UNIVERSITY OF CAPE COAST

DEPARTMENT OF GEOGRAPHY AND TOURISM

LAND USE DYNAMICS IN BIEHA,

SISSILI PROVINCE, SOUTHERN

BURKINA FASO

BY

ISSA OUEDRAOGO

A DISSERTATION SUBMITTED TO THE DEPARTMENT OF GEOGRAPHY

AND TOURISM OF THE FACULTY OF SOCIAL SCIENCES, UNIVERSITY

OF CAPE COAST, IN PARTIAL FULFILMENT OF THE REQUIREMENTS

FOR THE AWARD OF MASTER OF ARTS DEGREE IN GEOGRAPHY.

AUGUST 2007

DECLARATIONS

Candidate’s Declaration

I hereby declare that this dissertation is the result of my own original work and

that no part of it has been presented for another degree in this university or

elsewhere.

Candidate’s Signature: issadeh@yahoo.fr Date: 2007/08/30

Name: Issa Ouedraogo

Supervisors’ Declaration

We hereby declare that the preparation and presentation of the dissertation were

supervised in accordance with the guidelines on supervision of dissertation laid

down by the University of Cape Coast.

Principal Supervisor’s Signature: skendie2001@yahoo.com Date: 2007/08/30

Name: Professor Stephen B. Kendie

Co-Supervisor’s Signature: emmanjeb@yahoo.com Date: 2007/08/30

Name: Professor E. Jeurry Blankson.

i

ABSTRACT

Remote Sensing and Geographical Information Systems tools were used to

detect land use dynamics of Bieha District from 1986 to 2002 on the basis of

Landsat Thematic Mapper imageries processing.

During the 16-year period, important changes occurred on the main

geographical units of land use of the area, namely farming fields, shrubby and

wooded savannahs, and gallery forest. The farming surface increased from 3,438.7

hectares to 33,686.6 hectares and the shrubby savannah decreased from 67,427.5

hectares to 35,818.8 hectares. The wooded savannah and gallery forest remained

unchanged in terms of surface but spatially, each of these four units underwent

profound changes.

The deforestation caused by farming activities most amplified by high in-

migration of population was about 1,798.5 hectares annually. Wood extraction and

bushfires contributed to a loss of 2,105.5 hectares of forest per annum. Policy

initiatives that could lead to environmental conservation are suggested.

ii

ACKNOWLEDGEMENT

This work is the result of the support of consolidated goodwill persons

from Burkina Faso, Sweden and Ghana. I would like to express my gratitude to all

those who helped with the preparation and presentation of this thesis.

I wish to register my profound gratitude and appreciation to my

supervisors: Professor S. B. Kendie and Professor E. Jeurry Blankson for their

immeasurable contribution in the form of suggestions, guidance, constructive

criticisms and pieces of advice from the initiation of the research to its completion.

Without their intellectual dynamism, fruitful ideas and comments, this venture

would not have been possible. May God richly bless you in your endeavours.

I would like to specifically express my sincere gratitude towards Professor

K. Awusabo-Asare, Dean of the Faculty of Social Sciences and Professor Albert

M. Abane, Head of the Department of Geography and Tourism for receiving me at

the Department, taking time out of their busy schedules to go through my drafts,

and providing necessary conditions for the study at the University.

I express my thankfulness to all lecturers of the Department of Geography

and Tourism, particularly, Professor L. A. Dei, Dr. Roy Cole, Dr. Oheneba

Akyeampong, Dr. A. Kumi-Kyeremi, Mr. Tanle, Eshun and Afful for their

understanding and encouragement. To my fellow course mates, namely, Simon,

Foster and Gerard, I say a big thank you.

To all chiefs of Bieha District, the entire population, foresters and

translators who responded to the interview schedules during the study, I express

my gratitude. I am indebted to all the authors whose works I used as references.

iii

My thanks are also due to Dr. Basile Guissou, General Director of the

National Centre of Scientific and Technologic Research (CNRST) of Burkina

Faso; Dr. Jean-Marie Ouadba, Head of the Department of Forestry (DPF) of the

Institute for Environment and the Agronomic Research (INERA/CNRST), and Dr.

Maxim Compaoré, Director of the Scientific Coordination and Cooperation

(DCCS/CNRST) and National Coordinator of SIDA/SAREC Project for providing

the necessary fund for the study.

I am grateful to Professors Ulf Soderberg and Mats Sandewall of the

Department of Forest Resource Management and Geomatics at the Swedish

University of Agricultural Sciences (SLU-Umea) and Mr. Souleymane Paré, PhD

student of the same university for their contributions in terms of suggestions, and

encouragements.

Finally, my sincere thanks go to my family, friends and relatives who

endured my absence due to this work and for their moral and material support. I

am particularly grateful to my wife, Aguira Dera, whose patience and

encouragement enabled me to complete the study and my boy, Abdoul Razack

Ouédraogo, who was born during the early period of the study.

iv

DEDICATION

To my wife Aguira Dera and my son, Abdoul Razack Ouédraogo.

v

TABLE OF CONTENTS

Content Page

DECLARATIONS i

ABSTRACT ii

ACKNOWLEDGEMENT iii

DEDICATION v

TABLE OF CONTENTS vi

LIST OF TABLES xii

LIST OF FIGURES xiv

LIST OF PLATES xv

ACRONYMS xvi

CHAPTER ONE : BACKGROUND OF THE STUDY 1

Introduction 1

Statement of the problem 3

Objectives 4

Hypotheses 5

Rationale 5

Conceptual framework 6

The study area 9

Climate and vegetation 10

Fauna 12

Population and activities 13

Social infrastructure 15

vi

Tenure management 16

Structure of the dissertation 17

CHAPTER TWO : DEFINITION OF CONCEPTS AND TECHNIQUES 18

Introduction 18

Land use and land cover 18

Land use change and consequences 19

Remote sensing 20

Landsat thematic mapper sensor and multispectral imagery 23

Image classification techniques 25

Global positioning systems (GPS) 27

Geographic information systems (GIS) 28

Land use evolution detection 29

Competing models for land use dynamics 30

Summary 40

CHAPTER THREE : REVIEW OF ENVIRONMENTAL ISSUES IN

BURKINA FASO 41

Introduction 41

Agricultural practices and deforestation 43

Migration and environmental degradation 44

Overgrazing 46

Firewood and timber request 47

Bushfire 48

vii

CHAPTER FOUR : METHODS OF DATA COLLECTION AND

ISSUES FROM THE FIELD 49

Introduction 49

Data and sources 49

Images processing 50

Satellite images 50

Geometric correction 52

Classification 52

Detection of land changes 54

Problems encountered during the images processing 58

Population interviews 58

Instruments used 58

Method of sampling 59

Pre-survey activity in the villages 61

The fieldwork 62

Issues from the interview 62

Response rate 62

Problems encountered 63

Diagram of the methodology 64

Limitation of the study 67

viii

CHAPTER FIVE : LAND USE ASSESSMENT 69

Introduction 69

Land use detection 69

State of the land use in 1986 69

State of land use in 2002 71

Land use dynamics from 1986 to 2002 73

Respondents’ perception of the environment and their welfare 82

Dynamics of the environment 82

Dynamics of the vegetation 82

Wildlife dynamics 84

Soil fertility dynamics 85

Water dynamics 87

Dynamics of farming practices 88

Crops productivity 88

Farming techniques 89

Change in acreage per household 89

Dynamics of the population welfare 90

Food security 90

Drinking water 91

Income evolution 92

Population mobility 92

Summary 94

ix

CHAPTER SIX : FACTORS INFLUENCING CHANGES AND

IMPLICATIONS FOR LAND USE 96

Introduction 96

Changes detected 96

Factors affecting land use dynamics in Bieha 97

Leading factors in the farm fields dynamics 98

Population pressure 98

Agri-business 100

Poverty 101

Consequences of increasing farm lands 102

Forests dynamics 103

The shrubby savannah 103

The wooded savannah 103

The gallery forest 108

Consequences of the deforestation 109

CHAPTER SEVEN : SUMMARY, CONCLUSIONS AND

RECOMMENDATIONS 112

Introduction 112

Summary of the methodology 112

Summary of the findings 113

Summary of the discussion 114

Conclusion 115

Recommendations and strategies for further research 116

x

REFERENCES 119

APPENDIX : Questionnaire 132

xi

LIST OF TABLES

Table Page Table 1 Wild animal’s population in Bieha, 2004 13

Table 2 Dynamics of the population of Bieha 14

Table 3 Yields of the main crops in Bieha in 2002 15

Table 4 Livestock in Sissili 1986 and 2003 15

Table 5 Summary of land use dynamics models 34

Table 6 Satellite images used for land use detection of Sissili 51

Table 7 Land use classes considered in image classification 53

Table 8 Structure of the questionnaire 59

Table 9 Selected villages and number of respondents 61

Table 10 Respondents and response rate by selected villages 63

Table 11 Surface area and proportion of land use units in 1986 70

Table 12 Surface area and proportion of land use units in 2002 72

Table 13 Codification of land use units 73

Table 14 Legend of the land use dynamics 74

Table 15 Land use change in Bieha 78

Table 16 Dynamics of land use units 80

Table 17 Dynamics of the vegetation 83

Table 18 Causes of vegetation loss 84

Table 19 Dynamics of wild animals 85

Table 20 Causes of wildlife dynamics 85

Table 21 Soil fertility change 86

xii

Table 22 Causes of fertility change 86

Table 23 Solution to fertility reduction 86

Table 24 Water evolution in the rivers 87

Table 25 Causes of water reduction 87

Table 26 Evolution of crops productivity 88

Table 27 Dynamics of farming practices 89

Table 28 Change in acreage per household 90

Table 29 Food security according to population 91

Table 30 Evolution of the sources of drinking water 91

Table 31 Evolution of incomes according to population 92

Table 32 Permanent in-migration 93

Table 33 Causes of permanent in-migration 93

Table 34 Temporary in-migration 94

xiii

LIST OF FIGURES

Figure Page

Figure 1 Three-dimensional framework for land use change 7

Figure 2 Map of Bieha District (the study area) 9

Figure 3 Rainfall isohyets and floristic zone of Burkina Faso 11

Figure 4 Rainfall evolution of Bieha from 1988 to 2002 11

Figure 5 Monthly rainfall of Bieha district in 2002 12

Figure 6 GPS National Constellation 27

Figure 7 Landsat TM image mosaic of Burkina Faso 50

Figure 8 False colour composites of satellite imageries 56

Figure 9 Supervised classification of landsat image 57

Figure 10 Selected villages for the survey in Bieha 60

Figure 11 Methodological approaches for the land use dynamics 66

Figure 12 Land use units in Bieha in 1986 70

Figure 13 Land use units in Bieha in 2002 72

Figure 14 Land use dynamics in Bieha from 1986 to 2002 76

Figure 15 Comparison between land use 1986 - 2002 77

Figure 16 Observation of land use change in Bieha 77

Figure 17 Tendency curves of land use units in Bieha 78

Figure 18 Dynamics of land use units 81

xiv

LIST OF PLATES

Plate Page

Plate 1 Cashew plantation in Neboun 101

Plate 2 Pile of wood for sale 104

Plate 3 Wood transportation 104

Plate 4 Afzelia africana cut for animals 106

Plate 5 Burnt shrubby savannah 107

Plate 6 Burnt wooded savannah 108

xv

ACRONYMS

BNF Biological Nitrogen Fixation

CILSS Comite Inter-Etats de Lutte contre la Secheresse au Sahel

CLUE Conversion of Land Use and its Effects

CLUE-CR Conversion of Land Use and its Effects–Costa Rica

CNRST Centre National de la Recherche Scientifique et Technologique

(BF)

CONAGESS Comission Nationale de Gestion et de Securite

CUF California Urban Futures

CURBA California Urban an Biodiversity Analysis Model

DPAHRH Direction Provinciale de l’Agriculture de l’hydraulique et des

Ressources Halieutiques

DGEP Direction Générale des Etudes et de la Planification

DGSA Direction Générale des Statistiques Agricoles

DPECV Direction Provinciale de l’Environnement et de Cadre de Vie

ERTS 1 Earth Resources Technology Satellite – 1

ERTS 2 Earth Resources Technology Satellite – 2

ESRI Environmental Systems Research Institute

FAO Food and Agriculture Organization

GDP Gross Domestic Product

GEM General Ecosystem Model

GPS Global Positioning Systems

GIS Geographical Information System

xvi

IBS INYPSA/BDPA-SCETAGRI/SOPEX

INERA Institut pour l’Environment et de la Recherche Agricole

INSD Institut National des Statistiques et de la Demographie

IGB Institut Geographique du Burkina Faso

Landsat TM Landsat Thematic Mapper

MECV Ministère de l’Environnement et de Cadre de Vie

MEE Ministère de l’Environnement et de l’Eau

MEF Ministère de l’Economie et des Finances

MET Ministère de l’Environment et du Tourisme

MRA Ministère des Ressources Animales

NASA National Aeronautic and Space Administration

PLM Patuxent Landscape Model

PNGT Programme National de Gestion des Terroirs

PNK Phosphate – Nitrogen – Potassium

PNLD Programme National de Lutte contre la Désertification

PNLCD Plan National de Lutte Contre la Désertification

PSB Programme Sahel Burkina

PSSA Programme Sectoriel du Secteur Agricole

RAF Réorganisation Agraire et Foncière

RAV Responsable Administratif Villageois

RS/GIS Remote Sensing and Geographical Information Systems

SOM Soil Organic Matter

USD United States Dollars

xvii

CHAPTER ONE

BACKGROUND OF THE STUDY

Introduction

Burkina Faso, like the other Sub-Saharan Africa countries, is confronted

with problems of development in a context of accelerated degradation of her

natural resources caused by repeated droughts and human activities. The

phenomenon of deterioration became more pronounced these last three decades

due to increasing population growth (2.7 % per year) in conjunction with irregular

rainfall pattern (Yameogo, 2005). This situation has caused food deficits with

some corollary effects such as general poverty and the development of internal

migration from the northern and central parts to the southern and south-western

regions. These movements have also created new problems linked to the

concentration of people and their activities in opened up areas, and threats to

protected zones and national reserves (Ministere de l’Environment et du Tourisme,

1991). The nutrient production basis of the country has deteriorated because the

natural habitat has become fragile and incapable of satisfying the food needs of the

population.

Among the natural resources in decline are flora, fauna, soils and surface

waters. They are declining mainly because of the climatic risks and more

especially due to human activities such as over-harvesting of wood and wild

1

animals, unhealthy agricultural practices, overgrazing, and bush fires (M.E.E,

1999; Ganemtore and Aboubacar, 2002; Henry et al, 2002).

Development strategies have been instituted by the government since 1970

in order to increase food production, to improve population welfare and to reduce

natural resources depletion. Among these initiatives are:

• The National Desertification Control Program (PNLD) in 1970,

• The National Programme for Villagers’ Forestry in 1984,

• The Agrarian System Reorganization (RAF) in 1984,

• The adoption of the National Plan for Desertification Control (PNLCD) in

1986,

• The National Plan of Action for the Environment in 1991,

• The National Programme of Soil Management (PNGT) in 1992,

• The Adjustment Programmes of the Agricultural Sector (PASA) from 1991

to 1999,

• The Decentralization Programme since 2000.

Each of the programmes placed emphasis on natural resources

management and socio-economic development of the country in conformity with

the 1992 Rio declaration (Yameogo, 2005).

In spite of these efforts, the southern provinces of the country, namely

Sissili, Ziro and Nahouri where the natural resources were until recently almost

intact are today under the pressure of the agricultural and pastoral activities and of

bush fires. These pressures have the potential to impact adversely on the natural

resources base of the region.

2

Statement of the problem

Burkina Faso is a Sahelian country where agriculture and livestock rearing

constitute the mainstay of the economy. These two spheres of activities account

for 90 % of employment in the country and contribute about 34.5 % to the GDP

(CONAGESS, 1998; Ganemtore and Aboubacar, 2002). These activities are

undertaken in a rudimentary and extensive way, with a low level of intensification.

They are believed to contribute to degradation of the environment (Howorth and

O'Keefe, 1998; M.E.E, 1999). Indeed, the government through national and sub-

regional levels has initiated programs (PNGT, PSSA, PSB, and CILSS) to fight

environmental degradation.

The eastern and south-western parts of the country, where population

density is low, possess the largest forest reserve of the country (Ministere de

l’Environment et de Carde de Vie, 2004). However, during the last two decades,

the natural resources in these areas have been subjected to pressure because of

agricultural and pastoral migrations, domestic energy requirements and periodic

bush fires (M.E.E, 1996; Henry et al, 2002).

The Sissili province in southern Burkina Faso is currently concerned about

population migration. In 1985, 11,945 migrants arrived in this province which

contributed to a rise in the immigration rate to 4.88 % (Henry et al, 2002). This

figure seems to be rising considerably not only due to the current increase in

cotton and yam cultivation, the expansion of agro-businesses and the return of

Burkinabe migrants due to the political crisis in Cote d’Ivoire, but also, and

especially, due to the high birth rate of 5.01 % which is currently the highest in the

country (I.N.S.D, 1996).

3

Agrotechnik (1991) has suggested that Sissili province could only support

30 persons per km2 without irreparable damage and IBS (1994) forecasted that

some 43 % of the Sissili area would be deforested by 2010 due to land-use

activities. These estimations which were based on five-year interval studies seem

to have been over-generalized. In order to understand the real situation of the

natural resources, the questions that need to be addressed are:

1) To what extent are land use activities such as farming, harvesting of

fuelwood and bush burning degrading the environment in the Sissili

province?

2) Is the degradation sufficient enough to ultimately undermine ecosystem

balance, human welfare and its long-term sustainability?

This study therefore sought to provide answers to these questions and to

put the land use activities in the study area into their proper perspective.

Objectives

General aim of the study was to assess land use change in Sissili Province

from 1986 to 2002. In order to achieve the purpose of the study, four specific

objectives were set, namely to:

1. Trace in time series (1986 to 2002) the land-use in Sissili province and in

particular the Bieha District;

2. Assess local activities on natural resources management;

3. Analyse the dynamics of each type of land-use unit in the study area; and,

4. Based on the findings, suggest interventions for a more sustainable land

use for the province.

4

Hypotheses

Two main hypotheses guided the study. These were:

1. Natural resources in Bieha district have experienced significant degradation

since 1986 resulting from population pressure;

2. Agro-pastoral exploitation of the land has by far had significant impacts on

natural resources.

Rationale

Land-use in Sub-Saharan Africa is principally focused on food and cash

crop production. Land-use activities, whether converting landscapes for human use

or changing management practices in areas already under management have

transformed a large proportion of the planet’s land surface (Foley et al, 2005).

Therefore, understanding the changes in land-use has long been a major focus of

research in agronomy and in long-term environment sustainability perspectives.

The rationale of this study can be seen in two perspectives.

1. Contribution to environmental sustainability

The study aims to trace in time series, the land-use in Sissili province and

particularly in the Bieha area. Examination of the state of land-use (whether

retrogressive, stable, or progressive), should be a major contribution towards

generating strategies for the long-tem sustainability of natural resources (Human

Development Report, 2003). It will help build a sustainable society, defined as

one that manages its economy and controls its population size without doing

irreparable environmental harm; satisfying the needs of its people without

5

depleting the environment or jeopardizing the prospects of future generations of

humans or other species (Miller,1994). It will also help to build strategies or rules

on the use of the natural resources to reduce the over-use of the environment by

farmers.

2. Contribution to knowledge and further investigations

The study will be an opportunity to test the Pessimists’ and Optimists’

concepts of the relationship between population and environment. It will bring to

the fore knowledge on the persistent causes and consequences of environmental

degradation and the state of ecosystem dynamics and the development of agro-

pastoral areas.

The study was carried out within the context of a broad project being

undertaken by the National Institute of Scientific and Technology Research of

Burkina Faso (CNRST) in collaboration with the Department of Forest Resource

Management and Geomatics of the Swedish University of Agricultural Sciences in

Umeå (Sweden) titled “Sub-national approach to integrated natural resource

management in southern region of Burkina”. This present study should contribute

to understanding land use change as a fundamental basis of natural resources

management of the region.

Conceptual framework

Land use is determined by the interaction in space and time of biophysical

factors (constraints) such as soils, climate, topography, and human factors like

population, technology and economic conditions (Veldkamp and Fresco, 1996a).

6

To assess land- use dynamics, a framework based on three critical dimensions is

proposed for summarizing models of human-environmental dynamics. Time and

space are the first two dimensions and provide a common setting in which all

biophysical and human processes operate (Agarwal et al, 2002). In other words,

models of biophysical and/or human processes operate either in a temporal context

or a spatial context or both (Figure 1).

SPACE (Y)

TIME (X)

Human Decision-

Making (Z)

Figure 1: Three-dimensional framework for land use change

Source: Agarwal et al (2001).

In land use dynamics, two distinct and important attributes must be

considered; namely model scale and model complexity. Model scale refers to Time

step and duration; Spatial resolution and extent, and Scale of human decision

making. Time step is the smallest temporal unit of analysis for change to occur for

a specific process in a model. In this present study for instance, farm fields’

7

surface area may change annually. Duration on the other hand, refers to the length

of time that the model is applied. In this case, the duration is 16 years (1986 –

2002).

Resolution represents the smallest geographic unit of analysis of the model.

The study uses Landsat Thematic Mapper images with resolution 30m X 30 m

(900 m²). Extent describes the total geographic area to which the model is applied.

Here, the extent is Bieha District (1,754 km²).

To date, social scientists have not yet described human decision making in

terms that are as concise and widely accepted for modeling, as time step/duration

or resolution/extent (Agarwal et al, 2002). Therefore, an analogous approach can

be used to articulate scales of human decision-making in terms of agent and

domain. Agent refers to the human actor or actors in the model who are making-

decision and domain constitutes the broadest social organization incorporated in

the model. In this study, agent is the individual human and domain is the set of

communities in Bieha District.

Model complexity embraces temporal complexity, spatial complexity and

human decision making complexity. These represent, respectively, the extent to

which a model is explicit at temporal, spatial or at the human decision-making

scale. There are possible important interactions between temporal complexity and

human decision-making. For instance, some human decisions are made in very

short time intervals, such as the decision of which tree to cut for grazing a herd is

made daily. Other decisions such as to increase household farm fields are made

over longer term periods.

8

The study area

Bieha is one of the seven districts of Sissili province of Burkina Faso

(Figure 2). It covers 1,754.6 km2 and represents 25 % of the total area of the

province. The District comprises 22 villages.

Figure 2: Map of Bieha District (the study area)

Source: Geographical Institute of Burkina (IGB), 2006

9

Climate and vegetation

The study area is part of the humid sudanian climatic zone (Guinko, 1984,

Fontès and Guinko, 1995) characterized by an alternation of a dry season from

November to April and a rainy season from May to October (Figure 5). The

climate is determined by the swinging of the intertropical fronts. The intertropical

fronts represent the contact zone between the continental dry air mass of the

northeast (harmattan) and of the south-eastern humid air mass (monsoon). The dry

season is subdivided in two periods: a dry and cool period from November to

February during which blows the harmattan and a dry and hot period from March

that precedes the advent of rains in May-June until October ending. The average

annual precipitations are between 800 and 1000mm (Figure 3) and can often go up

beyond 1000mm or lower on this side of 800mm (Figure 4).

The vegetation of Bieha is the Soudano-Guinean type according to the

phytogeographical zoning made by Guinko (1984). Vegetation is dominated by

shrubby and wooded savannas. The woody vegetation is dominated by Vitelarea

paradoxa, Terminalia spp and Combretum spp. The dominant herbaceous

perennials are Andropogon ascinodis and Schizachyrium sanguineum. The woody

species of the valleys are Anageissus leiocarpus, Daniella oliveri and Mitragina

inermis, associated with Andropogon gayanus and Viteveria nigritina as the

dominant herbaceous perennials.

Bieha District is endowed with a classified forest (Safari Ranch Sissili)

with a surface area of 353.3 km² and a local forest (35 km²) at Bori (Figure 2).

Vegetation is especially dense in these forests because they are protected from

harvesting and animal grazing.

10

Figure 3: Rainfall isohyets and floristic zones of Burkina Faso (1971-2000)

Source: Guinko, 1984, 1995.

0

200

400

600

800

1000

1200

1400

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Rai

nfal

l (m

m)

Figure 4: Rainfall evolution of Bieha from 1988 to 2002

Source: Meteorological station of Po (2002)

11

0

50

100

150

200

250

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Month

Rai

nfal

l (m

m)

0

5

10

15

20

25

Days

Rainfall Number of day

Figure 5: Monthly rainfall of Bieha district in 2002

Source: Meteorological station of Po (2002)

Fauna

A wild animal inventory taken in 2004 in the Safari Ranch Sissili by

Bouché et al. (2004) showed a rich diversity of wildlife. Twelve hoofed species

shown in Table 1 were enumerated in the ranch.

12

Table 1: Wild animal’s population in Bieha, 2004

Animals population

Scientific English

Loxodonta africana Elephant 17

Cyncerus caffer brachyceros Buffalo 25

Hyppotragus equimus Hippotrague 233

Alcephalus buselaphus Bubale 175

Kobus ellipsyprymmus defassa Waterbuck 24

Reduna reduna and Kabus cob Cobe 2

Traelaphus scriptus Guibs harnaches 3

Ourebia ourebi, Ourebi 2

Phacochoerus aethiopicus Warthog 33

Sylvicarpia grimmia Cephaloph 4

- Baboon 8

Total 523

Source: Bouché et al (2004)

Population and activities

The District of Bieha is composed of 22 villages inhabited by three main

ethnic groups: the native Nuni, the migrant Mossi and the pastoralists Fulani. The

actual population as at January 2006 consists of 25,634 people with a crude

density of 14.6 inhabitants per square kilometre (Table 2).

13

Table 2: Dynamics of the population of Bieha

1985 1996 2002 2006 Year

Pop Density Pop Density Pop Density Pop Density

Bieha 15 043 08.4 17 728 09.93 20 643 11.56 25 634 14.6

Source: INSD, 1985 and 1996; Police headquarters of Bieha

The Nuni are autochthones and have a secular relationship with the area.

The Mossi are migrants, pushed from the northern and central region of the

country by the scarcity of arable lands, pastures and water. The Fulani are agro-

pastoralists and have recently come to Sissili, although some came earlier to herd

the cattle of the Nuni. In total, 7 per cent of the Fulani arrived more than 20 years

ago, the remaining, 93 per cent, have arrived in the last 15 years (Howorth and

O'Keefe, 1998). The main reason behind the immigration was resource

degradation in the north and a consequent lack of pasture and dry season watering

points.

The Fulani have now settled in most parts of Sissili. They tend to

concentrate their animal herding in the zones of low-intensive agriculture in the

periphery/wooded areas of the villages.

Agriculture and breeding constitute the main economic activities of the

district. Crops grown include yam, maize, red and white sorghum, millet,

groundnut, sweet potato, cowpea, black-eyed beans and cotton (Table 3). The

stock farming involves bovine, ovine, goats and donkeys (Table 4).

14

Table 3: Yields of the main crops in Bieha in 2002

Cereals Cotton Tubers and others

Area (ha)

Quant. (tons)

Yield (kg/ha)

Area (ha)

Quant (tons)

Yield (kg/ha)

Area (ha)

Quant. (tons)

Yield (kg/ha)

8,075 6,363 788 2,980 2,166 727 3,312 11,811 3,566

Source: Annual report of DPAHRH (2002)

Table 4: Livestock in Sissili 1986 and 2003 Years Bovine Ovine Goats Donkeys Horses

1986 145,000 45,000 25,000 10,000 60

2003 320,321 239,768 338,982 59,012 100

Source: DEP, (1986, 2003)

Social infrastructure

A monograph of Sissili province prepared in 2004 by the General Office of

Economy and Planning (DGEP) found the following infrastructures as available in

the District. First, in the area of health, Bieha had five dispensaries and five

maternity hospitals distributed among Bieha Centre, Yalle, Neboun, Koumbogoro

and Yelbouga. Four pharmacies exist in the district. A total of 12 medical staff

were working in the district.

For education, Bieha district had 14 primary schools and one college at

Bieha centre. The school enrolment rate was 36.6 % in 2000 of which 43.3 % were

males and 29.6 % were females.

15

Bieha district had a drinking water coverage rate of 126 % in 2003,

comprising 49 fountains, 87 modern wells and five solar fountains.

In terms of energy and communication, only Bieha centre had light

provided by solar energy and land line phones. Some areas of the district are

covered by cell phone networks (Celtel and Telmob). There are motorable roads

linking all the villages of the district but some of them are unusable during rainy

seasons.

Tenure management

Each village in Sissili has its own definite village territory that has its

origins in the local history of the area and in the first settlers. Tenure management

in the villages is controlled, under customary law arrangements by the Nuni land

chief (Howorth and O'Keefe, 1998). The principal roles of the land chief are to

oversee and to supervise everything that has to do with the land, including the

bush, the farms and wildlife. He is seen as the mediator between the human world

and the divine world of the ancestors and spirits. If a person needs new land to

farm, the land chief must first be consulted. He will indicate which piece of land

the person can cultivate and what he must do first, i.e. the sacrifices he must carry

out and how much land is available. Likewise, when the immigrants arrive in the

village territory with the desire to settle, the first person they address is the village

chief, then the land chief. It is the latter who decides whether there is land in the

territory for the immigrants to farm. Depending on the village, there are different

systems that the land chiefs use to allocate land and control the immigrant's effects

on the village environments.

16

Structure of the dissertation

The study is divided into six main chapters. Chapter one deals with the

background to the study. It looks at the introduction, statement of the problem,

objectives, hypotheses and a description of the study area. Following this chapter

is the second chapter which presents the concepts of land use; land use change;

remote sensing, GIS and GPS; classification systems; and techniques and the

review of local environmental issues. Chapter Three introduces the methods and

procedures employed in data collection from the field. Land use assessment in

1986 and 2002, and the analysis of land use evolution within these two periods

constitute the main components of the fourth chapter. Chapter Five interprets the

findings and establishes a link between dynamics of land use and local socio-

economic and cultural context; and a comparison between the specific case of

Bieha and other cases found elsewhere. Chapter Six summarises the study,

concludes the discussion while suggesting steps towards sustainable development

of the study area.

17

CHAPTER TWO

DEFINITION OF CONCEPTS AND TECHNIQUES

FOR LAND USE DETECTION

Introduction

This chapter defines concepts related to land use and the tools used for the

processes it entails by way of a literature review. It also looks at some issues of the

environment in Burkina in general, and in Sissili province in particular.

Land use and land cover

Land use refers to the purposes for which humans exploit the land cover

(Fresco, 1994). Land cover is defined as the layer of soils and biomass, including

natural vegetation, crops and human structures that cover the land surface. Land

cover change is the complete replacement of one cover type by another, while land

use dynamics also include the modification of land cover type, e.g., intensification

of agricultural use, without changing its overall classification (Turner II et al.

1993). According to Meyer (1995) and Bottomley (1998), every parcel of land on

the Earth’s surface is unique in the cover it possesses.

Land use is therefore the manner in which human beings employ the land

and its resources. Examples of land use include agriculture, urban development,

grazing, logging, and mining. In contrast, land cover describes the physical state of

the land surface. Land cover categories include cropland, forests, wetlands,

pasture, roads, and settlements. The term land cover originally referred to the kind

18

and state of vegetation, such as forest or grass cover, but it has broadened in

subsequent usage to include human structures such as buildings or pavement and

other aspects of the natural environment, such as soil type, biodiversity, and

surface and groundwater.

Land use is determined by the interaction in space and time of biophysical

factors (constraints) such as soils, climate, topography, etc., and human factors like

population, technology and economic conditions (Veldkamp and Fresco, 1996b).

Land use change and consequences

Land use affects land cover and changes in land cover affect land use

(Riebsame et al 1994). Land-use activities, whether converting natural landscapes

for human use or changing management practices on human-dominated land, have

transformed a large proportion of the planet’s land surface. Foley et al (2005)

reported that land-use practices vary greatly across the world; their ultimate

outcome is generally the same; namely the acquisition of natural resources for

immediate human needs, often at the expense of degrading environmental

conditions. They also argued that land use has caused decline in biodiversity

through the loss, modification, and fragmentation of the habitats; degradation of

soil and water; and overexploitation of native species. Land use thus presents us

with a dilemma. Foley et al (2005) further claimed that while on one hand, many

land-use practices are absolutely essential for humanity, because they provide

critical natural resources and ecosystem services such as food, fiber, shelter, and

fresh water; on the other hand, some forms of land use are degrading the

ecosystems and services upon which we depend.

19

However, Riebsame et al (1994) stated that changes in land cover as a

result of land use do not necessarily imply a degradation of the land. Land cover

can be altered by forces other than anthropogenic. Natural events such as weather,

flooding, fire, climate fluctuations, and ecosystem dynamics may also initiate

modifications upon land cover (Meyer, 1995).

Turner and Butzer (1992) noted two broad types of global change which

were systemic and cumulative. Systemic change operates directly on the bio-

chemical flows that sustain the biosphere and, depending on its magnitude, it can

lead to global change, just as fossil fuel consumption increases the concentration

of atmospheric carbon dioxide. Cumulative change has been the most common

type of human induced environmental change since antiquity. Cumulative changes

are geographically limited, but if repeated sufficiently, become global in

magnitude. Changes in landscape, cropland, grasslands, wetlands, or human

settlements are examples of cumulative change.

Riebsame, Meyer and Turner (1994) categorised changes in land cover

driven by land use into two types: “modification” and “conversion”.

“Modification” is a change of condition within a cover type; for example,

unmanaged forest modified to a forest managed by selective cutting, “conversion”

is a change from one cover type to another, such as deforestation to create

cropland.

Remote sensing

The acquisition of information about the environment without being in

direct contact with it is traditionally called remote sensing. Examples range from

20

aerial photography to multi-spectral scanning and radar (Lillesand and Kiefer,

1987; IDRISI, 1999). The focus of remote sensing is the measurement of emitted

or reflected electromagnetic radiation, or spectral characteristics, from a target

object by a multispectral satellite sensor. A multispectral sensor acquires multiple

images of the same target object at different wavelengths (bands). Each band

measures unique spectral characteristics about the target. A spectral band is a data

set collected by the sensor with information from discrete portions of the

electromagnetic spectrum. The electromagnetic (EM) spectrum is a range of

electromagnetic radiation ranging from cosmic waves to radio waves (Richards,

1986).

In most contemporary land use studies that employ remote sensing imagery

from multispectral sensors, the foremost task is the observation of spectral

characteristics of measured electromagnetic radiation from a target or landscape.

Analysts develop signatures based upon the detected energy’s measurement and

position in the electromagnetic spectrum. A signature is a set of statistics that

defines the spectral characteristic of a target phenomenon. Image analysts

determine the measurement of signature separability by determining quantitatively

the relation between class signatures. Signatures are refined by improved ground-

truth and accuracy assessment analysis. By utilizing the developed signatures in

multispectral classification and thematic mapping, the analyst generates new data

for analysis (ERDAS, 1999).

Resolution is an important term commonly used to describe remotely

sensed images. However, there are four distinct types of resolution that must be

considered. These four types of resolution are spatial, spectral, radiometric, and

21

temporal. These characteristics help to describe the functionality of both remote

sensing sensors and remotely sensed data.

Spatial resolution is the minimum size of terrain features that can be

distinguished from the background in an image, or the ability to differentiate

between two closely spaced features in an image. Spectral resolution refers to the

number and dimension of specific wavelength intervals in the electromagnetic

spectrum to which a sensor or sensor band is sensitive or can record. Radiometric

resolution refers to the dynamic range, or number of possible data files values in

each band. This is referred to by the number of bits into which the recorded energy

is divided. The total intensity of the energy, from 0 to the maximum amount, the

sensor measures is broken down, for example, into 256 brightness values for 8-bit

data. The data file values range from 0, for no energy return, to 255, for maximum

return, for each pixel.

Temporal resolution is a measure of how often a given sensor system

obtains imagery of a particular area, or how often an area can be revisited. The

temporal resolution of satellites is on a fixed schedule. The fixed schedule of

satellites allows for more repetitive views. This revisit capability makes it possible

to use several passes, covering perhaps two or three seasons or multiple years, for

interpretation.

Remote sensing has become an important tool applicable to developing and

understanding the global, physical processes affecting the earth. As current trends

continue, additional and higher resolution satellites will become available

providing the means to produce more accurate land use and land cover maps

characterized by finer levels of detail (Bottomley, 1998).

22

Landsat thematic mapper sensor and multispectral imagery

Lauer et al (1997, cited in Bottomley 1998) give a brief history of the

Landsat system and its renowned success. The United States has pioneered land

remote sensing from space and has been the leader in the development of earth

observing technology over the past thirty years. The evolution of the Landsat

programme has been a fundamental genesis for the better measurement and

monitoring of the Earth and its precious resources. Despite early

military/intelligence programmes in space during the 1950s and 1960s, the

scientific and industrial communities in the U.S. became aware of the potential of

earth observing vehicles in space. The National Aeronautics and Space

Administration (NASA), in cooperation with other federal agencies, successfully

launched on July 23, 1972, the first Earth Resources Technology Satellite (ERTS-

1), which was later renamed Landsat 1. Landsat 1 was a Nimbus-type platform

which carried sensor package and data-relay equipment. ERTS-2 was launched on

January 22, 1975, and was also renamed Landsat 2. Additional Landsats were

launched in 1978, 1982, and 1984 and renamed Landsats 3, 4, and 5 respectively.

Each successive satellite system has had improved sensor and communication

capabilities.

The Landsat programme has had an enormous impact on numerous

application arenas. In addition to the inauguration of global research, the Landsat

program use has also provided researchers with real-world data and access to

greatly enhanced spatial and analytical tools. The premise of the Landsat program

use is that the Earth’s features and landscapes can be discriminated, identified,

categorized, and mapped on the basis of their spectral reflectances and emissions.

23

The sensors of the Thematic Mapper record electromagnetic radiation in

seven bands. Bands 1, 2, and 3 are in the visible portion of the spectrum. Bands 4,

5, and 7 are in the reflective-infrared portion of the spectrum. Band 6 is in the

thermal portion of the spectrum. The following list describes each of the seven TM

bands:

Band 1 – Visible Blue, 0.45 – 0.52 um: useful for mapping coastal water areas,

differentiating between soil and vegetation, forest type mapping, and

detecting cultural features.

Band 2 – Visible Green, 0.52 – 0.60 um: corresponds to the green reflectance of

healthy vegetation. It is also used for cultural feature identification.

Band 3 – Visible Red, 0.63 – 0.69 um: useful for discriminating between many

plant species. It is also useful for determining soil boundary and geological

boundary delineations as well as cultural features.

Band 4 – Reflective - infrared, 0.76 – 0.90 um: this band is especially responsive

to the amount of vegetation biomass present in a scene. It is useful for crop

identification and emphasizes soil/crop and land/water contacts.

Band 5 – Mid - infrared, 1.55 – 1.74 um: sensitive to the amount of water in

plants. It is useful in crop drought studies and in plant health analyses. This

is also one of the few bands that can be used to discriminate between

clouds, snow, and ice.

Band 6 – Thermal - infrared, 10.40 – 12.50 um: useful for vegetation and crop

stress detection, heat intensity, insecticide applications, and for locating

thermal pollution. It can also be used to locate geothermal activity.

24

Band 7 – Mid - infrared, 2.08 – 2.35 um: important for the discrimination of

geologic rock type and soil boundaries, as well as soil and vegetation

moisture content.

Different combinations of the TM bands can be displayed to create

different composite effects. The following combinations are commonly used to

display images:

Bands 3, 2, and 1 create a true colour composite. True colour means that

objects look as though they would to the naked eye, similar to a photograph.

Bands 4, 3, and 2 create a false colour composite. False colour composites

appear similar to an infrared photograph where objects do have the same colours

or contrasts as they would naturally. For instance, in an infrared image, vegetation

appears red, water appears navy or black.

Bands 5, 4, and 2 create a pseudo colour composite. (A thematic image is

also a pseudo colour image.) In pseudo colour, the colours do not reflect the

features in natural colours. For instance, roads may be red, water yellow, and

vegetation blue.

With adequate knowledge of band properties and the appropriate

combination of Landsat TM bands, the extraction of numerous themes, land use

and land cover classes can be achieved for various mapping applications

(Bottomley, 1998).

Image classification techniques

Within the scope of this study, image classification is defined as the

extraction of distinct land use and land cover categories from satellite imagery.

25

There are two primary methods of image classification utilized by image analysts,

namely unsupervised and supervised classification.

Unsupervised image classification is a method in which the image

interpreting software separates the pixels in an image based upon their reflectance

values into classes or clusters with no direction from the analyst. Once this process

is completed, the image analyst determines the land cover type for each class

based on image interpretation, ground truth information, maps, field reports, etc.

and assigns each class to a specified category by aggregation (Bottomley, 1998;

IDRISI, 1999; ERDAS, 1999).

Supervised image classification is a method in which the analyst defines

small areas, called training sites, on the image which are representative of each

desired land cover category. The delineation of training areas to represent cover

types is most effective when an image analyst has knowledge of the geography of

a region and experience with the spectral properties of the cover classes

(Skidmore, 1989). The image analyst then trains the software to recognize spectral

values or signatures associated with the training sites. After the signatures for each

land cover category have been defined, the software then uses those signatures to

classify the remaining pixels (Bottomley, 1998; IDRISI, 1999; ERDAS, 1999).

When classifying satellite imagery, single supervised or unsupervised

classification techniques are often not enough to effectively classify an image.

Automated classification accuracies can often be unacceptably low, < 80%, at the

required level of categorical detail for many applications (Bolstad and Lillesand,

1992). Modifications of image classification techniques are most often required in

order to assess for classification accuracy. Experimentation with proven or

26

standardized classification techniques can produce accurate land cover classes as

well as lead to the development of new classification procedures. Modifications of

image classification techniques are often required in order to obtain adequate

classification accuracy.

Global positioning systems (GPS)

Global Positioning Systems (GPS) provide the mapping community with

powerful tools for acquiring accurate and current digital data. Combined with high

resolution remote sensing and Geographical Information System (GIS) for land

use studies, GPS can provide high accuracy ground-truth data for training-site

development (Pearson II and Frederick, 1990; Dana, 1995; Bottomley, 1998;

IDRISI, 1999). The constellation of satellites around the world which provide

geographical information to the receptors of GPS on Earth is shown in figure 6.

24 Satellites in 6 Orbital Planes 4 Satellites in each Plane 20,200 km Altitudes, 55 Degree Inclination

Figure 6: GPS National Constellation Source: Djebre (2004)

27

Essentially, the GPS satellites broadcast a continuously available time

signal using an on-board atomic clock. Receivers use these time signals to

calculate the distance to the satellite to establish an accurate position. However,

the accuracy of GPS positions can vary substantially and the user must be aware of

the factors that influence the precision of a GPS signal. The type of GPS service

accessed, the type of GPS equipment and processing techniques utilized, and

satellite geometry are some of the key factors affecting GPS precision (Bobbe,

1992).

Geographic information systems (GIS)

Another recent development in the use of satellite data is to take advantage

of increasing amounts of geographical data available in conjunction with

geographic information systems to assist in interpretation (Bottomley, 1998).

Geographical data describe objects from the real world in terms of (a) their

position with respect to a known coordinate system, (b) their attributes that are

unrelated to position (such as colour, type, cost, pH, incidence of disease, etc.) and

(c) their spatial interrelations with each other (topological relations), which describe

how they are linked together or how one can travel between them (Burrough, 1986).

The concept of geographic information system emerged during the 1960’s and

1970’s as new trends arose in the ways in which maps were being produced and

used for resource assessment, land evaluation, and planning. Essentially, this

concept focuses on the ability to develop a powerful set of tools for collecting,

storing, retrieving at will, transforming, and displaying spatial geographic data

from the real world for specific analysis and inquiry. This set of tools constitutes a

28

geographical or geographic information system. Geographic information systems

comprised three main components: computer hardware, sets of application

software modules, and a proper organization context (Gersmehl, 1991; ESRI,

1994; Burrough, 1986).

In addition, Robinove (1986) defines a geographic information system as a

collection of computer programs in a given hardware environment which operate

on a geographic database to analyze individual database elements or for synthesis

of multiple database elements.

With the increasingly widespread, combined implementation of remote

sensing and GIS technology namely, natural resource professionals have been

provided with efficient and accurate tools for mapping and maintaining

management information on forests and other natural resources in regional areas

(Bottomley, 1998). GIS technology is expanding, and allowing for greater

integration of remote sensing with digital cartography; thus providing the means to

produce more accurate land use and land cover maps.

Land use evolution detection

An increasingly common application of remotely sensed data is for change

detection. Change detection is the process of identifying differences in the state of

an object or phenomenon by observing it at different times (Singh, 1989; Turner

II, Ross, and Skole, 1993; Foley et al. 2005). Change detection is an important

process in monitoring and managing natural resources and urban development

because it provides quantitative analysis of the spatial distribution of the

population of interest. It is also useful in such diverse applications as land use

29

change analysis, monitoring shifting cultivation, assessment of deforestation, study

of changes in vegetation phenology, seasonal changes in pasture production,

damage assessment, crop stress detection, disaster monitoring, day/night analysis

of thermal characteristics as well as other environmental changes (Singh, 1989;

Zhongchao et al. 2002; You et al. 2004).

Macleod (1998, cited in Botomley, 1998) listed four aspects of change detection

which are important when monitoring natural resources: namely detecting the

occurrence of change, identifying the nature of the change, measuring the area

extent of change and assessing the spatial pattern of the change.

Scientific literature has revealed that digital change detection is a difficult

task to perform accurately and unfortunately many of the studies concerned with

comparative evaluation of these applications have not supported their conclusions

by quantitative analysis (Singh, 1989). All digital change detection is affected by

spatial, spectral, temporal, and thematic constraints. The type of method

implemented can profoundly affect the qualitative and quantitative estimates of the

change. Even in the same environment, different approaches may yield different

change maps. The selection of the appropriate method therefore takes on

considerable significance. Not all detectable changes, however, are equally

important to the resource manager. On the other hand, it is also probable that some

changes of interest will not be captured very well, or at all, by any given system.

Competing models for land use dynamics

Several models to be used for land use change exist depending on the

interest of each study. A summary of these models pointing out the variables used

30

31

for each model, including the strengths and weaknesses of each model are shown

in Table 5.

The weaknesses of most of the models concern their inability to take into

account one or two of the critical dimensions of time, space and human decision-

making, hence the choice of the model used by Agarwal et al (2002) to assess

human-environmental dynamics in this study. This model highly involves time

scale and complexity, spatial scale and complexity and human decision-making.

Table 5: Summary of land use dynamics models Model name

Model type Components/modules

Variables Strengths Weaknesses

1. General Ecosystem Model (GEM) (Fitz . et al1996)

Dynamic systems model

14 Sectors (modules), e.g. Hydrology Macrophytes Algae Nutrients Fire Dead organic matter Separate database for each secto

Captures feedback among abiotic and biotic ecosystem components

103 input parameters, in a set of linked databases, representing the modules, e.g., Hydrology Macrophytes Algae Nutrients Fire Dead organic matter

Spatially dependent model, with feedback between units and across time Includes many sectors Modular, can add or drop sectors Can adapt resolution, extent, and time step to match the process being modeled

Limited human decision making

2. Patuxent Landscape Model (PLM) (Voinov et al. 1999)

Dynamic systems model

Based on the GEM model (#1, above), includes the following modules, with some modification: 1) Hydrology 2) Nutrients 3) Macrophytes 4) Economic model

Predicts fundamental ecological processes and land-use patterns at the watershed level

In addition to the GEM variables, it -adds dynamics in carbon-to-nutrient ratios -introduces differences between evergreen and deciduous plant communities -introduces impact of land management through fertilizing, planting, and harvesting of crops and trees

In addition to the strengths of the GEM, the PLM incorporates several other variables that add to its applicability to assess the impacts of land management and best management practices

Limited consideration of institutional factors

3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp

Discrete, finite state model

1) Regional biophysical module 2) Regional land-use objectives module 3) Local land-use allocation module

Predicts land cover in the future

Biophysical drivers Land suitability for crops Temperature/Precipitation Effects of past land use (may explain both biophysical degradation and

Covers a wide range of biophysical and human drivers at differing temporal and spatial scales

Limited consideration of institutional and economic variables

34

Model name

Model type Components/modules

Variables Strengths Weaknesses Table 5 continued

and Fresco 1996a)

improvement of land, mainly for crops) Impact of pests, weeds, diseases Human Drivers Population size and density Technology level Level of affluence Political Structures (through command and control, or fiscal mechanisms) Economic conditions Attitudes and value

4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b)

Discrete finite state model

CLUE-CR an application of CLUE (#3, above) Same modules

Simulates top-down and bottom-up effects of land-use change in Costa Rica

Same as CLUE (#3, above)

Multiple scales - local, regional, and national Uses the outcome of a nested analysis, a set of 6x5 scaledependent land-use/landcover linear regressions as model input, which is reproducible, unlike a specific calibration exercise

Authors acknowledge limited consideration of institutional and economic factors

5. Chomitz et al. (1996)

Econometric (multinomial logit) model

Single module, with multiple equations

Predicts land use, aggregated in three classes: Natural vegetation Semi-subsistence agriculture Commercial farming

Soil nitrogen Available phosphorus Slope Ph Wetness Flood hazard Rainfall National land

Used spatially disaggregated information to calculate an integrated distance measure based on terrain and presence of roads Also, strong theoretical underpinning of Von Thünen’s model

Strong assumptions that can be relaxed by alternate specifications Does not explicitly incorporate prices

35

Model name

Model type Components/modules

Variables Strengths Weaknesses Table 5 continued

Forest reserve Distance to markets, based on impedance levels (relative costs of transport) Soil fertility

6. Wood et al. 1997

Spatial Markov model

Temporal and spatial land-use change Markov models

Land-use change

Models under development

Investigating Markov variations, which relax strict assumptions associated with the Markov approach Explicitly considers both spatial and temporal change

Not strictly a weakness, this is a work in progress and, hence, has not yet included HDM factors

7. CUF (California Urban Futures) (Landis 1995, Landis et al. 1998)

Spatial simulation

Population growth submodel Spatial database, various layers merged to project Developable Land Units (DLUs) Spatial Allocation submodel Annexation-incorporation submodel

Explains land use in a metropolitan setting, in terms of demand (population growth) and supply of land (underdeveloped land available for redevelopment)

Population growth, DLUs, and intermediate map layers with: Housing prices Zoning Slope Wetlands Distance to city center Distance to freeway or BART station Distance to sphere-of-influence boundaries

Underlying theory of parcel allocation by population growth projections and price, and incorporation of incentives for intermediaries - developers, a great strength Large-scale GIS map layers with detailed information for each individual parcel in 14 counties provide high realism and precision

Compresses long period (20 years) in a single model run Has no feedback of mismatch between demand and supply on price of developable land/housing stock Does not incorporate impact of interest rates, economic growth rates, etc.

8. Swallow et al. 1997

Dynamic model

Three components: 1) Timber model 2) Forage production function

Simulates an optimal harvest sequence

Present values of alternative possible states of the forest, using the three model components

The long time horizon, and the annual checking of present values under alternate possible states of the forest makes it a useful forest management tool

Authors note that the optimal management pattern on any individual stand or set of stands

36

37

Model name

Model type Components/modules

Variables Strengths Weaknesses

3) Non-timber benefit function

for maximizing multiple-use values

requires specific analysis rather than dependence on rules of thum

b

9. Clarke et al. 1998, Kirtland et al. 2000

Cellular automata model

Simulation module consists of complex rules Digital dataset of biophysical and human factors

Change in urban areas over time

Extent of urban areas Elevation Slope Roads

Allows each cell to act independently according to rules, analogous to city expansion as a result of hundreds of small decisions Fine-scale data, registered to a 30 m UTM grid

Does not unpackhuman

e

map

not

decisions that lead to spread of built areas Does not yet include biological factors

10. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998)

Overlay of GIS layers with statistical urban growth projections

1) Statistical model of urban growth 2) Policy simulation and evaluation model 3) Map and data layers of habitat types, biodiversity, and other natural factors

The interaction among the probabilities of urbanization, its interaction with habitat type and extent, and, impacts of policy changes on the two

Slope and elevation Location and types of roads Hydrographic features Jurisdictional boundaries Wetlands and flood zones Jurisdictional spheres of influence Various socioeconomic data Local growth policies Job growth Habitat type and extent maps

Increases understanding of factors behind recent urbanization patterns Allows projection of future urban growth patterns, and of the impact of projected urban growth on habitat integrity and quality

Human decision making not explicitly considered Further, errors arlikely from misclassification of data at grid level or misalignment offeature boundaries Errors also possible from limitations in explaining historical urban growth patterns

11. Gilruth et al. 1995

Spatial dynamic model

Several subroutines for different tasks

Predicts sites used for shifting cultivation in terms of topography and proximity to population centers

Site productivity (# of fallow years) Ease of clearing Erosion hazard Site proximity

Replicable Tries to mimic expansion of cultivation over time

Long gap between data collection; does include impact of landquality

Table 5 continued

38

Model name

Model type Components/modules

Variables Strengths Weaknesses

and socioeconomic variables

Population, as function of village size

Source: Literature review, 2006

Table 5 continued

Summary

The chapter has defined concepts and techniques related to land use

detection. These concepts are land use/land cover, land use change and its

consequences, land use evolution detection and competing models. Remote

sensing, GIS, GPS and Landsat TM imageries are tools to be used for land use

dynamics assessment. The local environmental issues concern the man who

contributes at a local level to the land use change.

The literature suggests that remote sensing and GIS are accurate tools for

land use change detection. Data from remote sensing platforms such as satellite

images provide information for GIS database. These data can be used for resource

monitoring, environmental analysis, forecasting and assessment. It was revealed

that several models for detecting land use change exist, but the one that takes into

account the dimensions Time, Space and Human decision-making ensures a better

understanding of the dynamics. Obviously, solutions must be found to resolve the

debate between the IBS and Howorth and O’Keefe’s findings on the state of the

environment in the province, and to generate results that best represent the reality

on the ground; hence the focus on land-use dynamics at a district scale (Bieha)

from 1986 to 2002 using Landsat TM images.

40

CHAPTER THREE

REVIEW OF ENVIRONMENTAL ISSUES

IN BURKINA FASO

Introduction

The Burkina Faso environment is characterised by great vulnerability; any

stability is only maintained by human management. However, several

unsustainable management practices are contributing to environmental

degradation, thus increasing people’s vulnerability through reduced productivity

and resilience to stress (Simonsson, 2005). According to MEE (1996) and MECV

(2004), from 1980 to 1992 the surface area of the forest of Burkina Faso reduced

from 15.42 million hectares to 14.16 million hectares. The annual loss of forest

was estimated at 105,000 ha (MEE, 1996). According to Kramer (2002), the

World Bank estimated the annual loss of wooded land surface in Burkina Faso at

80,000 to 100,000 ha while the FAO (2000) assumes an annual loss of 15.266 ha

equivalent to 0.2 % of wooded land surface, exclusively for those surfaces cleared

to make way for agriculture. According to Mongabay (2005), between 1990 and

2000, Burkina Faso lost an average of 24,000 ha of forest per year which

amounted to an average annual deforestation rate of 0.34 %. Between 2000 and

2005, the rate of forest change increased to 0.35 % per annum. In total, between

1990 and 2005, Burkina Faso lost 5.0 % of its forest cover, or around 360,000

hectares. Measuring the total rate of habitat conversion (defined as change in

41

forest area plus change in woodland area minus net plantation expansion) for the

1990-2005 intervals, Burkina Faso lost 2.8 % of its forest and woodland habitat.

Two studies related to land-cover using remote sensing and GIS tools have

been reported. The first was undertaken by IBS (1994) and aimed at examining

deforestation rates in Sissili since 1988 using Landsat and Spot images from 1988

and 1993. On the basis of maps produced from the study, extrapolations were

made on future deforestation which stated that the rate will increase from 21.6 %

in 1988 to 43.1 % by the year 2010. The second study was conducted by Howorth

and O’Keefe (1998) to investigate the new resource-use pattern that had developed

as a result of demographic changes. Based on maps of 1955 and 1983 and

interviews conducted in three villages of the province it was concluded that there

was a peaceful coexistence between the three ethnic groups living together in the

villages and there was no destruction of the environment at all in Sissili as IBS

forecasted. The study of Howorth and O’Keefe (1998) concluded that the

environment was improving in vegetation cover in Sissili.

The conclusions of the two studies were however contradictory. The time

interval used by IBS (1988 to 1993) may have been too limited to detect land use

changes, and worse of all, to forecast long-term changes in the environment. The

second study which used aerial photographs from 1955 and 1983 to map land use

changes in three villages of less than 40 km2 each, without detecting

environmental degradation, may be accurate at the village scale but, in terms of the

whole of Sissili province, it may be an exaggeration or overgeneralization.

As already reported in the literature the five broad and inter-related human

factors that lead to environmental degradation in Burkina Faso and specifically in

42

the study area have been identified as agricultural practices, migration,

overgrazing, fuel wood harvesting, timber logging and bushfires. Each of these is

briefly explained.

Agricultural practices and deforestation

The agricultural activities in Burkina Faso are undertaken in a rudimentary

and extensive way, with a low level of intensification (Bandre and Batta, 1998). In

this context, the only possible way to secure food security is to cultivate more land

(Reenberg and Lund, 2001). This is done by decreasing the ratio between fallow

and cultivated land within a village or by including new territory for cultivation,

thus contributing to the degradation of the environment (Howorth and O'Keefe,

1998; MEE, 1999). Under increasing population pressure, marginal lands are used

as farmers cultivate large area to maintain production but they do little to sustain

soil nutrient levels and the productive capacity of the soil. Short fallow periods and

inadequate use of fertilizers; coupled with overgrazing and deforestation through

fuelwood harvesting tend to cause loss of soil and vegetation cover, and water

degradation (MEF, 2000; Simonsson, 2005).

Elshout et al (2001) have argued that in the south and west zone of Burkina

Faso, the land clearance for extensive farming is the key contributor to vegetation

loss. In the study area, farming practices vary from one ethnic group to another.

Howorth and O'Keefe (1998) reported that the indigenous Nuni practice a gentle

form of agriculture which is exclusively manual with little inputs, relatively low

soil usage and use approximately 4.5 ha per family. This allows the retention of a

large number of trees and root systems, without causing great disturbance to the

43

agro-ecological system. The Fulani have settled extensively in Sissili, and tend to

concentrate their animal herding in the zones of low-intensive agriculture usually

in the periphery/wooded areas of villages. They cultivate about 1.5 ha per family

in old pasture zones containing high levels of cattle manure and, consequently,

have comparatively high yields. The Mossi, on the other hand, practise an

extensive form of agriculture with almost total field clearing, mainly for cereal

production. They tend so to exploit the lands in such a way as to degrade the

environment hence, there is an emergence of the relationship between migration

and deforestation.

Migration and environmental degradation

Migration is usually ignored in models of land use change (Veldkamp and

Fresco, 1996a; Shen, 2000; Stéphenne and Lambin, 2001), even though it is often

recognized to be the dominant demographic factor influencing land use (Lambin et

al, 2001). Many authors cite population growth as the single most important cause

of deforestation (Allen and Douglas, 1985; World Rainforest Movement, 1990;

World Bank, 1992). Population growth often leads to migration to the forest by

peasants seeking land to clear for subsistence farming.

One of the features of the population of Burkina Faso is its mobility (Kress,

2006). In the period between 1985 and 1991, 10 % of the Burkina Faso’s

population of 7.5 million inhabitants migrated from one province to another or

abroad (Jeune Afrique Atlases, 1998). Within the country, people tend to migrate

to the agricultural zone and to Ouagadougou and Bobo Dioulasso (Simonsson,

2005; Kress, 2006). SIDA’s Poverty Profile of Burkina Faso concluded that

44

migration seems to be a strategy for households to reduce poverty, more important

in rural than in urban areas; among men more than women; and among the poor

more than the rich (Haberg, 2000). Due to the migration, the population growth

rate in the savannah region was reduced by 1.1 % while, in the forest regions, it

increased by 0.6 % (Zachariah and Conde, 1981). The regions of departure lose

labour force while the social and economic infrastructure in regions of arrival may

have problems coping with the rapidly growing population (Henry et al, 2002).

A study on the inter-provincial migration in Burkina in 2003 (Henry et al,

2002) showed the highest immigration rate of 4.88 % in the Sissili province. With

large number of immigrants, the province faces difficulties related to land conflicts

and access to social infrastructure. As a consequence, the population of some

villages in the Province has more than doubled in 20 years. An example is the

Bieha district whose population increased from 15,043 inhabitants in 1985 to

25,634 inhabitants in 2006. The effect of this migration driven population growth

on land use was quickly visible. The newcomers tended to reproduce the same

extensive farming practices followed in the centre-north and, encouraged by large

private companies, are keen to produce cotton and maize (Gray, 1999). The land

requirement for migrants was thus larger than that for the sedentary populations

and land supply became limited (Mathieu, 1998).

Authors such as Hardin (1968), Ehrlich (1968), Ehrlich and Ehrlich (1990)

and Meadows et al (1972) have a pessimist view of the relationships between

population pressure and environment. According to them, population control must

be a part of any development strategy, otherwise environment will collapse. On the

other hand, some optimists argue that population pressure does not necessarily

45

lead to environment degradation. It stimulates development rather than slowing it

down, and moreover, it leads to innovation in agricultural technology and

techniques which support the increased number of population (Boserup, 1972;

Simon, 1980; Tiffen and Mortimore, 1994; Fairhead and Leach, 1996; Bassett and

Bi Zueli, 2000).

Overgrazing

Chikamai and Kigomo (2003) reported that overgrazing is the most notable

factor in causing de-vegetation and hence degradation. The heaviest impact of

overgrazing takes place in the Sahel countries especially areas falling within arid

and semi-arid zones. Overgrazing is concentrated around settlements and is often

related to recent sedentarisations of nomadic herders.

In Sissili province, the number of animals has been increasing substantially

due to the continued in-migration of the pastoralist Fulani (Howorth and O'Keefe,

1998). In 1986 the total number of bovines, ovine and goats was estimated at

215,000, but these increased to 900,000 in 2003, and thus jeopardizing the

carrying capacity of the province (DPAHRH, 2006). During the 1980s, the

government created a pastoral zone in Yalle (Bieha District) with the purpose of

settling about 75 families of breeders (Fulani), to promote the best quality of the

livestock and to limit the conflicts linked to competition for space between farmers

and breeders. Unfortunately, the breeders of the area refused this offer and

preferred to walk along the forest to graze their animals by cutting palatable

species.

46

Firewood and timber request

Wood fuel is the principal source of domestic energy in developing

countries (Openshaw, 1974; Eckholm, 1975; Arnold and Jongma, 1978). Wood

fuel includes charcoal as well as firewood, brushwood, twigs and cut branches

(Openshaw, 1986). Bandré and Batta (1998) argued that wood is used in Burkina

Faso for two main purposes: source of energy and as building material. Wood

represented 96 % of the domestic energy consumed in 1993 and accounted for 64

% of the national requirement in primary energy. The average per capita

consumption is 300 kg/year in the north while in the southern and western areas it

is over 800 kg/year (Bandré and Batta, 1998). The annual commercial value is

estimated at 35 billion CFA (58,000,000 USD) for the firewood and 6 billion CFA

(10,000,000 USD) for the timber (Kessler and Greeling, 1994). According to

Kramer (2002), the consumption of wood in Burkina Faso is higher than the

production. This means that there is no ecological sustainability. At the same time,

because of the low economic productivity, import of necessary amount of energy

cannot be envisaged neither on the macro-economic level nor on the family budget

level.

The rate of deforestation in the eastern, southern and western zones of the

country to meet the needs for energy and timber consumption became so high that

the Ministry in charge of environment and water took a decision to suspend

charcoal production from July, 15th 2005 (Le Pays, 2005). The Ministry estimated

at 593,092 tons the quantity of charcoal consumed in 2004 in Burkina Faso while

the annual loss of forest to charcoal production was estimated at 370,000 hectares.

47

Bushfire

Fire has always played a major role in most of Sub-Sahara Africa in

clearing the field, hunting, improving visibility, accelerating the re-growth of

perennial grasses and in customary rituals as reported by Bandré and Batta (1998);

who also argued that frequent fire, and especially late fires, not only killed most of

the perennial plants, but also impoverished the soil and reduced its productivity.

Fire also causes loss of certain nutrients (nitrogen and sulphur), which are usually

dispersed in the atmosphere, and loss of organic matter.

Bushfire practices are ancient in Burkina Faso going back to the pre-

colonial period (Aubreville, 1949; Belloward, 1959; Ministère de l’Environment et

du Tourisme, 1991; Kambou and Poussi, 1997; Yameogo, 2005). Three types of

fires exist in Burkina: early fire, intermediate fire and late fire (Yameogo, 2005).

According to Ministère de l’Environment et du Tourisme (1991) and Zida (1993),

the surface annually touched by fires is estimated at 98,568 km², which is about 55

% of the forest surface of the country. In the provinces of the south of the country

(Sissili, Ziro and Nahouri), which possess 12,305 km² of forest, 9,844 km² (80 %)

are fired each year (Ministère de l’Environment et du Tourisme, 1991). Bushfires

cause a loss of 200 million Euros in animal resources, 10.7 million Euros in wood

production and more than one million in wildlife and cotton production (Zida,

1993).

48

CHAPTER FOUR

METHODS OF DATA COLLECTION AND

ISSUES FROM THE FIELD

Introduction

This chapter describes the data and sources as well as the methods and

tools employed in the data collection. It also covers the sampling techniques and

the problems encountered in the field and how they were solved.

Data and sources

The data collected were basically quantitative arising from primary and

secondary sources. The primary data were divided into two broad categories:

a) Data resulting from the satellite image processing that dealt with

quantitative variables and concerned the surface areas of the land use units

in time series;

b) Data from interviews of sample population in the study area which dealt

with quantitative variables such as the perception of density of trees and

the wild animals, the productivity and availability of food, etc.

The secondary data were collected from textbooks in documentation

centres (offices of agriculture and animal resources, etc.) and centres of primary

data storage such as the national meteorology, the national statistics and

demography office. These data concern the agricultural and pastoral practices and

yields, the rainfall and the quality of soils in the study area.

49

Images processing

Satellite images

Remote sensing and Geographical Information System (RS/GIS) tools

were used to carry out the different land-use units and their respective surfaces

based on satellite data. The main data used in the research included Landsat

Thematic Mapper satellite images of 1986 and 2002 (hereafter referred to as TM

images). Sissili province is covered entirely by the images number 195/52 of

Landsat TM (Figure 7). A brief description of the satellite images used is shown in

Table 6. Digital topographic data with contour interval of 10 m produced by the

Geographic Institute of Burkina Faso (IGB) were also used.

Figure 7: Landsat TM image mosaic of Burkina Faso

Source: Database of the Geographical Institute of Burkina (IGB), 2006

50

Table 6: Satellite images used for land use detection of Sissili

Satellite

Type

Sensor Image

number

Number of

bands

Pixel

spacing

Observation

date

Landsat TM 195/52 7 30 x 30 18 Nov. 1986

Landsat TM 195/52 7 30 x 30 21 Oct.2002

Source: Landsat database (2006)

The TM images were provided by the Institute for Environmental and

Agronomic Research (INERA) of Ouagadougou (Burkina Faso). They were

acquired within the same season (end of the rainy season) and are at the same

resolution: 30 meters resolution. The two dates have the same vegetation

conditions according to the climate of the study area. According to the farming

practices, October and November are the harvesting periods during which

precocious bush fires occur. As a consequence, there is a lot of haze in the images.

Those factors give effects on vegetation status causing reflectance values of land

use quite difficult to compare. However, possible similar nomenclatures were set

up based on physical characteristics of land use.

The ground-truth information required for the classification and accuracy

assessment of the images was collected from the field during January, 2006 using

a training sample protocol. In addition, a self-designed format was used to collect

vegetation level information on vegetation types, condition and history of land use

provided by the local people and direct observation in the field.

51

Geometric correction

Subsets of satellite images were rectified first for their inherent geometric

errors using digital topographic maps in Modified Universal Transverse Mercator

coordinate system obtained as the reference material. The image was registered to

the digital topographic maps using distinctive features such as road intersections

and stream confluences that are also clearly visible in the image. A first-degree

Rotation Scaling and Translation transformation function and the Nearest

Neighbour re-sampling method were applied. This re-sampling method uses the

nearest pixel without any interpolation to create the warped image. A total of 20

points were used for registration of TM image subset with the rectification error of

0.1083 pixels.

A very high level of accuracy in the geo-referencing of the images was

possible because of the use of digital source as the reference data that allowed

zooming to the nearest possible point location.

Classification

The supervised Maximum Likelihood Classification method was used for

the classification of all the images. Training areas corresponding to each

classification item (or, land use class), were chosen from among the training

samples collected from the field.

To produce land use maps of 1986 and 2002 and to investigate changes

that occurred between these periods, the following four land use classes were

considered in image classification: gallery forest, wooded savannah, shrubby

savannah and farm fields. The choice of these land use classes was guided by: i)

52

the objective of the research, ii) expected certain degree of accuracy in image

classification, and iii) the easiness of identifying classes on false composite of the

images and the ground. A brief description of each of the land use classes is

presented in Table 7.

Table 7: Land use classes considered in image classification and change detection Land use class General description

Gallery forest Forest areas mostly along the rivers with estimated 75

percent or more of the existing crown covered by

broadleaf trees. The predominant species are: Pterocarpus

erinaceus, Afzelia africana, Kaya senegalensis,

Anageissus leiocarpus, Parkia biglobosa, Cassia

sieberiana,Mitragina innermis, etc.

Wooded

savannah

Wooded areas with estimated 50 percent or more of the

existing crown covered by naturally growing trees. It

includes also old fallows. Common species are Vitelaria

paradoxa, Parkia biglobosa, Lannea microcarpa, Lannea

acida, Sclerocarya birrea, Saba senegalensis, Diospyros

mespiliformis, detarium mocrocarpum, etc.

Shrubby

savannah

Land covered by shrubs, bushes and young broadleaf

regeneration including recent fallows. Degraded forest

areas with estimated <10 % tree crown cover are also

included. The common tree species are: Calotropis

procera, Peliostigma reticulatun, Guiera senegalensis,

53

Combretum micranthum, Vitelaria paradoxa, Lannea

acida, detarium mocrocarpum, etc.

Table 7 continued

Farm fields Agricultural lands with or without barren lands,

settlements, roads, construction sites and other built-up

areas. The main crops grown are: cereals (Sorghum,

millet, maize), oleaginous (groundnut, sesame), cash crops

(cotton), tubers (cassava, yam, potato) and plantation

(cashew, mangoes, orange, etc.).

Source: Landsat database (2006)

Detection of land changes

The Winships programme was used to convert the images from geotif

format to Idrisi raster (tfw). Bands 2, 3 and 4 were used for the image

classification because they are especially responsive to the amount of vegetation

biomass present in the images. Band 4 is put in the channel Red, band 3 in the

channel Green and band 2 in the channel Blue. A 432/RGB false colour

composites were produced (Figure 8). The specificity of each band of Landsat TM

images was described in Chapter Two.

After selectively combining classes, classified images were filtered before

producing the final output (Figure 9). A 3x3 median filter was applied to smooth

the classified images. All activities related to image processing were performed

with IDRISI 32.

54

Classified images were converted into vector format, and then exported to

ArcView-GIS Version 3.2 from IDRISI. In ArcView environment, the vectors

were clipped with the real limit of the study area and intersected each other in

order to detect the land use change within these two dates. All these operations

were done with the module “geo-processing”. The land use polygon themes for

1986 and 2002 were converted into MapInfo format with the module “universal

translator”. Land use units computing and the finishing of the maps were done

with MapInfo. The data base was exported to EXCEL (dbf format) for further

analyses.

55

Figure 8: False colour composites of satellite imageries

Source: Landsat TM image and author’s design, 2005.

56

Figure 9: Supervised classification of Landsat image of Bieha district in 2002

Source: Landsat image processing, 2006.

57

Problems encountered during the images processing

It was difficult to separate fallow from the other units since they appeared

like farm fields, shrubby savannah or wooded savannah in accordance with their

duration. Presence of cloud in parts of the TM image was the second major

problem encountered during image classification. The clouds were classified as

separate classes and later combined with their respective classes with the help of

ground-truth information. The third and last problem concerned the bushfire

detection. It was quite difficult to map the bushfires due to the fact that the early-

fires did not destroy completely the grasses since they were still green.

Immediately after the fire, grasses and leaves re-grew and affected the detection of

the impact of the fire in the ground by the satellite.

Population interviews

Instruments used

For the primary data, a structured questionnaire was developed to collect

information. The questionnaire was mostly close-ended and was categorised into

sixteen (16) sections (appendix 1). Each section represented a specific sub-theme

from the set of information to be collected (Table 8).

58

Table 8: Structure of the questionnaire

Section Sub-theme

1 Vegetation dynamics

2 Wild animals dynamics

3 Crops productivity

4 Food security at household level

5 Type of crops produced

6 Farming practices

7 Soil dynamics

8 Arable land dynamics

9 Household size

10 Income level

11 Drinking water sources

12 Living condition

13 Permanent migration

14 Temporary migration

15 Permanence of water in the rivers after the rainy season

16 Availability of fishes in the rivers

Source: Author’s construct, 2005.

Method of sampling

The target population was the total population (male and female) of 25,

634 in Bieha district who were 40 years old or more and have been living in the

district for at least 20 years. The assumption was that people who satisfied these

two conditions were old and qualified enough to provide accurate information

related to the sixteen sub-themes of the questionnaire in 1986, 1996 and in the

recent time. The target population was multiplied by 0.16 to obtain sampling frame

59

of 4,101 as the population of those old enough to provide the right information.

This was based on the fact that the population aged of at least forty years old

formed about 16 % of the total population in 2006 of Sissili province (I.N.S.D,

1996). Based on the sample frame, a sample fraction of 0.03 was purposively

chosen and used to generate a sample size of 123. From the 22 villages that made-

up Bieha district, 11 villages were randomly chosen for the survey (Figure 10).

The number of respondents selected from each village was based on the population

size of that village (Table 9).

Figure 10: Selected villages for the survey in Bieha district

Source: Author’s construct, 2005.

60

Table 9: Selected villages and number of respondents for survey

Selected villages Population in 2006 Sample size

Bieha 2,193 13

Binou 1,240 10

Boala 700 10

Danfina 1646 11

Prata 1,098 10

Kumbo 1,839 11

Kumbogoro 2,713 12

Livara 972 10

Pissai 1,933 12

Yalle 3,437 14

Yelbouga 1,549 10

Total 19,320 123

Source Author’s construct, 2005

Pre-survey activity in the villages

Official permission was first sought from the Prefect of Bieha district and

the central Chief of Bieha; while, in each of the selected villages, permission was

sought from the chief, elders and representatives (RAV) before interviews

commenced. The intension was to gain the support and cooperation of members of

the communities through these opinion leaders. At least one literate person in the

village was employed to translate the questions and answers from French to the

local language and vice-versa.

The Offices of Environment and Earth (DPECV) of Sissili and Bieha were

also informed of the survey to be conducted and its purpose. As the officers

responsible for the local environment, the foresters were also involved in the

61

administration of the questionnaire. One copy of the questionnaire translated into

French was given to each forester to enable them better understand the work.

The fieldwork

The fieldwork began on March 18, 2006 and ended on 28th of the same

month. In each of the selected villages, the questionnaire was administered using

snowball for two reasons: on one hand it was not evident to know at the first view

those fulfilling the age condition and on the other hand those who have lived in the

village since the last 20 years.

Issues from the interview

Response rate

Out of the sample size of 123 proposed, a total of 113 respondents were

interviewed. This gives a response rate of 91.8 % (Table 10).

62

Table 10: Respondents and response rate by selected village.

Selected villages Proposed

sample size

Total

respondents

Response

rate (%)

Bieha 13 11 85

Binou 11 11 100

Boala 10 10 100

Danfina 11 11 100

Prata 10 9 90

Kumbo 11 10 91

Kumbogoro 11 8 73

Livara 10 7 70

Pissai 11 11 100

Yalle 14 14 100

Yelbouga 11 11 100

Total 123 113 92

Source: Field work, 2006

Problems encountered

In Bieha, Prata, Kumbo, Kumbogoro and Livara, the total number of

respondents expected was not reached. This was due to the high number of recent

migrants in these villages. Most of the adults who were present during the survey

did not fulfil the condition of having lived in the village for 20 years. Some of

them had, due to the dry season, left for distant markets or towns to look for jobs.

Particularly in Kumbogoro, we were forced to leave before we reached the

proposed number for security reasons. In fact, a poacher from the village had been

apprehended carrying bush meat in the Safari Ranch of Bieha and had been

severely injured by the patrolmen of the ranch. According to the villagers, the

63

forester of Bieha district who was doing the survey with me that day was

responsible for the poacher being apprehended. One of the poacher’s parents

threatened the forester with a dagger. We therefore suspended immediately the

interview and drove quickly to Leo (the provincial city).

The second problem encountered was the lack of effective communication

with respondents. This was because our dialect i.e. the forester and researcher,

differs slightly from the Nuni dialect. Fortunately in each village, we could find

someone who could speak both Moore and Nuni or French and Nuni. However,

the translation of the names of plants and animals was a little bit difficult and we

were obliged to write these names in the local language and later find out the

corresponding names in French and English.

Diagram of the methodology

On the basis of the conceptual model previously discussed and on the

methods of data collection (image processing and interview of population), a

scheme in four steps that summarizes all the methodological processes is presented

in Figure 11.

Step 1: Use of Remote Sensing (RS)

This referred to the processing of Landsat TM images of 1986 and 2002 as

a modern method to assess land use change and also to estimate the extent to

which the natural resources have been depleted as a result of human activities.

This step involved the fieldwork, GIS processing and interpretation of data.

64

Step 2: Fieldwork activities

This combined checking and confirming real field situation through check

points and observations and the collection of data related to the various uses of the

natural resources by the local population. This step involved also RS, GIS and

interpretation of data.

Step 3: Use of Geographical Information System (GIS)

This integrated raster and vector information and traced the maps of the

land use dynamics. The GIS provided preliminary maps that facilitated fieldwork

and interpretation of data.

Step 4: Validation of the changes

This step validated the dynamics that have taken place from 1986 to 2002.

The extent of resources degradation in the area and its consequences on the long-

term sustainability were verified.

65

(1) REMOTE SENSING

(2) FIELD WORK

Figure 11: Methodological approaches for the land use dynamics

Source: Author’s construct, 2006

Processing of Landsat TM images: 2, 3, 4 RGB (1986 and 2002). Result: raster and vectors.

- Observation - Ground-truth - Questionnaires to assess resource use

(3) GIS

Use of vectors to map the dynamics of land use and to compute the surface areas of the land use units.

(4) INTERPRETATION

Maps interpretation will show the change in the area from 1986 to 2002 while the resource use assessment points out the role of the population in the degradation of the environment.

66

Limitation of the study

The multispectral mapping of the land associated with digital remote

sensing and GIS techniques is characterized by inherent limitations. No map

produced by digital manipulation of multispectral data is ever 100 % correct when

it is produced by a computer (Robinove, 1981). By nature, the process of

classifying such a broad range of the Earth’s features into specific and often

simplified land use and land cover classes introduces error by drawing boundaries

around geographically located classes that are ‘homogeneous’ or acceptably

heterogeneous (Bottomley, 1998). However, these limitations can often be

overcome by sound statistical analysis to produce acceptably accurate land use and

land cover maps as derived from multispectral satellite data.

Three main difficulties were encountered during the field work which may

constitute the limitations of this study. The first limitation focuses on the

separation between fallows and the other land use units. The geo-processing

module permitted to identify and quantify the fallows in 2002, but the

identification of those of 1986 requested training areas corresponding to fallows

unit for the supervised classification, which unfortunately was not possible

because of their confusion with the other units, namely farm fields, shrubby and

wooded savannas. The identification of the fallow in 1986 would have permitted

to know whether they are diminishing as the population reported.

The second limitation concerned the identification of the bushfires in the

images. Bushfires were not drawn on the land use maps because the dates of image

captures corresponded to the period of early fires which left very few marks in the

ground.

67

The third limitation is the ground truth data acquired for accuracy

assessment. By utilizing the process of obtaining the ground truth data by

extensive GPS field surveys, bias with respect to proximity to roads is

characteristic of the data. It should be noted that this is not critical to the overall

accuracy assessment of the land use map; however, it is important to mention.

The forth limitation dealt with the population interview. It was difficult to

reach the expected number of respondents who satisfied the selectivity conditions,

so that in some villages, the number of respondents interviewed was below the

needed number. The situation may affect the high representativeness of the results.

68

CHAPTER FIVE

LAND USE ASSESSMENT

Introduction

This chapter, in two broad sections, presents the output of images

processing and population interviews. The first section deals with the states of land

use detected in 1986 and 2002 and the changes which occurred during that period.

The second section discusses the population’s perception on the environmental

change since 1986.

Land use detection

State of the land use in 1986

In 1986, the farm fields’ area represented only 2 % of the entire Bieha

district. The area was mostly confined to just around the villages. The most

important unit was the shrubby savannah which occupied 38 % of the district,

followed by the wooded savannah (32 %). The gallery forest was located along the

rivers and represented 27 % of the district (Figure 12 and Table 11).

In all, 3,438 hectares were used for farming activities in Bieha district. The

rest of the district was occupied by natural vegetation: shrubby savannah, wooded

savannah or gallery forest. It was obvious that some portions of the shrubby and

wooded savannah included both recent and old fallows, even though the image

processing did not permit their detection. The relatively low cropping surface is

likely to be due to the fact that at that time the population was less dense in the

69

district and the cotton and maize cultivation as well as the cashew production were

not so much practised.

Figure 12: Land use units in Bieha in 1986

Source: Image processing and field work, 2006

Table 11: Surface area and proportion of land use units in 1986

Land use units Surface area in 1986 (ha) Proportion (%)

Farm fields 3,438.69 2.0

Shrubby savanna 67,427.46 38.5

Wooded savanna 56,967.57 32.5

Gallery forest 47,634.09 27.0

Total 175,467.81 100.0

Source: Image processing, 2006

70

State of land use in 2002

Land use units, surfaces and proportions in Bieha district in the year 2002

are presented in Figure 13 and Table 12. On the whole, the farming area reached

33,686 hectares, about 19 % of the whole district while the shrubby savannah

dropped to 20 % of the total surface area of Bieha. The wooded savannah and

gallery forest did not change in terms of surface area (Figure 13, Table 12). Apart

from the ranch and the forest reserve which are excluded from farming activities

and the extreme south-west of the district dominated by forests and perennial

rivers, the rest of the district was almost used for farming (Figure 13). The

remaining shrubby savannah was located in the centre-north of the district. The

cropping acreage increased at the detriment of the shrubby savannah; an indication

that most agricultural activities were practised in the shrubby savannah.

71

Figure 13: Land use units in Bieha in 2002

Source: Image processing and field work, 2006

Table 12: Area and proportion of land use units in 2002

Land use units Surface area in 2002 (ha) Proportion (%)

Farm fields 33,686.64 19.0

Shrubby savannah 35,818.88 20.5

Wooded savannah 58,714.6 33.5

Gallery forest 47,240.36 27.0

Total 175,460.48 100.0

Source: Image processing, 2006

72

Land use dynamics from 1986 to 2002

Two maps representing the period from 1986 to 2002 were superimposed

to enable significant evolution in land use dynamics to be determined. The four

land use units as previously defined were codified and used to present a better

understanding of the changes that were detected (see Table 13).

Table 13: Codification of land use units

Land use units Codes

Farm fields F

Shrubby savannah Ss

Wooded savannah Ws

Gallery forest Gf

Source: Author’s design, 2006

By using the Geo-processing module (ArcView) and the Cross Tabulation

module (Excel), the following combinations which present the dynamics of land

use in Bieha district was obtained (Table 14)

73

Table 14: Legend of the land use dynamics from 1986 to 2002

Combinations

(1986/2002)

significance

D y n a m i c s o f t h e f a r m f i e l d s

FF Farm fields in 1986, still Field in 2002

FGf Farm fields in 1986 changed into Gallery forest in 2002

FSs Farm fields in 1986 changed into Shrubby savannah in 2002

FWs Farm fields in 1986 changed into Wooded savannah in 2002

D y n a m i c s o f t h e g a l l e r y f o r e s t s

GfF Gallery forest in 1986 changed into Farm fields in 2002

GfGf Gallery forest in 1986, still Gallery forest in 2002

GfSs Gallery forest in 1986 changed into Shrubby savannah in

2002

GfWs Gallery forest changed in 1986 into Wooded savannah in

2002

D y n a m i c s o f t h e s h r u b b y s a v a n n a h

Shrubby savannah in 1986 changed into Farm fields in 2002

SsGf Shrubby savannah in 1986 changed into Gallery forest in

2002

SsSs Shrubby savannah in 1986, still Shrubby savannah in 2002

SsWs Shrubby savannah in 1986 changed into Wooded savannah in

2002

D y n a m i c s o f t h e w o o d e d s a v a n n a h

WsF Wooded savannah in 1986 changed into Farm fields in 2002

WsGf Wooded savannah in 1986 changed into gallery forest in 2002

WsSs Wooded savannah in 1986 changed into Shrubby savannah in

2002

WsWs Wooded savannah in 1986, still Wooded savannah in 2002

Source: author’s construct, 2006

74

75

The results of the combinations are presented in Figures 14, 15, 16, and 17.

Figures 14 and 15 illustrate the comparison between land use types in 1986 and

2002. The size of the total farm fields in 2002 was about nine times its original

size in 1986. The surface area of the shrubby savannah was reduced to half. The

sizes of the wooded savannah and the gallery forest had almost remained

unchanged.

Figure 16 presents the observations of land use change from 1986 to 2002.

The farm field and wooded savannah units increased in surface area (gain) while

the shrubby savannah and the gallery forest experienced losses in surface area.

Figure 17 shows the trend of land use in Bieha. The size of farm fields

increased within the period 1986 to 2002 with an expansion of 880 % in 16 years,

and annual expansion rate of 55 %. The surface area of the shrubby savannah was

reduced drastically from 65,427 hectares in 1986 to 35,818 hectares in 2002. The

reduction rate was 46.8 %; about 3 % annual average rate of reduction. The

tendency curves (Figure 17) of the wooded savannah and the gallery forest

remained horizontal, meaning that the change was not noticeable. The annual

expansion rate of the wooded savannah was 0.19 % and the reduction rate of the

gallery savannah was 0.05 % annually (Table 15).

76

Figure 14: Land use dynamics in Bieha district from 1986 to 2002

Source: Image processing, 2006

0

10000

20000

30000

40000

50000

60000

70000

Surface area (ha)

Farm field Shrubby savannah Wooded savannah Gallery forest

Land use units1986 2002

Figure 15: Comparison between the size of land use types in 1986 and 2002

Source: Image processing, 2006.

-40000 -30000 -20000 -10000 0 10000 20000 30000 40000

Farm Field

Shrubby savannah

Wooded savannah

Gallery forest

Surface area (ha)

GainLoss

Figure 16: Observation of the land use change in Bieha

Source: Image processing, 2006

77

Table 15: Land use change in Bieha

Land use

Units

Area in

1986

Area in

2002

Change Proportion

within

16 years

(%)

Annual

exten-

sion

rate

(%)

Farm fields 3,438.69 33,686.64 30,247.95 +879,6 +54,9

Shrubby

savannah

67,427.46 35,818.88 31,608.58 -46,8 -2,9

Wooded

savannah

56,967.57 58,714.6 1,747.03 +3,0 +0,19

Gallery forest 47,634.09 47,240.36 -393.73 -0,8 -0,05

Source: Image processing, 2006

0

10000

20000

30000

40000

50000

60000

70000

80000

1986 2002

Year

Surface area

Farm Field

Gallery forest

Shrubby savannahWooded savannah

Figure 17: Tendency curves of land use units in Bieha

Source: Image processing, 2006

78

Table 16 and Figure 18 present the dynamics of land use units in Bieha

and also define new types of land use units resulting from the combination of

the various units between 1986 and 2002.

It was observed that 66.8 % of the farm fields unit (i.e. 1.3 % of Bieha

district) mainly old farm fields was more than 16 years old, while about 33.2 %

had been transformed into shrubby savannah, wooded savannah and gallery

forest. These were classified as fallows and represent 0.6 % of the district.

Some areas which were covered by natural vegetation in 1986 were converted

into farm fields within the 16-year period. These new farm fields comprised 8

% of the gallery forest, 27.8 % of the shrubby savannah and 15.4 % of the

wooded savannah; representing 18 % of the district.

Some areas covered by natural vegetation in 1986 had remained intact

by 2002. These consisted of 46.9 % of the gallery forest, 24.3 % of the shrubby

savannah and 38.6 % of the wooded savannah; and represent 34.7 % of the

district.

In the 16-year period, crude deforestation to make way for agricultural

activities represented 17 % of the district. The net deforestation was calculated

by excluding the fallow areas from the crude deforestation. The average annual

deforestation rate due to farming activities was 1.025 %. This means about

1,798.5 hectares were cleared every year to make way for agriculture. The

deforestation caused by other factors in the natural vegetation concerned those

areas which lost vegetation cover and hence were transformed into degraded

units. These included 7 % of wooded savannah which changed into shrubby

savannah and 12.2 % of gallery forest which changed into wooded savannah

and shrubby savannah. That type of deforestation covered 19.2 % of the district

79

during the 16 years. The average annual deforestation rate due to agents other

than from agriculture was 1.2 %, which was about 2,105.5 ha.

Some areas did improve their vegetation cover during the 16-year

period. These included fallow covering 0.6 % of the district, gallery forest

cover of 7.9 % (which changed from wooded savannah), and 12.2 % of gallery

forest also from shrubby savannah. The afforestation rate of 26.9 % of the

district during the 16-year period covered 2,950 ha annually. It is worth noting

that the afforestation occurred exclusively in the protected areas where

vegetation suffered no stress from grazing, fuel wood extraction and bushfires.

Table 16: Dynamics of land use units

Land

use

change

Area (ha) General

rate (%)

in 1986

Change rate per

unit (%) in 2002

Observations

D y n a m i c s o f t h e F a r m f i e l d s

FF 2,287.87 1.3 66.83 Old farm fields

FGf 204.88 0.1 5.98 Fallow/Afforestation

FSs 538.01 0.3 15.72 Fallow/Afforestation

FWs 392.75 0.2 11.47 Fallow/Afforestation

Total 3,423.51 1.9 100.0 -

D y n a m i c s o f t h e G a l l e r y f o r e s t

GfF 3,810.7 2.2 8.03 New field/Deforestat

GfGf 22,275.66 12.8 46.96 Not disturbed

GfSs 6,501.11 3.7 13.70 Deforestation

GfWs 14,843.01 8.5 31.29 Deforestation

Total 47,430.48 27.2 100.0 -

80

Land

use

change

Area (ha) General

rate (%)

in 1986

Change rate per

unit (%) in 2002

Observations

D y n a m i c s o f t h e S h r u b b y s a v a n n a h

SsF 18,694.83 10.7 27.85 New field/Deforestat

SsGf 10,787.09 6.2 16.07 Afforestation

SsSs 16,367.74 9.4 24.38 Not disturbed

SsWs 21,285.01 12.2 31.70 Afforestation

Total 67,134.67 38.5 100.0 -

D y n a m i c s o f t h e W o o d e d s a v a n n a h

WsF 8,746.05 5.0 15.42 New field/Deforestat

WsGf 13,772.86 7.9 24.28 Afforestation

WsSs 12,258.55 7.0 21.61 Deforestation

WsWs 21,944.88 12.5 38.69 Not disturbed

Total 56,722.34 32.3 100.0 -

Source: Image processing, 2006

0

5000

10000

15000

20000

25000

Surface area (ha)

FF FGf

FSs

FWs

GfFGfG

fGfSs

GfWs

SsF

SsGf

SsSs

SsWs

WsFWsG

fWsS

s

WsWs

Dynamics of land use

Figure 18: Dynamics of land use units

Source: Image processing, 2006

81

Respondents’ perception of the environment and their welfare

The sample population was asked to indicate their views on the changes

that had occurred in the study area and the implication on their welfare. The

information collected is presented in the rest of the chapter

Dynamics of the environment

The environment refers to vegetation (trees and grasses), wild animals

(big and small animals, birds and fishes), water and soil. In general, the

perceptions are classified into “very dense, dense, medium and scarce” for

vegetation, “many, few and rare” for animals and “high, medium and low” for

soil fertility and water availability. The perceptions were estimated by the

respondents in three time-series of 20 years ago (1986), 10 years ago (1996)

and the current situation.

Dynamics of the vegetation

Table 17 indicates that the vegetation of the district had reduced from

“very dense” twenty years ago to “medium” at the present time through “dense”

ten years ago. The reduction was in terms of number, height and tree species.

The threatened tree species were Isoberlinia doka, Bombax costatum, Parkia

biglobosa, Acacia albida, Ficus gnafalocarpa, Khaya senegalensis,

Pterocarpus erinaceus, Lanea microcarpa, Burkea africana, Acacia seyal,

Cadaba forinosa, Maerua angolensis, Ceiba pentandra, Vitelaria paradoxa,

Tamarindus indica, Afzelia africana and Daniellia oliveri. Among the grass

species, the most threatened were Andropogon gayanus, Andropogon

pseudrapus, Eragrostis stremula, Cymbopogon citratus. These species were

82

important for the communities since they provide fruits, wood and folk

medicine to the population and pasture for their animals.

The proliferation of some species such as Detarium microcarpum,

Mangifera indica, Azadiratcha indica, Pileostigma thonningii, Tamarindus

indica, Anacardium occidentale, Zizyphus mauritiana, Balanites aegyptiaca,

Acacia siebrian and Dichrostachys cimeira was also observed. The new

grasses were Siderhom bifolia, Penicetum pedicellatum and Boheira diffusa.

This proliferation was caused by the reduction of the rainfall and the long

distance mobility of the herds which brought the grains of these new species

from the arid zones.

Also, the factors (Table 18) that led to the degradation of the vegetation

were overgrazing (83.2 %) and climate change (72.6 %) followed by

population growth (71.7 %), bushfires (58.4 %) and farming activities (53.1

%).

Table 17: Dynamics of the vegetation according to the population

Dynamics of the trees Dynamics of the grasses

1986 1996 2002 1986 1996 2002

Very dense 85.5 0 1.8 90.2 0.9 0.9

Dense 12.4 79.6 4.4 7.1 78.6 8.0

Medium 1.8 20.4 92 2.7 20.5 86.6

Scarce 0 0 1.8 0 0 4.5

Total 100% 100% 100% 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

83

Table 18: Causes of vegetation loss

Causes Yes (%) No (%) Total (%)

Population pressure 71.7 28.3 100

Over-grazing 83.2 16.8 100

Climate 72.6 27.4 100

Bushfires 58.4 41.6 100

Farming activities 53.1 46.9 100

Source: Field survey, 2006; Sample size (N) = 113

Wildlife dynamics

The population’s perception of wildlife changes was almost similar to

that of vegetation change (Table 19). All the classes of animals were reduced

from “many” in 1986 to “scarce” by 2006 as a result of forest reduction,

hunting, population growth, farming activities, bushfires, overgrazing and lack

of water in that order (Table 20). For the birds, they were observed to be

“many” or “few” but not scarce; which was the result of the efforts of the local

foresters to preserve wildlife and also to the creation of a ranch, which

constituted a safety habitat for animals and birds. Furthermore, the birds were

not often the focus of hunters and could easily move away from bushfires.

84

Table 19: Dynamics of wild animals

Big animals Small animals Birds

1986 1996 2002 1986 1996 2002 1986 1996 2002

Many 93.8 5.4 3.6 95.5 2.7 3.7 86.6 4.5 12.6

Few 1.8 90.2 12.5 1.8 92.7 19.3 9.0 91.0 36.9

Scarce 3.5 4.5 83.9 2.7 4.5 77.1 4.5 4.5 50.5

Total 100% 100% 100% 100% 100% 100% 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Table 20: Causes of wildlife dynamics

Causes Yes (%) No (%) Total (%)

Population pressure 69 31 100

Hunting 71.4 28.6 100

Bushfires 46.4 53.6 100

Farming activities 58.9 41.1 100

Forest reduction 80.5 19.5 100

Source: Field survey, 2006; Sample size (N) = 113

Soil fertility dynamics

The fertility of the soil as shown in Table 21 was observed to have

declined over the 16-year period; from high (98.2 %) through medium (91.1 %)

to low (89.3 %). About 72.3 % of the population attributed the change to the

reduction of the rainfall and 69 % to overuse (Table 22). Some also mentioned

overgrazing, and the use of modern production tools which disturbed the soil

and created room for harmful grasses. To address the problem, fertilizer

application was highly adopted (88.5 %), followed by animal ploughs (61.6 %)

85

and fallow (60.4 %). However, fallow could not be practised due to the non-

availability of land.

Table 21: Soil fertility change

Fertility of the soils

1986 1996 2002

High 98.2 3.6 5.4

Medium 1.0 91.1 5.4

Low 0 5.4 89.3

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Table 22: Causes of fertility change

Causes Yes (%) No (%) Total (%)

Overuse 69 31 100

Climate 72.3 27.7 100

Source: Field survey, 2006; Sample size (N) = 113

Table 23: Solution to fertility reduction

Solutions Yes (%) No (%) Total (%)

Fertilizers 88.5 11.5 100

Fallow 60.4 39.6 100

Plough 61.6 39.4 100

Migration 11 89 100

Source: Field survey, 2006; Sample size (N) = 113

86

Water dynamics

Table 24 reports that since 1996, availability of water in the rivers after

the rainy season has shortened. Water shortage is attributed, according to Table

25, to the reduction of rainfall (81.3 %), the sedimentation of the rivers (71.4

%) and to overgrazing (70.5 %). As a result, fishes were no more abundantly

available in the rivers.

Table 24: Water evolution in the rivers according to the respondents

Availability of water in the rivers after rainy

season

1986 1996 2002

Long time (≥3

months)

95.5 42.9 3.6

Short time (≤2

months)

4.5 57.1 96.4

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Table 25: Causes of water reduction in the rivers

Causes Yes (%) No (%) Total (%)

Population pressure 51.4 48.6 100

Animal pressure 70.5 29.5 100

Climate 81.3 18.7 100

Blinding 71.4 28.6 100

Source: Field survey, 2006; Sample size (N) = 113

87

Dynamics of farming practices

Crops produced in 1986 in order of magnitude were sorghum (red and

white), millet, maize, rice, beans, groundnut, yam, potato, cassava, cotton,

sesame and garden peas. All the crops produced were for household

consumption, apart from yam and cassava which were mostly sold. In 1996,

cotton and maize production as cash crops gained importance as peasants

became interested in the production of these two crops in addition to their

subsistence crops. Today, production is concentrated on cotton, maize and at

least sorghum, millet, beans and groundnut; with improved seeds developed to

satisfy the climate change, the poor soils and the growth of the population.

Crops productivity

Crop productivity which, as presented in Table 26, was high in 1986,

had after 20 years decreased substantially (more than 80 %). Factors

responsible for the decrease were reduction of rainfall, poor soils and lack of

labour in that order. Others were lack of education on sustainable use of the

soil, erosion due to surface runoff and overgrazing which tended to destroy the

soil.

Table 26: Evolution of crops productivity according to the population

Dynamics of crops productivity

Year 1986 1996 2002

High 97.3 3.6 4.5

Medium 0.9 93.8 12.5

Low 1.8 2.7 83.0

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

88

Farming techniques

The use of traditional tools in the farming system prevailed in the 1980s

(Table 27) with traditional skills based on the use of human energy in the

process of production. By 1996, the same system was still practised even

though the use of modern tools was fairly noticeable (42 %). The modern

system involves the use of ploughs, machines, and agro-chemicals (fertilizers,

pesticides, etc.) in the production process. Today, 61.6 % of the population

makes use of modern tools which, according to them, not only compensate for

the lack of labour and help produce higher yield but also improve the quality of

the soil.

Table 27: Dynamics of farming practices

Evolution of farming practices

Year 1986 1996 2002

Modern 10.8 42.0 61.6

Traditional 86.5 53.6 31.3

Both 2.7 4.5 7.1

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Change in acreage per household

In general, the households cultivated less than 5 hectares each even

though the current tendency is to cultivate between 5 and 10ha (Table 28), due

to increase in household sizes and reduction in crop yields per area. However,

89

most of the population is forced to maintain their field sizes intact due to lack

of new arable lands resulting from increased in-migration since 1996.

Table 28: Change in acreage per household

Evolution of acreage per household

Year 1986 1996 2002

≥ 10ha 2.7 3.7 15.2

5-10ha 14.3 35.7 42.0

≤5ha 83.0 60.6 42.9

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Dynamics of the population welfare

Human welfare is measured here by food security, access to potable

drinking water and income levels at household level.

Food security

Food security was observed to be dropping constantly (Table 29). Food

shortage was caused by low productivity of soils and increase in household

sizes. Large quantities of the cereals produced were sold and the proceeds

spent on education and health care. Others argued that there is now a conflict in

the production process between cotton and the cereals for two reasons: not only

that land suitable for cereals growing is being used to cultivate cotton, but also

cotton needs more maintenance and this is usually at the detriment of the other

crops.

90

Table 29: Food security according to the population

Food security

Year 1986 1996 2002

High 92.0 5.4 7.1

Medium 3.6 87.5 17.0

Low 4.5 7.1 75.9

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Drinking water

It was observed (Table 30) that sources of drinking water had improved

over the years due to construction of boreholes and solar pump which now

satisfies much of the demand (72 %) as against water from rivers and wells

which were more consumed (66.2 %) in 1986. While change was due mainly to

lack of water in the rivers and wells it has also been enhanced by improved

sanitation in the villages.

Table 30: Evolution of the sources of drinking water

Sources of drinking water

Year 1986 1996 2002

Rivers 62.2 4.5 0.9

Wells 37.8 75.7 27.0

Pipe/borehole 0 19.8 72.1

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

91

Income evolution

Incomes in general have not improved as faster as expected (Table 31).

Furthermore, the bulk of the revenue was based on the cattle trade and

agricultural production which were subject to or affected by fluctuations in

rainfall and external prices. Education and health care were expensive and

these consumed large portions of incomes.

However, the shift from huts to sheet metal house (45 %), from the use

of donkeys to bicycle or motorcycle as means of transportation (75 %), and

from traditional systems of farming to the use of ploughs, tractors and chemical

fertilizers were considered to be signs of improvement of income.

Table 31: Evolution of incomes according to the population

Dynamics of incomes

Year 1986 1996 2002

High 28.8 23.4 46.8

Steady 51.4 65.8 20.7

Low 19.8 10.8 32.4

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Population mobility

Population mobility refers to permanent and temporary migration in the

district. Table 32 indicates that in 1986, in-migration was popularly observed

(81.2 %) to be low which in 2002 it was observed to be high. The driven factor

92

of the mobility is said to be the abundance of natural resources in the area

(Table 33).

Table 32: Permanent in-migration

Dynamics of in-migration

Year 1986 1996 2002

High 18.8 61.6 76.6

Low 81.2 38.4 22.4

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Table 33: Causes of the permanent in-migration

Causes Yes (%) No (%) Total (%)

Abundance of resources 94.7 5.3 100

Parenthood relationships 9 91

Return migration 9.9 90.1 100

Source: Field survey, 2006; Sample size (N) = 113

Table 34 also shows that the temporary in-migration remained very low

from as far back as 1986 simply because it consisted of mainly herds men

coming to graze their animals.

93

Table 34: Temporary in-migration

Dynamics of in-migration

Year 1986 1996 2002

High 10.6 6.9 14.9

Low 89.4 93.1 85.1

Total 100% 100% 100%

Source: Field survey, 2006; Sample size (N) = 113

Summary

The results of the images processing show that in the 1986s, Bieha

district was dominated by flourishing natural vegetations composed of 38 % of

shrubby savannah, 32 % of wooded savannah and 27 % of gallery forest. The

land devoted to farming activities represented only 2 % of the district.

However, in the 2002, the farm fields increased in size and occupied 19 % of

the district. The shrubby savannah dropped to nearly 50 %. The wooded

savannah and the gallery forest kept nearly their original size within the 16-

year period.

The comparison of land use maps of the two periods permitted a clear

picture to be realised in terms of vegetation change. The net annual

deforestation caused by farming activities, wood supply, grazing and bushfires

was about 3,904 hectares in the district.

Respondents from the population helped to capture their perception on

the trend of change of the environment. They recognized that the vegetation

(both trees and grasses) was decreasing in size, number and species. This trend

also applied to the wild animals, the availability and the productivity of the

94

arable land, and to the perpetuity of the water in the district. To meet the needs

of the increasing household sizes, the peasants were forced to enlarge their

farm fields and so contributed to the environmental deterioration.

95

CHAPTER SIX

FACTORS INFLUENCING CHANGES AND

IMPLICATIONS FOR LAND USE

Introduction

This chapter establishes links between the changes detected and local

socioeconomic and cultural contexts, and also the national legislation on

environment. It also makes a comparison between the specific cases in Bieha

district and some other related case studies.

Changes detected

The land use pattern in Bieha through several years has had different

characteristics. In 1986, shrubby savannah was the major land use unit and was

uniformly distributed in the district. The second important unit was the wooded

savannah, followed by the gallery forest generally located along the rivers. The

natural vegetation covered 98 % of the total surface of Bieha. The farming area

was minute and represented only 2 % of the district. By 2002, important

changes had occurred in the land use. The shrubby savannah shifted sharply

from its rate of 38.5 % to 20.5 % of the district, and the farming surface (farm

fields) reached 19 %. The other units (wooded savannah and gallery forest)

remained nearly unchanged.

During the 16-year period, the land use dynamics had a noticeable

trend: 53 % of the gallery forest had altered to wooded savannah, shrubby

savannah and field crops; 61 % of the wooded savannah changed to farm

96

fields, shrubby savannah and gallery forest while 76 % of the shrubby

savannah was changed to field, wooded savannah and gallery forest. Among

the units, about 67 % of the 1986 estimated agricultural land, 47 % of the

gallery forest, 24 % of the shrubby savannah and 39 % of the wooded savannah

remained unchanged in 2002.

The change was most profound between the shrubby savannah and the

field crops within the period. The southwest and the northern zones were the

most affected (Figure 13). The land use types had common physical and

geographical interconnections; namely an increase in one type of land use

category was associated with a decrease in another land use category. The

change to farm fields happened in the areas with soil fertile enough to grow

crops and close to former field crops areas.

The annual deforestation rate in Bieha due to farming activities was

estimated at 1.025 % within the period. By implication, about 1,798.5 ha were

cleared annually to make way for agriculture. The deforestation caused by

factors other than agricultural activities was estimated at 1.2 % annually, about

2,105.5 ha. Some level of afforestation was observed and its rate was estimated

at 1.6 % annually, representing 2,807.3 ha. These changes may be associated

with several factors, which are examined next.

Factors affecting land use dynamics in Bieha

Natural and human factors may be responsible for the changes which

occurred in land uses in the district. The natural causes may be associated with

the fluctuations in rainfall, while the human factors may have their origins in

97

agricultural activities, poverty and population pressures, over-harvesting of fuel

wood and bushfires.

Leading factors in the farm fields dynamics

In 1986, the farming area in the district covered some 3,438.69

hectares. By 2002 however, the area had substantially increased to 33,686.64

hectares, an addition of 30,247.95 hectares during the 16-year period (Table

15). The fallow areas represented 33.2 % of the entire cultivated areas in 1986

and 3.3 % of farm fields in 2002 (Table 16). The annual deforestation rate

caused by farming activities was 1.025 % during the period; which is higher

than the national deforestation rate caused by agriculture estimated at 0.2 % by

FAO (2000) and 0.34 % by Mongabay (2005). These changes resulted mainly

from the population pressure, agri-businesses and poverty in the area.

Population pressure

The population of Bieha was 15,043 in 1985, 20,643 in 2002 (INSD,

1985, 1996) and 25,634 in 2006 (District Population Census, 2006). On the

basis of the natural growth rate of the population of Burkina Faso which was

estimated at 2.4 % in the 1996 census (INSD, 2004), the population of Bieha

should be roughly 21,983 inhabitants in 2006. The current high population

shows the importance of in-migration in the district. In fact, from 1996 to 2006,

about 3,651 immigrants arrived in the district. During the survey, a rising trend

in in-migration was observed (Table 32) over a twenty-year period namely 18.8

% in 1986, 61.6 % in 1996 and 76.6 % by 2006. The region had become one of

the main destinations for migrants from the poor and overexploited lands of the

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northern and central parts of the country. The migration flow was amplified by

the interest given to cotton and maize production that constitute the recent

basic cash crops. Following the rapid population growth through migration,

new lands have been brought under cultivation in the region, a move facilitated

by the wide diffusion of draught animals in recent years as reported in Chapter

Five. Yet, a shortening of the fallow period and an increasing use of fertilizers

were now noticeable tendencies that unmistakably reflected the rising pressure

on cultivated land, particularly land suitable for cotton growing.

Under conditions of increased demographic pressure, the most pressing

issue for farmers was to change land use practices or land use patterns, or both,

to ensure food security and income. The population of Bieha is facing this

reality by increasing farming acreage, intensifying the production, reducing

duration of fallow and even suspending it and resorting to the use of fertilizers

and draught animals.

The pressure driven by the migration was encouraged by the land

tenure system in the area. Breusers (2001), in his study on “land and mobility

in Burkina Faso”, reported that mobility was not only made possible by the

prevailing land tenure regime but also underpinned its flexibility and allowed

the merging and shifting of rights. In Burkina Faso, the land tenure system in

force is the Land and Agrarian Reforms (RAF) adopted in 1985 which

stipulated that the management of urban and rural lands, water, forests, fauna,

fisheries and mines belongs to the State. Unfortunately, the application of the

RAF is not yet widely accepted and in most of the villages in the country, it is

the local land right that is applied. In the Sissili province, each village has its

own defined village territory that has its origin in the local history of the area

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and in the first settlers. Tenure management, according to Howorth and

O’keefe (1998) was based on customary law arrangements between the land

chief and those searching for land to farm. In this context the population (or

migrants) forced out of their origins due to deteriorating physical conditions

easily acquired farm land in this region.

The population pressure also increased competition for resources in

Bieha and forced some farmers to abandon sustainable farming methods and

exploit marginal lands in an effort to secure their incomes and feed their

families. Conflict becomes highly likely when this process leads to deepened

poverty, widespread food insecurity, large scale in-migration, sharpened social

cleavage and weakened institutions.

Agri-business

There used to be large-scale farms in Bieha involving individuals and

mostly government ministers, directors of services and traders. These officers

and traders tend to be absentee farmers for most of them are based in the

capital city and employ casual labourers. Such labour was not surveyed since

the target populations in this study were the permanent residents of the district.

However, the information on the large-scale farms was demanded from

key informants such as the land chiefs since these absentee farmers formally

come to the land chiefs for the land on which to farm. According to the local

authorities, each farm covered 40 to 100 hectares of land for cotton and maize

growing and/or for cashew plantation (Plate 1). Once they got the land from the

chiefs, they quickly went back to Ouagadougou (capital city) or Leo (province

city) to register them on long term security. With the use of tractors and other

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machinery, and sometimes irrigation, such farming activities have contributed

to the immense change in land use and the landscape in Bieha district in

particular and in Sissili province in general.

Plate 1: Cashew plantation in Neboun

Source: Field work, 20/01/2006

Poverty

Haberg (2000) reported that migration in Burkina Faso seems to be a

strategy for households to reduce poverty. Poverty usually drives those affected

to rely more on natural resources for survival. The survey showed that the

population of Bieha was all farmers and/or breeders, thus depending deeply on

natural resources for their survival. The focus was more on resources for their

immediate needs rather than on those whose benefits may materialize only in

the long term. Furthermore, there is lack of relevant resources, hence reducing

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options available for proper conservation practices and inappropriate use of

land resulting in degradation.

Consequences of increasing farm lands

The immediate consequence of the increased farm land is degradation.

Land degradation is the aggregate or reduction of the productivity potential of

the land, including its major uses (arable, irrigated, forest, etc), its farming

system and its value as an economic resource (Stocking and Murnaghan,

2001). Land has been degrading in Bieha depending upon the modes of its use.

The degradation was observed from decrease in crop production, decrease in

soil fertility and the increasing food shortage. It was also worsened by non-

availability of water in the rivers immediately after the rains. Farmers have had

to resort to the application of chemical fertilizer and animal dung to replenish

exhausted soils.

Related long term consequences of the degradation may be conflicts

linked to competition for land between indigenes and migrants. Agrotechnik

(1991) suggested that Sissili province could only support 30 persons per km2

without irreparable damage while IBS (1994) forecasted that some 43 % of the

Sissili area would be deforested by 2010 due to land-use activities. The current

crude density of Bieha is 14.6 inhabitants per km2. When we exclude the

protected zones (Safari ranch of Bieha and the villagers’ forest of Bori) which

cover 388.31 km², the net density (population over habitable and exploitable

land) becomes 18.7 inhabitants per km2. Assuming that the rest of the district

could be used for farming, then it appears that 24.6 % of this arable land was

under cultivation in 2002. Considering the current rate of the population

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growth, the district will be in shortage of arable land in the near future. This

situation may cause the indigenes to reclaim their land from the migrants,

hence generating conflicts.

Forests dynamics

The shrubby savannah

The surface area of the shrubby savannah in 1986 reduced to 46.8 % in

2002 with an annual loss of about 3 %. Within the loss, 27.8% was converted

to farm fields while 46.7 % improved in vegetation cover and changed to

wooded savannah or gallery forest. The improvement occurred mostly in the

protected zone. Farming activities and the conservation systems initiated by the

creation of the ranch in 1985 were the factors that affected the dynamics of the

shrubby savannas. In fact, to the farmers, shrubby savannas were easily

accessible and exploitable because of the reduced number of trees and their

short height, as opposed to gallery forests and wooded savannas in which the

density of big trees discouraged the use of draughts.

The wooded savannah

During the 16-year period, the wooded savannah increased by 3 %

which it gained from the improvement in vegetation cover of the shrubby

savannah (12.2 %) and the degradation of the gallery forest (8.5 %). Other

factors like wood extraction, overgrazing and bushfires contributed to the loss

of 21.6 % of the wooded savannah to the benefit of the shrubby savannah.

Fuel wood constitutes the principal source of energy not only in Bieha

district but also in the whole country (Chapter Three). In the district, wood was

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cut for making charcoal for sale or sale directly as fuel wood. Several locations

of charcoal making and places of wood and charcoal selling were identified

during the field work (Plate 2). Fuel wood and charcoal were constantly sent to

Ouagadougou by truck loads daily (Plate 3).

Plate 2: Pile of wood fore sale Source: Field work, 24/03/2006

Plate 3: Wood transportation from Sissili to Ouagadougou Source: Field work, 24/03/06

104

In the district, two offices of foresters were in operation; one based in

Bieha village and the other at Neboun. These two posts were charged to plan

the wood cutting in the district, to give licence for cutting and to collect taxes

from cutting, selling and transporting wood or charcoal. However, some people

operated clandestinely and thus escaping the foresters’ patrols.

Grazing was allowed beyond the protected zones in the district. As

grasses were nearly always abundant and green, the area attracted several

breeders from Sissili and other distant provinces. There is no document which

provides the exact number of domestic animals at the district scale due to their

constant mobility along the countryside, but the 900,000 animals reported for

the whole Sissili province in 2003 was substantial (DEP, 2003). There was no

form of stabling or fodder cultivation for animals and worse of all, the project

on grazing zone management in the district was suspended due to the resistance

of the villagers. During dry seasons, the breeders frequently cut Afzelia

africana, Andansonia digitata, and other palatable species whose leaves

remained green to feed their animals (Plate 4). The effects of animals on

vegetation are numerous (Middleton, 1997; Middleton and Thomas, 1997;

Chikamai and Kigomo, 2003). On one hand, they destroy young trees by

grazing and stamping and on the other hand, their stamping on the land reduces

the infiltration of water.

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Plate 4: Afzelia africana cut for animals Source: Field work, 24/03/06

Bushfire is one of the monstrous factors that caused deforestation and

loss of species in most of Sub-Sahara Africa (Aubreville, 1949; Kambou and

Poussi, 1997; Yameogo, 2005). Most of the population of Bieha did not ignore

the negative role of bushfires on vegetation and wildlife as 58.6 % of the

respondents recognised the destructive effects of the fires on the vegetation.

They also estimated that the small number of foresters (two for the whole

district) rendered very ineffective the control of the fire. During the field work,

apart from the protected zones and the bushes of Biniou, Livara which were not

yet burnt, it was observed that the entire district had suffered from bushfire at

least once (Plate 5 and 6). Three types of bushfires are practised in the area,

namely early fire, intermediate fire and late fire (Yameogo, 2005). The early

fire takes place a month after the last rains (December). The ominous effects of

this fire on the vegetation are negligible because at that time, grasses and

leaves of the trees are still green and so, only the dry grasses are consumed.

106

The early fires are used by the foresters in the national parks to stimulate the

sprouting of grasses for wild-animals. The intermediate fires come at a time

when half of the grasses are dry (January-February). The latter occurs in

March-April, when grasses are dry. It causes severe effects on the vegetation

killing important number of trees and reducing the productivity of the trees,

because these periods correspond to the flowering period of Vitelaria

paradoxa, Bombax costatum, Sclerocarya birrea, Adansonia digitata, Lanea

microcarpa, Lanea acida, Parkia biglobosa, Saba senegalensis, detarium

microcarpum, etc.

Plate 5: Burnt shrubby savannah Source: Field work, 24/03/06

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Plate 6: Burnt wooded savannah Source: Field work, 24/03/06

There are punishments for people who cause bushfires (M.E.C.V, 2004)

but up to now no culprit of bushfire has been found. Accusations are levelled

against breeders (Fulani), hunters, cigar smokers but obviously there is lack of

clear political will to fight bushfires in the area.

The gallery forest

The gallery forest lost only 0.8 % of its surface area of 1986 but

internal changes occurred. About 8 % was converted to farm fields and 45 %

was degraded into wooded savannah or shrubby savannah. The pace of

degradation was more perceptible in this unit. The relative balance was due to

the compensation of improved shrubby savannah and wooded savannah which

contributed 43 % to gallery forests in 2002. The causes of degradation were the

same as discussed above. Up to now, gallery forests were not noticeable to the

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farmers in Bieha but rather the rapid decrease of the savannah could change

their attitudes towards this geographical unit.

Consequences of the deforestation

The degradation of vegetation cover in Sissili, if not checked, will lead

to unpleasant consequences which may be physical, demographic and

economic.

Physical consequences refer to soil degradation and desertification and

food insecurity. According to Sanchez et al. (1997), trees have a different

impact on soil properties than annual crops; because they remain in the soil

longer, have longer biomass accumulation, and longer-lasting, more extensive

root systems. There are four ways in which trees can have beneficial effects on

soil properties, crop production, and environmental protection. First, trees can

provide nutrients to crops in agroforestry systems through biological nitrogen

fixation (BNF), and nutrient cycling.

Biomass transferred from one site to another also provides nutrient

inputs. These nutrients become inputs to the soil when the tree biomass is

added to and is decomposed in the soil. Secondly, trees in agro-forestry

systems can increase the availability of nutrients in the soil through the

conversion of nutrients to more labile forms of soil organic matter (SOM).

Plants convert inorganic forms of nitrogen (N) and phosphate (P) in the soil

solution into organic forms in their tissues. Thirdly, trees decrease nutrient

losses from the soil due to agents such as winds and water. Smaling (1993)

reported that losses caused by surface runoff, erosion and leaching account for

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about half of the Nitrogen (N), Phosphate (P) and Potassium (K) depletion in

Africa.

Agro-forestry systems have been found to decrease nutrient losses by

surface runoff and erosion to minimal amounts (Lal 1989; Young 1989).

Finally, trees improve environmental benefits by protecting the soil surface

with their two layer canopies namely, the litter layer and the leaf canopy,

thereby decreasing runoff and erosion, dampening temperature and moisture

fluctuations and in most cases, maintaining or improving soil physical

properties (Sanchez et al. 1985). In agroforestry systems, the beneficial effects

of protecting the soil surface depend on the spatial and temporal coverage of

the tree component. Also, tree roots can loosen the topsoil by radial growth,

and improve porosity in the subsoil when roots decompose.

When forest is depleted, all these properties are lost, giving room to soil

degradation which leads, in turn, to desertification as defined by Grouzis

(1981), Thiombiano (2000) and Ouédraogo (2002); as “the loss of the

biological productivity of the arid soils which progressively transform to desert

or to skeletal irreparable soil”.

Bieha forest was home to hundreds of plant species and thousands of

animal species. Forest degradation will also cause the loss of its biological

diversity in terms of genetic, species and ecological losses.

Once soil is degraded, food production becomes low; hence food

insecurity and conflicts that in turn may lead to the mobility of the population

to other areas. Most of the population in the district depend on crop, wood and

charcoal production for their incomes. Furthermore, the women of the district

extract their livelihood from non wooded forest products such as shea nut

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harvesting to produce butter widely exported to Europe and America. They

also use the grain of Parkia biglobosa to produce Sumbala (Dawadawa)

widely consumed at the national scale. Fruits of Saba senegalensis, Detarium

microcarpum and zizuphus mauritiana are also commercialized by the women.

Loss of forest thus means loss of economic activities to the women.

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CHAPTER SEVEN

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

Introduction

This section deals with the summary of the methodology and findings

of the study, conclusions drawn from the study and recommendations for

improvement in land use in the Sisili Province and further studies.

Summary of the methodology

The basis of this research comprised the multitemporal classification of

Landsat TM satellite images to detect, delineate, and map the specific units of

land use in Bieha District from 1986 to 2002. Thus, the aim of this research

was to produce both current and past land use of Bieha from recent and historic

satellite imagery spanning the period of study to detect and map this land

change; in addition, assess the factors that led to the change.

The creation of the 1986 and 2002 land use maps was derived utilizing

standardized digital remote sensing classification techniques. The classification

employed two multitemporal Landsat scenes dated, November 18, 1986 and

October 21, 2002. A hierarchical level of land use classification comprised of

four classes namely; farm fields, shrubby savannah, wooded savannah and

gallery forest. Final classification accuracy was determined to be satisfactory

by means of employing standardized accuracy assessment measures and

comparison with ground data obtained from extensive GPS field surveys.

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The process of change detection was employed by utilizing a previously

documented, image differencing method and the three-dimensional model of

Agarwal et al (2002). Specifically, bands 2, 3 and 4 were used because of their

especially responsive to vegetation biomass, to make false colour composites

for the classification for each scene. The supervised classification type was

used to detect land use state for each period. The results of the image

processing were supported by an interview of the local population.

Summary of the findings

According to the statistics calculated from the land use change data, the

farming surface increased from 3,438.7 ha (2 %) to 33,686.6 ha (19 %); the

shrubby savannah decreased from 67,427.5 ha (38.4 %) to 35,818.8 ha (20.4

%); the wooded savannah increased from 56,967.5 ha (32.4 %) to 58,714.6 ha

(33.4 %) and the gallery forest declined slightly from 47,634 ha (21.7 %) to

47,240.3 ha (20.9 %) in Bieha District from 1986 to 2002 (see Chapter Five).

These statistics also indicated that farming activities contributed to forest

degradation which annual loss rate was estimated at 1.025 % (1,798.5 ha) and

that the fallow practices within the period was 33.1 % of the field area or 0.6 %

of the district. Other factors contributed to the degradation of the gallery

forests, wooded savannah and shrubby savannah estimated at 1.2 % annually

(2,105.5 ha). A form of afforestation was also indicated by the statistics which

represents about 1.6 % annually (2,950 ha); however, the map of the dynamics

shows the afforestation was taking place especially in the protected areas

namely; Safari Ranch Sissili and the forests. Assuming these statistics to be

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accurate and precise, the net amount of forest degraded annually was 0.623 %

or 1,098.8 ha in Bieha District over a time period of 16 years.

The statistics from the population interview revealed that the local

population is aware that their natural resources, namely the forests, arable

lands, soil, water, wild animals, etc. have been degraded since the 1980s. The

degradation resulted in lowering productivity of the crops, food insecurity, and

incomes reduction. The factors that led to the depletion of resources according

to the population included farming activities, over-harvesting of wood and

animals, overgrazing, bushfires and climate. They faced their environment

realities with increasing the acreage of the farm fields, using draughts and

fertilizers, and growing improved seeds; abandoning crops that needed much

more care.

Summary of the discussion

Comparison between the findings from the present study and other

related studies showed that the space of the deforestation in Bieha District was

higher than those estimated by MEE (1996), MECV (2004), FAO (2000) and

Mongabay (2005). The evolution of the cultivated lands was rapid and was

mostly focused on the shrubby savanna unit because of its aptitudes in

exploitation. The factors that led to the increase of the farming lands were

discussed and were mainly the population pressure, the emergence of agro-

businesses and poverty in the area. The demographic pressure was driven by

the in-migration of people from the crowded and infertile northern and central

regions of the country. The pressure was also facilitated by the smoothness of

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the tenure management which was based on customary and orally law

arrangements between new-comers and land chiefs.

Under condition of increasing cultivated land, marginal lands were also

put under cultivation, hence accelerating soil degradations, runoffs, and

bindings. Under such a tendency, sooner or latter, large scale degradation of

forest and lands; conflicts linked to competition for space between farmers and

breeders on one hand and between autochthones and migrants are likely

foreseeable.

The degradation of the vegetation in gallery forests, wooded and

shrubby savannas was caused, beyond the agricultural activities, by the wood

cutting, overgrazing and bushfires. Wood was cut and sent to towns as energy

sources in its natural form or transformed to charcoal; domestic animals were

too many in such a way that compromised the carrying capacity of the area,

and forests were burnt at least once annually, hardening the soils, killing plants

and animals, hence threatening the biological diversity.

Conclusions

The sixteen year time span, 1986 - 2002, considered in this study is a

short increment of time in a long history of land use dynamics. This time

period was chosen based upon the availability of current and compatible

satellite imagery for classification and change detection as well as a means to

provide current land use trends. This period also coincides with a period of

substantial increases in agricultural activity in the area due to the interest given

to maize and cotton cultivation, and cashew and mango plantation.

115

It is important to consider this time period in the grand scheme of land

use and land cover characteristics in Sissili province. Natural resources in the

province are degrading due to unsustainable agro-pastoral activities undertaken

by local stockholders in a context of high in-migration rate. The province was

mostly forested before migrant Fulani and Mossi settlement arrived in the

1980s following the drought of 1970s. From these findings, one may conclude

that the two hypotheses that guided the study are accepted.

Bieha District, which occupies 25 % of Sissili province surface area and

homed 11 % of the population of the province in 2002, is a representative

sample of the province. The findings from this study in Bieha District reflect

the real state of the natural resources in the whole Sissili. Natural resources are

degrading at a considerable pace in the province due mostly to human

activities, namely agriculture, grazing, wood fuel requests, hunting and

bushfires.

Recommendations and strategies for further research

Strategies that aim at sustainable management of natural resources

(conservation and restoration) in Sissili province must take into consideration:

a. The reduction of environmental refugees. Evidences showed that rural

to rural population mobility is driven by lack of farming land and

poverty in their provinces, and so large irrigation programmes around

dams may be foreseeable. Initiatives were taken in this way in Sourou,

Bagré, Kompienga, etc. but further efforts must be done to reinforce

irrigation programmes.

116

b. Intensification of crop productivities without large expansion of farm

lands. Sensitization programmes on water, soil and forest conservation

may be widely initiated in the area.

c. Reduction in overgrazing. The pasture zone creation project in Yallé

village must be resuscitated with large sensitization of the population.

Stalling programmes coupled with fodder cultivation may also be

encouraged.

d. Strategies to find other energy sources. Solar energy, gas and electricity

facilities must be promoted in order to reduce the dependence on wood

as main source of energy especially in towns.

e. Combat against bushfires. It may be possible through sensitizations and

the increase of the number of foresters in the area.

There are potential possibilities to consider which may give additional

strategies for further study upon the conclusion of this research. The detection

and delineation of forest loss and subsequent conversion to farm land in Bieha

district, Sissili province has been determined with satellite imagery. In

addition, an assessment of this land use change has been compiled with GIS

analysis. However, the resulting spatial data yielded from this study offers

prospects for further analysis.

For instance, the mapping of the bushfires may help to understand and

quantify its extension and its impacts on the natural resources. The study can

be possible by using Landsat TM images captured in indicated period during

which the marks of fires are perceptible in the ground. It may also be possible

by using GPS to trace the fires limits in a localised area.

117

The quantification of the wood used for fuel and transformed to charcoal per

year is also an interesting area of investigation. This is possible in collaboration

with the local foresters and the wood and charcoal sellers and transporters. It

may be interesting also to know the species of trees commonly cut to make

charcoal.

The third area of investigation concerns the migration. The exact

number of migrants living in Sissili province is unknown. The exact number

may help to make prediction on the future trend of population growth and

resource allocation and management in the area.

The last area of investigation to be considered in the province for more

sustainable land use is to develop participatory approach to natural resource

management that take into consideration local indigenous knowledge and

interdisciplinary scientific knowledge. The key issue here for resources

management is how to balance conservation values and the need to exploit

these resources to sustain life and economic development.

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