GIS-based multivariate statistical analysis for

landslide susceptibility zoning:

a first validation on different areas of Liguria

region (Italy)

R. Marzocchi, A. Rovegno, B. Federici,

R. Bovolenta & R. Berardi

FOSS4G-Europe 2015 July 14th – 17th, Como

DICCA – Polytechnic School – University of Genoa – Italy

Intr

oduction

6.9% of the territory is catalogued as landslide by the IFFI national inventory of landslide (ISPRA , 2013)

Several approaches to assess landslide hazard (Fell et al., 2008): heuristic analysis based on geological and geomorphological criteria and local observations collected by an expert statistical methods usually correlate an inventory of landslides occurred in the past with factors which are supposed to be responsible of slope failure (Cascini, 2008) process-based methods and numerical analysis local analysis

• affecting limited thickness of the loose soil • local contributing factors related to humans (eg. road cuts)

The type of landslides involved in this study are slides and flows (according to the UNESCO WP/WLI, 1993)

associated to intense meteorological events

Intr

oduction

not only emergency management but also prevention

Land use planning supported by

landslide susceptibility zoning

Intr

oduction

The aim Landslide susceptibility zoning on wide area through GIS-based multivariate statistical analysis

To express objectively the landslide susceptibility in accordance with many factors

Software GRASS GIS 7.0

The present research

Criticality analysis of the zoning procedure to develop a guideline for the landslide susceptibility zoning, as an instrument to non-GIS and non-statistical expert users for the choice of factors to be considered.

Predisposing factors to landslide

Logistic multiple regression

Critical analysis

Conclusions

Overview In

troduction

Literature analysis P

redis

posin

g f

acto

rs to landslid

e

1. Slope

2. Land use

3. Lithology

4. Aspect

5. Accumulation

6. Elevation above sea level

7. Distance from road network

8. Climatic aggressivity FFAO

geological Morphological anthropogenic climatic

Lith

olo

gy

stra

tigr

aph

y

Dis

tan

ce fr

om

tec

ton

ic a

lign

men

ts

Soil

typ

e

Soil

dep

th

slo

pe

Mo

rph

olo

gica

l par

amet

ers

asp

ect

Elev

atio

n a

bo

ve s

ea le

vel

Dis

tan

ce fr

om

th

e d

rain

age

net

wo

rk

accu

mu

lati

on

Lan

d u

se

vege

tati

on

Dis

tan

ce fr

om

ro

ad n

etw

ork

Clim

atic

agg

ress

ivit

y FF

AO

Mea

n m

on

thly

rai

n

Natali et al. (2010) x x x

Pradhan & Lee (2010) x x x x x x x x x

Pauditš & Bednárik

(2002) x x x

Lee (2007) (x) x x x x x x x

Dahal et al. (2008) x x x x x x x x x

Cencetti et al. (2010) x x x x x

Dai & Lee (2002) x x (x) x x (x) x

Ayalew & Yamagishi

(2005) x x x x x x x

Raster maps (20x20 m) reclassified for the area that you want to submit a zoning

proven factors of instability

Bivariate analysis

• to reduce number of classes

• to have monotonic (increasing or decreasing) trend of variables

Land use classification

1 Other

2 Buildings

3 Agricultural areas

4 Wood

5 Sparse vegetation

6 Bare soil/rock

𝑃 evento 𝑥 =𝑃 evento⋂𝑥

𝑃 𝑥

Pre

dis

posin

g f

acto

rs to landslid

e

Pre

dis

posin

g f

acto

rs to landslid

e a) FFAO

b) Log(accumulation) c) aspect d) slope e) lithology f) distance from road g) Elevation h) land cover

Bivariate analysis

Generalized linear model (GLM), appropriate for dichotomous data (0 = absence of landslide, 1 = presence of landslide)

𝑷 𝒆𝒗𝒆𝒏𝒕𝒐 𝑿 =𝟏

𝟏 + 𝒆−𝒁

𝒁 = 𝒍𝒐𝒈𝒊𝒕 𝒆𝒗𝒆𝒏𝒕𝒐 = 𝜷𝟎 + 𝜷𝟏 ∙ 𝑿𝟏 + ⋯ + 𝜷𝒑 ∙ 𝑿𝒑

Logis

tic m

ultip

le r

egre

ssio

n

Logistic multiple regression

Logis

tic m

ultip

le r

egre

ssio

n

Logistic multiple regression

1st phase: Calibration

𝑃 𝑒𝑣𝑒𝑛𝑡𝑜 𝑿 =1

1 + 𝑒−𝑍

inventory of landslide (IFFI) associating: P=1 if landslide area P=0 if NOT landslide area

chosen factors

𝑍 = 𝑙𝑜𝑔𝑖𝑡 𝑒𝑣𝑒𝑛𝑡𝑜 = 𝛽0 + 𝛽1 ∙ 𝑋1 + ⋯ + 𝛽𝑝 ∙ 𝑋𝑝

Estimation of regressions coefficients

Logis

tic m

ultip

le r

egre

ssio

n

Logistic multiple regression

2nd phase: Application

𝑃 𝑒𝑣𝑒𝑛𝑡𝑜 𝑿 =1

1 + 𝑒−𝑍

chosen factors

𝑍 = 𝑙𝑜𝑔𝑖𝑡 𝑒𝑣𝑒𝑛𝑡𝑜 = 𝛽0 + 𝛽1 ∙ 𝑋1 + ⋯ + 𝛽𝑝 ∙ 𝑋𝑝

Estimated regressions coefficients

Calculated landslide susceptibility

Critical analy

sis

Critical points of the procedure

• Classification of chosen factors bivariate analysis

• Selection of the indipendent variables of the regression model (factors)

• Choice of the proper area to calibrate the coefficient of the regression model

• Definition of a criterion of classification of the susceptibility values

Critical analy

sis

Test area

Liguria Region (Italy) and its 4 Provinces

Critical analy

sis

Selection of the indipendent factors

2(p

end

enza

e u

sosu

olo

) 3(+

lito

logi

a)

4(+

accu

mu

lazi

on

e)

5(+

esp

osi

zio

ne)

6(+

AC

)

7(+

clas

si q

uo

ta)

8(+

dis

tan

za s

trad

e)

22130000

22135000

22140000

22145000

22150000

22155000

n° regressori

AIC

AIC index (Akaike’s Information Criteria)

Defined the calibration area...

If the adding of a variable has a positive effect on the model, the values of AIC have to decrease

Regione Liguria provincia Imperia provincia Savona provincia Genova provincia La Spezia

0,0000

0,0002

0,0004

0,0006

0,0008

0,0010

0,0012

0,0014

R2

pendenza

uso suolo

litologia

accumulazione

esposizione

quota

distanza strade

aggress.climatica

Critical analy

sis

The proper area to calibrate

R2 correlation coefficient

Defined the factors... indication of the influence factor of the j-th factor on the susceptibility

Slope Land use Lithology Accumulation Aspect Elevation Distance to roads FFAO

Critical analy

sis

Factors vs calibration area

Regression coefficients bj reliability of the factors

Reliable factors • slope • Land use • accumulation • Distance from road

pendenza uso suolo litologia accumulazione esposizione quota distanza strade aggress.climatica

-0,27

-0,22

-0,17

-0,12

-0,07

-0,02

0,03

0,08

0,13

coe

ff. r

egr

ess

ion

e

Regione Liguria

provincia Imperia

provincia Savona

provincia Genova

provincia La Spezia

Slope land use lithology accumul aspect elevation dist road FFAO

Critical analy

sis

Factors vs calibration area

bassa media alta

Importance of spatial variability of factors

Low Medium high

FFAO map

Lithology map

Critical analy

sis

Factors vs calibration area

pendenza uso suolo litologiadist.dastrade

accumul. esposiz. quotaaggress.climatica

Piemonte 0,000136 0,000172 0,007310 0,000054 0,000045 0,001521 0,001026 0,000106

Liguria 0,000659 0,000342 0,000227 0,000196 0,000143 0,000033 0,000032 0,000005

0,0000

0,0010

0,0020

0,0030

0,0040

0,0050

0,0060

0,0070R2

R2 correlation coefficient indication of the influence factor of the j-th factor on the susceptibility

Piemonte

Liguria

Slope land use lithology dist road accumul. aspect elevation FFAO

Critical analy

sis

Classification of the susceptibility values

5.4 e-09

4.3 e-08

Estimated values of susceptibility • Relative results

• Very small values (order of 10-9 - 10-8)

Classification in 3 susceptibility level

low moderate high

Critical analy

sis

Results and validation

signs of instability incipient sliding

Low Moderate High

Critical analy

sis

Results and validation

Low Moderate High

Critical analy

sis

Results and validation

Low Moderate High

First conclusions C

on

clu

sio

ns

• Importance of preliminary analysis of the characteristics of the area to be subjected to zoning bivariate analysis

• Reliable factors: slope, land use, accumulation, distance to roads

• Importance of spatial variability of factors

• Strong influence of the characteristics of the calibration area on zoning results

• Calibration area: not too small, to be representative of all the conditions present in the area to be subjected to zoning, but of more homogeneous characteristics as possible

Future work C

on

clu

sio

ns

• Guidelines for the susceptibility zoning for non-GIS and non-statistical expert users

• Maps with indications of most suitable design solutions according to the characteristics of the critical zones

Co

nclu

sio

ns

This work is financed by the national research program (PRIN 2010-2011) called “Mitigation of landslide risk using sustainable actions” coordinated by University of Salerno.

Thank you for your attention