

shale, claystone, siltstone and

micaceous quarts sandstone

Atsabe Tertiary

and

Mesozoic

Miocene

and

middle to

Jurassic

Bobonaro

Complex,

Cablaci

Limestone

and

Wailuli

Formation

Sedimentary

rocks and littoral

deposit

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock ;contains marine

foraminifera, and also dominant by

sandstone, shale siltstone and

limestone.

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43

Table 3.2 ( …continued)

Maliana Tertiary Late to

Pleistocen

e and

Miocene

Viqueque

Formation

,

Bobonaro

Complex

and

Ainaro

Formation

Sedimentary

rocks and littoral

deposit

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock; alternating

conglomerate, conglomerate

sandstone, sandstone, a lot of

foraminifera in marl and

sandstone; mixture sand and clay

Ainaro Quaternar

y and

Mezosoic

Early

Miocene

and

middle to

Jurassic

Bobonaro

Complex,

Ainaro

Formation

and

Cablaci

Limestone

Sedimentary

rocks and littoral

deposit

Mainly composed by chaotic rock

with scaly matrix and blocks of

older rock ;contains marine

foraminifera, mixture sand and

clay

Hatolia Mezosoic Early

Jurassic

and late to

Jurassic

Wailuli

Formation

and Aileu

Formation

Sedimentary

rocks and a few

of Metamorphic

rocks and

volcanic rocks

Dominanted by sandstone, shale

silttone, limenstone; phylite, schist,

amphibolite, slate, metasandstone,

sandstone, shale and a few of

volcanic rocks

Hatobuilico Mesozoic Early

Jurassic

and late

Eosin

Wailuli

Formation

, Lolotoe

Formation

anf

Dartollu

Limestone

Sedimentary

Rocks and a few

of metamorphic

rock and

volcanic rocks

Dominanted by sandstone, shale

silttone, limenstone; doleritic lava,

volcanic breccia, tuff, green

sandstone, metagabro a,d

metadiorite

Table 3.3 Lithology

Lithology types and number of slopes failure

Site

Sedimentary

Rocks(SR)

Littoral

Deposit

Rocks(LR

)

Igneous

Rocks(IR)

Metamorphi

c

Rocks(MR)

Volcanic

Rocks(VR

)

Total

1. Bobonaro 101 19 25 0 22 167

2 Cailaco 87 46 0 0 0 133

3. Zumalai 57 18 0 0 0 75

4.Atsabe 21 18 0 0 0 39

5. Maliana 20 18 0 0 0 31

6.Ainaro 14 9 0 0 0 23

7. Hatolia 12 0 0 4 4 20

8. Hatobuilico 9 0 0 5 4 18

Total 321 121 25 9 30 506

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44

Figure 3.7. Lithology

3.2.2 Vegetation

Many studies have revealed a clear relationship between vegetation cover and slope

stability, especially for shallow landslides. Parameters, such as cohesion, internal friction

angle, weight of the soil and pore-water pressure, all tend to be substantially modified by the

presence of vegetation. Vegetation can both enhance effective soil cohesion due to root

matrix reinforcement and soil suction or negative water pressure through evapotranspiration

and interception. According to Selby (1993), tree-covered hillslopes are thought to increase

soil shear strength by about 60% depending on the tree type. Mehrotra et al. (1996) show that

landslide activity increases by up to 15% in those places where the original vegetation cover

Lithology of Slope Failure

0

10

20

30

40

50

60

70

80

90

100

110

SR LR IR MR VR

LithologyN

um

ber of Slo

pes F

ailure

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

Lithology of Unfailure Slopes

0

10

20

30

40

50

60

70

80

90

SR LR IR MR VR

Lithology

Num

ber of Un-failure

Slopes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

SR: sedimentary rocks

LR : Littoral deposit rocks

IR : Igneous rocks

MR: Metamorphic rocks

VR: Volcanic rocks

All Site

0

50

100

150

200

250

300

350

0 1 2 3 4 5 6

Lithology

Nu

mb

er

of

slo

pe

Un-failure Failure

SR LR IR MR VR

Failure

Unfailure

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45

has been removed or altered. In order to correlate vegetation cover with other factors

affecting slope failure, a vegetation classification was carried out in this study. The intention

was to discriminate between different vegetation cover types. Indeed, many studies have

pointed out that the degree of soil stability provided by vegetation decreases in the following

order: trees, shrub, grass and bare soil (Coppin and Richards, 1990).

The presence or absence of thick vegetation may affect slope failure. Due to the

characteristics of the study area, where land cover is not homogenous with the presence of

natural vegetation and for the purpose of this study and based on aerial photograph

interpretation these vegetation types were then simplified in to four types, namely woodland

or high tree (HT), scrublands or low tree (LT), grassland (G) and bare land or no vegetation

(NV).

To assess the effect of vegetation cover on the slope failure, the correlation between

vegetation type and number of slope failure is shown in Table 3.4 and Figure 3.8. It can be

seen that the number of slope failures on bare land and grassland is highest, and is lowest on

woodland and scrubland. This is in agreement with the fact that vegetation cover, especially

of a woody type with strong and big root systems, help to improve the stability of slopes.

Other cause of this agreement is many Timorese have been displaced to more marginal lands

and their former lands occupied by migrant farmers whose practices may not be adapted to Timorese

conditions. The situation has been exacerbated by deforestation, which has become more

substantial during the last three decades. Another problem is that many rural communities

rely on selling wood for fuel as source of family income and as a result, have contributed to

deforestation. Under such conditions, intense bombardment of the soil surface by rain can

quickly break down soil-organo aggregates, thus permitting slope failure.

Table 3.4 Distribution of vegetation Types of vegetation cover and Number of Slope failures Study Area

High Tree Low Tree Grassland No Vegetation

Total

Bobonaro 8 21 76 62 167

Cailaco 0 10 50 73 133

Zumalai 7 33 20 15 75

Atsabe 0 8 25 6 38

Maliana 1 8 10 12 31

Ainaro 4 5 10 4 23

Hatolia 0 6 11 3 20

Hatobuilico 1 3 2 12 18

Total 21 94 204 187 506

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46

Figure 3.8 Distribution of vegetation

3.2.3 Inclination angle of slope

Slope is the angle formed between any part of the surface of the earth and a horizontal

datum. It is the means by which gravity induces stress in the slope rocks, flux of water and

other materials; therefore, it is of great significance in hydrology and geomorphology. In fact,

slopes affect the velocity of both surface and subsurface flow and hence soil water content,

soil formation, erosion potential and a large number of important geomorphic processes. It

has been widely shown that landslides tend to occur more frequently on steeper slopes

(McDermid and Franklin, 1995; Cooke and Doornkamp, 1990). Slope failure tends to

Figure 3.3 Vegetation

Vegetation Cover of Slopes Failure

0

10

20

30

40

50

60

70

80

High Tree Low Tree Grassland No Vegetation

Vegetation

Nu

mb

er o

f S

lop

es F

ailu

re

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

Vegetation Cover of Unfailure Slopes

0

10

20

30

40

50

60

70

80

90

High Tree Low Tree Grassland No Vegetation

Vegetation

Num

ber of Un-F

ailure

slo

pes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

HT : High tree

LT : Low tree

G : Grassland

NV: No vegetation

All Site

0

50

100

150

200

250

300

0 1 2 3 4 5

Vegetation

Nu

mb

er

of

typ

e o

f s

lop

e

Un-failure Failure

HT LT G NV

Failure

Unfailure

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47

increase with slope angle but when the slope becomes near vertical, landsliding is scarce or

absent altogether. The reason is the lack of soil development and debris accumulation in such

topographic conditions (Selby, 1993; Derruau, 1983). A long slope may include sections that

can be affected by large movements originating further up the hills slope. The estimation of

the slope angle for this study was implemented using by topographic map investigation in

which slope is considered as the change in elevation over a fixed distance.

Inclination angle of slope is an essential component of slope stability and an important

control on slope failure. As slope inclination angle increases, the level of gravitation-induced

shear stress in the residual soil increases as well. Gentle hill slopes are expected to have a

flow frequency of slope failures because of generally lower shear stresses associated with

low inclination angle. In this study, inclination angle of slope has categories with ranges: 60 –

120, 12

0 – 18

0, 18

0 – 24

0, 24

0 – 30

0, 30

0 – 36

0, 36

0 – 42

0 and 42

0 - 48

0. In regional slope

failure (i.e., landslide and surface failure) susceptibility or hazard assessment, slope

inclination angle in terms of slope failure activity in taken into consideration as an

conditioning factor(Y. Duman et all, 2006). In the study site, the distribution number of

slope failure occurred with inclination angle of slope has shown in Table 3.5 and Figure 3.10 .

It can be seen that examination of the distribution of number of slope failure with

corresponding slope inclination angle ranges shows that most of slope failures with

inclination angle do have ranges increase in the 120 – 30

0 and gradually decrease in the

ranges 60 – 12

0 and 30

0 – 48

0. This is refection that steep natural slope with outcropping

bedrock and hence much higher shear strength may not susceptible to shallow landslide.

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48

Table 3.5 Distribution inclination angle of slope

Inclination angle and number of slope failure Site

60~12

0 12

0~18

0 18

0~24 24

0~30

0 30

0~36

0 36

0~42 42

0~48

0

Total

1. Bobonaro 17 38 38 37 23 13 1 167

2. Cailaco 19 42 34 18 17 3 0 133

3. Zumalai 5 26 17 5 12 9 1 75

4. Atsabe 9 6 8 6 6 3 1 39

5. Maliana 3 8 6 3 6 5 0 31

6. Ainaro 0 1 4 4 4 8 2 23

7. Hatolia 2 4 4 5 3 2 0 20

8. Hatobuilico 0 3 2 3 8 2 0 18

Total 55 128 113 81 79 45 5 506

Figure 3.9 Distribution of inclination angle of slopes

Slope Inclination Angle of Slopes Failure

0

5

10

15

20

25

30

35

40

45

6~12 12~18 18~24 24~30 30~36 36~42 42~48

Slope Inclination angle

Nu

mb

er

of

Slo

pe

s F

ail

ure

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

Slope Inclination Angle of Unfailure Slopes

0

10

20

30

40

50

60

6~12 12~18 18~24 24~30 30~36 36~42 42~48

Slope Inclination angle

Num

ber of Un-failure

slo

pes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

(o)

(o)

Failure

Unfailure

All Site

0

25

50

75

100

125

150

175

200

0 2 4 6 8

Inclination angle

Nu

mb

er

of

slo

pe

s

Un-failure Failure

6~12 12~18 8~24 24~30 30~36 36~42 42~48

( o )

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49

3.2.4 Direction of Slope

Aspect is often expressed as a compass direction .The aspect of slope failures ( i.e., the

direction) has the potential to influence its physical properties and its susceptibility to slope

failure. The processes that may be operating include exposure to sunlight, drying winds,

rainfall, earthquake and groundwater behavior. Although, the relation between slope aspect

(i.e., direction) and mass movement has long been investigated, no general agreement exists

on the effect of the aspect on slope failure occurrence (Carrara et al. 1991). However, slope

aspect is related to the general physiographic trend of the area and/or the main precipitation

direction, and direction of the slope failure is roughly perpendicular to general physiographic

trend. Several researchers have reported a relationship between slope orientation and

landslide occurrence. For example, DeGraff and Romesburg (1980) point out that, to some

extent, aspect gathers the structural and organic basic conditions of a slope including fault

planes and climatic factors, respectively. It is reported by Lineback et al. (2001) that larger

numbers of landslides occur in the wetter north-facing aspects than in drier, south facing

aspects. Marston et al. (1998) report a similar finding and highlight that soil exposed on

south-facing slopes are subject to several wetting and drying cycles, thus increasing landslide

activity in the Himalayas.

The distribution of direction among the aerial photograph and topography maps show that

the general physiographic trend of the study site is East to West and an important part of

slope failures in most of study area was highest number on North – Northeast and Northwest

facing slope, indicating that natural terrain slope failures is more common on these slopes.

The frequency of slope failures was lowest on those slopes facing, south and west, while the

frequency of slope failures remained moderate on the east, southeast and southwest facing

slopes. The distribution of slope failures direction has shown in Table 3.5 and Figure 3.10.

Page 61: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

50

Table 3.6 Distribution of direction of slope

Direction and number of slope failure

Site N NE E SE S SW W NW

Total

1. Bobonaro 42 49 10 25 5 16 12 8 167

2. Cailaco 19 53 21 11 0 0 0 29 133

3. Zumalai 4 28 5 11 0 17 3 7 75

4. Atsabe 3 4 0 0 0 6 0 26 39

5. Maliana 0 0 9 7 2 10 0 3 31

6. Ainaro 0 2 0 1 0 0 3 17 23

7. Hatolia 1 0 0 5 7 3 4 0 20

8. hatobuilico 5 3 0 9 0 0 0 1 18

Total 74 139 45 69 14 52 22 91 506

Figure 3.10 Distribution of direction of slope

Direction of Slopes Failure

0

10

20

30

40

50

60

N NE E SE S SW W NW

Direction

Num

ber o

f S

lopes F

ailu

re

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

Direction of Unfailure Slopes

0

5

10

15

20

25

30

35

40

45

N NE E SE S SW W NW

Direction

Num

ber of U

nfa

ilure

Slo

pes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

N : North S : South

NE : Northeast SW : Southwest

E : East W : West

SE : Southeast NW : Northwest

All Site

0

50

100

150

0 2 4 6 8 10

Direction

Nu

mb

er

of

slo

pe

Un-failure Failure

N NE E SE S SW W NW

Failure

Unfailure

Page 62: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

51

3.2.5 Landscape topography

Landscape topographic represents a theoretical measure of the accumulation of flow at

any point within a river basin. The landscape topography can be thought of as an abstract

parameter to be used as a basis for estimating the local soil moisture status and thus slope

failure areas due to surface topographic effects on hydrologic response. Soil moisture plays

an important role in slope instability, particularly for shallow landslides and surface failure.

Water operation may be through the accumulation of rainfall, as an agent of weathering,

hydration of fine soils (i.e. clayey soils), undercutting of slopes and spontaneous liquefaction.

In fact, according to Lamb (1996), hallow landslides can occur on slopes when water from

precipitation infiltrates the soil and eliminates the suction and lowers the apparent cohesion.

Modeling water in soil slopes in extensive areas is a difficult task as soil water content is

governed by a number of factors, some of which are estimated from laboratory tests. Since

landscape topographic is intended to represent the topographic control on soil wetness, it is

considered in this study as an indirect measurement of soil water content. According to the

topographic map investigation in this study, the landscape topographic index three variables

are required, which are namely Valley (V), Ridge (R) and Flat (F).

We interpret the flat, ridge and valley topography as the result of subaerial erosion. Most

of study areas located in hilly lands of mountainous landscape, while there is covered with

loose soil mantle of variable thickness. In such a slope failures (i.e., landslide and surface

failure) of soil mantle ridge, valley and flat topography, shallow slope failures typically only

involved the soil mantle and commonly occurred at or near the soil- bedrock boundary. The

distribution of landscape topography of slopes has shown in table 3.7 and figure 3.11. It can

be seen that landscape topography of study site where slope failure occurred with ridge and

valley topography that often dominates shallow landslide and surface failure location. This

assessment indicated that most of slope failure occurred in the hilly and mountainous terrain

of ridge and valley of study site. Overall distribution of the slope failure was determined

primarily by the intensity of ground shaking; the local density of slope failure reflected

differing local geology. Where ridge and valley tops have been severely fractured, abundant

landslides may develop later when saturated with water.

Page 63: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

52

Tabel 3.7 Landscape topography

Landscape topography and number of slope failure Site

Valley Ridge Flat

Total

1. Bobonaro 51 106 10 167

2. Cailaco 90 30 13 133

3. Zumalai 75 0 0 75

4. Atsabe 25 14 0 39

5. Maliana 13 18 0 31

6. Ainaro 23 0 0 23

7. Hatolia 11 9 0 20

8. hatobuilico 18 0 0 18

Total 306 177 23 506

Figure 3.11 Landscape topography

Landscape Topography of Slope Failure

0

20

40

60

80

100

120

Valley Ridge Flat

Landscape Topography

Nu

mb

er

of

Slo

pes F

ail

ure

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

0

10

20

30

40

50

60

70

Valley Ridge Flat

Landscape Topography

Num

ber of U

n-failure

Slo

pes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

All Site

0

50

100

150

200

250

300

350

0 1 2 3 4

Landscape topography

Nu

mb

er

of

slo

pe

s

Un-failure Failure

Valley Ridge Flat

Valley Ridge No Vegetation

Failure

Unfailure

Page 64: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

53

3.2.6 Elevation

Some researches have found that landslide activity, within a specific basin, occurs at

certain elevations (Greenbaum et al., 1995; Jordan et al., 2000), the relationship between

landslide activity and elevation is still unclear, hence it requires further studies. Pachauri and

Pant 1992 report that the elevation is a good indicator of slope failure susceptibility to

occurred. However, it is well known that elevation influences a large number of biophysical

parameters and anthropogenic activities. In turn, these conditions are likely to affect slope

stability and generate slope failure (Vivas, 1992). Elevation also affects soil characteristics

significantly. Ochoa (1978) relates the influence of elevation on physical–chemical soil

properties in the Cordillera de Me´rida. He argues that soil texture varies with elevation, as

the grain size increases with the altitude. Although, in the study site, there is a considerable

difference between the lowest and the highest elevation values has shown in Table 3.8 and

Figure 3.12. It can be seen that hill slopes between 200m to 800m in elevation had

frequencies of slope failure that were 2 times greater than those on hill slopes that are less

than 1400m to 2100m and greater than 800m to 1400m in elevation. At intermediate

elevations there are mountain summit, which is more prone to landslide that are usually

characterized by weather rocks, and the shear strength of these is much higher. At very low

elevations, the frequency of slope failure is low because the terrain is gentle, and is covered

by residual soil, and higher perched water table will required initiating slope failure (F.C Day

et al. 2000).

Table 3.8 Distribution of elevation

Elevation number of slope failure (m) Site

200~500 500~800 800~1100 1100~1400 1400~1700 1700~2100

Total

1. Bobonaro 29 57 26 36 19 0 167

2. Cailaco 68 38 24 3 0 0 133

3. Zumalai 39 36 0 0 0 0 75

4. Atsabe 6 11 0 14 8 0 39

5. Maliana 10 8 6 6 1 0 31

6. Ainaro 4 4 10 4 1 0 23

7. Hatolia 7 13 0 0 0 0 20

8. hatobuilico 0 0 5 4 5 4 18

Total 163 167 71 67 34 4 506

Page 65: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

54

Figure 3.12 Distribution of elevation

3.2.7 Slope width

Table 3.9 and Figure 3.13 shows that most of landslide occurred in all study sites

frequently do have width with ranging from 31 m to 90 m. Some site like Bobonaro, Cailaco,

Atsabe, Maliana, hatolia and Hatobuilico study site, landslide occurred moderately with

ranges greatest than 90m to 150m, and especially Bobonaro and Cailaco site, a few number

of landslide occurred with ranges greatest than 150m. Table 3.10 and Figure 3.14 shows that

most of surface failure occurred in all study sites frequently do have width with ranging from

31m to 150m, especially in case of Bobonaro site a few number of surface failure occurred

have ranges greatest than 150m to 360m. Table 3.11 and Figure 3.15 shows that in some site

Elevation of Slopes Failure

0

10

20

30

40

50

60

70

80

2~5 5~8 8~11 11~14 14~17 17~21

Elevation(x100m)

Num

ber

of slo

pes F

ailure

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

Elevation of Unfailure Slope

0

20

40

60

80

100

120

2~5 5~8 8~11 11~14 14~17 17~21

Elevation (x100m)

Num

ber of U

nfa

ilure

slo

pes

Bononaro Cailaco Zumalai Atsabe

Maliana Ainaro Hatolia Hatobuilico

All Site

0

50

100

150

200

250

300

0 1 2 3 4 5 6 7

Elevation (x100m)

Nu

mb

er

of

slo

pe

Un-failure Failure

2~5 5~8 8~1111~14 14~17 17~21

Failure

Unfailure

Page 66: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

55

like Bobonaro, Cailaco and Atsabe study site, most number of surface failure occurred in

older landslide area do have width with ranging 31m to 120m.

Based on aerial photograph and topographic maps investigation, most of slope failure

occurred in grassland and bare land areas where lateral roots strength of grassland could not

provide help to improve the stability of slopes. However, we assess that estimated slope

failure width (i.e., width of landslide and surface failure), assuming that root strength acts

primarily through a perimeter boundary. Reneau and Dietrich (1987) derived an expression

relating landslide width to length by assuming that the soil was saturated and that its strength

was composed of frictional term acting on a basal slide area and a root strength acting on the

perimeter of the slide.

In this study, we predict that slope failure width increases with decreasing root strength,

its means that as larger masses of soil are needed to overcome resisting forces. Perhaps

surprisingly, the drier the soil, the larger of slope failure mass and width, whereas the water

table rise reduces the size needed for failure. The comparison of this result with field data

suggests that slope failure size is controlled by the local patchiness of soil thickness, root

strength and topographically-driven relative saturation.

Table 3.9 Width of landslide

Ranges(m) Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico Total

0.1 - 30 0 0 3 0 0 1 0 0 4

30.1 - 60 31 27 24 4 5 1 1 1 94

60.1 - 90 22 31 11 10 5 3 6 3 91

90.1 - 120 14 5 2 4 5 0 4 1 35

120.1 - 150 10 22 0 2 1 0 1 0 36

150.1 - 180 7 2 2 1 0 0 0 0 12

180.1 - 210 1 1 0 0 0 0 1 0 3

210.1 - 240 1 2 0 0 0 0 0 0 3

240.1 - 270 1 2 0 0 0 0 0 0 3

270.1 - 300 1 1 0 0 0 0 0 0 2

Total 88 93 42 21 16 5 13 5 283

Page 67: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

56

0

5

10

15

20

25

30

350.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

240.1

- 2

70

270.1

- 3

00

Width (m)

Num

ber of Landslide

Bobonaro Cailaco Zumalai AtsabeMaliana Ainaro Hatolia Hatobuilico

Figure 3.13 Width of landslide

Table 3.10 Width of surface failure

Ranges(m) Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico Total

0.1 - 30 0 0 2 0 0 0 0 0 2

30.1 - 60 20 11 18 5 3 9 2 7 75

60.1 - 90 13 7 8 4 4 4 2 1 43

90.1 - 120 14 6 2 1 0 4 0 2 29

120.1 - 150 2 6 2 2 2 1 1 0 16

150.1 - 180 5 0 0 0 1 0 1 2 9

180.1 - 210 3 0 0 0 3 0 1 1 8

210.1 - 240 1 0 1 0 2 0 0 0 4

240.1 - 270 1 0 0 0 0 0 0 0 1

270.1 - 300 1 0 0 0 0 0 0 0 1

300.1 - 330 0 0 0 0 0 0 0 0 0

330.1 - 360 1 0 0 0 0 0 0 0 1

Total 61 30 33 12 15 18 7 13 189

Page 68: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

57

0

5

10

15

20

250.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

240.1

- 2

70

270.1

- 3

00

300.1

- 3

30

330.1

- 3

60

Width (m)

Num

ber of Surf

ace F

ailure

Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico

Figure 3.14 Width of surface failure

Table 3.11 Width of surface failure and landslide

Ranges(m) Bobonaro Cailaco Atsabe Total

0.1 - 30 0 0 0 0

30.1 - 60 6 3 0 9

60.1 - 90 6 2 4 12

90.1 - 120 4 2 2 8

120.1 - 150 1 2 0 3

150.1 - 180 0 1 0 1

180.1 - 210 0 0 0 0

210.1 - 240 0 0 0 0

240.1 - 270 1 0 0 1

Total 18 10 6 34

Page 69: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

58

0

1

2

3

4

5

6

7

0

0.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

240.1

- 2

70

Width (m)

Num

ber

of Landslide a

nd S

urf

ace F

ailure

Bobonaro(MIX) Cailaco(MIX) Atsabe(MIX)

Figure 15 Width of surface failure and landslide

3.2.8 Slope length

Slope length is the distance along a slope subject to uninterrupted overland flow, from of

the point at which overland flow begins to where deposition starts, or where flow enters a

well-defined channel (Wischmeier and Smith, 1978). This distance is computed on the

horizontal and normal to the contours of the surface of the slope. It is, in fact, the horizontal

projection of the slope distance, which is measured along the slope surface.

In this study, slope length was estimated using by topography map investigation which

generates real slope length values. Table 3.12 and figure 3.16 shows that most of landslide

occurred in all study sites frequently do have length with ranging from 31m to 150m and a

few numbers of landslides were occurred with length 180m to 210m. In some site as well as

Bobonaro and Cailaco study site, a few number of landslide occurred with ranges greatest

than 240m to 510m. Table 3.13 and figure3.17 shows that most of surface failure occurred in

all study site frequently do have length highest with ranging from 31m to 90m and

moderately number of surface failure were occurred with length 91m to 180m in Bobonaro,

Cailaco and Zumalai study site, and a few number of surface failure occurred in Bobonaro ,

Page 70: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

59

Cailaco and Hatobuilico site do have ranging greatest than 180m. Carrara et al. (1995) argue

that field and laboratory analyses show that slide density increases linearly with slope length

up to a threshold value of about 500 m. However, slope length may be considered an

important factor in landslide activity since longer slope lengths increase the potential of

erosive agents to dislodge and transport materials downslope. Moreover, downslope water

velocity is greater on longer slopes. The slope length is of paramount importance for the

travel distance of materials.

Table 3.12 Length of landslide

Ranges(m) Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico Total

0.1 - 30 0 0 0 0 0 0 0 0 0

30.1 - 60 13 6 12 2 2 0 3 0 38

60.1 - 90 24 11 6 3 4 2 3 0 53

90.1 - 120 19 18 5 5 6 0 3 0 56

120.1 - 150 14 20 5 4 3 1 2 3 52

150.1 - 180 7 4 7 3 0 0 0 0 21

180.1 - 210 1 11 4 1 1 2 1 1 22

210.1 - 240 2 7 2 0 0 0 0 1 12

240.1 - 270 1 2 1 0 0 0 0 0 4

270.1 - 300 3 2 0 0 0 0 0 0 5

300.1 - 330 2 1 0 0 0 0 1 0 4

330.1 - 360 1 6 0 1 0 0 0 0 8

360.1 - 390 0 1 0 0 0 0 0 0 1

390.1 - 420 0 1 0 1 0 0 0 0 2

420.1 - 450 0 1 0 1 0 0 0 0 2

450.1 - 480 0 1 0 0 0 0 0 0 1

480.1 - 510 1 1 0 0 0 0 0 0 2

Total 88 93 42 21 16 5 13 5 283

Page 71: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

60

0

5

10

15

20

25

0.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

240.1

- 2

70

270.1

- 3

00

300.1

- 3

30

330.1

- 3

60

360.1

- 3

90

390.1

- 4

20

420.1

- 4

50

450.1

- 4

80

480.1

- 5

10

Length (m)

Nu

mb

er

of L

an

dslid

e

Bobonaro Cailaco Zumalai AtsabeMaliana Ainaro Hatolia Hatobuilico

Figure 3.16 Length of landslide

Table 3.13 Length of Surface failure

Ranges(m) Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico Total

0.1 - 30 0 0 1 3 1 4 0 0 9

30.1 - 60 10 2 22 6 5 8 1 0 54

60.1 - 90 24 14 5 3 5 2 5 3 61

90.1 - 120 9 5 3 0 1 1 0 3 22

120.1 - 150 8 3 0 0 2 1 1 1 16

150.1 - 180 5 3 2 0 0 1 0 3 14

180.1 - 210 1 0 0 0 1 1 0 1 4

210.1 - 240 3 1 0 0 0 0 0 1 5

240.1 - 270 0 0 0 0 0 0 0 1 1

270.1 - 300 1 1 0 0 0 0 0 0 2

300.1 - 330 0 0 0 0 0 0 0 0 0

330.1 - 360 0 1 0 0 0 0 0 0 1

Total 61 30 33 12 15 18 7 13 189

Page 72: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

61

0

5

10

15

20

25

0.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

240.1

- 2

70

270.1

- 3

00

300.1

- 3

30

330.1

- 3

60

Length (m)

nu

mb

er

of S

urf

ac

e F

ailu

re

Bobonaro Cailaco Zumalai Atsabe Maliana Ainaro Hatolia Hatobuilico

Figure 3.17 Length of surface failure

Table 3.14 Length of surface failure and landslide

Ranges(m) Bobonaro Cailaco Atsabe Total

0.1 - 30 3 0 0 3

30.1 - 60 4 3 4 11

60.1 - 90 7 2 1 10

90.1 - 120 1 3 1 5

120.1 - 150 0 2 0 2

150.1 - 180 1 0 0 1

180.1 - 210 1 0 0 1

210.1 - 240 1 0 0 1

Total 18 10 6 34

Page 73: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

62

0

1

2

3

4

5

6

7

80

0.1

- 3

0

30.1

- 6

0

60.1

- 9

0

90.1

- 1

20

120.1

- 1

50

150.1

- 1

80

180.1

- 2

10

210.1

- 2

40

Length (m)

Num

ber

of Landslide a

nd S

urf

ace F

ailure

Bobonaro(MIX) Cailaco(MIX) Atsabe(MIX)

Figure 18. Length of surface failure and landslide

Page 74: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

63

CHAPTER IV

ANALYZING METHOD

4.1 Logistic Regression Analysis

Landslide susceptibility evaluation involves a high level of uncertainty due to data

limitation and model shortcomings (Zezere 2002). For this reason, the landslide researchers

have considered different techniques for analyzing factors contributing of landslide

occurrence and preparation landslide susceptibility maps. One of these techniques is

statistical analysis. In this study, a multivariate statistical analysis in the form of logistic

regression was used to analyzing the factors contributing of slope failure in East Timor. The

fundamental principle of logistic regression is based on the analysis of a problem, in which a

result measured with dichotomous variables (such as zero and one or true and false) is

determined from one or more independent factors (Menard 1995).

Logistic regression analysis is a multivariate technique that considers several physical

parameters that may affect probability. Logistic regression can be used to determine the

relation of slope failure occurrence and the related factors. The dependent variable (y) for this

analysis is the failure or un-failure of a slope.

Considering P independent variables, pxxx ,......,, 21 , affecting slope failure occurrences,

we define the vector X = ( pxxx ,......,, 21 ). In this study, the independent variables correspond

to the classes of the independent variables categories in Table 1; each of these variables is

binary, with values of 1 (failure) or 0 (un-failure). The reason we consider each class as an

independent variable is that we are interested in detailed relationships of these classes and not

just the relationships between the broader independent variables (or factors).

The conditional probability that a slope failure occurs is represented by ( )1( XyP = . The

logit of the multiple logistic regression models (Hosmer and Lemeshow, 2000) is:

Logit(y)=pp xbxbxbb ......21110 +++ (1)

Where b0 is the constant of the equation, and b1, b2,……,bp are the coefficients of

variables p

xxx ,......,, 21 .

The probability )1( XyP = can be expressed in the logistic regression model :

Page 75: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

64

)1( XyP = = )...,( 221101

1

ppxbxbxbbe

++++−

+ (2)

where e is the constant 2.718

Assume that we obtain a sample of n observations (Xj,xj), j = 1,2, …, n, Xj= (x1j,x2j,…xpj),

the yj is either 1 or 0, yj = 1 for a slope failure event, and yj = 0 for non event (un-failure). By

fitting the binary logistic regression analysis using the sample observations, we estimate the

logistic regression coefficient bi, i = 0, 1, ... , p. Based on this model, the probability of slope

failure occurrence in the future can be estimated using Eq. (2).

By examining the sign of a variable’s coefficient estimate, the effect of that variable on

the probability of slope failure occurrence could be determined. A positive coefficient

estimate indicates that the independent variable increases the probability of a slope failure,

assuming that the other variables in the model are held constant. Another method that can be

used to interpret the regression results and examine the significance of a variable in the

model involves determining the influence ratio.

The influence ratio is odds ratio of statistics used to assess the risk of a particular

outcome if a certain factor is present. The influence ratio is a relative measure of risk, telling

us how much more likely it is that some item of factor is exposed to the category under

study will develop the outcome as compares to some item of factor who is not exposed. Odds

are a way of presenting probabilities, but unless you know much about betting you will

probably need an explanation of how odds are calculated. The influence of an event

happening is the probability that event will happen divided by the probability that the event

will not happen.

By definition, the influence ratio is the ratio of the odds for variable xi = 1 (i = 1,2, ..., p)

to the odds for xi = 0 (Hosmer and Lemeshow 2000). In slope failure analysis, the influence

ratio approximates how much more likely it is for the slope failure to be present (y = 1)

among those events with variable xi = 1 than for those events with variable xi = 0. The

influence ratio is computed by exponentiations the coefficient estimate for each dichotomous

explanatory variable and it can be expressed:

Influnce ratio = be …………………………………………………….(3)

Page 76: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

65

Where e = 2.718 and b is coefficient value.

Using the logistic regression model, the spatial relationship between slope failure and the

factors influence to slope failure was assessed. The spatial databases of each factor were

converted use in the statistical package, and the correlations between slope failures were

calculated. Though there were two cases, in the first case, only one factor was analysis.

Besides, logistic regression mathematical equations were formulated for each case.

Finally, the probability that predicts the possibility of slope failure was calculated using

the spatial database, data from equation (1) and (2) with coefficient value of logistic

regression of each factor. However, in the second case, all factors were used and logistic

regression mathematical equations were formulated as shown in equation (2) and (4) for each

factor. Mathematically, probabilities of the possibility of slope failure can be express:

Zp = b0 + (Cl xLithology) + (Ci x Inclination angle)+(Cv x vegetation) + (Clt x Landscape

topography) + (Cd x Direction) + (Ce x Elevation) ……………………………(4)

Where Zp : probabilities of the possibility of slope failure Cl: coefficient of lithology, Ci:

coefficient of slope inclination angle, Cv : coefficient of vegetation, Clt : coefficient of

landscape topography, Cd : coefficient of direction, and Ce ; coefficient of elevation.

The model of analyzing building involves five main steps:

• Selection of variables based on a slope failure distribution analysis;

• Selection of statistically significant variables by a P-value significance test;

• Logistic regression analysis with those variables that passed the significance test;

• Logistic regression analysis with significant variables including the interaction terms;

and

• Evaluation of the analysis results.

In the first step, a slope failure distribution analysis is used to pre-select the variables that

are relevant for the regression. This analysis involves overlaying the variables of category of

slope failure occurrences and the variables of category of a factor (such as sedimentary

rocks), then calculating the percentage of coverage of the slope failure occurrence on each

class for each input factor, such as slope inclination angle within elevation factor. By

comparing the slope failure distributions, a preliminary ranking of the variables can be

developed. Important variables will be considered in the following significance tests.

Page 77: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

66

In the second step, the significance p-value of 0.05 is specified as the cut-off value to

choose the variable for further analyses and > 0.05 is chosen as the value for elimination of

insignificant variables. The variables that passed the significance test can be entered into the

logistic regression analysis in the next step (SPSS 1999). After the steps of pre-selection and

significance test, some independent variables will be out of the original independent

variables were selected for the regression analysis.

In the third step, the model is checked for its goodness of fit by entering a variable or

removing a variable. Following the SPSS procedures, iteration of some variables are

preferred to obtain optimal analysis. The final suitable logistic regression analysis is based on

the variables presented in the final step of the statistical calculation in the SPSS program, and

the regression coefficients are obtained.

In the fourth step, the interaction terms representing the interactions among variables are

entered into the logistic regression analysis. In particular, the interactions among variables

from six factors affecting slope failure are selected to form the interaction terms for the

regression. The interactions among two, three, and four variables at one time were tested.

Only significant interaction terms are retained for analysis. When interaction terms are

introduced into the model, the ranking of the significance of some of the variables will

change. Some of the variables showing significance in the previous step may become

insignificant, and some of the interaction terms showing significance are added into the

model. After many tests with the interaction terms, the model that produces the best

prediction result is adopted as the final optimal model. Despite the fact that all independent

variables including different interaction terms were introduced in the regression analysis,

logistic regression analysis will be showed significance in the final best model when

interaction terms were added.

In the fifth step, the models obtained from above and the factors influence to the slope

failure occurrences generated from the analysis are evaluated. Slope failure probability

values between 0 and 1 at each unique-condition unit are obtained from the final regression.

A general description of the slope failure probability is adopted in this study, and the range of

slope failure probability is grouped into five categories to create the final.

Page 78: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

67

Table 4.1 Classification of predicted the probabilities of slope failure from the logistic

regression analysis (Atkinson and Massara, 1998) Estimated Probabilities of occurrence Relative of probabilities class

0.75 ~ 1.0 Very high

0.55 ~ 0.75 High

0.30 ~ 0.55 Moderate

0.10 ~ 0.30 Low

0.00 ~ 0.10 Very low

4.2 Independent Variables and Sampling

Several different geological and geographical parameters considered to be relevant to the

occurrence of slope failure were selected as the independent variables. lithology, direction,

vegetation and landscape topography were treated as categorical independent variables,

whereas inclination angle of slope and elevation were continuous independent variables

(table 14).

For the purpose of the statistical analysis, sample data representing both failure and

unfailure of slopes must be provided to fit the logistic regression analysis. The way in which

these data are obtained will affect both the nature of the regression relation and the accuracy

of the resulting estimates (Atkinson and Massari, 1998).

In this study, the data set of slope failure inventory is an indispensable data source

representative of samples of slope failure occurrences. All locations of the slope failure scars

were thus used to extract the physical parameter (independent variables) automatically from

the existing data layers. Altogether, 506 locations were chosen for the representing the un-

failure area. These locations were obtained using a random sampling scheme.

In the present situation, the dependent variable is a binary variable representing the

failure and unfailure of slope. Where the dependent variable is binary, the logistic link

function is applicable (Atkinson and Massari 1998). The dependent variable must be input as

either 0 or 1, so the model analysis applies well to analysis for possibility of slope failure

occurrence. Logistic regression coefficients can be used to estimate ratio for each of the

independent variables in the model analysis. The training data were then used to input to the

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68

logistic regression analysis within the Statistical Package for Social Science (SPSS), desk-top

statistical software, to obtain the coefficient and odds ratio for the logistic regression analysis.

Table 4.2 Categories of the independent variables

Items Variable of categories Code

Sedimentary Rocks and mixtures with recent materials (i.e.,

heterogeneous soil and small rocks)

S_R

Littoral deposit and mixtures with recent materials (i.e.,

heterogeneous soil and small rocks

L_R

Igneous rocks and mixtures with recent materials (i.e.,

heterogeneous soil and small rocks

I_R

Metamorphic rocks and mixtures with recent materials (i.e.,

heterogeneous soil and small rocks

M_R

Lithology

Volcanic rocks and mixtures with recent materials (i.e.,

heterogeneous soil and small rocks

V_R

60 – 120 Inc_6

120 – 180 Inc_12

180 – 240 Inc_18

240 – 300 Inc_24

300 - 360 Inc_30

360 - 420 Inc_36

Inclination Angle

420 – 480 Inc_42

Woodland or High Tree HT

Scrubland or Low Tree LT

Grassland G

Vegetation

Bare land or n vegetation NV

Valley V

Ridge R

Landscape

Flat F

N N

NE NE

E E

SE SE

S S

SW SW

W W

Direction

NW NW

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69

Table 4.2 ( Continued….)

Items Variable of categories Code

200m – 500m Elev_200

500m – 800m Elev_500

800m – 1100m Elev_800

1100m – 1400m Elev_1100

1400m – 1700m Elev_1400

Elevation

1700m – 2100m Elev_1700

Start

Explanatory

Variable (X) Dependent

Variable (Y)

Y=f(x1,x2,…xn)

)...110

(1

1)(

nx

nbxbb

e

eventP++−

+

=

R2,X

2, test

P(event) test

P(event) ≤ 0.05

Finish

Yes NO

NO

Figure 4.1 Flow chart of logistic

regression analysis

R2= Hosmer and Lameshow

Test

X2= Fit goodness Test

Page 81: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

70

4.3 GIS Application for Slope Failure Mapping

In the last twenty years, Geographical Information Systems (GIS) and Remote Sensing

have become integral tools for the evaluation of natural hazard phenomena (Nagarajan et al

1998; Liu et al 2004). Moreover, GIS is an excellent and useful tool for the spatial analysis

of a multi-dimensional phenomenon such as landslides and for the landslide susceptibility

mapping (Carrara et al 1999; van Westen et al 1999; Lan et al 2004).

Slope failure events are associated with various physical factors and therefore almost all

methods of slope failure i.e., landslide susceptibility mapping focus on: a) the determination

of the physical factors which are directly or indirectly correlated with slope failure (slope

failure factors); b) the selection of the rating-weighting system of all factors and of the

classes of each one of them; c) the overall estimation of the relative role of causative factors

in producingslope failure; and d) the final susceptibility zoning by classifying the land

surface according to different hazard degrees (Anbalagan 1992; Guzzetti et al 1999; Dai et al

2002).

The slope failure i.e., landslide and surface failure map is a practical tool in natural and

urban planning; it can be applied for determining land use zones, in construction design and

planning various future projects. In this study, GIS based on predicting probability of slope

failure maps were generated; in the study site are western parts of East timor. This was

accomplished by methods for correlating factors, which affect slope failure occurrences. The

factors influence to slope failures which were taken into account was: lithology, slope

inclination angle, slope gradients, vegetation, landscape topography, slope aspect (direction)

and elevation. A frequency distribution of the number of the slope failure events of the study

area in each items of the factor category was performed in order to rate the classes. The

models used to combine the factors influence to slope failure and assess the overall

probability of slope failure by statistics logistic regression analysis. The produced maps were

classified into four zones: the Very Low, Low , Moderate, High and Very High probability

zone and validated using the other number of the slope failure events of the study area.

Evaluation of the results is optimized through a Landslide Models Indicator, the application

of which demonstrated that the best desired outcome is provided by the model. Moreover it

was estimated that this model is easier to set up and operate than the first model.

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71

Regarding the identification of the factors influence to slope failure, the used data are in

some cases either readily available or can be easily collected. In other cases statistical

analysis was performed. As for the assigned rates and weights, the methodology used

involves landslide inventory and frequency distribution, frequency ratio, density, multivariate

statistical methods, trial and error method, local experience, field knowledge and literature

(Gupta and Joshi 1990; Anbalagan 1992; Zêzere et al 1999; Temesgen et al 2001; Lee and

Min 2001; Donati and Turrini 2002; Saha et al 2002; Gritzner et al 2001; Liu et al 2004, Lee

and Sambath 2006). Most of the methods employed for the overall estimation of the relative

contribution factors influence to slope failure are based on statistical mathematical operations,

which combine the factors (Temesgen et al 2001; Saha et al 2002; Chau et al 2004, Ayalew

et al 2005). Finally, the goals of this study are: a) the production of probabilities of slope

failure susceptibility maps based on GIS techniques using the models of combining the

instability factors and estimation of overall slope failure susceptibility and b) the evaluation

of these models and produced maps. A GIS database has been developed using ArcGIS

version 3.3 software. The slope failure occurrences in the study area and the influence factors

have been recorded and saved as separate layers in the database. All the data layers were in

vector format, transformed in grids with cell size 30x30 meters.

Start

Slope Failure inventory map

Rating of each factors and category

where influence to slope failure

Apply Statistics Logistic regression

Analysis

Production of probabilities of slope

failure maps based on GIS techniques

Finish

Figure 4.2 Flow chart of Production of

probabilities of slope failure

maps based on GIS techniques

Page 83: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

72

CHAPTER V

ANALYSIS RESULT

5.1 Introduction

A logistic regression analysis was constructed initially based on the physical parameters

or independent variables as defined above. Then, each step, independent variables are

evaluated for removal one by one if they do not contribute sufficiently to the regression

equation. In this analysis, the likelihood-ratio test is always used for determining whether

variables should be added to the analysis. This involves estimating the model analysis with

each independent variable eliminated in turn and looking at the change in to the logarithm of

likelihood when each independent variable is deleted. If the result analysis observed

significance level is greater than probability for stepwise (0.05 in this analysis) for remaining

in the analysis, the variables is removed from the analysis and statistics analysis are

recalculated to see if any other independent variables are eligible for removal. The

independent variables in this analysis are: lithology, inclination angle, vegetation, landscape

topography, direction and elevation.

By studying and analysis the causal factors affecting for slope failure in regional

mountainous of these study site, this study tries to contribute to the restricted knowledge on

slope failure in East Timor. After a brief introduction of the study area and the spatial

distribution and characteristics of its slope failure, the preconditions, preparatory and

triggering causal factors will be discussed with attention to their spatial variation.

5.2 All Study site Analysis Result

Logistic regression analysis result of all study (i.e., Bobonaro, cailaco, Zumalai, Atsabe,

Maliana, Ainaro, Hatolia and Hatobuilico) are shows in table 5.1 and 5.2. It can be seen that

the model analysis produced a concordance rate of 90.3 % with the use of 0.50 as a

classification cutoff value. This result is in agreement with the work in northern Italy by

Carrara and others (1991).By examining this result to predict probabilities of slope failure

affecting by the independent variables, we can see what a different classification rule should

be adopted when applying the model analysis to each factor in the study area and obtained

regression model composed of significant variables.

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73

Table 5.1 Classification table of the cut value 0.50 Predicted

Status of Slope

Observed

Unfailure Failure

Percentage Correct

Un-failure 450 56 90.5 Status of slope Failure 50 456 90.1

Step

Overall Percentage 90.3

Table 5.2 Coefficient values and influence ratio of logistic regression of each item and

category in all study site Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

Ratio

Sed. rocks 321 63.4 3.66 39.03

Lit. dep. rocks 121 23.9 2.48 11.89

Igneous rocks 25 4.9 0.14 1.15

Meta. Rocks 9 1.9 -0.12 0.89

Lithology

Vol. rocks 30 5.9 3.22 25.2

6 ~ 12 55 10.9 -2.63 0.07

12.1 ~ 18 128 25.3 -2.03 0.13

18.1 ~ 24 113 22.3 -1.86 0.16

24.1 ~ 30 81 16.0 -1.14 0.32

30.1 ~ 36 79 15.6 0.75 2.11

36.1 ~ 42 45 8.9 1.1 3

Inclination

Angle ( o )

42.1 ~ 48 5 1.0 -0.18 0.83

High tree 21 4.3 -1.02 0.36

Low tree 94 18.6 1.02 2.78

Grassland 204 40.3 1.8 6.05

Vegetation

No vegetation 187 37.0 4.49 32.89

Valley 306 60.5 2.22 9.24

Ridge 177 35.0 1.57 4.79

Landscape

topography

Flat 23 4.5 -1.57 0.21

North 74 14.6 2.01 7.48

Northeast 139 27.5 2.79 16.31

East 45 8.9 0.72 2.05

Southeast 69 13.6 1.23 3.44

South 14 2.8 -0.32 0.73

Southwesr 52 10.3 0.64 1.90

West 22 4.3 0.32 1.38

Direction

Northwest 91 18.0 2.22 9.19

Page 85: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

74

Table 5.2 (continued …)

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

Ratio

200 ~ 500 163 32.2 -1.11 0.33

500.1 ~ 800 167 33.0 -0.71 0.49

800.1 ~ 1100 71 14.0 -0.22 0.80

1100.1 ~ 1400 67 13.3 0.04 1.04

1400.1 ~ 1700 34 6.7 -0.74 0.48

Elevation

( m )

1700.1 ~ 2100 4 0.8 0.22 1.02

Table 5.3 Coefficient values and influence ratio of the logistic regression of interaction term

when combined with other item and categories in all study site Slope Failure Item Category

Number Percentage

Coefficient

Value

Influence

Ratio

Sed. rocks 321 63.4 4.08 59.07

Lit. dep. rocks 121 23.9 2.8 16.41

Igneous rocks 25 4.9 0.26 1.3

Meta. Rocks 9 1.9 0.03 1.03

Lithology

Vol. rocks 30 5.9 4.44 84.64

6 ~ 12 55 10.9 -0.75 0.47

12.1 ~ 18 128 25.3 -0.16 0.85

18.1 ~ 24 113 22.3 -0.36 0.68

24.1 ~ 30 81 16.0 0.59 1.79

30.1 ~ 36 79 15.6 1.78 5.95

36.1 ~ 42 45 8.9 1.61 4.02

Inclination

Angle ( o )

42.1 ~ 48 5 1.0 NA NA

High tree 21 4.3 -1.34 0.26

Low tree 94 18.6 1.39 4.02

Grassland 204 40.3 3.11 22.46

Vegetation

No vegetation 187 37.0 4.9 134.71

Valley 306 60.5 2.59 13.3

Ridge 177 35.0 2.06 7.82

Landscape

topography

Flat 23 4.5 -2.06 0.13

North 74 14.6 2.15 8.56

Northeast 139 27.5 3.26 26.06

East 45 8.9 0.73 2.07

Southeast 69 13.6 1.54 4.65

Direction

South 14 2.8 0.53 1.7

Page 86: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

75

Table 5.3 (continued)

Slope Failure Item Category

Number Percentage

Coefficient

Value

Influence

Ratio

Southwest 52 10.3 0.87 2.83

West 22 4.3 -0.59 0.56

Direction

(Continued…)

Northwest 91 18.0 3.26 11.28

200 ~ 500 163 32.2 1.41 4.1

500.1 ~ 800 167 33.0 1.11 3.05

800.1 ~ 1100 71 14.0 1.21 3.35

1100.1 ~ 1400 67 13.3 2.61 13.6

1400.1 ~ 1700 34 6.7 3.28 26.67

Elevation

( m )

1700.1 ~ 2100 4 0.8 NA NA

From the analysis result (Table 5.2), the regression coefficients of the lithology item and

category of sedimentary rocks are 3.66, which is the highest among all items and category.

This variable most contributes and affecting to slope failures; the influence ratio of slope

failure against unfailure slope is 39 times when this variable is present and other items and

category are controlled.

The coefficient of the vegetation item and category of no vegetation or bare land is 3.49

which is the second highest, with an influence ratio of 33. And the next most categories is

volcanic rocks, northeast, littoral deposit rocks, valley side, northwest, north, grassland, and

ridge side (Figure 5.1).

Page 87: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

76

All site

0

5

10

15

20

25

30

35

40

45

Sedim

eta

ry

rocks

Bare

land

Volc

anic

s

rocks

Northeast

Litto

ral

deposit rocks

Valle

y

Nort

hw

est

Nort

h

Gra

ssla

nd

Rid

ge

Category

Infl

uen

ce r

ati

o

Fig. 5.1 Ranking of the top ten significant item and category

based on influence ratio

All site

0

20

40

60

80

100

120

140

160

Se

dim

eta

ry r

ocks

Ba

re la

nd

Vo

lca

nic

s r

ocks

No

rth

ea

st

Litto

ral d

ep

osit r

ocks

Va

lle

y

No

rth

we

st

No

rth

Gra

ssla

nd

Rid

ge

Category

Infl

uen

ce r

ati

o No interaction

Interaction with each item and category

Figure 5.2 The top ten ranking of interaction term when combined with other

variables based on the influence ratio

Page 88: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

77

The next is the interaction term when combined with other variables controlled by this

analysis, the regression coefficient and influence ratio of most of the variables gradually

increases 1 to 4.5 times an individual variable (Table 5.3 and Figure 5.2).

Based on this analysis result and the actual condition and characteristics slope failure

distribution in study area, it can be seen that geology features are most important variable in

this study, distribution of lithology as well as bedrock of sedimentary rocks and littoral

deposit rocks, surface materials, and the difference between surface aspect and dip direction

of bedding are more important than elevation and difference between slope and inclination

angle in controlling slope stability. Most slope failure occurred in study area where the

factors representing the terrain aspect nearly parallel to the dip direction of the bedrock

coexists with other influential conditions including the a few igneous rocks, metamorphic

rocks and volcanic rocks thin till or other unconsolidated material, steep slope and elevation

from 200 to 2100 m.

Vegetation variables were used in this analysis and shown significance, as the vegetation

used in this study might be different from that of the time the slope failure occurred, the

interpretation of the importance of the vegetation cover may vary over time, but in actual

condition in East Timor, most of study site where slope failure are covered by bare land and

grassland. However, when heavy rainfall infiltrates in to the soil slope, it will clearly increase

the moisture content of the soil above the phreatic surface, but as the water flows downward,

it may also result in a rise in the position of the phreatic surface. Such a rise could be the

caused of slope failure.

Landscape topography is one of the important variables affecting to slope failure

occurrences. Landscape of soil mantled ridge and valley topography, shallow landslides

typically only involve the soil mantle and commonly occur at or near the soil-bedrock

boundary. These landslides may mobilize and travel a short distance down slope before

coming to rest either still on the hillside. The analysis result shows that emerges from this

work on topography landslides shows that surface topography has a great bearing on the

location and frequency of shallow landslide. Importantly, it is not just the local slope that

matters, but also the curvature of the topography and how it focuses or spreads runoff down

slope. A physically, that quantifies the influence of surface topography on pore pressure in a

Page 89: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

78

shallow slope stability model may effectively capture the essential linkage between

topography and slope failure.

The direction of slope has the potential to influences its physical properties and it

susceptibility to failure. The process that may be operating including to sunlight, drying

winds and possibly rainfall (Evans and others 1999). The distribution of aspect among the

mapped and the significance analysis shows that the frequency of slope failure was highest

on northeast – northwest and north – facing slopes, indicating that natural terrain landslide is

more common on these slopes. The frequency of slope failure was lowest on those slopes

facing south and west, while the frequency of slope failure remained moderate on the East –

southeast and southwest-facing slopes. From the air photograph interpretation shows that this

may be attributed to fact that there is more vegetation cover on south and west slopes.

Based on the logistic regression analysis result and slope failure distribution analysis in

those areas, vegetation, lithology, landscape topography of slope and elevation are more

important than elevation and inclination angle of slope failure.

From the Figure 5.3 is the histogram to predict the probabilities of slope failures affected

by independent variables are used in this analysis. Theoretically, if we have an analysis

model that successfully distinguishes the two independent variables on a classification cutoff

value of 0.5, the cases for which slope failure has occurred should be to the right of 0.5,

whereas the cases for which slope failure has not occurred should be to the left of 0.5( Figure

5.3). A fivefold classification scheme, ranging from very high probabilities of slope failure,

to very low, was employed for the predicted probabilities of occurred. It should be noted that

the complexity of the failure processes means that any evaluation of stability contains a

considerable amount of uncertainty. The use of predicted probability of slope failure in this

study is limited and is not suitable for site specific evaluation. The reliability of the

assessment result depends on a multitude of factors ranging from the quality of the data base,

the introduction of potential errors associated with data entry to the limitations and

assumptions inherent in the statistical techniques ( Rowbotham and Dudycha 1998).

Page 90: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

79

Table 5.4 Classification of predicted the probabilities of slope failure

from the logistic regression analysis (Atkinson and Massara, 1998) Estimated Probabilities of occurrence Relative of probabilities class

0.75 ~ 1.0 Very high

0.55 ~ 0.75 High

0.30 ~ 0.55 Moderate

0.10 ~ 0.30 Low

0.00 ~ 0.10 Very low

The ranges individual classes presented in Table 5.4 were derived based on the histogram

of the estimated of probabilities of slope failure shown in Table 5.5 and Figure 5.3, and

Figure 5.4. Zones classified for predicting of slope failure in this study site as being of “very

high probabilities”, accounting for 65% of this study area and exhibit a strongly clustered

pattern of spatial distribution and cover by grassland and bare land. This category is

distinguished from the “high” category by relatively high elevations and steeper terrain. Most

of the locations of identified slope failure actually occurred within this class. The” high

probabilities class”, occupies 11% of the study area, is mainly distributed in the middle

section of slopes and bears a high potential for slope failure occurrence. The zone of

moderate class covers 11% of the study are, and are featured by lower sections of slopes and

ridges. The zone of low probabilities of slope failure occurred, covering 9% of this study area,

is relatively dispersed in its spatial distribution, and hence the chance for slope failure to

develop within this class is small. And finally, zone of “very low” covering 4% of total study

area are distributed on high mountains that are characterized by relatively gentle gradient of

slope. All these sites are highly table and are not favorable to development of slope failure.

In this study, a particular problem with uncertainty is that the 1:15,000-scale topographic

condition cannot fully reflect the micro-topography conditions prerequisite for slope failure

because slope failure in the all study area is characterized by small and bigger volumes, that a

slight change in micro-scale landform may have a strong influence on the slope failure. This,

however, has not been reflected in the topographic map. Another problem is the 1:350,000-

scale geological map used in this study cannot fully reflect the distribution of colluviums or

residual soils that are of critical significance to the slope failure.

Page 91: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

80

R 240 ô ô E ó ó Q ó ó U ó1 0ó E 160 ô0 1ô N ó0 1ó C ó0 1ó Y ó0 1ó 80 ô0 1ô ó0 1ó ó0 1ó ó0000 0010 010 10 10 10 10 110 01111 111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 20 Cases.

Figure 5.3 Observed Groups and Predicted Probabilities (Logistic regression analysis)

Table 5.5 Predicting for probability of slope failure

Failure Unfailure

Probability ranges Number Percentage Number Percentage

0 ~ 0.10 20 4.35 220 47.80

0.11 ~ 0.20 20 4.35 60 13.05

0.21 ~ 0.30 20 4.35 40 8.70

0.31 ~ 0.40 20 4.35 20 4.35

0.41 ~ 0.50 20 4.35 20 4.35

0.51 ~ 0.60 20 4.35 20 4.35

0.61 ~ 0.70 20 4.35 20 4.35

0.71 ~ 0.80 40 8.70 20 4.35

0.81 ~ 0.90 80 17.40 20 4.35

0.19 ~ 1.00 200 43.45 20 4.35

Total 460 100 460 100

Page 92: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

81

Figure 5.4 Histogram of predicted probabilities of slope failure

Figure 5.5 Map of relative slope failure susceptibility

All site

0 10 20 30 40 50 60

0~0.1

0.1~0.2

0.2~0.3

0.3~0.4

0.4~0.5

0.5~0.6

0.6~0.7

0.7~0.8

0.8~0.9

0.9~1P

robabilitie

s o

f occurr

ence

Percentage of occurrences

Unfailred

Failured

Page 93: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

82

5.3 Specific Site Analysis

5.3.1 Bobonaro site

Logistic regression analyses of Bobonaro site are shows in Table 5.6 and Table 5.7. It can

be seen that the model analysis produce a concordance rate of 88.6% with the use of 0.5 as a

classification cutoff value. By examining this result to predict probabilities of slope failure

affecting by the independent variables, we can see what a different classification rule should

be adopted when applying the model analysis to each factor in the study site and obtain the

regression model composed of significant variables.

Table 5.6 Classification table of the cut value 0.50 Predicted

Status of Slope

Observed

Unfailure Failure

Percentage Correct

Unfailure 153 14 91.6 Status of slope Failure 24 143 85.6

Step

Overall Percentage 88.6

Table 5.7 Coefficient values and influence ratio of logistic regression of each item and

category Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

value

Sed. rocks 101 60.5 1.66 5.28

Lit. dep. rocks 19 11.4 0.18 1.2

Igneous rocks 25 15 -0.77 0.46

Meta. Rocks 0 0 0 0

Lithology

Vol. rocks 22 13.1 0.2 1.23

6 ~ 12 17 10.2 -2.89 0.06

12.1 ~ 18 38 22.7 -2.06 0.13

18.1 ~ 24 38 22.7 -2.02 0.13

24.1 ~ 30 37 22.2 -1.21 0.3

30.1 ~ 36 23 13.8 -0.93 0.30

36.1 ~ 42 13 7.8 2.57 13

Inclination

Angle ( o )

42.1 ~ 48 1 0.6 -3.23 0.04

High tree 8 4.8 -0.37 0.69

Low tree 21 12.6 0.37 1.45

Grassland 76 45.5 1.93 6.89

Vegeatation

No vegetation 62 37.1 3.83 46.01

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83

Table 5.7 ( continued….)

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

value

Valley 51 30.5 1.45 4.28

Ridge 106 63.5 2.05 7.78

Landscape

topography

Flat 10 6.0 -1.45 0.21

North 42 25.1 2.32 10.12

Northeast 49 29.3 1.71 5.51

East 10 6 -0.73 0.48

Southeast 25 15 0.04 1

South 5 3 -0.27 0.77

Southwesr 16 9.6 0.04 1.04

West 12 7.2 0.24 1.27

Direction

Northwest 8 4.8 -0.04 0.96

200 ~ 500 29 17.4 -1.79 0.17

500.1 ~ 800 57 34 0.2 1.23

800.1 ~ 1100 26 15.6 1.24 3.47

1100.1 ~ 1400 36 21.6 3.77 43.5

1400.1 ~ 1700 19 11.4 -1.81 0.16

Elevation

( m )

1700.1 ~ 2100 0 0 0 0

Table 5.8 Coefficient values and influence ratio of the logistic regression of interaction term

when combined with other item and categories in Bobonaro site Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

Value

Sed. rocks 101 60.5 2.41 11.18

Lit. dep. rocks 19 11.4 0.73 2.07

Igneous rocks 25 15 No No

Meta. Rocks 0 0 No No

Lithology

Vol. rocks 22 13.1 No No

6 ~ 12 17 10.2 -2.76 0.62

12.1 ~ 18 38 22.7 -1.87 0.15

18.1 ~ 24 38 22.7 -1.64 0.11

24.1 ~ 30 37 22.2 -0.89 0.23

30.1 ~ 36 23 13.8 -0.61 0.46

36.1 ~ 42 13 7.8 2.91 18.36

Inclination

Angle ( o )

42.1 ~ 48 1 0.6 No No

Page 95: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

84

Table 5.8 (continued…)

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

Value

High tree 8 4.8 -1.19 0.3

Low tree 21 12.6 1.38 4

Grassland 76 45.5 3.11 22.37

Vegetation

No vegetation 62 37.1 5.01 149.81

Valley 51 30.5 3.91 49.68

Ridge 106 63.5 2.73 15.25

Landscape

topography

Flat 10 6.0 -1.81 0.16

North 42 25.1 3.08 21.79

Northeast 49 29.3 3.62 37.14

East 10 6 1.66 5.24

Southeast 25 15 1.85 6.36

South 5 3 0.33 1.38

Southwesr 16 9.6 0.3 1.36

West 12 7.2 -0.58 0.56

Direction

Northwest 8 4.8 0.17 1.19

200 ~ 500 29 17.4 0.61 1.84

500.1 ~ 800 57 34 2.89 17.94

800.1 ~ 1100 26 15.6 2.75 15.58

1100.1 ~ 1400 36 21.6 5.06 156.84

1400.1 ~ 1700 19 11.4 No No

Elevation

( m )

1700.1 ~ 2100 0 0 No No

From the analysis result (Table 5.7), the regression coefficients of the vegetation item and

category of no vegetation or bare land are 3.83, which is the highest among all items and

category. This variable most contributes and affecting to slope failures; the influence ratio of

slope failure against unfailure slope is 46 times when this category is present and other items

and category are controlled.

The coefficient of the elevation item and category of elevation 1100m ~ 1400m is 3.77

which is the second highest, with an influence ratio of 44. And the next most category is

inclination angle 36 o ~ 42 o , North, ridge, grassland, northeast, sedimentary rocks, valley

side and elevation 800m ~ 1100m ( Figure 5.6).

Page 96: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

85

The next is the interaction term when combined with other variables controlled by

this analysis, the regression coefficient and influence ratio of most of the item and category

gradually increases (Table 5.8 and Figure 5.7).

Bobonaro site

05

101520253035404550

Ba

re la

nd

Ele

v.1

10

0~

14

00

Incli

na

tio

n

an

gle

_3

6~

42

No

rth

Rid

ge

Gra

ssla

nd

No

rth

ea

st

Se

dim

eta

ry

rock

s

Va

lle

y

Ele

v.8

00

~1

10

0

Category

Infl

ue

nc

e r

ati

o

Figure 5.6 Ranking of the top ten significant item and category based on influence ratio

Page 97: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

86

Bobonaro site

0

20

40

60

80

100

120

140

160

180

Bare

land

Ele

v.1

100~

1400

Inclin

ation

angle

_36~

42

Nort

h

Rid

ge

Gra

ssla

nd

Nort

heast

Sedim

eta

ry

rocks

Valle

y

Ele

v.8

00~

1100

Category

Infl

uen

ce r

ati

o

No interaction

Interaction with each item and category

Figure 5.7 The top ten ranking of interaction term when combined with other variables based

on the influence ratio

Based on this analysis result, the actual condition and distribution of slope failure in

Bobonaro site, it can be seen that vegetation is most important factor were used in this

analysis and show significance. In this site, most of slope failure has occurred in the bare

land and grassland area. However, when heavy rainfall occurred and infiltrates in to the soil,

it will clearly increase the moisture content of the soil above phreatic surface and the water

flows downward, it may also result in a rise the position of the phreatic surface and could be

the caused of slope failure. While the variation in soil types and characteristics throughout

the Bobonaro ridge is large, some of them have a distinct boundary between the soil and the

underlying rock is common. During heavy rains, water stagnates on this continuity, creating

positive pore water pressures on this shear plane on which the soil can easily slide down.

Elevation is one of the important factors affecting for slope failure in this site. Most of

slope failure occurred frequently do have ranging from 200m to 1700m. The distribution of

elevation of slopes failure in this site shows that hill slopes between 200m to 500m in

elevation had frequencies of slope failure that were greater than those on hill slopes that are

Page 98: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

87

800m to 1100m and 1400m to 1700m and less than hill slopes that are 500m to 800m and

1100m to 1400m in elevation. At intermediate elevations there are mountain summit, which

is more prone to landslide that are usually characterized by weather rocks, and the shear

strength of these is much higher. At very low elevations, the frequency of slope failure is low

because the terrain is gentle, and is covered by residual soil, and higher perched water table

will required initiating slope failure.

In this site, slope inclination angle is an important variable occurrence and show

significance. Inclination angle is an essential component of slope stability analysis. As slope

inclination angle increases, the level of gravitation-induced shear stress in the residual soils

increases as well. It can be seen that examination of the distribution of number of slope

failure with corresponding slope inclination angle in Bobonaro study site shows that most of

slope failures has occurred with inclination angle ranges increase in the 120 – 36

0 and

gradually decrease in the ranges 60 – 120 and 360 – 480. This is refection that steep natural

slope with outcropping bedrock and hence much higher shear strength may be susceptible to

shallow landslide.

The direction of slope has the potential to influences its physical properties and it

susceptibility to failure. The process that may be operating including to sunlight, drying

winds and possibly rainfall (Evans and others 1999). The distribution of aspect among the

mapped and the significance analysis shows that the frequency of slope failure was highest

on north and northeast– facing slopes, indicating that natural terrain landslide is more

common on these slopes. The frequency of slope failure was lowest on those slopes facing

east - south – west and northwest, while the frequency of slope failure remained moderate on

the southeast and southwest -facing slopes. From the air photograph interpretation shows that

this may be attributed to fact that there is more vegetation cover on east, south, west and

northwest slopes.

Geology features are most important variable in this study, distribution of sedimentary

rocks and a few of igneous rocks and metamorphic rocks, surface materials, and the

difference between surface aspect and dip direction of bedding are more important than

elevation and difference between slope and inclination angle in controlling slope stability.

Most slope failure has occurred in study area where the factors representing the terrain aspect

Page 99: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

88

nearly parallel to the dip direction of the bedrock coexists with other influential conditions

including the littoral deposit bedrock thin till or other unconsolidated material, steep slope

and elevation from 200m to 1700 m.

It should be note that thin colluvium or residual soil in steep terrain, which is most

susceptible to slope failure, is not fully reflected in the geological map by lithological

characteristics of underlying bedrock. Structural information is also available from digital

geological maps. However, qualitative examination of spatial distributions suggests that the

correlation between slope failure and mapped linier structural feature at the 1:350,000- scale

is not good, and the structural information is, thus, excluded in this study.

Landscape topography is one of the important variables affecting to slope failure. In this

study sites, landscape of soil mantled ridge and valley topography, shallow landslides

typically only involve the soil mantle and commonly occur at or near the soil-bedrock

boundary. These landslides may mobilize and travel a short distance down slope before

coming to rest either still on the hillside. The analysis result shows that emerges from this

work on topography landslides shows that surface topography has a great bearing on the

location and frequency of shallow landslide. Importantly, it is not just the local slope that

matters, but also the curvature of the topography and how it focuses or spreads runoff down

slope. A physically, that quantifies the influence of surface topography on pore pressure in a

shallow slope stability model may effectively capture the essential linkage between

topography and slope failure.

Compared to other study site, the critical slope for slope failure in Bobonaro site is rather

higher, with slope failure occurring on slopes do have inclination angle from o6 onward.

Gentle slopes exhibiting slope failure are common in the Bobonaro zone, where soil

stratification and human interference are also important.

From the Figure 5.8 and Figure 5.9 are the histograms to predict the probabilities of slope

failures affected by independent variables are used in this analysis. Theoretically, if we have

an analysis model that successfully distinguishes the two independent variables on a

classification cutoff value of 0.5, the cases for which slope failure has occurred should be to

the right of 0.5, whereas the cases for which slope failure has not occurred should be to the

left of 0.5(Figure 5.8). A fivefold classification scheme, ranging from very high probabilities

Page 100: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

89

of slope failure, to very low, was employed for the predicted probabilities of occurrence. It

should be noted that the complexity of the failure processes means that any evaluation of

stability contains a considerable amount of uncertainty. The use of predicted probability of

slope failure in this study is limited and is not suitable for site specific evaluation. The

reliability of the assessment result depends on a multitude of factors ranging from the quality

of the data base, the introduction of potential errors associated with data entry to the

limitations and assumptions inherent in the statistical techniques ( Rowbotham and Dudycha

1998).

F ó 1ó R 60 ô 1ô E ó 1ó Q ó 1ó U ó 1ó E 40 ô0 1ô N ó0 1ó C ó0 0 1ó Y ó0 0 1ó 20 ô0 0 1ô ó0 0 0 0 11ó ó0 010 0 1 1 1 1 1 11ó ó0 000000 0 00 0 10 10 10 110 11110 10111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 5 Cases.

Figure 5.8 Observed Groups and Predicted Probabilities (Logistic regression analysis)

Page 101: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

90

Table 5.9 Predicting for probability of slope failure in Bobonaro Site

Failure Unfailure

Probability ranges Number Percentage Number Percentage

0 ~ 0.10 5 3 90 57

0.11 ~ 0.20 5 3 25 16

0.21 ~ 0.30 5 3 15 9

0.31 ~ 0.40 5 3 5 3

0.41 ~ 0.50 5 3 5 3

0.51 ~ 0.60 5 3 5 3

0.61 ~ 0.70 5 3 5 3

0.71 ~ 0.80 10 6 5 3

0.81 ~ 0.90 30 20 5 3

0.91 ~ 1.00 80 53 0 0

Total 155 100 160 100

Figure 5.9 Histogram of the predicted probabilities of slope failure

Bobonaro site

0 10 20 30 40 50 60

0~0.1

0.1~0.2

0.2~0.3

0.3~0.4

0.4~0.5

0.5~0.6

0.6~0.7

0.7~0.8

0.8~0.9

0.9~1

Pro

ba

bil

itie

s o

f o

cc

urr

en

ce

Percentage of occurrences

Failured

Unfailred

Page 102: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

91

The ranges individual classes presented in Table 5.4 were derived based on the histogram

of the estimated of probabilities of slope failure shown in Table 5.9 and Figure 5.8 and

Figure 5.9. Zones classified for predicting of slope failure in Bobonaro site as being of “very

high probabilities”, accounting for 76% of Bobonaro site and exhibit a strongly clustered

pattern of spatial distribution and cover by grassland and bare land. This category is

distinguished from the “high” category by relatively high elevations and steeper terrain. Most

of the locations of identified slope failure actually occurred within this class. The” high

probabilities class”, occupies 7.5% of the study area, is mainly distributed in the middle

section of slopes and bears a high potential for slope failure. The zone of moderate class

covers 7.5% of the study area, and is featured by lower sections of slopes and ridges. The

zone of “low probabilities” of slope failure, covering 6% of this study area, is relatively

dispersed in its spatial distribution, and hence the chance for slope failure to develop within

this class is small. And finally, zone of “very low” covering 3% of total study area are

distributed on high mountains that are characterized by relatively gentle gradient of slope. All

these sites are highly table and are not favorable to development of slope failure.

It should be noted that the complexity of the failure processes means that any evaluation

of stability contains a considerable amount of uncertainty. The use of predicted probability of

slope failure in this study is limited and is not suitable for site specific evaluation. The

reliability of the assessment result depends on a multitude of factors ranging from the quality

of the data base, the introduction of potential errors associated with data entry to the

limitations and assumptions inherent in the statistical techniques ( Rowbotham and Dudycha

1998).

Page 103: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

92

5.3.2 Cailaco site

Logistic regression analyses of Cailaco site are shows in Table 5.10 and Table 5.10. It

can be seen that the model analysis produce a concordance rate of 94% with the use of 0.5 as

a classification cutoff value. By examining this result to predict probabilities of slope failure

affecting by the independent variables, we can see what a different classification rule should

be adopted when applying the model analysis to each factor in the study site and obtain the

regression model composed of significant variables.

Table 5.10 Classification table of the cut value 0.50 Predicted

Status of Slope

Observed

Unfailure Failure

Percentage Correct

Unfailure 125 8 94.0 Status of

slope Failure 8 125 94.0

Step

Overall Percentage 94.0

Table 5.11 Coefficient values and influence ratio of logistic regression

of each item and category Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

ratio

Sed. rocks 87 65.4 2.26 9.54

Lit. dep. rocks 46 34.6 1.15 3.17

Igneous rocks 0 0 0 0

Meta. Rocks 0 0 0 0

Lithology

Vol. rocks 0 0 0 0

6 ~ 12 19 14.2 -1.07 0.42

12.1 ~ 18 42 31.6 -0.68 0.51

18.1 ~ 24 34 25.6 -0.28 0.76

24.1 ~ 30 18 13.5 0.41 1.5

30.1 ~ 36 17 12.8 2.43 11.33

36.1 ~ 42 3 2.3 -1.04 0.35

Inclination

Angle ( o )

42.1 ~ 48 0 0 0 0

High tree 0 0 0 0

Low tree 10 7.5 -1.76 0.17

Grassland 50 37.6 3.01 20.29

Vegeatation

No vegetation 73 54.9 4.61 100.74

Page 104: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

93

Table 5.11 (continued…)

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

ratio

Valley 90 67.6 1.51 4.52

Ridge 30 22.6 0.35 1.42

Landscape

topography

Flat 13 9.8 -0.35 0.7

North 19 14.3 2.38 3.17

Northeast 53 39.8 3.51 33.47

East 21 15.8 1.59 4.89

Southeast 11 8.3 1.58 4.86

South 0 0 0 0

Southwesr 0 0 0 0

West 0 0 0 0

Direction

Northwest 29 21.8 2.8 16.43

200 ~ 500 68 51.1 -0.41 0.67

500.1 ~ 800 30 22.6 0.53 1.7

800.1 ~ 1100 24 18 1.53 4.6

1100.1 ~ 1400 3 2.3 -1.53 0.22

1400.1 ~ 1700 0 0 0 0

Elevation

( m )

1700.1 ~ 2100 0 0 0 0

Table 5.12 Coefficient values and influence ratio of the logistic regression of interaction term

when combined with other item and categories Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

value

Sed. rocks 87 65.4 2.82 16.82

Lit. dep. rocks 46 34.6 2.04 7.69

Igneous rocks 0 0 0 0

Meta. Rocks 0 0 0 0

Lithology

Vol. rocks 0 0 0 0

6 ~ 12 19 14.2 -1.68 0.19

12.1 ~ 18 42 31.6 -0.83 0.44

18.1 ~ 24 34 25.6 -0.68 0.51

24.1 ~ 30 18 13.5 0.99 2.69

30.1 ~ 36 17 12.8 2.62 13.76

36.1 ~ 42 3 2.3 0 0

Inclination

Angle ( o )

42.1 ~ 48 0 0 0 0

Page 105: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

94

Table 5.12 (continued…)

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

value

High tree 0 0 0 0

Low tree 10 7.5 -2.1 0.12

Grassland 50 37.6 4.39 80.33

Vegeatation

No vegetation 73 54.9 6 404.67

Valley 90 67.6 1.67 5.29

Ridge 30 22.6 1.37 3.92

Landscape

topography

Flat 13 9.8 0.01 1.01

North 19 14.3 2.42 11.2

Northeast 53 39.8 3.85 47

East 21 15.8 1.8 6.04

Southeast 11 8.3 1.85 6.36

South 0 0 0 0

Southwesr 0 0 0 0

West 0 0 0 0

Direction

Northwest 29 21.8 3.38 29.41

200 ~ 500 68 51.1 1.69 5.42

500.1 ~ 800 30 22.6 .1.53 4.62

800.1 ~ 1100 24 18 2.11 8.22

1100.1 ~ 1400 3 2.3 0 0

1400.1 ~ 1700 0 0 0 0

Elevation

( m )

1700.1 ~ 2100 0 0 0 0

From the analysis result (Table 5.11), the regression coefficients of the vegetation item

and category of no vegetation or bare land are 3.83, which is the highest among all items and

category. This variable most contributes and affecting to slope failures; the influence ratio of

slope failure against unfailure slope is 46 times when this category is present and other items

and category are controlled.

The coefficient of the elevation item and category of elevation 1100m ~ 1400m is 3.77

which is the second highest, with an influence ratio of 44. And the next most category is

inclination angle 36 o ~ 42 o , grassland, northwest, inclination angle of slope are 30 o ~ 36 o ,

sedimentary rocks, east –southeast - facing of slopes, elevation 800m ~ 1100m and valley

side of slopes ( Figure 5.10).

Page 106: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

95

The next is the interaction term when combined with other variables controlled by this

analysis, the regression coefficient and influence ratio of most of the item and category

gradually increases 1 to 4 times an individual variables (Table 5.12 and Figure 5.11).

Based on the logistic regression analysis result and slope failure distribution analysis in

Cailaco site, vegetation, direction, inclination angle of slope, lithology and landscape

topography of slope are more important than slope elevation.

Cailaco site

0

20

40

60

80

100

120

Bare

land

Nort

heast

Gra

ssla

nd

Nort

hw

est

Inclin

ation

angle

_30~36

Sedim

eta

ry

rocks East

South

east

Ele

v.8

00~1100

Valle

y

Category

Infl

uen

ce r

ati

o

Fig. 5.10 Ranking of the top ten significant item and category based on influence ratio

Page 107: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

96

Cailaco site

0

50

100

150

200

250

300

350

400

450

Ba

re la

nd

No

rth

ea

st

Gra

ssla

nd

No

rth

we

st

Inclin

atio

n

an

gle

_3

0~

36

Se

dim

eta

ry

rocks Ea

st

So

uth

ea

st

Ele

v.8

00

~1

10

0

Va

lley

Category

Infl

ue

nc

e r

ati

o

No interaction

Interaction with each item and

category

Figure 5.11 The top ten ranking of interaction term when combined with

other variables based on the influence ratio

Vegetation variables were used in this analysis and shown significance, as the vegetation

used in this study might be different from that of the time the slope failure has occurred, the

interpretation of the importance of the vegetation cover may vary over time, but in actual

condition in Cailaco site, most of slope failure occurred are covered by bare land and

grassland. However, when heavy rainfall infiltrates in to the soil slope, it will clearly increase

the moisture content of the soil above the phreatic surface, but as the water flows downward,

it may also result in a rise in the position of the phreatic surface. Such a rise could be the

caused of slope failure.

The direction of slope has the potential to influences its physical properties and it

susceptibility to failure. The process that may be operating including to sunlight, drying

winds and possibly rainfall. The distribution of aspect among the mapped and the

significance analysis shows that the frequency of slope failure was highest on northeast and

Page 108: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

97

northwest facing slopes, indicating that natural terrain landslide is more common on these

slopes. The frequency of slope failure was lowest on those slopes facing south, while the

frequency of slope failure remained moderate on the East and east -facing slopes. From the

air photograph interpretation shows that this may be attributed to fact that there is more

vegetation cover on south – west and southwest facing slopes.

In this site, slope inclination angle is an important variable of slope failure and show

significance. Inclination angle is an essential component of slope stability analysis. As slope

inclination angle increases, the level of gravitation-induced shear stress in the residual soils

increases as well. It can be seen that examination of the distribution of number of slope

failure with corresponding slope inclination angle in Cailaco site shows that most of slope

failures occurred with inclination angle ranges increase in the 120 – 30

0 and gradually

decrease in the ranges 60 – 12

0 and 30

0 – 42

0.

Landscape topography is one of the important variables affecting to slope failure.

Landscape of soil mantled ridge and valley topography, shallow landslides typically only

involve the soil mantle and commonly occur at or near the soil-bedrock boundary. These

landslides may mobilize and travel a short distance down slope before coming to rest either

still on the hillside. The analysis result shows that emerges from this work on topography

landslides shows that surface topography has a great bearing on the location and frequency of

shallow landslide. Importantly, it is not just the local slope that matters, but also the curvature

of the topography and how it focuses or spreads runoff down slope. A physically, that

quantifies the influence of surface topography on pore pressure in a shallow slope stability

model may effectively capture the essential linkage between topography and slope failure.

Based on the logistic regression analysis result and slope failure distribution analysis in

this area, vegetation, lithology, landscape topography of slope and elevation are more

important than elevation and inclination angle of slope failure.

From the Figure 5.4 is the histogram to predict the probabilities of slope failures affected

by independent variables are used in this analysis. Theoretically, if we have an analysis

model that successfully distinguishes the two independent variables on a classification cutoff

value of 0.5, the cases for which slope failure has occurred should be to the right of 0.5,

whereas the cases for which slope failure has not occurred should be to the left of 0.5

Page 109: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

98

(Figure 5.12). A fivefold classification scheme, ranging from very high probabilities of slope

failure, to very low, was employed for the predicted probabilities of occurrence. It should be

noted that the complexity of the failure processes means that any evaluation of stability

contains a considerable amount of uncertainty. The use of predicted probability of slope

failure in this study is limited and is not suitable for site specific evaluation. The reliability of

the assessment result depends on a multitude of factors ranging from the quality of the data

base, the introduction of potential errors associated with data entry to the limitations and

assumptions inherent in the statistical techniques ( Rowbotham and Dudycha 1998).

The ranges individual classes presented in Table 5.4 were derived based on the histogram

of the estimated of probabilities of slope failure shown in Table 5.13 and Figure 5.13. Zones

classified for predicting of slope failure in Cailaco site as being of “very high probabilities”,

accounting for 80% of this study area and exhibit a strongly clustered pattern of spatial

distribution and cover by grassland and bare land. This category is distinguished from the

“high” category by relatively high elevations and steeper terrain. Most of the locations of

identified slope failure actually occurred within this class. The” high probabilities class”,

occupies 10% of the study area, is mainly distributed in the middle section of slopes and

bears a high potential for slope failure occurrence. The zone of moderate class covers 10% of

the study are, and are featured by lower sections of slopes and ridges.

Page 110: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

99

R 60 ô0 1ô E ó0 1ó Q ó0 1ó U ó0 1ó E 40 ô0 1ô N ó0 1ó C ó0 1ó Y ó0 1ó 20 ô0 1ô ó0 1ó ó0 0 0 0 11 1ó ó0 0 0 0 10 10 10 10 101 1011 111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 5 Cases.

Figure 5.12 Observed groups and predicted probabilities (Logistic regression analysis)

Table 5.13 Predicting for probability of slope failure in Cailaco Site

Failure Unfailure

Probability ranges Number Percentage Number Percentage

0 ~ 0.10 0 0 60 48

0.11 ~ 0.20 0 0 20 16

0.21 ~ 0.30 0 0 10 8

0.31 ~ 0.40 5 4 5 4

0.41 ~ 0.50 5 4 5 4

0.51 ~ 0.60 5 4 5 4

0.61 ~ 0.70 5 4 5 4

0.71 ~ 0.80 10 8 5 4

0.81 ~ 0.90 25 20 5 4

0.19 ~ 1.00 70 56 5 4

Page 111: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

100

Cailaco site

0 10 20 30 40 50 60

0~0.1

0.1~0.2

0.2~0.3

0.3~0.4

0.4~0.5

0.5~0.6

0.6~0.7

0.7~0.8

0.8~0.9

0.9~1

Pro

ba

bil

itie

s o

f o

cc

urr

en

ce

Percentage of occurrences

Failured

Unfailred

Figure 5.13 Histogram of the predicted probabilities of slope failure

5.3.3 Zumalai Site

Logistic regression analyses of Zumalai site are shows in Table 5.14 and Table 5.15. It

can be seen that the model analysis produce a concordance rate of 84.7% with the use of 0.5

as a classification cutoff value. By examining this result to predict probabilities of slope

failure affecting by the independent variables, we can see what a different classification rule

should be adopted when applying the model analysis to each factor in the study site and

obtain the regression model composed of significant variables.

Table 5.14 Classification table of the cut value 0.50 in Zumalai site Predicted

Status of Slope Observed

Unfailure Failure

Percentage

Correct

Unfailure 66 9 88.0 Status of slope Failure 14 61 81.3

Step

Overall Percentage 84.7

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101

Table 5.15 Coefficient values and influence ratio of logistic regression of each item and

category Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

ratio

Sed. rocks 57 76 3.15 23.22

Lit. dep. rocks 18 24 0.32 1.38

Igneous rocks 0 0 0 0

Meta. Rocks 0 0 0 0

Lithology

Vol. rocks 0 0 0 0

6 ~ 12 5 6.7 -0.53 0.59

12.1 ~ 18 26 34.7 0.73 2.08

18.1 ~ 24 17 22.6 0.46 1.55

24.1 ~ 30 5 6.7 0.51 1.67

30.1 ~ 36 12 16 3.18 24

36.1 ~ 42 9 12 2.2 9

Inclination

Angle ( o )

42.1 ~ 48 1 1.3 0.53 1.7

High tree 7 9.3 -1.15 0.32

Low tree 33 44 1.15 3.14

Grassland 20 26.7 1.08 2.96

Vegeatation

No vegetation 15 20 2.18 8.86

Valley 52 69.3 2.82 16.83

Ridge 20 26.7 1.32 3.73

Landscape

topography

Flat 3 4 -1.32 0.27

North 4 5.3 3.58 36

Northeast 28 37.3 3.92 50.4

East 5 6.7 1.32 3.75

Southeast 11 14.7 2 7.36

South 0 0 -2.2 0.11

Southwesr 17 22.7 2.09 8.05

West 3 4 2.2 9

Direction

Northwest 7 9.3 2.35 10.5

200 ~ 500 39 52 1.61 3.19

500.1 ~ 800 36 48 0.67 1.96

800.1 ~ 1100 0 0 0 0

1100.1 ~ 1400 0 0 0 0

1400.1 ~ 1700 0 0 0 0

Elevation

( m )

1700.1 ~ 2100 0 0 0 0

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102

Table 5.16 Coefficient values and influence ratio of the logistic regression of interaction term

when combined with other item and categories in Zumalai site

Slope Failure Item Category

Number Percentage

Coefficient

value

Influence

ratio

Sed. rocks 57 76 3.93 50.69

Lit. dep. rocks 18 24 1.26 3.51

Igneous rocks 0 0 0 0

Meta. Rocks 0 0 0 0

Lithology

Vol. rocks 0 0 0 0

6 ~ 12 5 6.7 60.02 1.02

12.1 ~ 18 26 34.7 1.41 4.11

18.1 ~ 24 17 22.6 0.78 2.19

24.1 ~ 30 5 6.7 0.98 2.67

30.1 ~ 36 12 16 3.85 46.87

36.1 ~ 42 9 12 2.87 17.62

Inclination

Angle ( o )

42.1 ~ 48 1 1.3 0 0

High tree 7 9.3 -1.26 0.29

Low tree 33 44 1.16 3.19

Grassland 20 26.7 2.53 15.52

Vegeatation

No vegetation 15 20 3.18 23.99

Valley 52 69.3 3.91 49.68

Ridge 20 26.7 2.73 15.25

Landscape

topography

Flat 3 4 -2.73 0.07

North 4 5.3 3.71 40.86

Northeast 28 37.3 4.19 65.72

East 5 6.7 2.72 15.24

Southeast 11 14.7 2.49 12.06

South 0 0 0 0

Southwesr 17 22.7 2.95 19.01

West 3 4 3.14 23.02

Direction

Northwest 7 9.3 4.42 83.15

200 ~ 500 39 52 2.68 14.54

500.1 ~ 800 36 48 1.81 6.11

800.1 ~ 1100 0 0 0 0

1100.1 ~ 1400 0 0 0 0

1400.1 ~ 1700 0 0 0 0

Elevation

( m )

1700.1 ~ 2100 0 0 0 0

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103

From the analysis result (Table 5.15), the regression coefficients of the direction item and

category of northeast are 3.92, this is the highest among all items and category. This variable

most contributes and affecting to slope failures; the influence ratio of slope failure against

unfailure slope is 50 times when this variable is present and other items and category are

controlled. The coefficient of the direction item and category of no north is 3.58 which is the

second highest, with an influence ratio of 36. And the next most important categories are

inclination angle of slope are 30 o ~ 36 o , sedimentary rocks, south, valley, northwest, bare

land, southwest, southeast and ridge side of slope (Figure 5.14).

The next is the interaction term when combined with other variables controlled by this

analysis, the regression coefficient and influence ratio of most of the item and category

gradually increases 1 to 4 times an individual variables (Table 5.16 and Figure 5.15).

Based on the logistic regression analysis result and slope failure distribution analysis in

Zumalai area, direction, inclination angle of slope, lithology, vegetation and landscape

topography of slope are more important than slope elevation.

Zumalai site

0

10

20

30

40

50

60

Northeast

North

Inclin

ation

angle

_30~36

Sedim

eta

ry

rocks

Valle

y

Northw

est

Bare

land

South

west

South

east

Rid

ge

Category

Influence ratio

Fig. 5.14 Ranking of the top ten significant item and category based on influence

Page 115: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

104

Zumalai site

0

10

20

30

40

50

60

70

80

90

Nort

heast

Nort

h

Inclin

ation

angle

_30~

36

Sedim

eta

ry

rocks

Valle

y

Nort

hw

est

Bare

land

South

west

South

east

Rid

ge

Category

infl

ue

nc

e r

ati

o

No interaction Interaction with each item and category

Figure 5.15 The top ten ranking of interaction term when combined with other variables

based on the influence ratio

The direction of slope has the potential to influences its physical properties and it

susceptibility to failure. The process that may be operating including to sunlight, drying

winds and possibly rainfall. The distribution of aspect among the mapped and the

significance analysis shows that the frequency of slope failure was highest on northeast and

southwest facing slopes, indicating that natural terrain landslide is more common on these

slopes. The frequency of slope failure was lowest on those slopes facing north-east-west-

northwest, while the frequency of slope failure remained moderate on the southeast -facing

slopes. From the air photograph interpretation shows that this may be attributed to fact that

there is more vegetation cover on north – east - west – south and west facing slopes.

In this site, slope inclination angle is important variable slope failure and show

significance. Inclination angle is an essential component of slope stability analysis. As slope

inclination angle increases, the level of gravitation-induced shear stress in the residual soils

increases as well. It can be seen that examination of the distribution of number of slope

failure with corresponding slope inclination angle in Zumalai site shows that most of slope

Page 116: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

105

failures occurred with inclination angle ranges increase in the 12 o – 24 o and gradually

decrease in the ranges 6 o – 12 o and 24 o – 48 o .

Landscape topography is one of the important variables affecting to slope failure.

Landscape of soil mantled ridge and valley topography, shallow landslides typically only

involve the soil mantle and commonly occur at or near the soil-bedrock boundary. These

landslides may mobilize and travel a short distance down slope before coming to rest either

still on the hillside. The analysis result shows that emerges from this work on topography

landslides shows that surface topography has a great bearing on the location and frequency of

shallow landslide. Importantly, it is not just the local slope that matters, but also the curvature

of the topography and how it focuses or spreads runoff down slope. A physically, that

quantifies the influence of surface topography on pore pressure in a shallow slope stability

model may effectively capture the essential linkage between topography and slope failure.

Geology features are most important variable in this study site, distribution of

sedimentary rocks, surface materials, and the difference between surface aspect and dip

direction of bedding are more important than elevation and difference between slope and

inclination angle in controlling slope stability. Most slope failure occurred in study area

where the factors representing the terrain aspect nearly parallel to the dip direction of the

bedrock coexists with other influential conditions including the littoral deposit bedrock thin

till or other unconsolidated material, steep slope and elevation from 200m to 800 m.

It should be note that thin colluvium or residual soil in steep terrain, which is most

susceptible to slope failure, is not fully reflected in the geological map by lithological

characteristics of underlying bedrock. Structural information is also available from digital

geological maps. However, qualitative examination of spatial distributions suggests that the

correlation between slope failure and mapped linier structural feature at the 1:350,000- scale

is not good, and the structural information is, thus, excluded in this study.

Based on the logistic regression analysis result and slope failure distribution analysis in

that area, vegetation, lithology, landscape topography of slope and elevation are more

important than elevation and inclination angle of slope failure.

From the Figure 5.4 is the histogram to predict the probabilities of slope failures affected

by independent variables are used in this analysis. Theoretically, if we have an analysis

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106

model that successfully distinguishes the two independent variables on a classification cutoff

value of 0.5, the cases for which slope failure has occurred should be to the right of 0.5,

whereas the cases for which slope failure has not occurred should be to the left of 0.5(Figure

5.16). A fivefold classification scheme, ranging from very high probabilities of slope failure,

to very low, was employed for the predicted probabilities of occurrence. It should be noted

that the complexity of the failure processes means that any evaluation of stability contains a

considerable amount of uncertainty. The use of predicted probability of slope failure in this

study is limited and is not suitable for site specific evaluation (Figure 5.3). The reliability of

the assessment result depends on a multitude of factors ranging from the quality of the data

base, the introduction of potential errors associated with data entry to the limitations and

assumptions inherent in the statistical techniques ( Rowbotham and Dudycha 1998).

The ranges individual classes presented in Table 5.4 were derived based on the histogram

of the estimated of probabilities of slope failure occurrence shown in Table 5.17 and Figure

5.17. Zones classified for predicting of slope failure in this study site as being of “very high

probabilities”, accounting for 75% of this study area and exhibit a strongly clustered pattern

of spatial distribution and cover by grassland and bare land. This category is distinguished

from the “high” category by relatively high elevations and steeper terrain. Most of the

locations of identified slope failure actually occurred within this class. The” high

probabilities class”, occupies 11.50% of the study area, is mainly distributed in the middle

section of slopes and bears a high potential for slope failure. The zone of moderate class

covers 7.5% of the study are, and are featured by lower sections of slopes and ridges. And

finally, zone of “very low” covering 5% of total study area are distributed on high mountains

that are characterized by relatively gentle gradient of slope. All these sites are highly table

and are not favorable to development of slope failure.

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107

F ó ó R 24 ô ô E ó ó Q ó ó U ó 00 ó E 16 ô 00 ô N ó 00 ó C ó 00 11 ó Y ó 00 11 ó 8 ô 00 11 ô ó 00 00 11 11 ó ó 00 001 00 1 1 1111 1111 ó ó 00 000 10 100 10 10 10 110 1111 1111 ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 2 Cases.

Figure 5.16 Observed Groups and Predicted Probabilities (Logistic regression analysis)

Table 5.17. Predicting for probability of slope failure in Zumalai Site

Failure Unfailure

Probability ranges Number Percentage Number Percentage

0 ~ 0.10 0 0 36 52

0.11 ~ 0.20 2 3 14 21

0.21 ~ 0.30 2 3 6 9

0.31 ~ 0.40 2 3 4 6

0.41 ~ 0.50 2 3 2 3

0.51 ~ 0.60 2 3 2 3

0.61 ~ 0.70 4 6 2 3

0.71 ~ 0.80 6 8 2 3

0.81 ~ 0.90 20 28 0 0

0.19 ~ 1.00 32 43 0 0

Total 72 100 60 100

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108

Zumalai site

0 20 40 60

0~0.1

0.1~0.2

0.2~0.3

0.3~0.4

0.4~0.5

0.5~0.6

0.6~0.7

0.7~0.8

0.8~0.9

0.9~1

Pro

bab

ilit

ies o

f o

ccu

rren

ce

Percentage of occurrences

Failured

Unfailred

Figure 5.17 Histogram of predicted probabilities of slope failure

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109

CHAPTER VI

CONCLUSIONS AND FUTURE SUBJECT

6.1 Conclusions

By studying and analysis on the causal factors affecting for slope failure in East

Timor, this study is contributed to the restricted knowledge on slope failure in East Timor.

Type of slope failure in East Timor dominantly by landslide and surface failure, and the

actual condition and characteristics of slope failure were investigated based on analysis of

aerial photograph and topography map. After a brief introduction of the study area and

knowing the actual condition and characteristics of slope failure, and determine the

factors influencing slope failure in East Timor by logistic regression analysis, the

following conclusions can be obtained:

• Types of slope failure occurred in East Timor dominantly by landslide 56% with

density 0.24 Number/km2 ,surface failure are 37% with density 0.16 Number/km2

and mix of landslide and surface failure are 7% with density 0.06 Number/km2.

• Distribution of slope failure in study area relatively highest density in sedimentary

rocks and littoral deposit rocks and lowest in igneous rocks, metamorphic rocks and

volcanic rocks.

• Most of slope failure occurred on bare land and grassland in highest and lowest on

woodland and scrubland.

• The direction of slope failure was highest on northeast – northwest and north – facing

slopes, the frequency of slope failure was lowest on those slopes facing south and

west, while the frequency of slope failure remained moderate on the East – southeast

and southwest-facing slopes.

• Most slope failures occurred with inclination angle ranges increase in the 12o – 36o

and gradually decrease in the ranges 6o – 12

o and 36

o – 48

o.

• Based on the multivariate statistical analysis results and the observed distribution of

slope failure in those study sites, vegetation, lithology, landscape topography, slope

inclination angle, slope direction, and elevation were found to be the most important

factors affecting to the of slope failure in mountainous study area.

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110

• By logistic regression analysis, the interaction term were introduce, the proportion of

the observed all items and category predicted as high influence ratio increased by 1 to

4 times of individual category.

• Zone classified for predicting probability of slope failure in East Timor as being of

“very high probabilities” occupies 8.6% of study site, The “high probabilities”

occupies 73.7 %, “moderate class “ occupies 12.2%, “low probabilities” occupies 4%,

and very low probabilities occupies 1.5% of the study site.

6.2 Future Subject

To predicted probability of slope failure in East Timor is limited and is not suitable

for site specific evaluation. The reliability of the analysis result depends on a multitude of

factors ranging from the quality of the data base, the introduction of potential errors

associated with data entry to the limitations and assumptions inherent in the statistical

techniques.

In this study, a particular problem with uncertainty is that the 1:15,000-scale

topographic condition cannot fully reflect the micro-topography conditions prerequisite

for the slope failure because slope failure in the study area is characterized by small and

bigger volumes that a slight change in micro-scale landform may have a strong influence

on the slope failure.

Another problem is the 1:350,000-scale geological map used in this study cannot

fully reflect the distribution of residual soils that are of critical significance to the slope

failure.

Intensity of rainfall with a failure time are most important data to analyst of slope

failure hazard but unfortunately at this time it is difficult to find out in East Timor,

However, it difficult to assess whether climate is changing related to the environmental

hazard like slope failure in East Timor. Therefore, for further study on investigation and

analyzing of slope failure in East Timor, those data has to be considered.

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111

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Page 128: Landslide, slope failure, Timor-Leste, Multivariate Statistical Analysis

Appendix A: Physical data of slope failure and unfailure slope

A.1 Physical data of slope failureA.1.1 Bobonaro site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure

*)

1 107 230 168 214 231 1138 1225 1181 88 22 NE LS SR HT RIDGE

2 46 77 61 230 239 510 578 544 68 16 NW LS SR NV RIDGE

3 77 77 77 92 94 856 875 866 19 12 E LS SR 1 VALLEY

4 107 199 153 490 507 750 880 815 130 15 N LS SR NV RIDGE

5 92 107 99 153 155 838 860 849 23 8 SE LS SR G RIDGE

6 107 168 138 306 315 800 875 838 75 14 SE LS SR NV VALLEY

7 168 168 168 306 340 850 998 924 148 26 N LS SR NV RIDGE

8 92 168 130 168 171 1000 1030 1015 30 10 NW LS SR NV RIDGE

9 92 153 122 138 146 975 1025 1000 50 20 NE LS SR G RIDGE

10 77 153 115 122 130 475 518 497 43 19 SE LS SR G VALLEY

11 122 230 176 61 62 500 513 506 13 12 SE LS SR G VALLEY

12 153 153 153 153 158 340 378 359 38 14 SE LS SR G RIDGE

13 46 77 61 77 84 550 585 568 35 25 N LS SR NV RIDGE

14 31 46 38 77 78 433 448 440 15 11 SE LS SR NV RIDGE

15 46 92 69 138 141 313 344 328 31 13 SE LS SR G RIDGE

16 46 46 46 92 93 563 575 569 13 8 N LS SR G VALLEY

17 61 61 61 77 85 538 575 556 38 26 NE LS IR NV FLAT

18 61 61 61 92 99 475 513 494 38 22 NE LS IR G FLAT

19 46 46 46 92 95 388 413 400 25 15 N LS IR G RIDGE

20 61 107 84 61 66 388 413 400 25 22 N LS IR NV RIDGE

21 46 122 84 153 165 406 469 438 63 22 N LS IR NV RIDGE

22 46 46 46 138 143 438 475 456 38 15 N LS IR NV RIDGE

23 61 77 69 138 146 363 413 388 50 20 SE LS SR G RIDGE

24 46 62 54 77 78 335 350 343 15 11 SE LS SR G RIDGE

25 46 46 46 77 82 290 319 304 29 21 SE LS SR G RIDGE

26 92 92 92 61 62 263 275 269 13 12 SE LS SR G FLAT

27 47 109 78 155 165 563 620 591 58 20 NE LS SR NV RIDGE

28 78 124 101 78 84 663 695 679 33 23 W LS SR NV VALLEY

29 233 233 233 31 33 400 413 406 13 22 W LS SR G VALLEY

30 153 184 168 77 82 675 705 690 30 21 SW LS SR LT VALLEY

117

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Slope failure data of Bobonaro study site (continued)

31 31 61 46 184 192 663 720 691 58 17 N LS SR LT VALLEY

32 31 46 38 92 97 580 613 596 33 19 S LS SR G RIDGE

33 46 46 46 61 63 1219 1235 1227 16 15 SE LS SR LT RIDGE

34 31 31 31 61 62 1213 1225 1219 13 12 SE LS SR LT RIDGE

35 46 46 46 61 63 1125 1140 1133 15 14 SE LS SR HT RIDGE

36 31 61 46 92 96 530 558 544 28 17 NW LS SR LT RIDGE

37 62 62 62 31 33 428 438 433 10 18 W LS SR G VALLEY

38 93 124 109 93 97 413 440 426 28 16 W LS SR G RIDGE

39 31 31 31 47 48 438 450 444 13 15 W LS SR G RIDGE

40 31 61 46 92 96 530 558 544 28 17 NW LS SR LT RIDGE

41 47 62 54 31 33 405 415 410 10 18 W LS SR G VALLEY

42 47 62 54 47 52 415 438 426 23 26 W LS VR G VALLEY

43 46 46 46 122 134 395 450 423 55 24 NE LS VR NV RIDGE

44 46 46 46 61 66 438 463 450 25 22 NE LS VR NV RIDGE

45 109 140 124 93 99 588 620 604 33 19 SE LS VR LT RIDGE

46 47 47 47 124 134 713 763 738 50 22 SE LS VR NV VALLEY

47 78 155 116 78 79 723 740 731 18 13 SE LS VR G RIDGE

48 93 124 109 78 84 525 558 542 33 23 E LS IR NV VALLEY

49 46 46 46 122 134 395 450 423 55 24 NE LS IR NV RIDGE

50 46 46 46 61 66 438 463 450 25 22 NE LS IR NV RIDGE

51 61 61 61 46 47 635 644 639 9 11 S LS IR G RIDGE

52 31 47 39 124 128 495 525 510 30 14 SW LS SR G RIDGE

53 47 62 54 62 63 475 488 481 13 11 SW LS SR LT RIDGE

54 47 78 62 109 111 450 475 463 25 13 SW LS SR LT RIDGE

55 78 109 93 62 65 500 518 509 18 16 SW LS SR G VALLEY

56 47 109 78 132 134 567 590 579 23 10 SW LS SR NV VALLEY

57 62 109 85 155 158 550 582 566 32 12 SW LS SR NV VALLEY

58 47 78 62 47 48 525 538 531 13 15 SW LS SR NV VALLEY

59 62 109 85 124 128 535 565 550 30 14 SW LS SR NV VALLEY

60 78 109 93 54 56 485 500 493 15 15 SW LS SR LT RIDGE

61 155 155 155 62 63 375 388 381 13 11 SW LS SR NV RIDGE

62 47 47 47 93 96 470 495 483 25 15 NE LS IR NV RIDGE

63 31 31 31 93 99 463 495 479 33 19 NE LS IR G VALLEY

64 31 62 47 47 52 438 460 449 23 26 E LS IR G RIDGE

65 171 233 202 93 100 563 600 581 38 22 E LS SR NV RIDGE

66 77 77 77 31 33 450 463 456 13 22 S LS SR G RIDGE

118

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Slope failure data of Bobonaro study site (continued)

67 124 124 124 155 165 705 763 734 58 20 E LS SR G RIDGE

68 124 155 140 62 67 550 575 563 25 22 NE LS SR G VALLEY

69 124 155 140 62 67 738 763 750 25 22 NE LS SR G RIDGE

70 78 124 101 93 96 725 750 738 25 15 N LS SR NV VALLEY

71 78 124 101 140 148 600 650 625 50 20 N LS SR G RIDGE

72 93 186 140 93 98 700 730 715 30 18 N LS SR NV RIDGE

73 31 31 31 109 111 515 538 526 23 12 NE LS SR G RIDGE

74 47 47 47 124 128 500 530 515 30 14 NE LS SR NV VALLEY

75 109 171 140 279 289 625 700 663 75 15 NE LS IR NV VALLEY

76 78 140 109 279 283 550 600 575 50 10 NE LS IR NV VALLEY

77 248 279 264 248 259 475 550 513 75 17 NE LS IR NV VALLEY

78 109 155 132 93 100 500 538 519 38 22 NE LS SR NV RIDGE

79 61 153 107 306 314 750 819 784 69 13 NE LS SR NV RIDGE

80 77 77 77 46 48 1025 1038 1031 13 15 N LS SR G RIDGE

81 77 77 77 61 62 575 588 581 13 12 S LS SR G RIDGE

82 77 92 84 46 48 550 563 556 13 15 S LS SR G RIDGE

83 31 46 38 92 95 500 525 513 25 15 N LS VR G RIDGE

84 46 46 46 92 95 488 513 500 25 15 N LS VR G VALLEY

85 77 77 77 138 149 444 500 472 56 22 N LS VR G FLAT

86 31 31 31 78 80 582 600 591 18 13 W LS VR LT RIDGE

87 93 124 109 54 56 813 825 819 13 13 NW LS VR LT RIDGE

88 264 310 287 279 286 688 750 719 63 13 E LS VR NV RIDGE

89 92 92 92 122 150 1525 1613 1569 88 36 NE SF VR HT FLAT

90 31 46 38 92 105 550 600 575 50 29 SE SF VR G RIDGE

91 46 77 61 153 165 1288 1350 1319 63 22 NE SF IR LT RIDGE

92 61 61 61 61 72 1150 1188 1169 38 31 SE SF IR HT VALLEY

93 77 77 77 46 54 698 725 711 28 31 NE SF IR G RIDGE

94 77 122 99 61 72 575 613 594 38 31 N SF IR LT RIDGE

95 92 92 92 61 72 875 913 894 38 31 N SF SR G RIDGE

96 31 47 39 47 53 400 425 413 25 28 W SF SR G VALLEY

97 31 62 47 47 53 400 425 413 25 28 W SF SR G RIDGE

98 47 62 54 62 72 575 613 594 38 31 E SF SR LT RIDGE

99 93 140 116 202 231 500 613 556 113 29 E SF SR NV RIDGE

100 78 124 101 186 224 550 675 613 125 34 E SF SR NV RIDGE

101 155 186 171 62 80 500 550 525 50 39 E SF SR G RIDGE

102 46 61 54 77 85 513 550 531 38 26 SW SF SR LT FLAT

119

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Slope failure data of Bobonaro study site (continued)

103 109 186 147 124 145 600 675 638 75 31 NE SF SR NV RIDGE

104 77 153 115 77 85 388 425 406 38 26 NE SF SR NV RIDGE

105 93 140 116 93 107 563 615 589 53 29 NE SF SR LT VALLEY

106 31 62 47 93 112 700 763 731 63 34 NE SF SR NV VALLEY

107 46 61 54 77 85 513 550 531 38 26 SW SF SR LT FLAT

108 124 264 194 140 172 600 700 650 100 36 NW SF SR NV VALLEY

109 77 153 115 77 85 388 425 406 38 26 NE SF SR NV RIDGE

110 186 326 256 124 149 563 645 604 83 34 NE SF SR NV RIDGE

111 47 109 78 78 98 530 590 560 60 38 NE SF SR NV RIDGE

112 46 61 54 31 36 475 494 484 19 31 N SF SR G RIDGE

113 31 31 31 39 54 500 538 519 38 44 NE SF SR G VALLEY

114 124 202 163 93 112 388 450 419 63 34 NE SF SR NV VALLEY

115 31 31 31 31 34 425 440 433 15 26 N SF SR G FLAT

116 31 31 31 93 102 483 525 504 43 25 NE SF SR G RIDGE

117 78 109 93 186 206 425 513 469 88 25 NE SF SR NV VALLEY

118 62 78 70 62 77 830 875 853 45 36 NW SF SR G RIDGE

119 78 78 78 39 46 575 600 588 25 33 NE SF IR G RIDGE

120 45 45 45 61 68 860 892 876 32 28 NE SF IR HT VALLEY

121 77 107 92 77 88 838 880 859 43 29 N SF IR HT RIDGE

122 31 107 69 122 149 790 875 833 85 35 SE SF IR NV VALLEY

123 168 383 275 153 172 938 1015 976 78 27 N SF IR G RIDGE

124 77 122 99 122 137 863 925 894 63 27 N SF SR NV RIDGE

125 92 122 107 107 134 920 1000 960 80 37 N SF SR NV RIDGE

126 47 47 47 62 69 475 505 490 30 26 W SF SR G RIDGE

127 155 217 186 47 61 650 690 670 40 41 N SF SR NV VALLEY

128 124 186 155 78 100 588 650 619 63 39 NE SF SR NV VALLEY

129 47 62 54 78 87 600 640 620 40 27 NW SF SR G RIDGE

130 155 217 186 124 166 590 700 645 110 42 SW SF SR NV VALLEY

131 62 109 85 47 63 538 580 559 43 42 W SF SR G RIDGE

132 76 106 91 61 79 850 900 875 50 40 SW SF SR HT VALLEY

133 31 31 31 31 40 1025 1050 1038 25 39 N SF SR LT RIDGE

134 92 245 168 77 91 563 613 588 50 33 N SF SR G RIDGE

135 46 92 69 61 72 413 450 431 38 31 N SF SR G VALLEY

136 536 138 337 61 72 550 588 569 38 31 NE SF SR LT RIDGE

137 138 138 138 61 72 513 550 531 38 31 N SF SR G RIDGE

138 138 168 153 122 141 398 468 433 70 30 N SF SR NV RIDGE

120

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Slope failure data of Bobonaro study site (continued)

139 31 61 46 61 71 403 438 420 35 30 N SF SR G RIDGE

140 92 138 115 61 77 438 485 461 48 38 N SF SR G RIDGE

141 46 46 46 92 105 488 538 513 50 29 N SF SR NV VALLEY

142 77 77 77 38 44 388 410 399 23 30 N SF VR G FLAT

143 61 77 69 107 127 400 469 434 69 33 SE SF VR G RIDGE

144 31 31 31 47 60 613 650 631 38 39 SW SF VR G RIDGE

145 202 248 225 62 72 875 913 894 38 31 NE SF VR G RIDGE

146 47 62 54 62 80 750 800 775 50 39 NE SF VR NV RIDGE

147 30 30 30 121 149 888 975 931 88 36 NE SF VR HT VALLEY

148 61 91 76 242 273 838 963 900 125 27 NE SF VR NV VALLEY

149 76 91 83 182 232 775 920 848 145 39 NE SF VR NV VALLEY

150 61 153 107 184 197 931 1003 967 71 21 NE MIX SR G RIDGE

151 31 31 31 23 26 1013 1025 1019 13 29 N MIX SR G RIDGE

152 62 78 70 155 172 425 500 463 75 26 NE MIX SR NV VALLEY

153 46 46 46 23 25 1050 1060 1055 10 24 N MIX SR LT RIDGE

154 233 264 248 186 211 750 850 800 100 28 NE MIX SR NV RIDGE

155 124 155 140 47 53 738 763 750 25 28 NE MIX SR NV RIDGE

156 109 109 109 23 26 588 600 594 13 28 NE MIX SR NV VALLEY

157 92 122 107 61 66 1100 1125 1113 25 22 NE MIX SR G RIDGE

158 153 46 99 61 67 518 544 531 26 23 SE MIX SR G VALLEY

159 46 61 54 46 52 425 450 438 25 29 N MIX SR G RIDGE

160 54 54 54 77 85 475 513 494 38 26 N MIX SR G RIDGE

161 61 77 69 46 51 463 485 474 23 26 N MIX SR NV VALLEY

162 61 61 61 77 83 494 525 509 31 22 N MIX SR G RIDGE

163 61 61 61 77 83 388 419 403 31 22 N MIX SR G VALLEY

164 61 61 61 46 50 406 425 416 19 22 N MIX SR G FLAT

165 46 46 46 77 85 363 400 381 38 26 SE MIX SR G RIDGE

166 47 78 62 93 101 465 505 485 40 23 SE MIX SR G RIDGE

167 31 31 31 78 81 650 675 663 25 18 SE MIX SR G RIDGE

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

121

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A.1.2 Cailaco site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) of Slope

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 62 233 147 310 334 425 550 488 125 22 SE LS LR NV RIDGE

2 78 155 116 248 267 450 550 500 100 22 NE LS LR NV RIDGE

3 109 155 132 279 283 600 650 625 50 10 N LS LR NV RIDGE

4 47 78 62 62 67 625 650 638 25 22 SE LS LR NV VALLEY

5 109 171 140 124 133 663 710 686 48 21 SE LS LR NV VALLEY

6 78 78 78 47 48 650 663 656 13 15 NE LS LR NV RIDGE

7 62 62 62 39 44 655 675 665 20 27 NE LS SR G RIDGE

8 78 78 78 47 48 638 650 644 13 15 NE LS SR G VALLEY

9 78 186 132 372 392 363 488 425 125 19 NE LS SR NV VALLEY

10 62 62 62 47 48 400 413 406 13 15 NE LS SR G VALLEY

11 31 31 31 31 33 363 375 369 13 22 NE LS SR G VALLEY

12 47 78 62 140 144 325 363 344 38 15 NE LS SR NV VALLEY

13 62 93 78 124 130 350 388 369 38 17 E LS SR G VALLEY

14 124 140 132 62 67 250 275 263 25 22 E LS SR LT VALLEY

15 109 140 124 78 80 305 325 315 20 14 E LS SR NV VALLEY

16 155 202 178 93 96 310 335 323 25 15 E LS SR NV VALLEY

17 47 47 47 171 182 363 425 394 63 20 NE LS SR G VALLEY

18 47 78 62 155 167 438 500 469 63 22 NE LS SR G VALLEY

19 47 62 54 310 338 515 650 583 135 24 NE LS SR NV VALLEY

20 31 62 47 186 201 575 650 613 75 22 NE LS SR NV VALLEY

21 248 279 264 140 148 260 310 285 50 20 N LS SR G RIDGE

22 109 124 116 202 211 275 338 306 63 17 NE LS SR NV RIDGE

23 140 388 264 140 148 300 350 325 50 20 NW LS SR NV VALLEY

24 31 47 39 62 65 305 325 315 20 18 NW LS SR G VALLEY

25 31 62 47 155 159 315 350 333 35 13 N LS SR G VALLEY

26 93 171 132 434 441 345 425 385 80 10 N LS SR NV VALLEY

27 47 47 47 217 227 300 365 333 65 17 NW LS SR G RIDGE

28 31 62 47 217 233 315 400 358 85 21 NW LS SR LT VALLEY

29 47 93 70 341 350 325 405 365 80 13 NE LS LR G RIDGE

30 47 47 47 124 128 500 530 515 30 14 NE LS LR G RIDGE

31 47 62 54 101 112 750 800 775 50 26 E LS LR G RIDGE

32 47 78 62 217 226 738 800 769 63 16 N LS LR NV RIDGE

122

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Slope failure data of Cailaco study site (continued)

33 47 78 62 78 80 20 14 N LS LR NV VALLEY

34 124 140 132 78 83 810 840 825 30 21 NE LS LR NV VALLEY

35 47 93 70 93 95 870 890 880 20 12 E LS LR NV VALLEY

36 109 140 124 326 331 838 895 866 58 10 E LS LR NV VALLEY

37 78 124 101 217 221 910 950 930 40 10 NW LS LR NV VALLEY

38 47 62 54 124 128 963 995 979 33 15 N LS LR NV RIDGE

39 140 155 147 31 33 1005 1015 1010 10 18 N LS LR NV RIDGE

40 47 109 78 93 96 920 945 933 25 15 E LS LR NV RIDGE

41 31 47 39 93 99 1045 1080 1063 35 21 NE LS LR NV VALLEY

42 31 62 47 124 134 925 975 950 50 22 NE LS LR G VALLEY

43 47 62 54 124 126 830 853 841 23 10 NE LS LR NV VALLEY

44 31 62 47 93 99 975 1010 993 35 21 E LS LR NV VALLEY

45 62 78 70 171 188 945 1025 985 80 25 NE LS LR NV VALLEY

46 62 202 132 93 101 850 890 870 40 23 NE LS LR NV VALLEY

47 78 171 124 186 191 785 830 808 45 14 NE LS LR NV VALLEY

48 31 109 70 124 128 245 275 260 30 14 NE LS LR NV VALLEY

49 62 93 78 109 110 235 250 243 15 8 NE LS LR NV VALLEY

50 186 248 217 93 101 300 340 320 40 23 NW LS LR G VALLEY

51 155 233 194 93 99 315 350 333 35 21 NW LS LR G VALLEY

52 47 47 47 124 130 360 400 380 40 18 NW LS LR G VALLEY

53 140 310 225 155 161 338 380 359 43 15 NW LS LR G VALLEY

54 31 47 39 140 143 380 413 396 33 13 NW LS SR NV VALLEY

55 31 31 31 93 96 375 400 388 25 15 NW LS SR NV VALLEY

56 78 186 132 341 345 390 440 415 50 8 N LS SR NV VALLEY

57 47 109 78 310 316 350 413 381 63 11 NW LS SR NV VALLEY

58 93 171 132 341 355 400 500 450 100 16 NW LS SR NV VALLEY

59 31 47 39 93 95 480 500 490 20 12 N LS SR G VALLEY

60 47 124 85 217 223 450 500 475 50 13 N LS SR NV VALLEY

61 93 186 140 434 452 500 625 563 125 16 N LS SR NV VALLEY

62 47 93 70 124 129 600 635 618 35 16 N LS SR NV VALLEY

63 93 186 140 155 159 525 560 543 35 13 N LS SR NV VALLEY

64 62 78 70 140 142 600 625 613 25 10 NE LS SR NV VALLEY

65 264 295 279 264 271 638 700 669 63 13 NE LS SR NV VALLEY

66 93 155 124 465 492 650 810 730 160 19 N LS SR G VALLEY

67 78 202 140 202 212 750 815 783 65 18 NW LS SR G VALLEY

68 93 155 124 341 363 700 825 763 125 20 NE LS SR G VALLEY

123

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Slope failure data of Cailaco study site (continued)

69 93 171 132 186 188 30 9 E LS SR NV FLAT

70 109 140 124 186 188 415 440 428 25 8 NE LS SR NV FLAT

71 62 109 85 124 126 375 400 388 25 11 NE LS SR NV FLAT

72 62 78 70 124 126 345 365 355 20 9 NE LS SR NV FLAT

73 62 109 85 186 190 300 338 319 38 11 NE LS SR NV FLAT

74 47 93 70 140 142 250 275 263 25 10 NE LS SR G FLAT

75 62 78 70 186 189 240 275 258 35 11 N LS SR G FLAT

76 155 202 178 140 144 265 300 283 35 14 N LS SR NV FLAT

77 62 78 70 109 111 325 350 338 25 13 N LS SR NV FLAT

78 62 62 62 124 126 350 375 363 25 11 NW LS SR LT VALLEY

79 47 47 47 62 64 275 290 283 15 14 E LS SR G VALLEY

80 124 124 124 85 92 350 385 368 35 22 NE LS LR G RIDGE

81 47 47 47 62 65 355 375 365 20 18 NE LS LR G RIDGE

82 93 140 116 171 181 1180 1240 1210 60 19 NE LS LR NV VALLEY

83 78 140 109 248 255 790 850 820 60 14 NW LS LR NV RIDGE

84 78 78 78 109 111 675 700 688 25 13 NE LS LR G VALLEY

85 47 47 47 109 111 600 625 613 25 13 SE LS LR NV RIDGE

86 47 62 54 186 197 425 490 458 65 19 NE LS LR G VALLEY

87 47 47 47 78 81 775 800 788 25 18 NE LS LR G RIDGE

88 62 62 62 124 132 755 800 778 45 20 N LS LR G RIDGE

89 47 47 47 93 99 565 600 583 35 21 NW LS LR G VALLEY

90 31 31 31 78 81 500 525 513 25 18 NE LS LR NV VALLEY

91 47 93 70 186 195 650 710 680 60 18 NE LS LR G VALLEY

92 47 78 62 62 64 513 530 521 18 16 NE LS LR G FLAT

93 47 47 47 93 99 338 370 354 33 19 NE LS LR G FLAT

94 93 171 132 62 77 605 650 628 45 36 NW SF LR G RIDGE

95 31 47 39 54 67 335 375 355 40 36 NE SF SR G VALLEY

96 47 140 93 62 72 638 675 656 38 31 NE SF SR NV VALLEY

97 47 47 47 93 106 613 663 638 50 28 E SF SR G RIDGE

98 62 93 78 47 55 290 320 305 30 33 E SF SR LT VALLEY

99 31 47 39 62 67 325 350 338 25 22 E SF SR G VALLEY

100 109 171 140 78 87 310 350 330 40 27 NW SF SR G RIDGE

101 78 124 101 78 97 980 1038 1009 58 37 E SF SR NV VALLEY

102 109 171 140 109 130 953 1025 989 73 34 E SF SR NV VALLEY

103 93 124 109 47 55 1140 1170 1155 30 33 NE SF SR LT VALLEY

104 93 155 124 310 349 1105 1265 1185 160 27 NE SF SR NV VALLEY

124

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Slope failure data of Cailaco study site (continued)

105 78 155 116 54 66 268 305 287 37 34 NW SF SR LT VALLEY

106 47 62 54 78 92 250 300 275 50 33 NW SF SR LT RIDGE

107 31 47 39 78 92 250 300 275 50 33 NW SF SR LT RIDGE

108 47 47 47 78 88 940 982 961 42 28 E SF SR G VALLEY

109 47 47 47 62 72 950 988 969 38 31 NE SF SR NV VALLEY

110 16 47 31 140 165 315 404 360 89 33 NW SF SR G VALLEY

111 47 109 78 171 214 410 540 475 130 37 NW SF SR G VALLEY

112 47 78 62 248 290 450 600 525 150 31 NW SF SR G VALLEY

113 47 109 78 155 177 540 625 583 85 29 NW SF SR NV VALLEY

114 78 186 132 140 163 500 585 543 85 31 E SF SR NV VALLEY

115 78 140 109 109 135 870 950 910 80 36 NE SF SR NV VALLEY

116 93 155 124 54 62 250 280 265 30 29 NE SF SR G VALLEY

117 47 47 47 93 115 338 405 371 68 36 NE SF SR NV VALLEY

118 31 62 47 109 124 1000 1060 1030 60 29 E SF SR NV VALLEY

119 78 124 101 54 66 475 513 494 38 35 SE SF SR LT RIDGE

120 62 62 62 62 71 475 510 493 35 29 NE SF SR NV VALLEY

121 62 62 62 62 80 870 920 895 50 39 NE SF SR G VALLEY

122 78 78 78 54 67 435 475 455 40 36 SE SF SR NV VALLEY

123 47 47 47 62 71 450 485 468 35 29 NE SF SR G FLAT

124 62 93 78 47 53 650 675 663 25 28 SE MIX SR NV RIDGE

125 31 78 54 93 101 400 440 420 40 23 NW MIX SR NV VALLEY

126 31 47 39 109 123 338 395 367 57 28 NW MIX SR NV VALLEY

127 78 140 109 124 139 550 613 581 63 27 E MIX SR NV VALLEY

128 78 78 78 47 51 905 925 915 20 23 SE MIX SR G VALLEY

129 124 186 155 93 106 375 425 400 50 28 SE MIX SR NV RIDGE

130 93 202 147 93 106 500 550 525 50 28 NW MIX SR G RIDGE

131 47 62 54 62 67 425 450 438 25 22 SE MIX SR NV FLAT

132 140 140 140 70 79 713 750 731 38 28 E MIX SR NV VALLEY

133 78 124 101 47 51 330 350 340 20 23 SE MIX SR LT VALLEY

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

125

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A.1.3 Zumalai site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 30 45 38 61 64 500 520 510 20 18 E LS SR LT Valley

2 30 61 45 106 109 525 550 538 25 13 SE LS SR LT Valley

3 61 121 91 106 113 450 490 470 40 21 W LS SR NV Valley

4 76 136 106 151 160 463 515 489 53 19 SW LS SR NV Valley

5 61 106 83 151 155 485 520 503 35 13 SW LS SR G Valley

6 61 91 76 182 186 500 540 520 40 12 SW LS SR G Valley

7 45 91 68 212 218 525 575 550 50 13 SW LS SR G Valley

8 30 30 30 121 124 563 590 576 28 13 SE LS SR G Valley

9 45 45 45 167 170 590 625 608 35 12 SW LS SR G Valley

10 45 45 45 61 62 410 425 418 15 14 NE LS SR LT Valley

11 30 61 45 151 154 490 520 505 30 11 SW LS SR G Flat

12 45 45 45 106 109 615 640 628 25 13 SE LS SR NV Valley

13 30 45 38 45 48 600 615 608 15 18 SE LS SR NV Valley

14 30 45 38 30 33 650 663 656 13 22 SW LS SR NV Valley

15 30 45 38 167 174 513 563 538 50 17 E LS SR HT Valley

16 45 61 53 212 227 520 600 560 80 21 NE LS SR NV Valley

17 45 61 53 242 258 550 640 595 90 20 NE LS SR NV Valley

18 15 30 23 45 47 313 325 319 13 15 NE LS SR LT Valley

19 30 45 38 45 47 338 350 344 13 15 NE LS SR G Valley

20 15 45 30 136 151 550 615 583 65 26 N LS SR LT Valley

21 30 45 38 136 145 500 550 525 50 20 N LS SR LT Ridge

22 30 76 53 182 189 538 590 564 53 16 NE LS SR NV Ridge

23 45 61 53 38 40 450 463 456 13 18 W LS LR HT Ridge

24 30 30 30 45 47 413 425 419 13 15 E LS LR HT Ridge

25 15 30 23 38 40 288 300 294 13 18 E LS LR HT Ridge

26 45 76 61 61 62 315 330 323 15 14 SW LS LR LT Ridge

27 30 45 38 76 77 388 403 395 15 11 SW LS LR LT Flat

28 45 61 53 38 40 425 438 432 13 19 NW LS LR G Valley

29 61 76 68 121 124 350 375 363 25 12 SE LS LR LT Valley

30 45 76 61 197 206 468 530 499 62 17 W LS LR LT Valley

31 45 91 68 121 127 463 500 481 38 17 E LS LR LT Valley

32 136 167 151 30 32 450 460 455 10 18 NW LS LR G Valley

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Slope failure data of Zumalai study site (continued)

33 45 76 61 91 94 475 500 488 25 15 NE LS LR LT Flat

34 30 30 30 76 81 413 440 426 28 20 NE LS LR LT Ridge

35 30 30 30 45 47 525 538 531 13 15 NE LS SR G Ridge

36 30 61 45 91 94 400 425 413 25 15 NE LS SR G Ridge

37 45 61 53 151 160 338 390 364 53 19 NE LS SR NV Ridge

38 15 30 23 61 64 425 445 435 20 18 NE LS SR NV Ridge

39 76 91 83 45 51 563 585 574 23 26 SE LS SR LT Ridge

40 136 167 151 121 125 360 390 375 30 14 SW LS SR G Ridge

41 61 76 68 45 48 385 400 393 15 18 SW LS SR G Ridge

42 45 121 83 182 194 400 468 434 68 21 NE LS SR G Ridge

43 76 212 144 30 38 635 658 647 23 37 SW SF SR LT Ridge

44 15 30 23 76 91 600 650 625 50 33 NW SF SR NV Ridge

45 30 30 30 45 54 620 650 635 30 33 NW SF SR NV Valley

46 30 30 30 76 94 325 380 353 55 36 SE SF SR HT Valley

47 76 106 91 30 36 310 330 320 20 33 NW SF LR LT Valley

48 45 61 53 38 48 590 620 605 30 38 NE SF LR NV Valley

49 45 76 61 45 52 525 550 538 25 29 SW SF LR G Valley

50 45 61 53 30 39 490 515 503 25 40 SE SF LR LT Valley

51 45 61 53 30 38 388 410 399 23 37 NE SF LR HT Valley

52 61 61 61 30 33 438 450 444 13 22 NW SF LR G Valley

53 76 106 91 45 53 388 415 401 28 31 SE SF SR LT Valley

54 151 293 222 61 79 475 525 500 50 40 SW SF SR LT Valley

55 30 61 45 76 81 525 555 540 30 22 SW SF SR LT Valley

56 30 30 30 30 33 588 600 594 13 22 NE SF SR LT Valley

57 61 76 68 45 59 500 538 519 38 40 NE SF SR NV Valley

58 30 61 45 151 171 550 630 590 80 28 NE SF SR LT Valley

59 30 30 30 30 33 525 538 531 13 22 NE SF SR HT Valley

60 45 45 45 30 33 538 550 544 13 22 NE SF SR LT Valley

61 15 30 23 45 48 435 450 443 15 18 NE SF SR LT Valley

62 30 45 38 38 45 525 550 538 25 33 NE SF SR LT Valley

63 30 30 30 45 56 438 470 454 33 36 NE SF SR LT Valley

64 30 30 30 30 39 450 475 463 25 40 NE SF SR LT Valley

65 30 30 30 76 81 485 515 500 30 22 NE SF SR G Valley

66 30 45 38 76 98 475 538 507 63 40 N SF SR LT Valley

67 45 76 61 53 73 550 600 575 50 43 N SF SR G Valley

127

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Slope failure data of Zumalai study site (continued)

68 61 61 61 30 39 600 625 613 25 40 SE SF SR LT Valley

69 45 45 45 23 27 410 425 418 15 33 SW SF SR LT Valley

70 76 91 83 38 44 388 410 399 23 31 SW SF SR G Valley

71 61 76 68 61 69 475 508 492 33 29 NE SF SR LT Valley

72 45 61 53 151 165 430 495 463 65 23 NE SF SR LT Ridge

73 121 151 136 38 45 475 500 488 25 33 NW SF SR LT Ridge

74 45 45 45 30 36 770 790 780 20 33 SE SF SR NV Ridge

75 45 106 76 30 36 300 320 310 20 33 NE SF SR G Valley

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

A.1.4 Atsabe Site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 47 155 101 403 410 500 575 538 75 11 NW LS SR G VALLEY

2 62 62 62 109 112 485 513 499 28 14 NW LS SR LT RIDGE

3 78 109 93 155 163 388 438 413 50 18 NW LS SR G RIDGE

4 78 109 93 78 81 388 413 400 25 18 NW LS SR G VALLEY

5 62 78 70 341 349 375 450 413 75 12 NW LS SR G VALLEY

6 93 155 124 186 189 350 385 368 35 11 NW LS SR NV VALLEY

7 62 93 78 155 159 365 400 383 35 13 NW LS SR G VALLEY

8 31 31 31 62 63 388 400 394 13 11 NW LS SR G VALLEY

9 47 62 54 93 100 438 475 456 38 22 NW LS SR NV VALLEY

10 124 186 155 434 438 413 475 444 63 8 NW LS SR NV RIDGE

11 78 93 85 140 142 460 488 474 28 11 NW LS SR LT RIDGE

12 78 93 85 124 126 500 525 513 25 11 NW LS SR LT RIDGE

13 47 62 54 124 126 550 575 563 25 11 NW LS LR G RIDGE

14 78 140 109 93 100 550 588 569 38 22 NW LS LR G RIDGE

15 78 171 124 155 167 575 638 606 63 22 NE LS LR NV RIDGE

16 31 31 31 47 48 313 325 319 13 15 SW LS LR G RIDGE

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Slope failure data of Atsabe study site (continued)

17 62 78 70 93 94 515 530 523 15 9 NE LS LR G RIDGE

18 47 78 62 39 42 500 515 508 15 21 NW LS LR LT RIDGE

19 62 93 78 124 131 572 615 594 43 19 NW LS LR NV VALLEY

20 62 78 70 62 64 535 550 543 15 14 NW LS LR LT RIDGE

21 62 62 62 93 99 330 363 346 33 19 SW LS LR G RIDGE

22 124 124 124 39 46 388 413 400 25 33 NW SF LR G RIDGE

23 78 109 93 47 60 375 413 394 38 39 NW SF LR G RIDGE

24 31 62 47 39 48 385 413 399 28 35 NW SF LR G RIDGE

25 47 78 62 62 69 375 405 390 30 26 N SF LR G VALLEY

26 62 93 78 62 77 350 395 373 45 36 N SF LR G RIDGE

27 31 47 39 23 26 438 450 444 13 28 NW SF SR LT RIDGE

28 78 78 78 39 46 363 388 375 25 33 NW SF SR G RIDGE

29 109 186 147 54 66 538 575 556 38 35 N SF SR G RIDGE

30 31 31 31 23 29 383 400 391 18 37 SW SF SR G RIDGE

31 47 62 54 23 30 382 400 391 18 38 SW SF SR G RIDGE

32 62 78 70 31 37 380 400 390 20 33 SW SF SR G RIDGE

33 31 47 39 31 43 495 525 510 30 44 NW SF SR G RIDGE

34 93 140 116 78 86 363 400 381 38 26 NE MIX SR G VALLEY

35 62 78 70 47 53 350 375 363 25 28 SW MIX SR G RIDGE

36 78 93 85 47 51 300 320 310 20 23 NW MIX LR NV RIDGE

37 62 62 62 93 101 460 500 480 40 23 NE MIX LR G RIDGE

38 62 93 78 31 34 475 490 483 15 26 NW MIX LR LT RIDGE

39 78 109 93 54 60 400 425 413 25 25 NW MIX LR LT RIDGE

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

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A.1.5 Maliana Site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 109 124 116 78 81 475 500 488 25 18 SW LS SR G RIDGE

2 78 109 93 109 114 475 510 493 35 18 SW LS SR G RIDGE

3 124 124 124 47 48 450 463 456 13 15 SW LS SR G RIDGE

4 39 54 47 93 96 825 850 838 25 15 SE LS SR NV VALLEY

5 47 124 85 140 142 675 700 688 25 10 SE LS SR NV RIDGE

6 47 47 47 62 63 650 663 656 13 11 SE LS SR NV RIDGE

7 47 124 85 124 133 863 910 886 48 21 E LS SR NV VALLEY

8 78 78 78 171 186 575 650 613 75 24 SW LS SR NV RIDGE

9 54 54 54 109 113 894 925 909 31 16 SE LS SR G RIDGE

10 78 93 85 93 100 875 913 894 38 22 S LS SR G RIDGE

11 93 124 109 78 81 695 720 708 25 18 S LS LR NV RIDGE

12 62 124 93 93 100 650 688 669 38 22 SE LS LR NV RIDGE

13 78 93 85 140 147 600 645 623 45 18 SE LS LR NV RIDGE

14 47 47 47 93 95 794 813 803 19 11 SE LS LR NV RIDGE

15 31 31 31 78 81 463 488 475 25 18 E LS LR LT VALLEY

16 109 109 109 31 33 463 475 469 13 22 E LS LR G VALLEY

17 168 260 214 153 190 1188 1300 1244 113 36 NW SF LR LT VALLEY

18 168 225 197 77 91 1275 1325 1300 50 33 NW SF LR LT VALLEY

19 46 46 46 46 58 1663 1698 1680 35 37 NW SF LR HT VALLEY

20 62 93 78 62 72 1125 1163 1144 38 31 SW SF LR LT RIDGE

21 47 78 62 47 60 1175 1213 1194 38 39 SW SF LR LT VALLEY

22 124 124 124 39 43 1275 1294 1284 19 26 SW SF SR LT RIDGE

23 31 31 31 62 68 435 463 449 28 24 E SF SR NV VALLEY

24 78 78 78 47 60 413 450 431 38 39 E SF SR NV VALLEY

25 47 62 54 109 125 450 513 481 63 30 E SF SR NV VALLEY

26 124 140 132 47 58 463 498 480 35 37 SW SF SR G RIDGE

27 155 233 194 62 80 575 625 600 50 39 SW SF SR G VALLEY

28 171 171 171 62 72 475 513 494 38 31 SW SF SR LT RIDGE

29 202 202 202 78 90 700 745 723 45 30 E SF SR G RIDGE

30 168 260 214 107 129 1165 1238 1201 73 34 E SF SR LT RIDGE

31 62 62 62 23 28 950 965 958 15 33 E SF SR G VALLEY

130

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A.1.6 Ainaro Site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 62 108 85 170 189 1063 1145 1104 83 26 SE LS LR G VALLEY

2 62 93 77 139 149 935 990 963 55 22 NW LS LR G VALLEY

3 45 91 68 76 81 235 263 249 28 20 NW LS LR G VALLEY

4 15 30 23 61 66 300 325 313 25 22 NW LS LR HT VALLEY

5 30 45 38 182 188 775 825 800 50 15 NW LS LR HT VALLEY

6 30 61 45 23 27 510 525 518 15 33 W SF LR LT VALLEY

7 30 45 38 23 30 480 500 490 20 41 NW SF LR HT VALLEY

8 30 76 53 121 165 788 900 844 113 43 NW SF LR LT VALLEY

9 61 61 61 45 59 1163 1200 1181 38 40 NW SF LR HT VALLEY

10 30 61 45 45 61 1325 1365 1345 40 41 NW SF SR G VALLEY

11 61 76 68 30 39 1538 1563 1550 25 40 NW SF SR G VALLEY

12 30 45 38 23 26 1538 1550 1544 13 29 NW SF SR G VALLEY

13 45 76 61 45 59 1350 1388 1369 38 40 NW SF SR G VALLEY

14 45 61 53 45 56 813 845 829 33 36 NW SF SR LT VALLEY

15 30 61 45 91 118 900 975 938 75 40 NW SF SR LT VALLEY

16 45 45 45 23 26 1075 1088 1081 13 29 NW SF SR NV VALLEY

17 61 136 98 23 30 1000 1020 1010 20 41 NW SF SR G VALLEY

18 76 76 76 23 26 950 963 956 13 29 NW SF SR G VALLEY

19 30 30 30 30 39 900 925 913 25 40 NW SF SR LT VALLEY

20 77 123 100 154 187 695 800 748 105 34 NE SF SR NV VALLEY

21 62 123 93 108 149 713 815 764 103 44 NE SF SR NV VALLEY

22 123 139 131 39 46 800 825 813 25 33 W SF SR G VALLEY

23 61 167 114 45 61 200 240 220 40 41 W SF SR NV VALLEY

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

131

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A.1.7 Hatolia Site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Mean difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 47 47 47 54 56 575 590 583 15 15 SW LS VR G RIDGE

2 93 140 116 124 132 505 550 528 45 20 SW LS VR G RIDGE

3 62 78 70 124 126 650 675 663 25 11 SE LS VR G VALLEY

4 78 109 93 54 59 660 682 671 22 22 SE LS VR G VALLEY

5 78 186 132 93 100 688 725 706 38 22 S LS SR NV VALLEY

6 109 264 186 62 71 665 700 683 35 29 S LS SR NV VALLEY

7 47 109 78 62 64 675 690 683 15 14 S LS SR NV VALLEY

8 78 109 93 186 191 275 320 298 45 14 SE LS SR G RIDGE

9 78 124 101 39 43 388 405 396 18 24 W LS SR LT RIDGE

10 47 78 62 62 64 410 427 419 17 15 W LS SR LT RIDGE

11 62 78 70 93 106 650 700 675 50 28 S LS SR G RIDGE

12 62 78 70 109 119 475 525 500 50 25 W LS SR LT VALLEY

13 62 93 78 310 313 185 230 208 45 8 W LS SR LT RIDGE

14 140 279 209 62 80 685 735 710 50 39 SE SF SR LT VALLEY

15 140 171 155 62 77 680 725 703 45 36 SE SF SR G VALLEY

16 47 47 47 62 71 585 620 603 35 29 S SF SR G VALLEY

17 62 93 78 47 53 600 625 613 25 28 S SF MR G VALLEY

18 140 140 140 54 65 650 685 668 35 33 SW SF MR G RIDGE

19 62 109 85 109 132 400 475 438 75 35 N SF MR G VALLEY

20 31 31 31 62 80 210 260 235 50 39 S SF MR LT RIDGE

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

132

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A.1.8 Hatobuilico Site

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max difference angle Slope **) Cover ***) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure*)

1 62 93 77 216 225 800 863 831 63 16 SE LS VR NV VALLEY

2 77 93 85 139 148 800 850 825 50 20 SE LS VR NV VALLEY

3 46 77 62 170 180 1030 1090 1060 60 19 SE LS VR LT VALLEY

4 77 108 93 123 126 863 890 876 28 13 SE LS VR G VALLEY

5 46 62 54 140 144 1290 1325 1308 35 14 SE LS MR HT VALLEY

6 31 46 39 123 144 1288 1363 1325 75 31 NE SF MR LT VALLEY

7 31 77 54 62 79 1250 1300 1275 50 39 N SF MR NV VALLEY

8 93 139 116 231 263 1550 1675 1613 125 28 SE SF MR LT VALLEY

9 123 216 170 154 179 1650 1740 1695 90 30 SE SF MR G VALLEY

10 31 62 46 123 151 1988 2075 2031 88 35 N SF SR NV VALLEY

11 31 31 31 93 119 2025 2100 2063 75 39 N SF SR NV VALLEY

12 46 46 46 170 227 2000 2150 2075 150 41 N SF SR NV VALLEY

13 31 62 46 93 112 2000 2063 2031 63 34 N SF SR NV VALLEY

14 62 77 69 123 153 1625 1715 1670 90 36 NE SF SR NV VALLEY

15 77 108 93 62 74 1585 1625 1605 40 33 NE SF SR NV VALLEY

16 31 62 46 154 184 1500 1600 1550 100 33 NW SF SR NV VALLEY

17 139 201 170 77 87 1000 1040 1020 40 27 SE SF SR NV VALLEY

18 185 231 208 77 92 1200 1250 1225 50 33 SE SF SR NV VALLEY

*)Types of Slope Failure : - SF = Surface Failure; LS = Landslide, MIX = Landslide and Surface Failure

**) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

***)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

133

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A.2 Physical data of unfailure slope

A.2.1 BOBONARO SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) (m) Failure

1 77 153 115 153 173 1100 1180 1140 80 28 SE unfailured IR HT RIDGE

2 61 69 65 92 112 1510 1575 1543 65 35 SE unfailured IR HT RIDGE

3 61 46 54 138 151 495 558 526 63 24 SE unfailured IR HT VALLEY

4 69 92 80 77 78 841 855 848 14 10 SE unfailured IR LT FLAT

5 77 122 99 122 139 916 983 949 66 28 E unfailured IR LT VALLEY

6 77 153 115 184 219 735 855 795 120 33 NE unfailured IR LT VALLEY

7 46 107 77 214 223 735 799 767 64 17 NE unfailured IR HT RIDGE

8 61 92 77 92 99 823 860 841 38 22 NE unfailured IR HT RIDGE

9 107 138 122 69 73 660 685 673 25 20 N unfailured IR LT RIDGE

10 46 69 57 107 119 648 700 674 53 26 N unfailured IR LT RIDGE

11 31 61 46 77 81 565 593 579 28 20 SE unfailured IR G RIDGE

12 61 54 57 107 116 535 580 558 45 23 SE unfailured IR G RIDGE

13 77 92 84 122 124 823 840 831 18 8 SW unfailured IR LT FLAT

14 92 122 107 184 192 785 840 813 55 17 SW unfailured IR G RIDGE

15 54 46 50 77 89 775 820 798 45 30 SW unfailured IR G RIDGE

16 46 61 54 92 108 1273 1330 1301 58 32 SW unfailured IR LT RIDGE

17 31 61 46 77 77 1204 1215 1209 11 8 SW unfailured IR G RIDGE

18 46 46 46 69 70 1198 1210 1204 13 10 N unfailured SR G FLAT

19 54 61 57 77 77 1110 1120 1115 10 7 N unfailured SR HT FLAT

20 69 31 50 84 90 1135 1168 1151 33 21 N unfailured SR HT RIDGE

21 122 138 130 92 113 934 1000 967 66 36 SE unfailured SR LT VALLEY

22 46 61 54 46 50 1009 1028 1018 19 22 SE unfailured SR LT RIDGE

23 61 92 77 61 64 1021 1040 1031 19 17 SE unfailured SR LT RIDGE

24 46 61 54 38 44 1021 1043 1032 22 30 SE unfailured SR LT VALLEY

25 54 61 57 46 49 1046 1063 1055 17 20 SE unfailured SR LT RIDGE

26 61 77 69 92 110 859 920 889 62 34 S unfailured SR HT RIDGE

27 69 92 80 122 148 916 1000 958 84 34 S unfailured SR HT RIDGE

28 107 107 107 168 212 846 975 911 129 37 S unfailured SR HT RIDGE

29 69 99 84 138 140 996 1020 1008 24 10 SW unfailured IR G FLAT

30 107 122 115 122 128 971 1010 991 39 18 SW unfailured IR LT RIDGE

31 92 61 77 46 53 694 720 707 27 30 SW unfailured IR LT RIDGE

134

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Un-failure slopes data of Bobonaro study site (continued)

32 84 92 88 77 84 571 605 588 34 24 NE unfailured IR HT RIDGE

33 107 77 92 77 78 871 885 878 14 10 NE unfailured IR HT RIDGE

34 77 138 107 77 87 559 600 579 42 28 NE unfailured IR HT RIDGE

35 61 54 57 61 63 409 425 417 17 15 NE unfailured IR HT FLAT

36 61 69 65 92 94 509 530 519 22 13 SE unfailured IR LT FLAT

37 69 92 80 69 77 526 560 543 34 26 SE unfailured IR LT VALLEY

38 92 107 99 92 97 384 415 399 32 19 SE unfailured IR LT VALLEY

39 31 61 46 92 104 391 440 416 49 28 SE unfailured IR G VALLEY

40 38 77 57 69 74 434 460 447 27 21 NW unfailured IR G VALLEY

41 92 138 115 92 98 471 505 488 34 20 NW unfailured IR G VALLEY

42 107 122 115 77 79 496 515 506 19 14 SE unfailured IR NV FLAT

43 107 122 115 107 111 336 365 351 29 15 SE unfailured IR NV FLAT

44 214 153 184 77 82 546 575 561 29 21 NE unfailured IR NV RIDGE

45 61 92 77 92 97 1096 1128 1112 32 19 NE unfailured IR LT RIDGE

46 153 130 142 92 102 971 1015 993 44 26 SW unfailured IR G RIDGE

47 77 77 77 107 120 584 638 611 54 27 SE unfailured IR G RIDGE

48 61 46 54 92 97 646 678 662 32 19 SE unfailured IR G FLAT

49 54 77 65 61 65 544 565 554 22 19 N unfailured IR G FLAT

50 77 61 69 46 56 534 565 549 32 34 N unfailured IR G VALLEY

51 77 92 84 92 95 560 585 573 25 15 SW unfailured IR LT RIDGE

52 61 46 54 92 93 473 485 479 12 7 SW unfailured IR LT FLAT

53 31 77 54 107 108 633 649 641 16 9 SE unfailured IR LT FLAT

54 84 61 73 84 86 448 468 458 20 13 SE unfailured IR LT FLAT

55 107 69 88 92 94 573 593 583 20 12 SE unfailured IR HT FLAT

56 92 77 84 92 98 515 549 532 34 20 SE unfailured IR HT FLAT

57 61 122 92 107 109 548 568 558 20 11 SE unfailured IR HT FLAT

58 77 77 77 46 51 430 453 441 23 26 E unfailured IR G VALLEY

59 107 107 107 92 100 310 349 329 39 23 E unfailured IR G VALLEY

60 61 122 92 153 159 510 555 533 45 16 E unfailured IR G RIDGE

61 61 138 99 122 145 395 473 434 78 32 E unfailured IR LT RIDGE

62 77 77 77 77 83 423 455 439 33 23 E unfailured IR LT RIDGE

63 61 69 65 107 115 400 443 421 43 22 E unfailured IR LT RIDGE

64 77 77 77 61 76 473 518 495 45 36 E unfailured IR LT VALLEY

65 46 77 61 107 112 498 530 514 33 17 S unfailured IR LT RIDGE

66 46 107 77 107 120 435 490 463 55 27 S unfailured LR LT RIDGE

67 38 77 57 107 109 560 580 570 20 11 S unfailured LR HT FLAT

135

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Un-failure slopes data of Bobonaro study site (continued)

68 61 46 54 77 80 423 445 434 23 16 S unfailured LR HT FLAT

69 46 31 38 77 89 535 580 558 45 30 E unfailured LR LT RIDGE

70 77 46 61 61 76 473 518 495 45 36 E unfailured LR LT VALLEY

71 31 77 54 46 56 385 418 401 33 35 E unfailured LR LT VALLEY

72 31 107 69 77 83 385 418 401 33 23 E unfailured LR LT VALLEY

73 77 92 84 122 141 404 474 439 70 30 E unfailured LR LT VALLEY

74 77 54 65 153 159 435 480 458 45 16 E unfailured LR LT FLAT

75 31 92 61 77 82 460 490 475 30 21 E unfailured LR LT RIDGE

76 61 46 54 92 108 485 543 514 58 32 SE unfailured LR NV RIDGE

77 46 61 54 99 105 485 518 501 33 18 SE unfailured LR NV RIDGE

78 61 77 69 92 100 491 530 511 39 23 SE unfailured LR G RIDGE

79 31 77 54 61 72 385 424 404 39 32 SE unfailured LR G RIDGE

80 92 99 96 122 138 441 505 473 64 28 SE unfailured LR G RIDGE

81 92 69 80 61 67 401 428 414 26 23 E unfailured LR LT VALLEY

82 61 77 69 77 82 383 413 398 30 21 E unfailured LR LT VALLEY

83 92 61 77 107 116 395 440 418 45 23 E unfailured LR LT VALLEY

84 92 107 99 122 130 358 403 380 45 20 E unfailured LR HT FLAT

85 46 107 77 77 90 358 405 381 48 32 W unfailured LR HT VALLEY

86 77 138 107 107 109 330 353 341 23 12 W unfailured LR HT FLAT

87 77 92 84 46 55 285 315 300 30 33 W unfailured LR LT VALLEY

88 38 122 80 77 79 258 278 268 20 15 W unfailured LR LT FLAT

89 78 93 85 47 48 588 600 594 13 15 SW Unfailure LR LT FLAT

90 47 124 85 62 72 563 600 581 38 31 SW Unfailure LR LT VALLEY

91 62 140 101 78 81 470 495 483 25 18 E Unfailure LR LT RIDGE

92 62 78 70 93 99 463 495 479 33 19 E Unfailure LR HT RIDGE

93 78 47 62 62 72 483 520 501 38 31 E Unfailure LR HT VALLEY

94 109 47 78 78 95 425 480 453 55 35 E Unfailure LR HT VALLEY

95 124 47 85 155 160 465 505 485 40 14 E Unfailure LR G FLAT

96 124 62 93 171 172 438 460 449 23 8 E Unfailure LR G FLAT

97 78 62 70 93 99 663 695 679 33 19 NW Unfailure LR G RIDGE

98 93 78 85 109 113 475 505 490 30 15 NW Unfailure LR G RIDGE

99 171 78 124 109 109 400 413 406 13 7 NW Unfailure SR LT FLAT

100 78 124 101 109 111 400 425 413 25 13 NW Unfailure SR LT FLAT

101 78 47 62 109 109 428 438 433 10 5 NW Unfailure SR LT FLAT

102 78 47 62 93 97 413 440 426 28 16 NW Unfailure SR LT FLAT

103 62 78 70 78 79 438 450 444 13 9 E Unfailure SR LT FLAT

136

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Un-failure slopes data of Bobonaro study site (continued)

104 62 93 78 70 74 400 425 413 25 20 E Unfailure SR LT RIDGE

105 62 93 78 54 55 405 415 410 10 10 E Unfailure SR LT FLAT

106 62 78 70 54 59 415 438 426 23 23 E Unfailure SR LT RIDGE

107 171 93 132 62 71 663 698 680 36 30 E Unfailured SR HT VALLEY

108 93 124 109 78 97 600 658 629 58 37 SW Unfailured SR HT VALLEY

109 78 109 93 93 97 600 628 614 28 17 SW Unfailured SR LT RIDGE

110 62 62 62 109 118 725 771 748 46 23 SW Unfailured SR LT RIDGE

111 78 124 101 93 94 735 748 742 13 8 SW Unfailured SR LT FLAT

112 93 93 93 109 112 538 566 552 29 15 SW Unfailured SR G FLAT

113 62 78 70 155 159 613 648 630 36 13 NW Unfailured SR G FLAT

114 62 78 70 109 113 625 658 642 33 17 NW Unfailured SR HT RIDGE

115 78 47 62 62 65 663 683 673 21 18 NW Unfailured SR HT RIDGE

116 124 93 109 140 170 603 700 651 98 35 NW Unfailured LR HT VALLEY

117 93 62 78 109 109 595 608 601 14 7 NW Unfailured LR HT FLAT

118 62 93 78 109 115 550 588 569 38 19 NW Unfailured LR HT RIDGE

119 93 62 78 62 67 508 533 520 26 22 SW Unfailured LR LT RIDGE

120 47 78 62 109 109 488 500 494 13 7 SW Unfailured LR LT FLAT

121 78 93 85 140 141 463 483 473 21 8 SW Unfailured LR LT FLAT

122 78 124 101 93 95 582 603 592 21 12 SE Unfailured LR G FLAT

123 47 62 54 124 127 565 595 580 30 13 SE Unfailured LR LT FLAT

124 62 93 78 171 172 540 565 553 25 8 SE Unfailured LR LT FLAT

125 47 78 62 78 82 550 578 564 28 20 SE Unfailured LR LT RIDGE

126 62 93 78 62 63 500 513 506 13 11 SE Unfailured LR LT FLAT

127 78 93 85 109 111 390 415 403 25 13 SE Unfailured SR HT FLAT

128 93 124 109 78 106 515 588 551 73 43 SE Unfailured SR HT FLAT

129 47 78 62 78 81 828 850 839 23 16 W Unfailured SR G VALLEY

130 78 124 101 78 85 890 925 908 35 24 W Unfailured SR LT VALLEY

131 140 109 124 93 99 590 625 608 35 21 W Unfailured SR LT VALLEY

132 78 62 70 124 159 515 615 565 100 39 E Unfailured LR LT VALLEY

133 62 93 78 140 175 565 670 618 105 37 E Unfailured LR LT VALLEY

134 109 78 93 155 162 515 563 539 48 17 E Unfailured LR G VALLEY

135 140 109 124 93 99 578 613 595 35 21 E Unfailured LR G VALLEY

136 124 155 140 109 130 615 688 651 73 34 E Unfailured LR G VALLEY

137 78 109 93 124 126 590 613 601 23 10 NE Unfailured LR G VALLEY

138 93 124 109 78 95 720 775 748 55 35 NE Unfailured LR LT RIDGE

139 109 93 101 140 152 703 763 733 60 23 NE Unfailured LR LT RIDGE

137

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Un-failure slopes data of Bobonaro study site (continued)

140 155 140 147 171 196 765 863 814 98 30 NE Unfailured LR LT RIDGE

141 171 186 178 109 118 765 813 789 48 24 NE Unfailured LR HT VALLEY

142 78 78 78 109 112 665 693 679 28 14 NE Unfailured LR HT VALLEY

143 62 78 70 109 119 585 633 609 48 24 SW Unfailured IR LT VALLEY

144 93 124 109 78 80 573 593 583 21 15 SW Unfailured IR LT RIDGE

145 78 93 85 109 123 723 781 752 58 28 SW Unfailured IR LT VALLEY

146 93 171 132 62 65 760 781 770 21 18 SW Unfailured IR LT RIDGE

147 93 109 101 78 80 760 781 770 21 15 SW Unfailured IR G RIDGE

148 78 140 109 62 65 748 768 758 21 18 SW Unfailured IR G VALLEY

149 109 140 124 109 118 623 668 645 46 23 W Unfailured IR G RIDGE

150 62 186 124 109 133 623 700 661 78 36 W Unfailured IR G VALLEY

151 47 155 101 78 82 723 748 735 26 18 W Unfailured IR G RIDGE

152 62 217 140 109 134 585 663 624 78 36 W Unfailured IR HT VALLEY

153 78 93 85 78 95 553 608 580 56 36 SE Unfailured IR HT VALLEY

154 78 47 62 124 125 538 556 547 18 8 SE Unfailured IR HT FLAT

155 109 78 93 62 70 523 556 539 33 28 SE Unfailured IR HT VALLEY

156 124 62 93 109 111 523 548 535 26 13 SE Unfailured IR HT FLAT

157 62 109 85 78 94 648 700 674 53 34 SE Unfailured IR LT VALLEY

158 47 140 93 93 104 573 618 595 46 26 SE Unfailured IR LT VALLEY

159 155 124 140 62 78 498 545 521 48 37 S Unfailured IR LT VALLEY

160 124 124 124 140 143 523 556 539 33 13 S Unfailured IR LT FLAT

161 109 78 93 171 185 448 518 483 71 22 S Unfailured IR G VALLEY

162 124 124 124 109 123 410 468 439 58 28 S Unfailured IR G VALLEY

163 61 106 83 76 83 878 912 895 35 25 W Unfailured IR HT RIDGE

164 45 76 61 121 151 905 995 950 90 37 W Unfailured IR HT VALLEY

165 76 91 83 182 222 855 983 919 128 35 W Unfailured IR LT VALLEY

166 76 106 91 151 211 793 940 866 148 44 W Unfailured IR LT VALLEY

167 76 106 91 76 92 868 920 894 53 35 W Unfailured IR G VALLEY

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

138

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A.2.2 CAILACO SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 109 140 124 109 119 375 425 400 50 25 SW Unfailured LR LT RIDGE

2 109 140 124 171 211 375 500 438 125 36 SW Unfailured LR HT VALLEY

3 93 124 109 109 119 450 500 475 50 25 SW Unfailured LR HT VALLEY

4 93 124 109 186 193 600 650 625 50 15 SW Unfailured LR G FLAT

5 78 124 101 93 96 600 625 613 25 15 SW Unfailured LR G FLAT

6 109 140 124 109 113 525 555 540 30 15 SW Unfailured LR LT RIDGE

7 62 124 93 78 87 600 640 620 40 27 SW Unfailured LR LT RIDGE

8 78 140 109 109 111 700 725 713 25 13 E Unfailured LR G FLAT

9 47 78 62 93 94 725 740 733 15 9 E Unfailured LR G FLAT

10 78 124 101 109 110 800 818 809 18 9 E Unfailured LR G RIDGE

11 47 109 78 93 98 915 945 930 30 18 N Unfailured LR G RIDGE

12 78 93 85 78 79 670 685 678 15 11 N Unfailured LR LT FLAT

13 62 93 78 78 79 668 683 675 16 11 N Unfailured LR LT FLAT

14 78 109 93 62 63 650 658 654 8 7 N Unfailured LR LT FLAT

15 93 109 101 93 99 725 758 742 33 20 E Unfailured LR HT RIDGE

16 93 140 116 186 222 385 506 445 121 33 S Unfailured LR G VALLEY

17 78 93 85 62 64 423 440 431 18 16 S Unfailured LR G FLAT

18 62 62 62 78 79 385 400 393 15 11 SW Unfailured LR NV FLAT

19 78 62 70 109 125 360 423 392 63 30 SW Unfailured SR NV VALLEY

20 62 93 78 109 113 348 381 364 33 17 SW Unfailured SR NV FLAT

21 78 109 93 109 116 360 400 380 40 20 E Unfailured SR G VALLEY

22 124 140 132 62 66 258 280 269 23 20 SE Unfailured SR LT RIDGE

23 31 47 39 62 66 333 355 344 23 20 W Unfailured SR HT RIDGE

24 31 47 39 54 66 343 380 361 38 35 W Unfailured SR HT VALLEY

25 62 62 62 124 126 358 380 369 23 10 W Unfailured SR HT FLAT

26 47 47 47 62 64 283 300 291 18 16 W Unfailured SR HT FLAT

27 109 140 124 78 79 313 330 321 18 13 W Unfailured SR LT FLAT

28 155 202 178 93 96 318 340 329 23 14 NW Unfailured SR LT FLAT

29 62 93 78 47 54 298 325 311 28 31 NW Unfailured SR LT VALLEY

30 78 124 101 47 50 338 355 346 18 21 NW Unfailured SR LT VALLEY

31 109 171 140 78 86 318 355 336 38 26 NW Unfailured SR LT VALLEY

32 124 124 124 85 91 358 390 374 33 21 NW Unfailured SR NV VALLEY

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Un-failure slopes data of Cailaco study site (continued)

33 47 47 47 62 64 363 380 371 18 16 S Unfailured SR NV RIDGE

34 47 47 47 171 181 370 430 400 60 19 S Unfailured SR NV RIDGE

35 47 78 62 155 166 445 505 475 60 21 S Unfailured SR LT RIDGE

36 47 62 54 310 337 523 655 589 133 23 S Unfailured SR LT RIDGE

37 47 62 54 186 196 433 495 464 63 19 S Unfailured SR LT VALLEY

38 31 78 54 93 100 408 445 426 38 22 S Unfailured SR G VALLEY

39 31 62 47 186 200 583 655 619 73 21 S Unfailured SR G RIDGE

40 248 279 264 140 147 268 315 291 48 19 S Unfailured SR HT RIDGE

41 93 140 116 171 181 285 345 315 60 19 S Unfailured SR HT RIDGE

42 109 186 147 109 118 310 358 334 48 24 S Unfailured SR HT RIDGE

43 93 124 109 78 85 278 313 295 35 24 E Unfailured SR HT RIDGE

44 62 78 70 93 104 260 308 284 48 27 E Unfailured SR HT VALLEY

45 47 78 62 62 78 260 308 284 48 37 E Unfailured IR LT VALLEY

46 47 62 54 93 95 315 333 324 18 11 E Unfailured IR LT FLAT

47 62 78 70 140 143 325 358 341 33 13 N Unfailured IR LT FLAT

48 78 140 109 217 230 355 433 394 78 20 N Unfailured IR LT RIDGE

49 47 78 62 202 211 310 373 341 63 17 W Unfailured IR LT RIDGE

50 62 93 78 155 176 325 408 366 83 28 W Unfailured IR LT RIDGE

51 78 93 85 186 202 335 413 374 78 23 W Unfailured IR LT RIDGE

52 47 62 54 109 121 348 403 375 55 27 W Unfailured IR G RIDGE

53 62 78 70 124 127 510 538 524 28 13 SE Unfailured IR G RIDGE

54 78 109 93 78 85 485 520 503 35 24 SE Unfailured IR G VALLEY

55 78 54 66 93 104 623 670 646 48 27 SE Unfailured IR LT VALLEY

56 62 47 54 109 118 760 808 784 48 24 SE Unfailured IR LT VALLEY

57 78 47 62 62 66 785 808 796 23 20 NE Unfailured IR G VALLEY

58 93 62 78 109 117 765 808 786 43 21 NE Unfailured IR LT RIDGE

59 62 78 70 155 166 748 808 778 60 21 NE Unfailured IR HT RIDGE

60 78 62 70 124 128 575 608 591 33 15 NE Unfailured IR HT VALLEY

61 47 78 62 93 95 870 888 879 18 11 NE Unfailured IR G FLAT

62 93 109 101 78 82 820 848 834 28 20 NW Unfailured IR G RIDGE

63 109 78 93 62 64 915 933 924 18 16 NW Unfailured IR LT RIDGE

64 124 93 109 109 110 880 898 889 18 9 NW Unfailured IR LT FLAT

65 124 109 116 233 239 848 903 875 55 13 NW Unfailured IR LT FLAT

66 93 124 109 171 175 915 954 935 39 13 S Unfailured IR G FLAT

67 62 93 78 140 143 968 999 983 32 13 S Unfailured IR G FLAT

68 109 124 116 78 79 1010 1025 1018 15 11 S Unfailured SR G FLAT

140

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Un-failure slopes data of Cailaco study site (continued)

69 47 109 78 62 66 925 949 937 24 21 S Unfailured SR LT RIDGE

70 78 109 93 62 77 985 1030 1008 45 36 S Unfailured SR LT VALLEY

71 93 140 116 78 97 958 1015 986 58 37 W Unfailured SR LT VALLEY

72 47 62 54 78 97 1005 1064 1035 59 37 W Unfailured SR LT VALLEY

73 62 78 70 109 114 1050 1084 1067 34 17 W Unfailured SR LT VALLEY

74 62 78 70 78 88 945 986 966 41 28 W Unfailured SR HT VALLEY

75 78 109 93 93 105 930 979 955 49 28 W Unfailured SR HT VALLEY

76 62 155 109 93 95 835 857 846 22 13 W Unfailured SR HT RIDGE

77 93 93 93 47 59 955 992 973 37 38 W Unfailured SR HT RIDGE

78 78 93 85 47 58 980 1014 997 34 36 SW Unfailured SR G RIDGE

79 109 124 116 109 134 950 1029 990 79 36 SW Unfailured IR G RIDGE

80 124 140 132 109 115 855 894 875 39 20 SW Unfailured IR G RIDGE

81 109 140 124 124 132 790 834 812 44 20 NW Unfailured IR LT RIDGE

82 62 93 78 93 97 1145 1174 1160 29 17 NW Unfailured IR HT VALLEY

83 47 93 70 116 150 1110 1205 1158 95 39 NW Unfailured IR HT VALLEY

84 109 109 109 78 97 1185 1244 1215 59 37 NW Unfailured IR G VALLEY

85 62 109 85 140 151 795 854 825 59 23 N Unfailured IR LT VALLEY

86 93 78 85 155 158 250 279 265 29 11 N Unfailured IR LT FLAT

87 124 124 124 93 94 240 254 247 14 9 N Unfailured IR NV FLAT

88 140 140 140 109 115 305 344 325 39 20 N Unfailured IR NV RIDGE

89 109 155 132 78 85 320 354 337 34 24 N Unfailured IR LT RIDGE

90 78 62 70 109 115 365 404 385 39 20 N Unfailured IR LT RIDGE

91 109 140 124 124 130 353 393 373 40 18 SW Unfailured IR LT VALLEY

92 47 124 85 109 113 395 425 410 30 15 SW Unfailured IR LT RIDGE

93 62 93 78 155 157 390 413 401 23 8 SW Unfailured IR LT FLAT

94 47 62 54 124 151 330 417 373 87 35 SW Unfailured IR HT VALLEY

95 62 140 101 217 222 405 453 429 48 12 SW Unfailured IR HT FLAT

96 62 93 78 186 195 365 425 395 60 18 S Unfailured IR HT RIDGE

97 109 140 124 155 183 415 513 464 98 32 S Unfailured IR HT VALLEY

98 93 155 124 140 141 495 515 505 20 8 S Unfailured LR LT FLAT

99 47 93 70 155 162 465 513 489 48 17 S Unfailured LR LT RIDGE

100 31 93 62 140 186 515 638 576 123 41 SW Unfailured LR LT VALLEY

101 62 109 85 171 213 425 553 489 128 37 SW Unfailured LR LT VALLEY

102 47 78 62 186 237 465 613 539 148 38 SW Unfailured LR G VALLEY

103 62 109 85 140 162 555 638 596 83 31 E Unfailured LR G VALLEY

104 62 93 78 124 128 615 648 631 33 15 E Unfailured LR G RIDGE

141

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Un-failure slopes data of Cailaco study site (continued)

105 93 155 124 93 99 540 573 556 33 19 E Unfailured LR LT RIDGE

106 62 78 70 109 111 615 638 626 23 12 E Unfailured LR LT RIDGE

107 62 93 78 78 81 515 538 526 23 16 E Unfailured LR LT FLAT

108 78 109 93 124 128 490 523 506 33 15 E Unfailured LR NV FLAT

109 78 93 85 78 81 690 713 701 23 16 NE Unfailured LR NV FLAT

110 140 186 163 171 181 653 713 683 60 19 NE Unfailured LR NV RIDGE

111 109 140 124 109 124 565 625 595 60 29 E Unfailured LR LT RIDGE

112 140 186 163 124 149 515 598 556 83 34 E Unfailured LR LT VALLEY

113 93 140 116 217 268 665 823 744 158 36 N Unfailured LR LT VALLEY

114 109 140 124 155 165 665 723 694 58 20 E Unfailured LR G VALLEY

115 78 171 124 155 167 765 828 796 63 22 E Unfailured LR HT VALLEY

116 78 109 93 217 247 723 840 781 118 28 SE Unfailured SR LT VALLEY

117 109 140 124 109 135 893 973 933 81 37 SE Unfailured SR LT VALLEY

118 47 93 70 78 93 893 943 918 51 33 SE Unfailured SR G VALLEY

119 47 78 62 78 82 448 473 460 26 18 SE Unfailured SR G VALLEY

120 62 78 70 62 74 458 498 478 41 33 SE Unfailured SR NV VALLEY

121 47 78 62 78 81 535 560 548 25 18 W Unfailured SR NV FLAT

122 124 155 140 140 147 473 520 496 48 19 W Unfailured SR NV FLAT

123 47 78 62 78 94 473 525 499 53 34 W Unfailured SR LT VALLEY

124 109 140 124 140 147 438 485 461 48 19 W Unfailured SR LT RIDGE

125 78 109 93 109 117 398 440 419 43 21 W Unfailured SR LT RIDGE

126 62 93 78 124 128 368 400 384 33 15 W Unfailured SR LT RIDGE

127 31 47 39 78 95 360 415 388 55 35 W Unfailured SR G VALLEY

128 62 109 85 140 160 323 400 361 78 29 W Unfailured SR G VALLEY

129 78 93 85 109 139 273 360 316 88 39 S Unfailured SR LT VALLEY

130 109 140 124 78 83 273 303 288 31 21 S Unfailured SR LT RIDGE

131 62 78 70 124 129 263 298 280 36 16 S Unfailured SR LT RIDGE

132 124 155 140 93 100 288 325 306 38 22 S Unfailured SR LT RIDGE

133 78 93 85 109 117 348 390 369 43 21 S Unfailured SR LT RIDGE

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

142

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A.2.3 ZUMALAI SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 61 91 76 61 62 465 480 473 15 14 SE Unfailured LR HT FLAT

2 45 76 61 61 62 428 443 435 15 14 W Unfailured LR HT FLAT

3 30 76 53 45 48 303 318 310 15 18 W Unfailured LR HT FLAT

4 45 61 53 76 95 340 398 369 58 37 SW Unfailured LR HT VALLEY

5 76 106 91 45 51 325 348 336 23 26 SW Unfailured LR LT VALLEY

6 76 91 83 61 63 330 348 339 18 16 SW Unfailured LR LT RIDGE

7 91 121 106 106 111 375 408 391 33 17 SW Unfailured LR LT RIDGE

8 76 91 83 61 63 400 418 409 18 16 SW Unfailured LR LT RIDGE

9 76 91 83 61 63 425 443 434 18 16 SW Unfailured LR LT RIDGE

10 91 121 106 61 66 403 428 415 25 22 NE Unfailured LR LT RIDGE

11 61 76 68 76 81 540 568 554 28 20 NE Unfailured LR LT VALLEY

12 61 76 68 45 53 505 533 519 28 31 NE Unfailured LR LT VALLEY

13 91 121 106 91 93 403 420 411 18 11 NE Unfailured LR LT VALLEY

14 91 106 98 61 70 490 526 508 36 30 NE Unfailured LR LT RIDGE

15 61 106 83 121 140 415 486 450 71 30 SW Unfailured LR HT RIDGE

16 76 121 98 106 126 445 513 479 68 32 SW Unfailured LR HT RIDGE

17 76 106 91 61 63 425 443 434 18 16 SW Unfailured LR HT RIDGE

18 61 91 76 121 125 505 538 521 33 15 SW Unfailured LR HT RIDGE

19 106 136 121 45 53 490 518 504 28 31 E Unfailured LR HT RIDGE

20 76 106 91 61 66 403 428 415 25 22 E Unfailured LR HT RIDGE

21 76 91 83 61 62 453 468 460 15 14 E Unfailured LR LT FLAT

22 106 121 114 76 77 440 456 448 16 12 E Unfailured LR LT FLAT

23 45 76 61 106 109 365 393 379 28 15 E Unfailured LR LT FLAT

24 106 136 121 61 68 403 433 418 30 26 E Unfailured LR LT VALLEY

25 106 185 146 76 92 490 543 516 53 35 E Unfailured LR LT VALLEY

26 61 76 68 151 165 486 550 518 65 23 E Unfailured LR LT RIDGE

27 45 91 68 121 128 480 520 500 40 18 E Unfailured LR LT RIDGE

28 61 91 76 61 65 518 540 529 23 20 E Unfailured LR LT RIDGE

29 61 91 76 76 81 543 570 556 28 20 SW Unfailured LR LT RIDGE

30 76 106 91 76 87 468 510 489 43 29 SW Unfailured LR HT VALLEY

31 76 121 98 136 147 480 535 508 55 22 SW Unfailured LR HT VALLEY

32 91 106 98 76 82 543 575 559 33 23 NW Unfailured LR HT RIDGE

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Un-failure slopes data of Zumalai study site (continued)

33 106 182 144 76 80 653 678 665 26 19 NW Unfailured LR HT RIDGE

34 76 106 91 136 141 503 540 521 38 15 NW Unfailured SR HT RIDGE

35 61 106 83 121 128 518 560 539 43 19 NW Unfailured SR HT RIDGE

36 76 106 91 151 160 543 595 569 53 19 E Unfailured SR G RIDGE

37 61 91 76 121 125 580 610 595 30 14 E Unfailured SR G FLAT

38 61 91 76 167 171 608 645 626 38 13 E Unfailured SR G FLAT

39 121 151 136 61 62 468 480 474 13 12 W Unfailured SR G FLAT

40 61 76 68 91 95 493 520 506 28 17 SW Unfailured SR G FLAT

41 45 91 68 76 92 618 670 644 53 35 SW Unfailured SR LT RIDGE

42 61 76 68 76 82 638 670 654 33 23 SW Unfailured SR LT RIDGE

43 61 91 76 106 109 633 660 646 28 15 S Unfailured SR LT FLAT

44 45 76 61 61 63 618 635 626 18 16 S Unfailured SR LT FLAT

45 61 76 68 76 77 668 683 675 15 11 S Unfailured SR HT FLAT

46 45 76 61 136 146 545 598 571 53 21 S Unfailured SR HT RIDGE

47 61 76 68 121 147 553 635 594 83 34 S Unfailured IR LT VALLEY

48 76 121 98 151 177 583 675 629 93 31 S Unfailured IR LT VALLEY

49 76 106 91 76 82 623 655 639 33 23 S Unfailured IR LT RIDGE

50 61 106 83 61 62 620 635 628 15 14 S Unfailured IR LT FLAT

51 45 61 53 121 139 583 650 616 68 29 SE Unfailured IR LT VALLEY

52 61 91 76 121 132 533 585 559 53 23 SE Unfailured IR LT VALLEY

53 61 136 98 61 73 533 573 553 40 33 SE Unfailured IR HT VALLEY

54 76 106 91 151 161 570 625 598 55 20 SE Unfailured IR HT VALLEY

55 61 106 83 121 147 583 665 624 83 34 SE Unfailured IR HT VALLEY

56 45 121 83 61 62 558 573 565 15 14 SE Unfailured IR HT FLAT

57 76 136 106 76 77 570 585 578 15 11 S Unfailured IR LT FLAT

58 76 106 91 76 78 468 485 476 18 13 S Unfailured IR LT FLAT

59 91 121 106 61 67 558 585 571 28 24 S Unfailured IR LT RIDGE

60 61 76 68 76 81 445 475 460 30 22 S Unfailured IR LT RIDGE

61 45 76 61 61 70 470 505 488 35 30 S Unfailured IR LT RIDGE

62 45 91 68 76 81 483 510 496 28 20 SE Unfailured IR G RIDGE

63 61 76 68 76 82 518 550 534 33 23 SE Unfailured IR G RIDGE

64 45 76 61 76 77 558 573 565 15 11 SE Unfailured IR LT FLAT

65 76 91 83 91 95 433 460 446 28 17 S Unfailured IR LT RIDGE

66 61 76 68 106 119 370 425 398 55 27 S Unfailured IR LT VALLEY

67 76 91 83 61 65 458 480 469 23 20 SE Unfailured IR HT RIDGE

68 45 61 53 61 65 333 355 344 23 20 SW Unfailured IR HT RIDGE

144

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Un-failure slopes data of Zumalai study site (continued)

69 45 76 61 106 107 345 360 353 15 8 SW Unfailured IR HT FLAT

70 61 76 68 76 77 370 385 378 15 11 SW Unfailured IR G FLAT

71 76 106 91 76 102 510 578 544 68 42 NW Unfailured IR HT VALLEY

72 61 121 91 61 82 585 640 613 55 42 NW Unfailured IR LT VALLEY

73 45 76 61 61 66 805 830 818 25 22 S Unfailured SR LT RIDGE

74 61 76 68 45 54 635 665 650 30 33 S Unfailured SR LT VALLEY

75 76 106 91 61 67 598 625 611 28 24 S Unfailured SR LT VALLEY

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

A.2.4 ATSABE SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 62 124 93 186 190 587 628 607 41 12 E Unfailured LR HT RIDGE

2 78 109 93 155 163 500 550 525 50 18 E Unfailured LR HT RIDGE

3 109 78 93 109 118 403 450 426 48 24 E Unfailured LR HT RIDGE

4 93 62 78 140 146 845 888 866 43 17 E Unfailured LR G RIDGE

5 93 78 85 124 128 398 430 414 33 15 SW Unfailured LR G VALLEY

6 124 124 124 109 114 373 408 390 35 18 SW Unfailured LR G VALLEY

7 109 93 101 62 66 398 420 409 23 20 W Unfailured LR NV RIDGE

8 93 124 109 78 85 385 420 403 35 24 W Unfailured LR NV RIDGE

9 78 93 85 62 67 395 420 408 25 22 W Unfailured LR LT RIDGE

10 62 78 70 78 82 385 413 399 28 20 SW Unfailured LR LT RIDGE

11 47 78 62 93 102 360 403 381 43 25 SW Unfailured LR LT RIDGE

12 62 47 54 62 68 323 350 336 28 24 SW Unfailured LR LT RIDGE

13 93 62 78 78 83 340 370 355 30 21 SW Unfailured LR LT RIDGE

14 47 62 54 62 68 393 420 406 28 24 SW Unfailured LR LT VALLEY

15 47 62 54 78 89 392 435 413 43 29 SW Unfailured LR LT VALLEY

16 78 62 70 47 58 390 425 408 35 37 SW Unfailured LR HT VALLEY

17 78 93 85 62 74 360 400 380 40 33 SW Unfailured LR HT VALLEY

18 93 109 101 62 68 373 400 386 28 24 SW Unfailured LR HT RIDGE

19 62 93 78 78 81 310 335 323 25 18 SW Unfailured LR HT FLAT

145

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Un-failure slopes data of Atsabe study site (continued)

20 47 78 62 62 63 448 460 454 13 11 W Unfailured LR LT FLAT

21 78 109 93 47 53 410 435 423 25 28 W Unfailured LR LT VALLEY

22 62 78 70 217 231 385 465 425 80 20 W Unfailured LR G VALLEY

23 93 140 116 155 158 360 393 376 33 12 W Unfailured LR LT FLAT

24 62 93 78 140 143 375 408 391 33 13 W Unfailured SR LT FLAT

25 47 93 70 109 111 398 420 409 23 12 W Unfailured SR LT FLAT

26 78 109 93 93 107 448 500 474 53 29 W Unfailured SR G VALLEY

27 124 155 140 140 160 423 500 461 78 29 W Unfailured SR G VALLEY

28 78 93 85 124 129 470 505 488 35 16 W Unfailured SR LT RIDGE

29 31 62 47 47 50 513 530 521 18 21 SW Unfailured SR LT RIDGE

30 62 78 70 124 127 513 540 526 28 13 SW Unfailured SR LT FLAT

31 78 62 70 109 117 473 515 494 43 21 SW Unfailured SR G RIDGE

32 47 78 62 47 50 488 505 496 18 21 SW Unfailured SR LT RIDGE

33 62 78 70 47 57 508 540 524 33 35 W Unfailured SR HT VALLEY

34 47 78 62 47 50 548 565 556 18 21 W Unfailured SR HT VALLEY

35 78 62 70 109 112 563 590 576 28 14 W Unfailured SR HT FLAT

36 93 109 101 78 87 563 603 583 40 27 W Unfailured SR G VALLEY

37 109 124 116 124 140 588 653 620 65 28 W Unfailured LR G VALLEY

38 78 109 93 78 87 550 590 570 40 27 W Unfailured LR G FLAT

39 62 78 70 93 95 528 545 536 18 11 S Unfailured LR LT RIDGE

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

146

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A.2.5 MALIANA SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 107 122 115 92 114 1150 1218 1184 68 36 SE unfailured LR LT RIDGE

2 118 138 128 107 131 1200 1275 1238 75 35 W unfailured LR LT VALLEY

3 122 153 138 69 82 1260 1305 1283 45 33 W unfailured LR LT VALLEY

4 61 92 77 77 82 1648 1678 1663 30 21 W unfailured LR HT VALLEY

5 124 155 140 93 98 473 503 488 30 18 W Unfailured LR LT FLAT

6 62 93 78 78 87 473 513 493 40 27 W Unfailure LR LT RIDGE

7 78 62 70 78 79 448 465 456 18 13 W Unfailure LR LT RIDGE

8 93 62 78 78 83 460 490 475 30 21 W Unfailure LR LT FLAT

9 109 124 116 70 84 573 620 596 48 34 W Unfailure LR HT VALLEY

10 109 62 85 109 115 473 510 491 38 19 W Unfailure LR HT VALLEY

11 93 109 101 62 72 573 610 591 38 31 W Unfailure LR HT VALLEY

12 93 62 78 78 84 1123 1155 1139 33 23 W Unfailure SR HT VALLEY

13 62 140 101 78 86 1173 1210 1191 38 26 W Unfailure SR LT VALLEY

14 93 155 124 93 95 1273 1290 1281 18 11 W Unfailure SR LT FLAT

15 62 47 54 124 127 823 850 836 28 13 E Unfailure SR NV FLAT

16 62 47 54 109 111 891 915 903 24 12 E Unfailure SR NV FLAT

17 93 62 78 78 86 873 910 891 38 26 E Unfailure SR NV RIDGE

18 155 140 147 109 113 693 723 708 30 15 E Unfailure SR G FLAT

19 78 93 85 109 117 648 690 669 43 21 N Unfailure SR G RIDGE

20 62 93 78 47 52 598 620 609 23 26 N Unfailure SR G VALLEY

21 62 93 78 47 55 673 703 688 30 33 N Unfailure LR G VALLEY

22 78 124 101 155 157 791 815 803 24 9 N Unfailure LR G FLAT

23 78 116 97 171 172 648 670 659 23 8 N Unfailure LR LT FLAT

24 93 109 101 47 55 860 890 875 30 33 SE Unfailure LR LT VALLEY

25 93 78 85 47 51 948 968 958 20 23 SE Unfailure LR LT RIDGE

26 109 155 132 62 70 698 730 714 33 28 SE Unfailure LR LT VALLEY

27 78 62 70 93 98 460 490 475 30 18 SE Unfailure LR HT FLAT

28 78 47 62 93 99 433 465 449 33 19 SE Unfailure LR HT FLAT

29 93 109 101 62 69 410 440 425 30 26 SE Unfailure LR HT VALLEY

30 47 109 78 78 95 450 505 478 55 35 SW Unfailured LR LT VALLEY

31 78 93 85 62 63 463 475 469 13 11 SW Unfailure LR LT FLAT

147

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A.2.6 AINARO SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Avr Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 62 93 77 154 173 1073 1150 1111 77 27 N Unfailured SR LT VALLEY

2 93 108 100 123 159 705 805 755 100 39 N Unfailured SR LT VALLEY

3 77 123 100 108 145 723 820 771 97 42 N Unfailured SR LT VALLEY

4 108 154 131 62 65 810 830 820 20 18 W Unfailured SR LT RIDGE

5 77 108 93 108 119 945 995 970 50 25 W Unfailured SR HT VALLEY

6 76 121 98 91 101 235 280 258 45 26 S Unfailured SR LT RIDGE

7 61 91 76 76 82 270 303 286 33 23 S Unfailured IR LT RIDGE

8 45 91 68 45 54 335 365 350 30 33 S Unfailured IR LT RIDGE

9 61 106 83 61 64 545 565 555 20 18 S Unfailured IR LT RIDGE

10 76 106 91 182 183 515 540 528 25 8 E Unfailured IR LT FLAT

11 61 121 91 151 161 810 865 838 55 20 E Unfailured IR HT RIDGE

12 45 76 61 106 158 823 940 881 118 48 E Unfailured IR HT VALLEY

13 61 91 76 76 87 1198 1240 1219 43 29 E Unfailured IR HT VALLEY

14 76 121 98 61 75 1360 1405 1383 45 37 E Unfailured IR HT VALLEY

15 91 106 98 45 54 1573 1603 1588 30 33 SW Unfailured IR HT RIDGE

16 91 106 98 45 49 1573 1590 1581 18 21 SW Unfailured VR HT VALLEY

17 61 76 68 45 62 1385 1428 1406 43 43 SW Unfailured VR HT RIDGE

18 61 91 76 61 71 848 885 866 38 32 SW Unfailured VR HT VALLEY

19 76 91 83 91 121 935 1015 975 80 41 SW Unfailured VR HT RIDGE

20 91 121 106 76 78 1110 1128 1119 18 13 SW Unfailured VR LT RIDGE

21 61 121 91 106 109 1035 1060 1048 25 13 NW Unfailured VR HT RIDGE

22 45 76 61 91 93 985 1003 994 18 11 NW Unfailured VR HT RIDGE

23 61 136 98 91 96 935 965 950 30 18 NW Unfailured VR HT VALLEY

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

148

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A.2.7 HATOLIA SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 78 47 62 62 65 400 420 410 20 18 S Unfailured MR LT RIDGE

2 62 93 78 62 65 423 442 432 20 17 S Unfailured MR LT RIDGE

3 31 62 47 62 64 588 605 596 18 16 S Unfailured MR LT RIDGE

4 109 109 109 93 104 518 565 541 48 27 S Unfailured MR LT RIDGE

5 93 109 101 78 86 663 700 681 38 26 S Unfailured MR LT VALLEY

6 47 93 70 109 112 663 690 676 28 14 N Unfailured MR HT FLAT

7 62 93 78 78 81 673 697 685 25 18 N Unfailured MR HT FLAT

8 93 155 124 62 75 698 740 719 43 34 N Unfailured MR HT VALLEY

9 78 124 101 78 91 693 740 716 48 32 N Unfailured MR HT VALLEY

10 78 124 101 109 116 700 740 720 40 20 N Unfailured MR HT RIDGE

11 93 140 116 78 86 678 715 696 38 26 NE Unfailured MR LT RIDGE

12 47 78 62 78 79 688 705 696 18 13 NE Unfailured MR LT RIDGE

13 47 62 54 78 86 598 635 616 38 26 NE Unfailured MR LT RIDGE

14 78 93 85 62 68 613 640 626 28 24 NE Unfailured MR HT RIDGE

15 62 78 70 93 104 658 705 681 48 27 N Unfailured MR G VALLEY

16 62 78 70 109 118 483 530 506 48 24 N Unfailured MR G VALLEY

17 62 109 85 109 130 408 480 444 73 34 N Unfailured MR G VALLEY

18 78 109 93 186 191 283 325 304 43 13 SE Unfailured SR LT FLAT

19 31 31 31 62 78 218 265 241 48 37 SE Unfailured SR LT VALLEY

20 62 93 78 310 313 193 235 214 43 8 SE Unfailured SR LT FLAT

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

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A.2.8 HATOBUILICO SITE

Number Width Length Elevation Height Inclination Direction Type of Lithology Vegetation Landscape

Min Max Mean Horizontal inclined Min Max Avr difference angle Slope *) Cover **) Topography

(m) (m) (m) (m) (m) (m) (m) (m) Failure

1 46 62 54 77 96 810 868 839 57 37 W Unfailured VR HT VALLEY

2 62 77 69 123 131 810 855 833 45 20 W Unfailured VR HT VALLEY

3 62 77 69 139 149 1040 1095 1068 55 22 W Unfailured VR HT VALLEY

4 77 108 93 123 125 873 895 884 22 10 W Unfailured MR LT RIDGE

5 108 154 131 77 85 1010 1045 1028 35 24 SW Unfailured MR LT RIDGE

6 123 139 131 77 89 1210 1255 1233 45 30 SW Unfailured MR G RIDGE

7 62 77 69 123 142 1298 1368 1333 70 29 SW Unfailured MR G VALLEY

8 62 77 69 62 76 1260 1305 1283 45 36 SW Unfailured MR LT RIDGE

9 77 108 93 139 183 1560 1680 1620 120 41 S Unfailured MR LT VALLEY

10 77 154 116 108 137 1660 1745 1703 85 38 S Unfailured MR G VALLEY

11 77 170 123 123 148 1998 2080 2039 82 34 S Unfailured MR G VALLEY

12 77 108 93 93 116 2035 2105 2070 70 37 S Unfailured MR G VALLEY

13 77 93 85 108 154 2010 2120 2065 110 45 S Unfailured MR LT VALLEY

14 77 108 93 93 109 2010 2068 2039 57 32 SW Unfailured MR LT RIDGE

15 77 123 100 123 150 1635 1720 1678 85 34 SW Unfailured VR LT VALLEY

16 77 108 93 77 85 1595 1630 1613 35 24 SW Unfailured VR HT RIDGE

17 62 77 69 123 156 1510 1605 1558 95 38 SW Unfailured VR HT VALLEY

18 46 62 54 109 113 1300 1330 1315 30 15 SW Unfailured VR HT RIDGE

*) Lithology : - SR = Sedimentary rocks; LR = Littoral deposit rocks; IR = Igneous rocks, MR = Metamorphic rocks; VR = Volcanics rocks

**)Vegetation Cover : - HT =High tree; LT= Low Tree; G= Grass, NV=No Vegetation

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Appendix B : Logistic Regression Analysis

B.1 All study site

Case Processing Summary

Unweighted Cases(a) N Percent

Selected Cases Included in Analysis 1012 100.0

Missing Cases 0 .0

Total 1012 100.0

Unselected Cases 0 .0

Total 1012 100.0

a If weight is in effect, see classification table for the total number of cases.

Dependent Variable Encoding

Original Value Internal Value

.00 0

1.00 1

Classification Table(a)

Observed Predicted

status_slope Percentage

Correct

.00 1.00

Step 1 status_slope .00 450 56 890.5

1.00 50 456 390.1

Overall Percentage 90.3

a The cut value is .500 Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.903 1.128 2.846 1 .042 .149

elev_500.1_800 -1.499 1.126 1.771 1 .013 .223

elev_800.1_1100 -1.004 1.132 .787 1 .035 .366

elev_1100.1_1400 -.756 1.144 .437 1 .008 .469

elev_1400.1_1700 -.742 1.164 .406 1 .024 .476

Step 1(a)

Constant 1.495 1.123 1.772 1 .183 4.461

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.111 .314 12.514 1 .000 .329

elev_500.1_800 -.708 .316 5.033 1 .025 .492

elev_800.1_1100 -.218 .352 .385 1 .035 .804

elev_1100.1_1400 .036 .367 .010 1 .022 1.036

elev_1700.1_2100 .021 .302 4.835 1 1.021

Step 1(a)

Constant .703 .297 5.596 1 .018 2.020

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1700.1_2100. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.822 .852 4.574 1 .032 .162

inc_ang12.1_18 -1.218 .845 2.077 1 .049 .296

inc_ang18.1_24 -1.049 .847 1.535 1 .015 .350

inc_ang24.1_30 -.329 .857 .147 1 .002 .720

inc_ang30.1_36 .745 .883 .712 1 .039 2.107

inc_ang36.1_42 1.099 .943 1.358 1 .044 3.000

Step 1(a)

Constant .916 .837 1.199 1 .273 2.500

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -2.633 .416 40.116 1 .000 .072

inc_ang12.1_18 -2.028 .401 25.587 1 .000 .132

inc_ang18.1_24 -1.860 .405 21.110 1 .000 .156

inc_ang24.1_30 -1.139 .426 7.142 1 .008 .320

inc_ang30.1_36 -.066 .476 .019 1 .040 .936

inc_ang42.1_48 -.182 .491 .137 1 .012 .833

Step 1(a)

Constant 1.727 .384 20.264 1 .000 5.625

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang42.1_48. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

low_tree 1.022 .262 15.194 1 .000 2.780

grass 2.822 .265 113.354 1 .000 16.809

no_veg 4.515 .355 161.660 1 .000 91.422

Step 1(a)

Constant -1.992 .233 73.361 1 .000 .136

a Variable(s) entered on step 1: low_tree, grass, no_veg.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

high_tree -1.022 .262 15.194 1 .000 .360

grass 1.800 .176 105.121 1 .000 6.047

no_veg 3.493 .294 140.767 1 .000 32.891

Step 1(a)

Constant -.970 .121 64.153 1 .000 .379

a Variable(s) entered on step 1: high_tree, grass, no_veg.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 3.797 .371 104.560 1 .000 44.583

L_R 2.609 .375 48.439 1 .000 13.587

I_R .137 .407 .114 1 .036 1.147

V_R 3.360 .496 45.914 1 .000 28.788

Step 1(a)

Constant -2.357 .349 45.659 1 .000 .095

a Variable(s) entered on step 1: S_R, L_R, I_R, V_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 3.664 .246 221.555 1 .000 39.027

L_R 2.476 .251 96.957 1 .000 11.894

M_R -.123 .408 .090 1 .044 .885

V_R 3.227 .411 61.769 1 .000 25.200

Step 1(a)

Constant -2.224 .211 111.533 1 .000 .108

a Variable(s) entered on step 1: S_R, L_R, M_R, V_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.330 .356 42.745 1 .000 10.278

northeast 3.110 .354 77.270 1 .000 22.419

east 1.034 .347 8.879 1 .003 2.812

southeast 1.553 .337 21.176 1 .000 4.726

southwest .956 .339 7.943 1 .005 2.600

west .318 .379 .706 1 .001 1.375

northwest 2.537 .353 51.697 1 .000 12.639

Step 1(a)

Constant -1.609 .293 30.220 1 .000 .200

a Variable(s) entered on step 1: north, northeast, east, southeast, southwest, west, northwest.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.012 .315 40.770 1 .000 7.475

northeast 2.791 .312 79.998 1 .000 16.305

east .716 .304 5.526 1 .019 2.045

southeast 1.235 .294 17.695 1 .000 3.437

south -.318 .379 .706 1 .001 .727

southwest .637 .295 4.655 1 .031 1.891

northwest 2.218 .311 50.878 1 .000 9.192

Step 1(a)

Constant -1.291 .241 28.758 1 .000 .275

a Variable(s) entered on step 1: north, northeast, east, southeast, south, southwest, northwest. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 2.223 .246 81.983 1 .000 9.239

Ridge 1.566 .249 39.472 1 .000 4.786

Step 1(a)

Constant -1.693 .227 55.667 1 .000 .184

a Variable(s) entered on step 1: Valley, Ridge.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley .658 .139 22.248 1 .000 1.931

Flat -1.566 .249 39.472 1 .000 .209

Step 1(a)

Constant -.127 .103 1.522 1 .217 .881

a Variable(s) entered on step 1: Valley, Flat.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -.534 1.208 .195 1 .029 .586

elev_500.1_800 -.220 1.204 .033 1 .015 .803

elev_800.1_1100 .133 1.207 .012 1 .012 1.142

elev_1100.1_1400 .361 1.223 .087 1 .048 1.434

elev_1400.1_1700 .252 1.243 .041 1 .039 1.287

inc_ang6.0_12 -1.819 .856 4.512 1 .034 .162

inc_ang12.1_18 -1.222 .849 2.069 1 .050 .295

inc_ang18.1_24 -1.075 .851 1.597 1 .006 .341

inc_ang24.1_30 -.438 .862 .258 1 .612 .645

inc_ang30.1_36 .621 .889 .488 1 .485 1.860

inc_ang36.1_42 .933 .949 .968 1 .325 2.543

Step 1(a)

Constant 1.185 1.466 .653 1 .419 3.271

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .199 3.110 .004 1 .049 1.220

elev_500.1_800 .328 3.107 .011 1 .016 1.388

elev_800.1_1100 .574 3.106 .034 1 .003 1.775

elev_1100.1_1400 1.227 3.118 .155 1 .004 3.409

elev_1400.1_1700 1.138 3.132 .132 1 .116 3.121

inc_ang6.0_12 -2.361 1.032 5.229 1 .022 .094

inc_ang12.1_18 -1.647 1.021 2.601 1 .007 .193

inc_ang18.1_24 -1.466 1.023 2.054 1 .052 .231

inc_ang24.1_30 -.844 1.037 .664 1 .015 .430

inc_ang30.1_36 .541 1.059 .262 1 .009 1.719

inc_ang36.1_42 1.286 1.116 1.328 1 .049 3.619

low_tree 1.128 .299 14.267 1 .000 3.091

grass 3.091 .304 103.567 1 .000 21.988

no_veg 4.924 .390 159.298 1 .000 137.594

Step 1(a)

Constant -1.315 3.272 .161 1 .688 .268

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .199 3.110 .004 1 .049 1.220

elev_500.1_800 .328 3.107 .011 1 .016 1.388

elev_800.1_1100 .574 3.106 .034 1 .003 1.775

elev_1100.1_1400 1.227 3.118 .155 1 .004 3.409

elev_1400.1_1700 1.138 3.132 .132 1 .016 3.121

inc_ang6.0_12 -2.361 1.032 5.229 1 .022 .094

inc_ang12.1_18 -1.647 1.021 2.601 1 .007 .193

inc_ang18.1_24 -1.466 1.023 2.054 1 .052 .231

inc_ang24.1_30 -.844 1.037 .664 1 .015 .430

inc_ang30.1_36 .541 1.059 .262 1 .009 1.719

inc_ang36.1_42 1.286 1.116 1.328 1 .049 3.619

grass 1.962 .198 98.690 1 .000 7.114

no_veg 3.796 .314 145.683 1 .000 44.519

high_tree -1.128 .299 14.267 1 .000 .324

Step 1(a)

Constant -.187 3.265 .003 1 .954 .830

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, high_tree.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .817 4.799 .029 1 .865 2.265

elev_500.1_800 .962 4.795 .040 1 .841 2.618

elev_800.1_1100 .976 4.794 .041 1 .839 2.654

elev_1100.1_1400 2.301 4.808 .229 1 .632 9.983

elev_1400.1_1700 2.496 4.842 .266 1 .606 12.130

inc_ang6.0_12 -1.757 1.277 1.893 1 .169 .173

inc_ang12.1_18 -1.037 1.265 .672 1 .412 .354

inc_ang18.1_24 -.819 1.267 .418 1 .518 .441

inc_ang24.1_30 -.239 1.286 .035 1 .852 .787

inc_ang30.1_36 1.318 1.316 1.003 1 .317 3.736

inc_ang36.1_42 1.271 1.369 .863 1 .353 3.565

grass 3.180 .358 78.725 1 .000 24.053

no_veg 5.239 .482 117.934 1 .000 188.574

low_tree 1.317 .348 14.312 1 .000 3.733

S_R 3.978 .486 67.134 1 .000 53.432

L_R 2.980 .489 37.156 1 .000 19.693

I_R .264 .511 .268 1 .605 1.303

V_R 4.229 .698 36.680 1 .000 68.634

Step 1(a)

Constant -5.333 4.990 1.142 1 .285 .005

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, I_R, V_R.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .817 4.799 .029 1 .035 2.264

elev_500.1_800 .962 4.796 .040 1 .041 2.618

elev_800.1_1100 .976 4.795 .041 1 .039 2.653

elev_1100.1_1400 2.301 4.808 .229 1 .032 9.983

elev_1400.1_1700 2.495 4.842 .265 1 .006 12.120

inc_ang6.0_12 -1.757 1.277 1.893 1 .039 .173

inc_ang12.1_18 -1.037 1.265 .672 1 .012 .354

inc_ang18.1_24 -.819 1.267 .418 1 .018 .441

inc_ang24.1_30 -.239 1.286 .035 1 .052 .787

inc_ang30.1_36 1.318 1.316 1.003 1 .017 3.736

inc_ang36.1_42 1.271 1.369 .863 1 .003 3.565

grass 3.180 .358 78.722 1 .000 24.053

no_veg 5.240 .482 117.942 1 .000 188.613

low_tree 1.317 .348 14.308 1 .000 3.733

S_R 3.716 .330 126.958 1 .000 41.093

L_R 2.718 .333 66.576 1 .000 15.144

V_R 3.966 .595 44.385 1 .000 52.782

M_R -.259 .511 .256 1 .013 .772

Step 1(a)

Constant -5.070 4.976 1.038 1 .308 .006

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, V_R, M_R.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .955 5.064 .036 1 .045 2.598

elev_500.1_800 .849 5.060 .028 1 .017 2.338

elev_800.1_1100 1.082 5.059 .046 1 .031 2.949

elev_1100.1_1400 2.259 5.073 .198 1 .046 9.576

elev_1400.1_1700 2.627 5.117 .263 1 .008 13.826

inc_ang6.0_12 -1.413 1.369 1.066 1 .002 .243

inc_ang12.1_18 -.683 1.357 .253 1 .015 .505

inc_ang18.1_24 -.622 1.359 .209 1 .047 .537

inc_ang24.1_30 .274 1.383 .039 1 .043 1.315

inc_ang30.1_36 1.557 1.421 1.201 1 .023 4.744

inc_ang36.1_42 1.482 1.461 1.030 1 .010 4.403

grass 3.112 .381 66.594 1 .000 22.457

no_veg 4.903 .510 92.426 1 .000 134.705

low_tree 1.305 .373 12.231 1 .000 3.686

S_R 3.792 .508 55.632 1 .000 44.360

L_R 2.681 .519 26.712 1 .000 14.593

V_R 4.059 .742 29.924 1 .000 57.894

I_R -.228 .549 .173 1 .047 .796

north 1.340 .586 5.236 1 .022 3.820

northeast 2.606 .582 20.017 1 .000 13.540

east .080 .561 .021 1 .016 1.084

southeast 1.023 .542 3.564 1 .051 2.781

southwest .350 .545 .412 1 .021 1.418

west -.385 .578 .444 1 .005 .681

northwest 1.875 .562 11.139 1 .001 6.520

Step 1(a)

Constant -6.273 5.274 1.415 1 .234 .002

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, V_R, I_R, north, northeast, east, southeast, southwest, west, northwest.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .955 5.064 .036 1 .850 2.598

elev_500.1_800 .849 5.060 .028 1 .867 2.338

elev_800.1_1100 1.082 5.059 .046 1 .831 2.949

elev_1100.1_1400 2.259 5.073 .198 1 .656 9.576

elev_1400.1_1700 2.627 5.117 .263 1 .608 13.826

inc_ang6.0_12 -1.413 1.369 1.066 1 .302 .243

inc_ang12.1_18 -.683 1.357 .253 1 .615 .505

inc_ang18.1_24 -.622 1.359 .209 1 .647 .537

inc_ang24.1_30 .274 1.383 .039 1 .843 1.315

inc_ang30.1_36 1.557 1.421 1.201 1 .273 4.744

inc_ang36.1_42 1.482 1.461 1.030 1 .310 4.403

grass 3.112 .381 66.594 1 .000 22.457

no_veg 4.903 .510 92.426 1 .000 134.705

low_tree 1.305 .373 12.231 1 .000 3.686

S_R 3.792 .508 55.632 1 .000 44.360

L_R 2.681 .519 26.712 1 .000 14.593

V_R 4.059 .742 29.924 1 .000 57.894

I_R -.228 .549 .173 1 .677 .796

north 1.725 .500 11.895 1 .001 5.613

northeast 2.990 .494 36.660 1 .000 19.894

east .465 .456 1.038 1 .308 1.592

southeast 1.408 .441 10.200 1 .001 4.086

southwest .734 .440 2.790 1 .095 2.084

northwest 2.260 .459 24.192 1 .000 9.579

south .385 .578 .444 1 .505 1.469

Step 1(a)

Constant -6.658 5.289 1.584 1 .208 .001

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, V_R, I_R, north, northeast, east, southeast, southwest, northwest, south.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.411 6.235 .051 1 .821 4.100

elev_500.1_800 1.114 6.232 .032 1 .858 3.046

elev_800.1_1100 1.208 6.231 .038 1 .846 3.345

elev_1100.1_1400 2.610 6.245 .175 1 .676 13.602

elev_1400.1_1700 3.283 6.275 .274 1 .601 26.665

inc_ang6.0_12 -.754 1.372 .302 1 .582 .470

inc_ang12.1_18 -.159 1.356 .014 1 .907 .853

inc_ang18.1_24 -.379 1.357 .078 1 .780 .684

inc_ang24.1_30 .585 1.381 .179 1 .672 1.794

inc_ang30.1_36 1.784 1.422 1.574 1 .210 5.952

inc_ang36.1_42 1.611 1.461 1.216 1 .270 5.007

grass 3.288 .397 68.576 1 .000 26.783

no_veg 5.233 .552 89.772 1 .000 187.286

low_tree 1.390 .382 13.247 1 .000 4.015

S_R 4.075 .541 56.702 1 .000 58.876

L_R 2.785 .545 26.117 1 .000 16.195

V_R 4.432 .762 33.830 1 .000 84.129

I_R -.009 .570 .000 1 .988 .991

north 1.541 .608 6.416 1 .011 4.670

northeast 2.693 .607 19.685 1 .000 14.773

east .151 .585 .066 1 .797 1.163

southeast .950 .563 2.849 1 .091 2.586

southwest .272 .566 .231 1 .631 1.313

northwest 1.891 .587 10.377 1 .001 6.625

west -.588 .607 .938 1 .333 .555

Valley 2.588 .454 32.431 1 .000 13.297

Ridge 2.057 .460 19.993 1 .000 7.824

Step 1(a)

Constant -9.451 6.440 2.154 1 .142 .000

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, V_R, I_R, north, northeast, east, southeast, southwest, northwest, west, Valley, Ridge.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.411 6.235 .051 1 .021 4.100

elev_500.1_800 1.114 6.232 .032 1 .058 3.046

elev_800.1_1100 1.208 6.231 .038 1 .046 3.345

elev_1100.1_1400 2.610 6.245 .175 1 .046 13.602

elev_1400.1_1700 3.283 6.275 .274 1 .001 26.665

inc_ang6.0_12 -.754 1.372 .302 1 .032 .470

inc_ang12.1_18 -.159 1.356 .014 1 .007 .853

inc_ang18.1_24 -.379 1.357 .078 1 .040 .684

inc_ang24.1_30 .585 1.381 .179 1 .002 1.794

inc_ang30.1_36 1.784 1.422 1.574 1 .010 5.952

inc_ang36.1_42 1.611 1.461 1.216 1 .027 5.007

grass 3.288 .397 68.576 1 .000 26.783

no_veg 5.233 .552 89.772 1 .000 187.286

low_tree 1.390 .382 13.247 1 .000 4.015

S_R 4.075 .541 56.702 1 .000 58.876

L_R 2.785 .545 26.117 1 .000 16.195

V_R 4.432 .762 33.830 1 .000 84.129

I_R -.009 .570 .000 1 .028 .991

north 1.541 .608 6.416 1 .011 4.670

northeast 2.693 .607 19.685 1 .000 14.773

east .151 .585 .066 1 .007 1.163

southeast .950 .563 2.849 1 .041 2.586

southwest .272 .566 .231 1 .031 1.313

northwest 1.891 .587 10.377 1 .001 6.625

west -.588 .607 .938 1 .033 .555

Valley .530 .261 4.129 1 .042 1.700

Flat -2.057 .460 19.993 1 .000 .128

Step 1(a)

Constant -7.393 6.421 1.326 1 .250 .001

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, low_tree, S_R, L_R, V_R, I_R, north, northeast, east, southeast, southwest, northwest, west, Valley, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.411 6.235 .051 1 .021 4.100

elev_500.1_800 1.114 6.232 .032 1 .048 3.046

elev_800.1_1100 1.208 6.231 .038 1 .046 3.345

elev_1100.1_1400 2.610 6.245 .175 1 .006 13.602

elev_1400.1_1700 3.283 6.275 .274 1 .001 26.665

inc_ang6.0_12 -.754 1.372 .302 1 .032 .470

inc_ang12.1_18 -.159 1.356 .014 1 .007 .853

inc_ang18.1_24 -.379 1.357 .078 1 .020 .684

inc_ang24.1_30 .585 1.381 .179 1 .032 1.794

inc_ang30.1_36 1.784 1.422 1.574 1 .010 5.952

inc_ang36.1_42 1.611 1.461 1.216 1 .040 5.007

low_tree 1.390 .382 13.247 1 .000 4.015

grass 3.288 .397 68.576 1 .000 26.783

no_veg 5.233 .552 89.772 1 .000 187.286

S_R 4.075 .541 56.702 1 .000 58.876

L_R 2.785 .545 26.117 1 .000 16.195

I_R -.009 .570 .000 1 .038 .991

V_R 4.432 .762 33.830 1 .000 84.129

north 1.541 .608 6.416 1 .011 4.670

northeast 2.693 .607 19.685 1 .000 14.773

east .151 .585 .066 1 .037 1.163

southeast .950 .563 2.849 1 .041 2.586

southwest .272 .566 .231 1 .031 1.313

west -.588 .607 .938 1 .033 .555

northwest 1.891 .587 10.377 1 .001 6.625

Valley 2.588 .454 32.431 1 .000 13.297

Ridge 2.057 .460 19.993 1 .000 7.824

Step 1(a)

Constant -9.451 6.440 2.154 1 .042 .000

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, I_R, V_R, north, northeast, east, southeast, southwest, west, northwest, Valley, Ridge.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.777 .712 6.227 1 .013 .169

elev_500.1_800 -2.056 .716 8.244 1 .004 .128

elev_800.1_1100 -1.983 .781 6.450 1 .011 .138

elev_1100.1_1400 -.574 .766 .563 1 .053 .563

inc_ang6.0_12 -2.097 .649 10.448 1 .001 .123

inc_ang12.1_18 -1.493 .607 6.053 1 .014 .225

inc_ang18.1_24 -1.720 .615 7.821 1 .005 .179

inc_ang24.1_30 -.752 .657 1.313 1 .052 .471

inc_ang30.1_36 .438 .739 .351 1 .054 1.549

high_tree -1.335 .377 12.556 1 .000 .263

grass 1.901 .278 46.654 1 .000 6.692

no_veg 3.873 .467 68.737 1 .000 48.085

S_R 4.079 .391 108.835 1 .000 59.069

L_R 2.798 .383 53.453 1 .000 16.414

M_R .027 .570 .002 1 .042 1.028

V_R 4.438 .654 46.077 1 .000 84.640

north 2.146 .526 16.650 1 .000 8.555

northeast 3.261 .519 39.462 1 .000 26.063

east .725 .478 2.299 1 .129 2.065

southeast 1.537 .463 11.023 1 .001 4.650

south .532 .610 .760 1 .033 1.702

southwest .868 .461 3.551 1 .045 2.383

northwest 2.423 .488 24.653 1 .000 11.279

Valley .517 .260 3.946 1 .047 1.678

Flat -2.060 .460 20.084 1 .000 .127

Step 1(a)

Constant -2.067 1.048 3.889 1 .049 .127

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, high_tree, grass, no_veg, S_R, L_R, M_R, V_R, north, northeast, east, southeast, south, southwest, northwest, Valley, Flat.

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Step number: 1

Observed Groups and Predicted Probabilities

320 ô ô ó ó ó ó F ó ó R 240 ô ô E ó ó Q ó ó U ó1 0ó E 160 ô0 1ô N ó0 1ó C ó0 1ó Y ó0 1ó 80 ô0 1ô ó0 1ó ó0 1ó ó0000 0010 010 10 10 10 10 110 01111 111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 20 Cases.

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B.2 Specific site

B.2.1 Bobonaro site

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 280.423 10 .000

Block 280.423 10 .000

Model 280.423 10 .000

Model Summary

Step -2 Log

likelihood Cox & Snell R Square

Nagelkerke R Square

1 182.600(a) .568 .757

a Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.

Classification Table(a)

Observed Predicted

status_slope Percentage

Correct

.00 1.00

Step 1 status_slope .00 153 14 91.6

1.00 24 143 85.6

Overall Percentage 88.6

a The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.792 .375 22.831 1 .000 .167

elev_500.1_800 -1.588 .342 21.524 1 .000 .204

elev_1100.1_1400 2.485 1.057 5.530 1 .019 12.000

Step 1(a)

Constant 1.099 .298 13.578 1 .000 3.000

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_1100.1_1400.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_500.1_800 -.300 .258 1.359 1 .044 .741

elev_800.1_1100 .739 .378 3.817 1 .051 2.094

elev_1100.1_1400 3.773 1.032 13.354 1 .000 43.500

Step 1(a)

Constant -.189 .195 .941 1 .332 .828

a Variable(s) entered on step 1: elev_500.1_800, elev_800.1_1100, elev_1100.1_1400.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_500.1_800 .204 .283 .518 1 .042 1.226

elev_800.1_1100 1.243 .396 9.853 1 .002 3.467

elev_1100.1_1400 4.277 1.039 16.943 1 .000 72.000

elev_1400.1_1700 -1.811 .392 21.343 1 .048 .163

Step 1(a)

Constant -.693 .227 9.289 1 .002 .500

a Variable(s) entered on step 1: elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, elev_1400.1_1700.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.017 1.442 .497 1 .041 .362

inc_ang12.1_18 -.191 1.431 .018 1 .034 .826

inc_ang18.1_24 -.147 1.431 .010 1 .018 .864

inc_ang24.1_30 .666 1.442 .214 1 .044 1.947

inc_ang30.1_36 .938 1.468 .409 1 .023 2.556

inc_ang36.1_42 2.565 1.754 2.138 1 .014 13.000

Step 1(a)

Constant .000 1.414 .000 1 1.000 1.000

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -2.889 .811 12.701 1 .000 .056

inc_ang12.1_18 -2.063 .791 6.809 1 .009 .127

inc_ang18.1_24 -2.018 .791 6.508 1 .011 .133

inc_ang24.1_30 -1.205 .810 2.213 1 .037 .300

inc_ang30.1_36 -.934 .855 1.191 1 .025 .393

inc_ang42.1_48 -3.225 .898 12.898 1 .001 .040

Step 1(a)

Constant 1.872 .760 6.073 1 .014 6.500

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang42.1_48. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

low_tree .370 .457 .654 1 .019 1.447

grass 2.299 .431 28.444 1 .000 9.963

no_veg 4.199 .604 48.398 1 .000 66.650

Step 1(a)

Constant -1.682 .385 19.077 1 .000 .186

a Variable(s) entered on step 1: low_tree, grass, no_veg.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

high_tree -.370 .457 .654 1 .019 .691

grass 1.929 .313 37.988 1 .000 6.885

no_veg 3.830 .526 53.036 1 .000 46.057

Step 1(a)

Constant -1.312 .246 28.489 1 .000 .269

a Variable(s) entered on step 1: high_tree, grass, no_veg. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 1.664 .294 31.935 1 .000 5.279

I_R -.770 .306 6.323 1 .012 .463

Step 1(a)

Constant -.381 .203 3.531 1 .060 .683

a Variable(s) entered on step 1: S_R, I_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 2.077 .277 56.395 1 .000 7.982

L_R .183 .335 .300 1 .054 1.201

Step 1(a)

Constant -.794 .176 20.420 1 .000 .452

a Variable(s) entered on step 1: S_R, L_R.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 2.433 .275 78.379 1 .000 11.395

V_R .203 .347 .342 1 .018 1.225

Step 1(a)

Constant -1.150 .173 44.221 1 .000 .317

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.580 .676 14.550 1 .000 13.200

northeast 1.972 .615 10.291 1 .001 7.187

east -.464 .648 .514 1 .043 .629

southeast .270 .596 .205 1 .051 1.310

southwest .265 .625 .180 1 .031 1.304

west .501 .661 .574 1 .049 1.650

northwest .229 .698 .107 1 .043 1.257

Step 1(a)

Constant -.788 .539 2.137 1 .144 .455

a Variable(s) entered on step 1: north, northeast, east, southeast, southwest, west, northwest.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.315 .516 20.132 1 .000 10.125

northeast 1.707 .432 15.615 1 .000 5.512

east -.730 .478 2.333 1 .027 .482

southeast .004 .404 .000 1 .041 1.004

south -.265 .625 .180 1 .031 .767

west .236 .495 .226 1 .034 1.266

northwest -.036 .544 .004 1 .047 .964

Step 1(a)

Constant -.523 .315 2.751 1 .097 .593

a Variable(s) entered on step 1: north, northeast, east, southeast, south, west, northwest. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.351 .603 15.227 1 .000 10.500

northeast 1.743 .532 10.721 1 .001 5.717

east -.693 .570 1.478 1 .024 .500

southeast .041 .510 .006 1 .036 1.042

south -.229 .698 .107 1 .043 .795

southwest .036 .544 .004 1 .047 1.037

west .272 .585 .216 1 .042 1.313

Step 1(a)

Constant -.560 .443 1.594 1 .207 .571

a Variable(s) entered on step 1: north, northeast, east, southeast, south, southwest, west.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 1.454 .398 13.319 1 .000 4.280

Ridge 2.052 .383 28.778 1 .000 7.784

Step 1(a)

Constant -1.548 .348 19.748 1 .000 .213

a Variable(s) entered on step 1: Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Ridge .598 .250 5.721 1 .017 1.819

Flat -1.454 .398 13.319 1 .000 .234

Step 1(a)

Constant -.094 .194 .233 1 .629 .911

a Variable(s) entered on step 1: Ridge, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.644 1.042 19.853 1 .000 .010

elev_500.1_800 -4.530 1.029 19.373 1 .000 .011

elev_800.1_1100 -3.655 1.074 11.583 1 .001 .026

inc_ang6.0_12 -2.565 .864 8.821 1 .003 .077

inc_ang12.1_18 -1.904 .841 5.123 1 .024 .149

inc_ang18.1_24 -1.732 .843 4.221 1 .040 .177

inc_ang24.1_30 -.892 .857 1.082 1 .028 .410

inc_ang30.1_36 -.601 .900 .446 1 .004 .548

Step 1(a)

Constant 5.649 1.288 19.225 1 .000 283.891

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.590 .399 15.876 1 .000 .204

elev_500.1_800 -1.477 .364 16.440 1 .000 .228

inc_ang6.0_12 -2.502 .865 8.365 1 .004 .082

inc_ang12.1_18 -1.797 .843 4.545 1 .033 .166

inc_ang18.1_24 -1.633 .845 3.734 1 .053 .195

inc_ang24.1_30 -.827 .862 .920 1 .037 .438

inc_ang30.1_36 -.533 .904 .348 1 .055 .587

elev_1100.1_1400 2.664 1.069 6.214 1 .013 14.360

Step 1(a)

Constant 2.511 .849 8.744 1 .003 12.318

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, elev_1100.1_1400.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -.989 .428 5.353 1 .021 .372

elev_500.1_800 -.875 .395 4.914 1 .027 .417

inc_ang6.0_12 -2.571 .863 8.867 1 .003 .076

inc_ang12.1_18 -1.905 .841 5.133 1 .023 .149

inc_ang18.1_24 -1.732 .842 4.228 1 .040 .177

inc_ang24.1_30 -.897 .857 1.095 1 .025 .408

inc_ang30.1_36 -.608 .900 .456 1 .000 .545

elev_1100.1_1400 3.271 1.081 9.160 1 .002 26.338

elev_1400.1_1700 20.687 8855.465 .000 1 .008

964465908.825

Step 1(a)

Constant 1.997 .846 5.569 1 .018 7.366

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, elev_1100.1_1400, elev_1400.1_1700.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.621 1.042 19.651 1 .000 .010

elev_500.1_800 -4.524 1.029 19.312 1 .000 .011

elev_800.1_1100 -3.668 1.075 11.644 1 .001 .026

inc_ang6.0_12 -1.497 1.453 1.061 1 .003 .224

inc_ang12.1_18 -.836 1.440 .337 1 .021 .434

inc_ang18.1_24 -.666 1.438 .215 1 .043 .514

inc_ang24.1_30 .175 1.448 .015 1 .004 1.192

inc_ang30.1_36 .470 1.478 .101 1 .050 1.600

inc_ang36.1_42 1.553 1.786 .756 1 .035 4.724

Step 1(a)

Constant 4.573 1.747 6.852 1 .009 96.803

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.857 1.115 18.977 1 .000 .008

elev_500.1_800 -4.948 1.094 20.460 1 .000 .007

elev_800.1_1100 -3.642 1.141 10.184 1 .001 .026

inc_ang6.0_12 -2.358 1.877 1.579 1 .020 .095

inc_ang12.1_18 -1.869 1.869 1.000 1 .017 .154

inc_ang18.1_24 -1.639 1.858 .778 1 .038 .194

inc_ang24.1_30 -.829 1.869 .197 1 .057 .436

inc_ang30.1_36 -.331 1.898 .030 1 .041 .718

inc_ang36.1_42 1.297 2.238 .336 1 .052 3.660

low_tree 1.194 .655 3.324 1 .038 3.301

grass 3.014 .634 22.601 1 .000 20.376

no_veg 5.056 .780 41.981 1 .000 157.003

Step 1(a)

Constant 3.396 2.101 2.611 1 .106 29.831

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.857 1.115 18.977 1 .000 .008

elev_500.1_800 -4.948 1.094 20.460 1 .000 .007

elev_800.1_1100 -3.642 1.141 10.184 1 .001 .026

inc_ang6.0_12 -2.358 1.877 1.579 1 .009 .095

inc_ang12.1_18 -1.869 1.869 1.000 1 .017 .154

inc_ang18.1_24 -1.639 1.858 .778 1 .038 .194

inc_ang24.1_30 -.829 1.869 .197 1 .057 .436

inc_ang30.1_36 -.331 1.898 .030 1 .031 .718

inc_ang36.1_42 1.297 2.238 .336 1 .052 3.660

grass 1.820 .384 22.434 1 .000 6.172

no_veg 3.862 .584 43.749 1 .000 47.559

high_tree -1.194 .655 3.324 1 .018 .303

Step 1(a)

Constant 4.590 2.174 4.457 1 .035 98.477

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, grass, no_veg, high_tree. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -5.029 1.138 19.545 1 .000 .007

elev_500.1_800 -5.108 1.117 20.901 1 .000 .006

elev_800.1_1100 -3.624 1.185 9.356 1 .002 .027

inc_ang6.0_12 -2.129 2.808 .575 1 .048 .119

inc_ang12.1_18 -1.613 2.799 .332 1 .034 .199

inc_ang18.1_24 -1.289 2.795 .213 1 .045 .275

inc_ang24.1_30 -.446 2.805 .025 1 .034 .640

inc_ang30.1_36 .256 2.831 .008 1 .028 1.292

inc_ang36.1_42 .600 3.013 .040 1 .042 1.822

low_tree .964 .678 2.022 1 .055 2.622

grass 2.859 .661 18.705 1 .000 17.448

no_veg 5.009 .829 36.482 1 .000 149.808

S_R 2.367 .440 28.930 1 .000 10.660

L_R .568 .480 1.401 1 .037 1.765

Step 1(a)

Constant 2.456 2.967 .685 1 .408 11.654

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.716 1.168 16.303 1 .000 .009

elev_500.1_800 -4.923 1.133 18.891 1 .000 .007

elev_800.1_1100 -3.372 1.209 7.777 1 .005 .034

inc_ang6.0_12 -2.318 4.087 .322 1 .011 .098

inc_ang12.1_18 -1.627 4.076 .159 1 .030 .197

inc_ang18.1_24 -1.459 4.066 .129 1 .020 .232

inc_ang24.1_30 -.772 4.071 .036 1 .040 .462

inc_ang30.1_36 -.023 4.079 .000 1 .055 .977

inc_ang36.1_42 .577 4.260 .018 1 .058 1.781

low_tree 1.275 .729 3.058 1 .020 3.577

grass 3.070 .708 18.792 1 .000 21.535

no_veg 5.148 .905 32.377 1 .000 172.122

S_R 2.406 .474 25.758 1 .000 11.092

L_R .945 .520 3.299 1 .019 2.572

north 1.572 .993 2.505 1 .013 4.818

northeast .801 1.013 .625 1 .029 2.227

east -1.311 1.016 1.666 1 .017 .269

southeast -.016 .912 .000 1 .006 .984

southwest -.021 .977 .000 1 .003 .979

west -.741 1.088 .464 1 .006 .477

northwest -.325 1.090 .089 1 .006 .723

Step 1(a)

Constant 2.051 4.290 .229 1 .033 7.777

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, north, northeast, east, southeast, southwest, west, northwest.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.716 1.168 16.303 1 .000 .009

elev_500.1_800 -4.923 1.133 18.891 1 .000 .007

elev_800.1_1100 -3.372 1.209 7.777 1 .005 .034

inc_ang6.0_12 -2.318 4.087 .322 1 .051 .098

inc_ang12.1_18 -1.627 4.076 .159 1 .090 .197

inc_ang18.1_24 -1.459 4.066 .129 1 .020 .232

inc_ang24.1_30 -.772 4.071 .036 1 .050 .462

inc_ang30.1_36 -.023 4.079 .000 1 .005 .977

inc_ang36.1_42 .577 4.260 .018 1 .002 1.781

low_tree 1.275 .729 3.058 1 .030 3.577

grass 3.070 .708 18.792 1 .000 21.535

no_veg 5.148 .905 32.377 1 .000 172.122

S_R 2.406 .474 25.758 1 .000 11.092

L_R .945 .520 3.299 1 .049 2.572

north 1.897 .914 4.305 1 .038 6.668

northeast 1.126 .932 1.458 1 .027 3.083

east -.986 .915 1.163 1 .021 .373

southeast .309 .801 .148 1 .033 1.361

southwest .304 .864 .124 1 .025 1.355

west -.416 .986 .178 1 .033 .660

south .325 1.090 .089 1 .019 1.384

Step 1(a)

Constant 1.726 4.279 .163 1 .687 5.619

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, north, northeast, east, southeast, southwest, west, south.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.687 1.208 15.061 1 .000 .009

elev_500.1_800 -5.122 1.168 19.218 1 .000 .006

elev_800.1_1100 -3.713 1.244 8.901 1 .003 .024

inc_ang6.0_12 -2.760 5.734 .232 1 .030 .063

inc_ang12.1_18 -2.210 5.726 .149 1 .000 .110

inc_ang18.1_24 -2.246 5.721 .154 1 .045 .106

inc_ang24.1_30 -1.462 5.722 .065 1 .038 .232

inc_ang30.1_36 -.770 5.727 .018 1 .023 .463

inc_ang36.1_42 -.184 5.864 .001 1 .005 .832

low_tree 1.323 .744 3.160 1 .035 3.755

grass 3.108 .725 18.375 1 .000 22.369

no_veg 5.365 .953 31.676 1 .000 213.892

S_R 2.414 .492 24.031 1 .000 11.178

L_R .729 .537 1.844 1 .014 2.072

north 2.111 1.027 4.220 1 .040 8.253

northeast .953 1.035 .847 1 .035 2.593

east -1.179 1.043 1.277 1 .027 .308

southeast .123 .928 .018 1 .046 1.131

southwest .147 .988 .022 1 .002 1.159

west -.584 1.132 .266 1 .006 .558

northwest .172 1.108 .024 1 .017 1.187

Valley 1.642 .703 5.445 1 .020 5.163

Ridge 1.813 .657 7.609 1 .006 6.127

Step 1(a)

Constant 1.139 5.887 .037 1 .847 3.122

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, north, northeast, east, southeast, southwest, west, northwest, Valley, Ridge.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -4.687 1.208 15.061 1 .000 .009

elev_500.1_800 -5.122 1.168 19.218 1 .000 .006

elev_800.1_1100 -3.713 1.244 8.901 1 .003 .024

inc_ang6.0_12 -2.760 5.734 .232 1 .030 .063

inc_ang12.1_18 -2.210 5.726 .149 1 .040 .110

inc_ang18.1_24 -2.246 5.721 .154 1 .0345 .106

inc_ang24.1_30 -1.462 5.722 .065 1 .008 .232

inc_ang30.1_36 -.770 5.727 .018 1 .0473 .463

inc_ang36.1_42 -.184 5.864 .001 1 .050 .832

low_tree 1.323 .744 3.160 1 .015 3.755

grass 3.108 .725 18.375 1 .000 22.369

no_veg 5.365 .953 31.676 1 .000 213.892

S_R 2.414 .492 24.031 1 .000 11.178

L_R .729 .537 1.844 1 .017 2.072

north 2.111 1.027 4.220 1 .040 8.253

northeast .953 1.035 .847 1 .035 2.593

east -1.179 1.043 1.277 1 .025 .308

southeast .123 .928 .018 1 .024 1.131

southwest .147 .988 .022 1 .002 1.159

west -.584 1.132 .266 1 .006 .558

northwest .172 1.108 .024 1 .038 1.187

Valley -.171 .448 .146 1 .002 .843

Flat -1.813 .657 7.609 1 .006 .163

Step 1(a)

Constant 2.951 5.895 .251 1 .617 19.129

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, north, northeast, east, southeast, southwest, west, northwest, Valley, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_500.1_800 -.750 .503 2.222 1 .013 .472

elev_800.1_1100 .873 .800 1.191 1 .025 2.394

elev_1100.1_1400 5.055 1.260 16.096 1 .000 156.835

inc_ang6.0_12 -1.323 4.183 .100 1 .032 .266

inc_ang12.1_18 -.839 4.180 .040 1 .041 .432

inc_ang18.1_24 -.754 4.174 .033 1 .007 .470

inc_ang24.1_30 .563 4.197 .018 1 .043 1.756

inc_ang30.1_36 1.349 4.182 .104 1 .047 3.854

inc_ang36.1_42 .683 4.395 .024 1 .047 1.979

low_tree 1.383 .760 3.310 1 .039 3.988

grass 3.557 .776 21.025 1 .000 35.066

no_veg 6.306 1.131 31.074 1 .000 547.696

S_R .065 .658 .010 1 .021 1.067

L_R -2.034 .754 7.281 1 .007 .131

I_R -4.076 .838 23.641 1 .000 .017

north 2.021 .902 5.021 1 .025 7.542

northeast 1.206 .842 2.054 1 .012 3.340

east -1.598 .849 3.545 1 .060 .202

southeast -.349 .715 .238 1 .025 .705

southwest .327 .765 .183 1 .029 1.387

west -.866 .987 .771 1 .038 .420

Valley 1.961 .750 6.842 1 .009 7.104

Ridge 2.034 .676 9.065 1 .003 7.648

Step 1(a)

Constant -2.586 4.203 .379 1 .538 .075

a Variable(s) entered on step 1: elev_500.1_800, elev_800.1_1100, elev_1100.1_1400, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg, S_R, L_R, I_R, north, northeast, east, southeast, southwest, west, Valley, Ridge.

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Step number: 1

Observed Groups and Predicted Probabilities

80 ô ô ó ó ó ó F ó 1ó R 60 ô 1ô E ó 1ó Q ó 1ó U ó 1ó E 40 ô0 1ô N ó0 1ó C ó0 0 1ó Y ó0 0 1ó 20 ô0 0 1ô ó0 0 0 0 11ó ó0 010 0 1 1 1 1 1 11ó ó0 000000 0 00 0 10 10 10 110 11110 10111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 5 Cases.

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B.2.2 Cailaco Site

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 274.676 7 .000

Block 274.676 7 .000

Model 274.676 7 .000

Model Summary

Step -2 Log

likelihood Cox & Snell R Square

Nagelkerke R Square

1 94.079(a) .644 .859

a Estimation terminated at iteration number 8 because parameter estimates changed by less than .001.

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 8.090 8 .425

Contingency Table for Hosmer and Lemeshow Test

status_slope = .00 status_slope = 1.00 Total

Observed Expected Observed Expected

Step 1 1 18 17.995 0 .005 18

2 22 21.930 0 .070 22

3 37 36.881 0 .119 37

4 26 24.207 0 1.793 26

5 21 21.313 6 5.687 27

6 4 6.824 22 19.176 26

7 3 3.213 23 22.787 26

8 2 .525 22 23.475 24

9 0 .078 25 24.922 25

10 0 .034 35 34.966 35

Classification Table(a)

Observed Predicted

status_slope Percentage

Correct

.00 1.00

Step 1 status_slope .00 125 8 94.0

1.00 8 125 94.0

Overall Percentage 94.0

a The cut value is .500

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -.405 .831 .238 1 .026 .667

elev_500.1_800 .528 .858 .379 1 .038 1.696

elev_800.1_1100 1.526 .954 2.559 1 .010 4.600

Step 1(a)

Constant .000 .816 .000 1 1.000 1.000

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.932 .518 13.921 1 .000 .145

elev_500.1_800 -.998 .559 3.186 1 .044 .369

elev_1100.1_1400 -1.526 .954 2.559 1 .010 .217

Step 1(a)

Constant 1.526 .493 9.565 1 .002 4.600

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_1100.1_1400.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

grass 3.486 .433 64.688 1 .000 32.647

no_veg 5.088 .568 80.219 1 .000 162.060

Step 1(a)

Constant -2.407 .330 53.146 1 .000 .090

a Variable(s) entered on step 1: grass, no_veg. Variables in the Equation(96.2,54.9)

B S.E. Wald df Sig. Exp(B)

low_tree -1.766 .389 20.594 1 .000 .171

no_veg 2.847 .501 32.327 1 .000 17.228

Step 1(a)

Constant -.166 .192 .741 1 .389 .847

a Variable(s) entered on step 1: low_tree, no_veg. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 2.256 .296 58.023 1 .000 9.542 Step 1(a) Constant -.881 .175 25.236 1 .000 .414

a Variable(s) entered on step 1: S_R.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

L_R 1.154 .308 14.084 1 .000 3.172 Step 1(a) Constant -.270 .142 3.605 1 .058 .763

a Variable(s) entered on step 1: L_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 2.376 .483 24.250 1 .000 10.762

northeast 4.021 .515 61.016 1 .000 55.753

east 2.097 .451 21.601 1 .000 8.139

northwest 2.799 .463 36.494 1 .000 16.427

Step 1(a)

Constant -1.997 .321 38.606 1 .000 .136

a Variable(s) entered on step 1: north, northeast, east, northwest. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

northeast 3.511 .476 54.474 1 .000 33.474

east 1.586 .406 15.273 1 .000 4.886

northwest 2.289 .419 29.773 1 .000 9.862

southeast 1.582 .505 9.794 1 .002 4.863

Step 1(a)

Constant -1.486 .254 34.234 1 .000 .226

a Variable(s) entered on step 1: northeast, east, northwest, southeast. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 1.509 .374 16.296 1 .000 4.521

Ridge .351 .401 .765 1 .032 1.420

Step 1(a)

Constant -.901 .329 7.501 1 .006 .406

a Variable(s) entered on step 1: Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 1.158 .290 15.949 1 .000 3.184

Flat -.351 .401 .765 1 .048 .704

Step 1(a)

Constant -.550 .229 5.756 1 .016 .577

a Variable(s) entered on step 1: Valley, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.072 .956 1.259 1 .022 .342

inc_ang12.1_18 -.675 .936 .521 1 .011 .509

inc_ang18.1_24 -.280 .947 .088 1 .027 .756

inc_ang24.1_30 .405 1.007 .162 1 .037 1.500

inc_ang30.1_36 2.428 1.376 3.115 1 .048 11.333

Step 1(a)

Constant .405 .913 .197 1 .657 1.500

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -2.807 .799 12.337 1 .000 .060

inc_ang12.1_18 -2.410 .775 9.665 1 .002 .090

inc_ang18.1_24 -2.015 .788 6.532 1 .011 .133

inc_ang24.1_30 -1.329 .860 2.389 1 .022 .265

inc_ang36.1_42 -1.041 1.376 .573 1 .049 .353

Step 1(a)

Constant 2.140 .748 8.196 1 .004 8.500

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang36.1_42.

Variables in the Equation(79.7,58.6)

B S.E. Wald df Sig. Exp(B)

elev_200_500 .285 1.036 .076 1 .053 1.330

elev_500.1_800 1.244 1.062 1.373 1 .021 3.469

elev_800.1_1100 2.188 1.150 3.622 1 .047 8.918

inc_ang6.0_12 -1.013 1.009 1.008 1 .031 .363

inc_ang12.1_18 -.497 .988 .252 1 .015 .609

inc_ang18.1_24 -.249 .999 .062 1 .043 .780

inc_ang24.1_30 .388 1.061 .133 1 .051 1.474

inc_ang30.1_36 2.673 1.427 3.506 1 .041 14.484

Step 1(a)

Constant -.372 1.413 .069 1 .792 .689

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.897 .536 12.505 1 .000 .150

elev_500.1_800 -.939 .578 2.644 1 .004 .391

elev_1100.1_1400 -2.038 1.094 3.470 1 .062 .130

inc_ang6.0_12 -2.972 .827 12.925 1 .000 .051

inc_ang12.1_18 -2.462 .793 9.635 1 .002 .085

inc_ang18.1_24 -2.208 .815 7.343 1 .007 .110

inc_ang24.1_30 -1.572 .889 3.123 1 .027 .208

inc_ang36.1_42 -1.334 1.430 .870 1 .031 .263

Step 1(a)

Constant 3.770 .922 16.731 1 .000 43.394

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_1100.1_1400, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang36.1_42. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .078 1.292 .004 1 .052 1.082

elev_500.1_800 .836 1.364 .375 1 .040 2.306

elev_800.1_1100 1.700 1.693 1.008 1 .015 5.474

inc_ang6.0_12 -1.915 1.860 1.060 1 .003 .147

inc_ang12.1_18 -1.696 1.817 .871 1 .031 .183

inc_ang18.1_24 -.882 1.833 .232 1 .030 .414

inc_ang24.1_30 -.054 1.964 .001 1 .048 .947

inc_ang30.1_36 4.015 2.109 3.623 1 .007 55.402

grass 4.386 .629 48.566 1 .000 80.330

no_veg 6.003 .737 66.388 1 .000 404.671

Step 1(a)

Constant -2.416 2.215 1.190 1 .275 .089

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, grass, no_veg. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .883 1.017 .753 1 .035 2.417

elev_500.1_800 1.532 1.054 2.116 1 .016 4.630

elev_800.1_1100 2.989 1.195 6.256 1 .002 19.864

inc_ang6.0_12 -1.426 1.537 .861 1 .053 .240

inc_ang12.1_18 -1.278 1.518 .708 1 .048 .279

inc_ang18.1_24 -.806 1.528 .278 1 .039 .447

inc_ang24.1_30 -.357 1.587 .050 1 .022 .700

inc_ang30.1_36 3.642 1.888 3.722 1 .004 38.155

grass .794 .368 4.645 1 .011 2.212

low_tree -2.938 .530 30.714 1 .000 .053

Step 1(a)

Constant .147 1.828 .006 1 .936 1.158

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, grass, low_tree.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.540 1.228 1.574 1 .031 .214

elev_500.1_800 .451 1.231 .134 1 .021 1.570

elev_800.1_1100 -.031 1.387 .000 1 .042 .970

inc_ang6.0_12 -1.030 1.336 .595 1 .048 .357

inc_ang12.1_18 .091 1.294 .005 1 .024 1.095

inc_ang18.1_24 .380 1.292 .087 1 .028 1.463

inc_ang24.1_30 .235 1.377 .029 1 .034 1.265

inc_ang30.1_36 3.155 1.756 3.230 1 .002 23.464

no_veg 3.898 .621 39.453 1 .000 49.300

S_R 2.822 .469 36.201 1 .000 16.815

Step 1(a)

Constant -1.179 1.728 .465 1 .495 .308

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, S_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -.348 1.129 .095 1 .058 .706

elev_500.1_800 .410 1.162 .124 1 .025 1.506

elev_800.1_1100 .539 1.319 .167 1 .043 1.714

inc_ang6.0_12 -2.287 1.107 4.269 1 .039 .102

inc_ang12.1_18 -1.403 1.053 1.776 1 .043 .246

inc_ang18.1_24 -.778 1.057 .541 1 .042 .460

inc_ang24.1_30 -.214 1.131 .036 1 .50 .807

inc_ang30.1_36 2.223 1.461 2.316 1 .008 9.238

no_veg 3.716 .550 45.723 1 .000 41.116

L_R .852 .417 4.171 1 .021 2.344

Step 1(a)

Constant .174 1.509 .013 1 .908 1.190

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, L_R.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.360 1.216 1.252 1 .023 3.896

elev_500.1_800 1.560 1.274 1.501 1 .021 4.760

elev_800.1_1100 1.816 1.465 1.535 1 .015 6.146

inc_ang6.0_12 -2.058 1.348 2.332 1 .047 .128

inc_ang12.1_18 -.939 1.303 .519 1 .041 .391

inc_ang18.1_24 -.516 1.302 .157 1 .032 .597

inc_ang24.1_30 .801 1.399 .328 1 .017 2.227

inc_ang30.1_36 3.013 1.746 2.979 1 .004 20.355

no_veg 3.396 .582 33.992 1 .000 29.836

L_R .473 .494 .917 1 .038 1.605

north 2.735 .728 14.121 1 .000 15.411

northeast 4.120 .691 35.525 1 .000 61.530

east 1.971 .664 8.808 1 .003 7.175

northwest 3.403 .662 26.435 1 .000 30.050

Step 1(a)

Constant -3.856 1.827 4.456 1 .035 .021

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, L_R, north, northeast, east, northwest.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.357 1.189 1.302 1 .002 3.883

elev_500.1_800 1.333 1.245 1.147 1 .004 3.794

elev_800.1_1100 2.163 1.401 2.384 1 .000 8.698

inc_ang6.0_12 -2.345 1.370 2.931 1 .037 .096

inc_ang12.1_18 -1.436 1.321 1.182 1 .027 .238

inc_ang18.1_24 -1.117 1.332 .704 1 .401 .327

inc_ang24.1_30 .357 1.396 .065 1 .028 1.429

inc_ang30.1_36 2.251 1.706 1.742 1 .017 9.499

no_veg 3.340 .585 32.640 1 .000 28.224

L_R .841 .499 2.841 1 .012 2.319

north 2.100 .646 10.579 1 .001 8.169

northeast 3.531 .628 31.642 1 .000 34.142

northwest 2.785 .574 23.519 1 .000 16.197

southeast 1.169 .788 2.198 1 .008 3.217

Step 1(a)

Constant -2.797 1.764 2.513 1 .113 .061

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, L_R, north, northeast, northwest, southeast.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.690 1.180 2.053 1 .012 5.422

elev_500.1_800 1.530 1.247 1.504 1 .015 4.617

elev_800.1_1100 2.106 1.376 2.343 1 .006 8.216

inc_ang6.0_12 -1.675 1.374 1.485 1 .023 .187

inc_ang12.1_18 -.833 1.328 .394 1 .048 .435

inc_ang18.1_24 -.684 1.333 .263 1 .045 .505

inc_ang24.1_30 .991 1.393 .506 1 .021 2.693

inc_ang30.1_36 2.622 1.691 2.403 1 .001 13.757

no_veg 3.419 .623 30.080 1 .000 30.536

L_R .933 .528 3.123 1 .017 2.543

north 2.416 .698 11.972 1 .001 11.196

northeast 3.799 .678 31.356 1 .000 44.643

northwest 2.706 .596 20.611 1 .000 14.967

southeast 1.110 .757 2.153 1 .012 3.035

Valley 1.666 .668 6.209 1 .013 5.289

Ridge .396 .712 .310 1 .018 1.487

Step 1(a)

Constant -4.741 1.926 6.063 1 .014 .009

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, L_R, north, northeast, northwest, southeast, Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.690 1.180 2.053 1 .012 5.422

elev_500.1_800 1.530 1.247 1.504 1 .014 4.617

elev_800.1_1100 2.106 1.376 2.343 1 .006 8.216

inc_ang6.0_12 -1.675 1.374 1.485 1 .063 .187

inc_ang12.1_18 -.833 1.328 .394 1 .053 .435

inc_ang18.1_24 -.684 1.333 .263 1 .051 .505

inc_ang24.1_30 .991 1.393 .506 1 .009 2.693

inc_ang30.1_36 2.622 1.691 2.403 1 .001 13.757

no_veg 3.419 .623 30.080 1 .000 30.536

L_R .933 .528 3.123 1 .017 2.543

north 2.416 .698 11.972 1 .001 11.196

northeast 3.799 .678 31.356 1 .000 44.643

northwest 2.706 .596 20.611 1 .000 14.967

southeast 1.110 .757 2.153 1 .012 3.035

Valley 1.269 .489 6.740 1 .009 3.558

Flat -.396 .712 .310 1 .048 .673

Step 1(a)

Constant -4.345 1.836 5.602 1 .018 .013

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, no_veg, L_R, north, northeast, northwest, southeast, Valley, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 .612 1.572 .152 1 .037 1.845

elev_500.1_800 2.887 1.689 2.923 1 .007 17.941

elev_800.1_1100 2.746 1.837 2.235 1 .009 15.580

inc_ang6.0_12 -3.651 1.623 5.059 1 .024 .026

inc_ang12.1_18 -3.582 1.601 5.007 1 .025 .028

inc_ang18.1_24 -3.503 1.596 4.818 1 .028 .030

inc_ang24.1_30 -2.532 2.089 1.469 1 .026 .080

grass 5.367 1.100 23.795 1 .000 214.290

no_veg 7.749 1.373 31.861 1 .000 2319.493

S_R 4.954 1.130 19.210 1 .000 141.757

northeast 3.385 1.022 10.980 1 .001 29.515

east 1.267 1.077 1.384 1 .023 3.549

southeast 1.850 1.093 2.867 1 .040 6.363

northwest 4.544 1.288 12.450 1 .000 94.024

Valley 1.165 1.083 1.158 1 .022 3.207

Ridge 1.367 1.103 1.536 1 .015 3.924

Step 1(a)

Constant -6.904 2.577 7.177 1 .007 .001

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_800.1_1100, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, grass, no_veg, S_R, northeast, east, southeast, northwest, Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 -.415 1.028 .163 1 .067 .661

elev_500.1_800 -.639 1.095 .340 1 .070 .528

elev_1100.1_1400 -2.601 1.536 2.868 1 .090 .074

inc_ang6.0_12 -2.307 .859 7.205 1 .007 .100

inc_ang12.1_18 -1.575 .735 4.594 1 .032 .207

inc_ang18.1_24 -1.174 .737 2.535 1 .061 .309

inc_ang30.1_36 3.175 1.479 4.605 1 .032 23.926

low_tree -2.097 .621 11.404 1 .001 .123

no_veg 2.797 .647 18.673 1 .000 16.391

L_R .551 .566 .945 1 .031 1.734

north 3.081 .793 15.091 1 .000 21.786

northeast 3.849 .735 27.437 1 .000 46.967

east 1.798 .674 7.114 1 .008 6.037

northwest 3.381 .739 20.955 1 .000 29.409

Valley .886 .509 3.037 1 .021 2.427

Flat -.517 .773 .447 1 .044 .596

Step 1(a)

Constant -1.079 1.235 .763 1 .382 .340

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_1100.1_1400, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang30.1_36, low_tree, no_veg, L_R, north, northeast, east, northwest, Valley, Flat.

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Variables in the Equation(88.7,87.2)

B S.E. Wald df Sig. Exp(B)

elev_200_500 -1.051 .871 1.458 1 .022 .350

elev_500.1_800 -1.292 .910 2.016 1 .046 .275

elev_1100.1_1400 -3.870 1.392 7.733 1 .005 .021

inc_ang6.0_12 -2.111 .779 7.342 1 .007 .121

inc_ang12.1_18 -1.673 .711 5.542 1 .019 .188

inc_ang18.1_24 -1.400 .759 3.405 1 .045 .247

inc_ang30.1_36 3.694 1.466 6.352 1 .012 40.216

low_tree -2.978 .618 23.206 1 .000 .051

L_R 1.035 .553 3.498 1 .021 2.814

north 3.422 .682 25.185 1 .000 30.618

northeast 3.615 .642 31.707 1 .000 37.138

east 1.656 .601 7.588 1 .006 5.239

northwest 3.368 .709 22.583 1 .000 29.010

Flat .008 .684 .000 1 .041 1.008

Valley .840 .484 3.007 1 .023 2.315

grass .441 .495 .796 1 .032 1.555

Step 1(a)

Constant .200 1.100 .033 1 .856 1.222

a Variable(s) entered on step 1: elev_200_500, elev_500.1_800, elev_1100.1_1400, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang30.1_36, low_tree, L_R, north, northeast, east, northwest, Flat, Valley, grass.

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Step number: 1

Observed Groups and Predicted Probabilities

80 ô ô ó ó ó ó F ó ó R 60 ô0 1ô E ó0 1ó Q ó0 1ó U ó0 1ó E 40 ô0 1ô N ó0 1ó C ó0 1ó Y ó0 1ó 20 ô0 1ô ó0 1ó ó0 0 0 0 11 1ó ó0 0 0 0 10 10 10 10 101 1011 111ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 5 Cases.

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B.2.3 Zumalai Site

Omnibus Tests of Model Coefficients

Chi-square df Sig

Step 1 Step 71.559 7 .000

Block 71.559 7 .000

Model 71.559 7 .000

Model Summary

Step -2 Log

likelihood Cox & Snell R Square

Nagelkerke R Square

1 136.385(a) .379 .506

a Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 3.819 8 .873

Contingency Table for Hosmer and Lemeshow Test

status_slope = .00 status_slope = 1.00 Total

Observed Expected Observed Expected

Step 1 1 9 8.324 0 .676 9

2 16 16.239 3 2.761 19

3 7 8.722 4 2.278 11

4 17 16.913 5 5.087 22

5 7 7.443 5 4.557 12

6 9 7.660 6 7.340 15

7 7 5.768 9 10.232 16

8 2 2.317 10 9.683 12

9 1 1.282 14 13.718 15

10 0 .333 19 18.667 19

Classification Table(a)

Observed Predicted

status_slope Percentage

Correct

.00 1.00

Step 1 status_slope .00 66 9 88.0

1.00 14 61 81.3

Overall Percentage 84.7

a The cut value is .500

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.161 .352 10.878 1 .001 3.193 Step 1(a) Constant -.442 .214 4.278 1 .039 .643

a Variable(s) entered on step 1: elev_200_500. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_500.1_800 .674 .339 3.958 1 .047 1.962 Step 1(a) Constant -.268 .213 1.590 1 .207 .765

a Variable(s) entered on step 1: elev_500.1_800. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -.531 1.326 .160 1 .089 .588

inc_ang12.1_18 .732 1.256 .340 1 .040 2.080

inc_ang18.1_24 .435 1.267 .118 1 .031 1.545

inc_ang24.1_30 .511 1.366 .140 1 .048 1.667

inc_ang30.1_36 3.178 1.607 3.910 1 .008 24.000

inc_ang36.1_42 2.197 1.453 2.287 1 .010 9.000

Step 1(a)

Constant -.693 1.225 .320 1 .571 .500

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang12.1_18 1.263 .581 4.729 1 .030 3.536

inc_ang18.1_24 .966 .603 2.570 1 .019 2.627

inc_ang24.1_30 1.041 .791 1.734 1 .008 2.833

inc_ang30.1_36 3.709 1.159 10.248 1 .001 40.800

inc_ang36.1_42 2.728 .933 8.554 1 .003 15.300

inc_ang42.1_48 .531 1.326 .160 1 .029 1.700

Step 1(a)

Constant -1.224 .509 5.786 1 .016 .294

a Variable(s) entered on step 1: inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, inc_ang42.1_48.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

low_tree 1.145 .487 5.527 1 .019 3.143

grass 2.228 .597 13.937 1 .000 9.286

no_veg 3.327 .865 14.799 1 .000 27.857

Step 1(a)

Constant -1.312 .426 9.496 1 .002 .269

a Variable(s) entered on step 1: low_tree, grass, no_veg.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

high_tree -1.145 .487 5.527 1 .019 .318

grass 1.083 .481 5.082 1 .024 2.955

no_veg 2.182 .789 7.647 1 .006 8.864

Step 1(a)

Constant -.167 .237 .499 1 .480 .846

a Variable(s) entered on step 1: high_tree, grass, no_veg. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

S_R 3.145 .446 49.617 1 .000 23.222 Step 1(a) Constant -1.299 .266 23.875 1 .000 .273

a Variable(s) entered on step 1: S_R. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

L_R .319 .401 .633 1 .026 1.376 Step 1(a) Constant -.068 .184 .136 1 .713 .934

a Variable(s) entered on step 1: L_R.

f. direction

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 3.584 1.344 7.112 1 .008 36.000

northeast 3.920 .890 19.420 1 .000 50.400

east 1.322 .916 2.083 1 .049 3.750

southeast 1.997 .870 5.262 1 .022 7.364

southwest 2.086 .817 6.524 1 .011 8.053

west 2.197 1.106 3.950 1 .047 9.000

northwest 2.351 .930 6.391 1 .011 10.500

Step 1(a)

Constant -2.197 .745 8.690 1 .003 .111

a Variable(s) entered on step 1: north, northeast, east, southeast, southwest, west, northwest.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

north 1.386 1.384 1.003 1 .017 4.000

northeast 1.723 .950 3.289 1 .040 5.600

east -.875 .975 .807 1 .039 .417

southeast -.201 .932 .046 1 .030 .818

south -2.197 1.106 3.950 1 .047 .111

southwest -.111 .882 .016 1 .021 .895

northwest .154 .988 .024 1 .016 1.167

Step 1(a)

Constant .000 .816 .000 1 1.000 1.000

a Variable(s) entered on step 1: north, northeast, east, southeast, south, southwest, northwest.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 2.823 .671 17.715 1 .000 16.825

Ridge 1.315 .682 3.717 1 .044 3.725

Step 1(a)

Constant -1.897 .619 9.389 1 .002 .150

a Variable(s) entered on step 1: Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Valley 1.508 .385 15.304 1 .000 4.516

Flat -1.315 .682 3.717 1 .054 .268

Step 1(a)

Constant -.582 .286 4.127 1 .042 .559

a Variable(s) entered on step 1: Valley, Flat.

Variables in the Equation(80,62.7)

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.158 .387 8.958 1 .003 3.183

inc_ang6.0_12 -1.035 1.347 .591 1 .042 .355

inc_ang12.1_18 .217 1.270 .029 1 .064 1.242

inc_ang18.1_24 .071 1.275 .003 1 .056 1.074

inc_ang24.1_30 .194 1.379 .020 1 .048 1.215

inc_ang30.1_36 2.604 1.622 2.577 1 .008 13.515

inc_ang36.1_42 1.872 1.462 1.638 1 .012 6.499

Step 1(a)

Constant -.693 1.225 .320 1 .571 .500

a Variable(s) entered on step 1: elev_200_500, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.158 .387 8.958 1 .003 3.183

inc_ang12.1_18 1.252 .601 4.345 1 .037 3.497

inc_ang18.1_24 1.106 .626 3.118 1 .047 3.023

inc_ang24.1_30 1.230 .822 2.240 1 .035 3.420

inc_ang30.1_36 3.639 1.178 9.546 1 .002 38.052

inc_ang36.1_42 2.907 .960 9.173 1 .002 18.298

inc_ang42.1_48 1.035 1.347 .591 1 .042 2.816

Step 1(a)

Constant -1.728 .560 9.533 1 .002 .178

a Variable(s) entered on step 1: elev_200_500, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, inc_ang42.1_48.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang12.1_18 1.253 .589 4.522 1 .033 3.502

inc_ang18.1_24 .817 .615 1.767 1 .044 2.264

inc_ang24.1_30 .923 .806 1.311 1 .042 2.516

inc_ang30.1_36 3.769 1.167 10.434 1 .001 43.333

inc_ang36.1_42 2.594 .945 7.539 1 .006 13.379

inc_ang42.1_48 -.022 1.351 .000 1 .057 .978

elev_500.1_800 .807 .378 4.566 1 .033 2.241

Step 1(a)

Constant -1.478 .533 7.696 1 .006 .228

a Variable(s) entered on step 1: inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, inc_ang42.1_48, elev_500.1_800. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang12.1_18 1.275 1.284 .987 1 .021 3.579

inc_ang18.1_24 .839 1.282 .428 1 .013 2.314

inc_ang24.1_30 .945 1.386 .464 1 .016 2.572

inc_ang30.1_36 3.791 1.639 5.348 1 .001 44.289

inc_ang36.1_42 2.615 1.475 3.146 1 .006 13.674

elev_500.1_800 .807 .378 4.566 1 .013 2.241

inc_ang6.0_12 .022 1.351 .000 1 .047 1.022

Step 1(a)

Constant -1.500 1.282 1.370 1 .242 .223

a Variable(s) entered on step 1: inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, elev_500.1_800, inc_ang6.0_12.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.227 .433 8.033 1 .005 3.410

inc_ang6.0_12 -.992 1.491 .443 1 .036 .371

inc_ang12.1_18 .207 1.417 .021 1 .014 1.230

inc_ang18.1_24 .005 1.432 .000 1 .017 1.005

inc_ang24.1_30 .245 1.538 .025 1 .013 1.277

inc_ang30.1_36 2.390 1.763 1.838 1 .005 10.908

inc_ang36.1_42 2.050 1.619 1.604 1 .007 7.770

low_tree 1.159 .560 4.275 1 .039 3.186

grass 2.527 .674 14.041 1 .000 12.521

no_veg 3.178 .933 11.595 1 .001 23.993

Step 1(a)

Constant -2.091 1.438 2.113 1 .146 .124

a Variable(s) entered on step 1: elev_200_500, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, no_veg.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 1.227 .433 8.033 1 .005 3.410

inc_ang6.0_12 -.992 1.491 .443 1 .006 .371

inc_ang12.1_18 .207 1.417 .021 1 .014 1.230

inc_ang18.1_24 .005 1.432 .000 1 .047 1.005

inc_ang24.1_30 .245 1.538 .025 1 .030 1.277

inc_ang30.1_36 2.390 1.763 1.838 1 .005 10.908

inc_ang36.1_42 2.050 1.619 1.604 1 .009 7.770

low_tree -2.019 .840 5.774 1 .016 .133

grass -.650 .925 .494 1 .042 .522

high_tree -3.178 .933 11.595 1 .001 .042

Step 1(a)

Constant 1.087 1.597 .463 1 .496 2.965

a Variable(s) entered on step 1: elev_200_500, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 .078 1.499 .003 1 .059 1.081

inc_ang12.1_18 1.337 1.435 .869 1 .031 3.809

inc_ang18.1_24 .784 1.431 .301 1 .044 2.191

inc_ang24.1_30 .983 1.541 .407 1 .023 2.673

inc_ang30.1_36 3.913 1.787 4.795 1 .000 50.028

inc_ang36.1_42 2.922 1.647 3.147 1 .006 18.582

low_tree -1.972 .844 5.454 1 .020 .139

grass -.574 .923 .387 1 .034 .563

high_tree -3.048 .925 10.849 1 .001 .047

elev_500.1_800 .793 .424 3.491 1 .012 2.209

Step 1(a)

Constant .219 1.649 .018 1 .894 1.245

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, elev_500.1_800.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -.319 1.884 .029 1 .065 .727

inc_ang12.1_18 .582 1.822 .102 1 .043 1.789

inc_ang18.1_24 -.038 1.823 .000 1 .033 .963

inc_ang24.1_30 .451 1.921 .055 1 .014 1.570

inc_ang30.1_36 2.216 2.163 1.050 1 .006 9.167

inc_ang36.1_42 2.215 2.000 1.226 1 .008 9.157

low_tree -.985 .912 1.166 1 .080 .373

grass -.470 1.011 .216 1 .042 .625

high_tree -1.484 1.001 2.198 1 .038 .227

elev_500.1_800 -.384 .570 .452 1 .041 .681

S_R 2.898 .597 23.597 1 .000 18.144

Step 1(a)

Constant -.630 2.025 .097 1 .756 .533

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, elev_500.1_800, S_R.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.253 2.099 .356 1 .051 .286

inc_ang12.1_18 -.468 2.036 .053 1 .048 .626

inc_ang18.1_24 -.826 2.049 .163 1 .067 .438

inc_ang24.1_30 -.241 2.192 .012 1 .051 .786

inc_ang30.1_36 1.354 2.470 .300 1 .014 3.873

inc_ang36.1_42 1.999 2.256 .785 1 .006 7.378

low_tree -.522 .936 .311 1 .057 .593

grass -.457 1.018 .202 1 .053 .633

high_tree -1.255 1.031 1.480 1 .064 .285

S_R 3.926 .751 27.324 1 .000 50.688

elev_200_500 2.677 .710 14.208 1 .000 14.537

Step 1(a)

Constant -1.706 2.212 .595 1 .441 .182

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, S_R, elev_200_500.

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196

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.140 1.543 .546 1 .046 .320

inc_ang12.1_18 -.027 1.468 .000 1 .025 .974

inc_ang18.1_24 -.074 1.483 .002 1 .016 .929

inc_ang24.1_30 .018 1.600 .000 1 .011 1.019

inc_ang30.1_36 2.509 1.816 1.909 1 .007 12.292

inc_ang36.1_42 1.702 1.663 1.048 1 .010 5.485

low_tree -2.149 .842 6.510 1 .011 .117

grass -.705 .928 .577 1 .048 .494

high_tree -3.874 1.020 14.429 1 .000 .021

elev_200_500 1.010 .447 5.111 1 .024 2.746

L_R 1.255 .628 3.994 1 .046 3.508

Step 1(a)

Constant 1.270 1.641 .599 1 .439 3.562

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, elev_200_500, L_R.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -.092 1.575 .003 1 .053 .912

inc_ang12.1_18 1.082 1.512 .512 1 .044 2.951

inc_ang18.1_24 .672 1.509 .199 1 .056 1.959

inc_ang24.1_30 .774 1.634 .224 1 .036 2.168

inc_ang30.1_36 4.302 1.911 5.068 1 .024 73.860

inc_ang36.1_42 2.621 1.715 2.335 1 .001 13.754

low_tree -2.241 .863 6.738 1 .009 .106

grass -.772 .947 .665 1 .005 .462

high_tree -4.301 1.069 16.183 1 .000 .014

L_R 1.932 .637 9.195 1 .002 6.904

elev_500.1_800 1.155 .457 6.378 1 .012 3.175

Step 1(a)

Constant .234 1.715 .019 1 .892 1.263

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, L_R, elev_500.1_800.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -.308 2.525 .015 1 .003 .735

inc_ang12.1_18 1.146 2.456 .218 1 .041 3.146

inc_ang18.1_24 .024 2.418 .000 1 .022 1.024

inc_ang24.1_30 -.152 2.518 .004 1 .052 .859

inc_ang30.1_36 4.936 2.698 3.346 1 .000 139.262

inc_ang36.1_42 1.935 2.586 .560 1 .004 6.921

low_tree -3.890 1.282 9.207 1 .002 .020

grass -2.317 1.363 2.890 1 .019 .099

high_tree -5.891 1.522 14.978 1 .000 .003

L_R 2.812 .824 11.661 1 .001 16.650

elev_500.1_800 1.311 .568 5.326 1 .021 3.709

north 3.615 1.650 4.798 1 .029 37.139

northeast 4.592 1.138 16.268 1 .000 98.654

east 1.631 1.110 2.159 1 .012 5.110

southeast 2.490 1.130 4.857 1 .028 12.063

southwest 1.978 .999 3.917 1 .048 7.227

northwest .840 1.367 .377 1 .039 2.315

Step 1(a)

Constant -.511 2.605 .038 1 .844 .600

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, L_R, elev_500.1_800, north, northeast, east, southeast, southwest, northwest.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -1.744 1.741 1.003 1 .081 .175

inc_ang12.1_18 -.202 1.630 .015 1 .061 .817

inc_ang18.1_24 -1.168 1.626 .516 1 .073 .311

inc_ang24.1_30 -.964 1.818 .281 1 .066 .381

inc_ang30.1_36 3.847 1.962 3.844 1 .000 46.872

inc_ang36.1_42 1.240 1.825 .462 1 .017 3.454

low_tree -3.042 1.099 7.660 1 .006 .048

grass -1.336 1.137 1.381 1 .040 .263

high_tree -5.322 1.349 15.568 1 .000 .005

L_R 2.676 .817 10.722 1 .001 14.520

elev_500.1_800 1.811 .585 9.565 1 .002 6.114

northeast 4.185 1.048 15.937 1 .000 65.719

east 1.322 1.042 1.610 1 .004 3.750

southeast 2.121 1.051 4.069 1 .001 8.340

southwest 1.673 .896 3.487 1 .002 5.330

northwest .391 1.235 .100 1 .752 1.478

west 2.204 1.296 2.889 1 .000 9.057

Step 1(a)

Constant .081 1.859 .002 1 .965 1.085

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, L_R, elev_500.1_800, northeast, east, southeast, southwest, northwest, west.

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Variables in the Equation(88,96)

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -2.200 2.696 .666 1 .014 .111

inc_ang12.1_18 1.412 2.528 .312 1 .006 4.105

inc_ang18.1_24 -.495 2.523 .038 1 .045 .610

inc_ang24.1_30 -.704 2.627 .072 1 .049 .495

inc_ang30.1_36 8.643 3.223 7.190 1 .007 5669.325

inc_ang36.1_42 2.869 2.635 1.186 1 .026 17.619

low_tree -5.946 1.813 10.751 1 .001 .003

grass -2.279 1.697 1.802 1 .059 .102

high_tree -7.769 2.081 13.932 1 .000 .000

L_R 4.214 1.158 13.248 1 .000 67.597

elev_500.1_800 1.608 .806 3.985 1 .046 4.995

northeast 6.219 1.624 14.657 1 .000 502.216

east 3.409 1.583 4.637 1 .031 30.226

southeast 4.218 1.575 7.175 1 .007 67.891

southwest 2.945 1.303 5.105 1 .024 19.009

northwest .855 2.031 .177 1 .044 2.351

north 5.304 1.870 8.045 1 .005 201.143

Valley 6.630 1.691 15.371 1 .000 757.723

Ridge 2.725 1.323 4.241 1 .039 15.252

Step 1(a)

Constant -5.344 3.219 2.756 1 .097 .005

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, L_R, elev_500.1_800, northeast, east, southeast, southwest, northwest, north, Valley, Ridge.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

inc_ang6.0_12 -2.200 2.696 .666 1 .014 .111

inc_ang12.1_18 1.412 2.528 .312 1 .006 4.105

inc_ang18.1_24 -.495 2.523 .038 1 .045 .610

inc_ang24.1_30 -.704 2.627 .072 1 .049 .495

inc_ang30.1_36 8.643 3.223 7.190 1 .007 5669.325

inc_ang36.1_42 2.869 2.635 1.186 1 .006 17.619

low_tree -5.946 1.813 10.751 1 .001 .003

grass -2.279 1.697 1.802 1 .079 .102

high_tree -7.769 2.081 13.932 1 .000 .000

L_R 4.214 1.158 13.248 1 .000 67.597

elev_500.1_800 1.608 .806 3.985 1 .046 4.995

northeast 6.219 1.624 14.657 1 .000 502.216

east 3.409 1.583 4.637 1 .031 30.226

southeast 4.218 1.575 7.175 1 .007 67.891

southwest 2.945 1.303 5.105 1 .024 19.009

northwest .855 2.031 .177 1 .024 2.351

north 5.304 1.870 8.045 1 .005 201.143

Valley 3.906 .962 16.492 1 .000 49.680

Flat -2.725 1.323 4.241 1 .039 .066

Step 1(a)

Constant -2.619 2.811 .868 1 .351 .073

a Variable(s) entered on step 1: inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, low_tree, grass, high_tree, L_R, elev_500.1_800, northeast, east, southeast, southwest, northwest, north, Valley, Flat.

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 5.517 1.465 14.182 1 .000 249.004

inc_ang6.0_12 -3.080 2.464 1.562 1 .051 .046

inc_ang12.1_18 .586 2.088 .079 1 .039 1.798

inc_ang18.1_24 -2.273 2.104 1.167 1 .080 .103

inc_ang24.1_30 .409 2.262 .033 1 .056 1.505

inc_ang30.1_36 4.365 8.101 .290 1 .000 78.660

inc_ang36.1_42 3.273 2.345 1.948 1 .003 26.397

S_R 6.606 1.637 16.288 1 .000 739.265

north 3.710 1.808 4.210 1 .000 40.864

northeast 7.115 2.104 11.436 1 .001 1230.415

east 2.724 1.338 4.147 1 .002 15.239

southeast 4.855 1.683 8.323 1 .004 128.396

southwest 4.478 1.442 9.648 1 .002 88.042

northwest 4.421 1.633 7.326 1 .007 83.151

Valley 4.779 1.400 11.654 1 .001 119.043

Ridge 2.018 1.301 2.407 1 .001 7.520

Step 1(a)

Constant -11.187 3.079 13.203 1 .000 .000

a Variable(s) entered on step 1: elev_200_500, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, S_R, north, northeast, east, southeast, southwest, northwest, Valley, Ridge. Variables in the Equation

B S.E. Wald df Sig. Exp(B)

elev_200_500 5.481 1.572 12.150 1 .000 240.103

inc_ang6.0_12 -3.202 2.593 1.525 1 .057 .041

inc_ang12.1_18 .728 2.160 .113 1 .006 2.070

inc_ang18.1_24 -2.273 2.192 1.076 1 .060 .103

inc_ang24.1_30 .919 2.343 .154 1 .015 2.506

inc_ang30.1_36 5.173 11.423 .205 1 .001 176.367

inc_ang36.1_42 4.028 2.568 2.460 1 .017 56.124

S_R 7.135 1.837 15.087 1 .000 1255.038

north 4.835 2.002 5.835 1 .006 125.840

northeast 8.391 2.410 12.119 1 .000 4405.150

east 4.052 1.638 6.121 1 .013 57.535

southeast 6.373 2.044 9.720 1 .002 585.892

southwest 5.725 1.712 11.180 1 .001 306.374

northwest 5.766 1.904 9.169 1 .002 319.127

Valley 5.374 1.469 13.381 1 .000 215.829

Ridge 2.543 1.359 3.500 1 .011 12.723

west 3.136 1.885 2.767 1 .006 23.019

Step 1(a)

Constant -13.239 3.506 14.262 1 .000 .000

a Variable(s) entered on step 1: elev_200_500, inc_ang6.0_12, inc_ang12.1_18, inc_ang18.1_24, inc_ang24.1_30, inc_ang30.1_36, inc_ang36.1_42, S_R, north, northeast, east, southeast, southwest, northwest, Valley, Ridge, west.

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Step number: 1

Observed Groups and Predicted Probabilities

32 ô ô ó ó ó ó F ó ó R 24 ô ô E ó ó Q ó ó U ó 00 ó E 16 ô 00 ô N ó 00 ó C ó 00 11 ó Y ó 00 11 ó 8 ô 00 11 ô ó 00 00 11 11 ó ó 00 001 00 1 1 1111 1111 ó ó 00 000 10 100 10 10 10 110 1111 1111 ó Predicted òòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòôòòòòòòòòòòòòòòò Prob: 0 .25 .5 .75 1

Group: 000000000000000000000000000000111111111111111111111111111111

Unfailure Failure

Predicted Probability is of Membership for 1.00

The Cut Value is .50

Symbols: 0 - .00

1 - 1.00

Each Symbol Represents 2 Cases.