Enjoh_Eunice_FOMBAD_17422_NRM_MSC_Thesis

THE LOSS OF FOREST RESOURCES AND SOCIETAL
ADAPTIVE STRATEGIES IN THE SOUTHERN PART OF
ASHANTI MAMPONG DISTRICT, GHANA
Enjoh Eunice Fombad
March, 2009

THE LOSS OF FOREST RESOURCES AND SOCIETAL
ADAPTIVE STRATEGIES IN THE SOUTHERN PART OF
ASHANTI MAMPONG DISTRICT, GHANA
By
Enjoh Eunice Fombad
Thesis submitted to the International Institute for Geo-information Science and Earth Observation
(ITC), The Netherlands and Kwame Nkrumah University of Science and Technology (KNUST),
Ghana in partial fulfilment of the requirements for the degree of Master of Science in Geo-
information Science and Earth Observation, Specialisation: GIS for Natural Resources Management
Thesis Assessment Board
Prof. Dr. Ir. Eric M.A. Smaling, Chairman (ITC)
Dr. B. E. K. Prah, External Examiner (Kumasi Polytechnic)
Ir. L. M. van Leeuwen, Supervisor (ITC)
Prof. S. J. Quashie-Sam, Supervisor (KNUST)
Prof. S. K. Oppong, Internal Examiner (KNUST)
Supervisors: Prof. S. J. Quashie-Sam and Ir. L. M. van Leeuwen
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS AND
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA
KNUST

THE LOSS OF FOREST RESOURCES AND SOCIETAL ADAPTIVE STRATEGIES IN THE SOUTHERN PART OF ASHANTI MAMPONG DISTRICT, GHANA
Disclaimer
This document describes work undertaken as part of a programme of study at the International
Institute for Geo-information Science and Earth Observation and Kwame Nkrumah University
of Science and Technology. All views and opinions expressed therein remain the sole
responsibility of the author, and do not necessarily represent those of the two institutions.

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Abstract
Most rural dwellers in Africa rely on the surrounding natural resources for their subsistence. There
has been a loss/decline of natural resources in most of rural Africa due to rapid population increases
that has led to agricultural and settlement expansions. The rural population therefore, do not get
enough natural resources to sustain their livelihoods. Studies on how fringe communities are coping
with the loss/decline of natural resources, on which they rely, are very scarce. This research sought to
assess the loss/decline of forest resources in the southern part of Ashanti Mampong District (SPAMD)
in Ghana and to understand the coping adaptive strategies that the fringe communities have put in
place. The research used satellite image analysis by applying remote sensing and GIS techniques to
locate communities which have recently lost tree resources as a result of land cover changes. Socio-
economic surveys were carried out using participatory approaches, focus group interviews with semi-
structured questionnaires and household survey with structured questionnaires on 33% households
each in 4 communities which gave a total of 182 households. Appropriate softwares were used for
data analysis. The results show a loss of 19.84% area covered by tree resources and a gain of 16.42%
and 3.43% area covered by grass/farm and built-up/bare lands respectively between 2002 and 2007.
The quantities of forest resources to fringe communities had decreased relatively from 80% in 2002 to
20% in 2007. Cola nuts and wild cocoyam were extinct. These make the communities move further
away from their settlement to get forest resources that could sustain their livelihood and at times buy
to supplement and substitute. The loss/decline of forest resources also affected the soil fertility and
climatic conditions which has negatively impacted on their agricultural yields. This has made them
resort to use of fertilizers or completely abandon farming as main occupation and turn to other
livelihood options. Several coping strategies were put in place by the fringe communities which were
however, not sustainable to the natural resources base and to the communities’ natural resources
needs. More sustainable strategies like tree planting and the domestication of some of the forest
resources should be encouraged for long term benefit to both the fringe communities and the natural
resources base.
Key words: Land cover change, forest resources, fringe communities, adaptive strategies

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Acknowledgements
I am very grateful to the Lord Almighty for His Grace, Mercy and Guidance throughout this study. I
am indebted to my supervisors; Ir. L. M. van Leeuwen, Prof. S.J. Quashie-Sam, Mr. John Ayer and
Dr. A. A. Duker for their constructive comments which have helped shaped this thesis. I owe a lot of
gratitude to ITC and the Tropical Biology Association for the scholarship awarded me. I am equally
thankful to the Director of the Natural Resources Programme, Dr. M. Weir and the coordinators of the
GISNATUREM, Ir. L. M. van Leeuwen and Prof. S. K. Oppong for all their support and guidance
during my study both in the Netherlands and Ghana. I appreciate the contributions of every ITC staff
especially Ms. Ir. Liza Groenendijk and Dr. Mike McCall for their constructive criticisms during the
proposal development and their colleagues at KNUST especially Dr. E. M. Osei Jnr., Mr. J. Quaye-
Ballard and Ms VNA Asare.
I am grateful to the staff of the forestry commission office in Ashanti Mampong District especially to
Mr. R. Twum-Damoah, Mr. A. Buabeng and Mr. K. Kyeremeh and all those who served as local
translators for making my fieldwork a success. I am grateful to the community members for their
hospitality and cooperation during the data collection. I extend my appreciation to the
GISNATUREM 2008 and all colleagues of the natural resources department, ITC 2008 who
contributed in one way or the other to make the course work and proposal development a success. I
say thank you to Mr. K. Asubonteng, Mr. D. Tutu and Mr. L. Addae-Wireko for all the words of
encouragement.
I express my gratitude to the Asiedu-Kumi family for making me feel at home while in Ghana. I say a
big thank you to the Fombad’s family for the support and encouragements throughout my studies.
May God richly bless all who contributed to the success of this work.

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Dedication
Dedicated to the FOMBAD’s family

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Table of contents
ABSTRACT ..................................................................................................................................................................................................... i
AKNOWLEDGEMENT.................................................................................................................................................................................. ii
DEDICATION................................................................................................................................................................................................. iii
TABLE OF CONTENTS................................................................................................................................................................................. iv
LIST OF FIGURES AND TABLES................................................................................................................................................................. vi
LIST OF ABBREVIATIONS.......................................................................................................................................................................... vii
1. INTRODUCTION........................................................................................................................... 1
1.1. Background.............................................................................................................................. 1
1.2. Problem statement and justification........................................................................................ 2
1.3. Research objectives and questions.......................................................................................... 3
1.3.1. Specific objectives ........................................................................................................... 3
1.3.2. Research questions.......................................................................................................... 4
1.4. Thesis outline .......................................................................................................................... 4
1.5. Research approach................................................................................................................... 4
2. CONCEPTS AND DEFINITIONS ................................................................................................. 6
2.1. Tree, forest and tree resources loss ......................................................................................... 6
2.2. Drivers/causes of land cover changes and forest resources loss............................................. 6
2.3. Effects of land cover changes and forest resources loss ......................................................... 7
2.4. Responses/adaptations to deforestation and forest resources loss .......................................... 8
2.5. Image processing and classification........................................................................................ 9
2.5.1. Image pre-processing and classification ........................................................................ 9
2.5.2. Change detection............................................................................................................. 9
2.6. Participatory rural appraisal in natural resources.................................................................. 10
3. MATERIALS AND METHODS .................................................................................................. 11
3.1. Location and description of the study area ........................................................................... 11
3.1.1. Vegetation...................................................................................................................... 12
3.1.2. Climate, topography and drainage ............................................................................... 12
3.1.3. Demographic characteristics and economic activities ................................................. 13
3.1.4. Impact of human activities ............................................................................................ 13
3.2. Data sources .......................................................................................................................... 13
3.3. Satellite image analysis ......................................................................................................... 14
3.3.1. Image pre-processing.................................................................................................... 14
3.3.2. Image classification....................................................................................................... 14
3.3.3. Accuracy assessment ..................................................................................................... 15
3.3.4. Change detection........................................................................................................... 15
3.4. Socio-economic surveys........................................................................................................ 16
3.4.1. Community selection ..................................................................................................... 16
3.4.2. Participatory mapping and focus group discussions.................................................... 16
3.4.3. Selection of households and questionnaire administration .......................................... 17
4. RESULTS...................................................................................................................................... 19
4.1. Land cover classification in SPAMD.................................................................................... 19

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4.1.1. Classified land cover maps of 2002 and 2007 in SPAMD.............................................19
4.1.2. Accuracy assessment......................................................................................................21
4.1.3. Land cover change between 2002 and 2007 in SPAMD................................................21
4.2. Forest resources collected and collection areas of fringe communities.................................24
4.2.1. Forest resources collected by fringe communities per location in SPAMD..................26
4.2.2. Forest resources collection areas per community in SPAMD.......................................26
4.3. Changes in forest resources to fringe communities and effects in SPAMD..........................28
4.3.1. Changes in availability of forest resources in SPAMD .................................................28
4.3.2. Changes in quantity of forest resources in SPAMD ......................................................29
4.3.3. Effects of forest resources loss/decline on fringe communities in SPAMD...................29
4.4. Adaptive strategies to forest resources loss/decline and effects in SPAMD .........................30
5. DISCUSSIONS ..............................................................................................................................31
5.1. Land cover classification in SPAMD.....................................................................................31
5.1.1. Land cover maps of 2002 and 2007 and change map between 2002-2007 ...................31
5.1.2. Accuracy assessment......................................................................................................32
5.2. Forest resources collected and collection areas of fringe communities.................................33
5.3. Changes in forest resources to fringe communities and effects in SPAMD..........................33
5.3.1. Changes in availability and quantity of forest resources ..............................................33
5.3.2. Effects of the loss of forest resources on fringe communities........................................34
5.4. Adaptive strategies to forest resources loss and effects in SPAMD......................................35
6. CONCLUSIONS AND RECOMMENDATIONS.........................................................................36
6.1. Conclusions............................................................................................................................36
6.2. Recommendations ..................................................................................................................38
REFERENCES.......................................................................................................................................39
APPENDICES........................................................................................................................................46
Appendix 1: Questionnaires used for socio-economic survey...........................................................46
Appendix 1a: Focus group questionnaire......................................................................................46
Appendix 1b: Household questionnaire.........................................................................................49
Appendix 2: Village sketch maps ......................................................................................................52
Appendix 2a: Adidwan village sketch map ....................................................................................52
Appendix 2b: Atonsuagya village sketch map................................................................................52
Appendix 2c: Damang village sketch map.....................................................................................53
Appendix 2d: Sataso village sketch map........................................................................................53
Appendix 3: Classification accuracy assessment...............................................................................54
Appendix 4: Ghana projection system...............................................................................................54

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List of figures and tables
Figures
Figure 1.1: Research approach....................................................................................................... 5
Figure 3.1: Location of the study area............................................................................................ 12
Figure 3.2: Flow of Methods.......................................................................................................... 18
Figure 4.1: Land cover map of Landsat ETM+ 2002 in SPAMD.................................................. 19
Figure 4.2: Land cover map of Landsat ETM+ 2007 in SPAMD.................................................. 20
Figure 4.3: Land cover change map of Landsat ETM+ 2002-2007 in SPAMD ............................ 21
Figure 4.4: Area covered (Ha) by the different land cover classes in 2002 and 2007 in SPAMD ....... 22
Figure 4.5: Area change (%) per land cover class between 2002 and 2007 in SPAMD ............... 23
Figure 4.6: Percent respondents who expanded farms and located new farms in SPAMD........... 23
Figure 4.7: Land cover where farms expanded and new farms were located in SPAMD ............. 24
Figure 4.8: No. of households sampled with respect to total
households per community in SPAMD......................................................................... 24
Figure 4.9: Tree resources presence and ownership per studied
community in SPAMD.................................................................................................. 25
Figure 4.10: Percent respondents with trees on farms per community in SPAMD ....................... 25
Figure 4.11: Forest resources collection areas in around 4 communities in SPAMD ................... 27
Figure 4.12: Proportion of respondents who said
forest resources are and are not extinct........................................................................ 28
Figure 4.13: Percent respondents against forest resources that are extinct in SPAMD................. 28
Figure 4.14: Comparison of past and present quantities of forest resources in SPAMD .............. 29
Tables
Table 3.1: Description of land cover classes in SPAMD........................................................15
Table 4.1: Change matrix ............................................................................................................... 22
Table 4.2: Average quantity (%) of forest resources collected in
2002 and 2007 per location in SPAMD ......................................................................... 26
Table 4.3: Adaptive strategies and effects in SPAMD................................................................... 30

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List of abbreviations
AOI Area of Interest
DN Digital Number
ETM Enhanced Thematic Mapper
FAO Food and Agricultural Organisation of the United Nations
GDP Gross Domestic Product
GIS Geographic Information System
GMLC Gausian Maximum Likelihood Classifier
GPS Global Positioning System
Ha Hectare
ITC International Institute for Geo-information Science and Earth Observation
ITTO International Tropical Timber Organisation
IUCN International Union for the Conservation of Nature
Km2
Square kilometre
KNUST Kwame Nkrumah University of Science and Technology
MS Microsoft
NRMP Natural Resources Management Program
NTFP Non Timber Forest Product
PRA Participatory Rural Appraisal
RMSE Root Mean Square Error
RS Remote Sensing
SPAMD Southern Part of Ashanti Mampong District
SPSS Statistical Package for Social Sciences
USDA United States Development Agency
USGS United States Geological Survey
m3
Cubic Metre

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1. INTRODUCTION
1.1. Background
Forests ecosystems have diverse functions of production, regulation, protection and recreation. They
provide several harvestable and non harvestable goods and services to humans. They have global
importance in climate change adaptation and mitigation. Most importantly, people in Africa particularly
depend on forests and forestry activities for their livelihoods (FAO, 2006). A large proportion of the
population utilize forest resources which serve as important components of livelihoods (Marfo et al., 2002)
and probably prevent people from slipping into deep poverty (Shackleton et al., 2007). Despite this
importance, human domination of the biosphere has greatly altered forest ecosystems, often reducing their
capacity to provide ecosystem functions, goods and services (DeFries et al., 2004; Kremen, 2005). Humans
face the challenge of managing or balancing the inherent trade-offs between immediate needs and
maintaining the capacity of the biosphere to provide goods and services in the long term (DeFries et al.,
2004; Foley et al., 2005). Ghana and many other developing countries, where the majority of the
population relies on natural resources for their livelihood, are faced with balancing immediate human
needs and the long term productivity of the biosphere.
Ecologically, Ghana is divided into a high-forest zone in the south, accounting for about a third of the land
area (8 million hectares), a savannah zone (14.7 million hectares) mostly in the north and a transition zone
(1.1 million hectares) (SFM Tropics, 2005). One third of Ghana’s land area (23,850,000 Ha) was covered
by natural tropical forest (Wagner and Cobbinah, 1993). The total area of forests in the country is
estimated to be between 2.72 and 6.34 million hectares (FAO, 2005a; FAO, 2005c; FAO, 2007).
Agriculture and forestry constitutes the backbone of the Ghanaian economy as it provides 43% of the
Gross Domestic Product (GDP), 50% of export earnings and 70% of total employment (Agyarko, 2001).
The forestry sector alone accounts for 6% of GDP, 11% of export earnings and employs a labour force of
100,000 people (Agyarko, 2001). These values imply agriculture which is usually done at the detriment of
forest resources contributes more to the country’s economy. Local population depend on forest for
products and foods that supplement and complement what is obtained from agriculture lands. It also
provides a wide range of medicines and other products that contribute to health and hygiene (Oduro, 2002).
Deforestation in Ghana as in most parts of the world usually commences with the degradation of well-
stocked forests by excessive (often illegal) logging, slash-and-burn agriculture, mining and quarrying, and
fuel wood collection. Degraded forests are then often completely deforested by wildfire, illegal occupation
and/or land-use changes (ITTO, 2006; SFM Tropics, 2005). Howthorne (1989) and Repetto (1990)
estimated that 78% of Ghana’s tropical forest had disappeared. There has not been recent estimate of
deforestation in Ghana, though IUCN (2006) estimated the deforestation rate in Ghana to be 3% per year.
According to Forest Watch Ghana (2006) Ghana lost 80% (from 8 million Ha to 1.6 million Ha) of its
forest cover between 1900 and 1990. A comparison of satellite images of state managed forest reserves in
1990 and 2000 showed rapid deforestation within the reserves with some losing as much as 90% of forest
cover over the period (Forest Watch Ghana, 2006). Almost all forests have suffered depletion, creating
eroded hillsides in some cases and destroying genetic diversity in others. The estimated average annual
CHAPTER ONE

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area deforested between 1990 and 2000 was 120,000 hectares; the average growing stock in the remaining
forest area is only 49 m3
per hectare (FAO, 2005c). Official estimates suggest that logging is proceeding at
about four million m3
per year which is four times the annual allowable cut (Forest Watch Ghana, 2006). It
is most likely that this rate of deforestation will increase in the future; especially in off-forest reserve areas
where communities live and is under less control. Most of the uncontrolled timber harvesting and
deforestation that took place occurred in the off-forest reserved areas (Osafo, 2005). Much forest lands
outside the forest reserves have been converted to farmland over the past decade (Osafo, 2005; Mayers et
al., 1996; Kotey et al., 1998). Hawthorne and Abu-Juam (1995) reported that most of the forest conversion
activities were intentional, legal and necessary for national development since Ghana has depended on
these lands over the past 60 years for the country’s major export earner, cocoa. At some point in time, the
off-reserved areas provided more timber than the forest reserves (Kotey et al., 1998). Today the area
outside forest reserves is a mosaic of agricultural fields, fallow lands and secondary forest patches and
settlements (Oduro, 2002).
Appiah et al., (2007) reported that poverty-driven agriculture, lack of alternative rural wage employment
other than farming, household population increase levels, and traditional land practices are the four main
causes of deforestation at district level in Ghana. The authors further found that the average total income is
constituted of 60% from agriculture; 38% from forest, while 2% comes from off-farm. Yiridoe and Nanang
(2001) also added that forest product exports, fuel wood energy consumption, cocoa production and food
crop production compete for forest land use or forest products. This is unlike livestock grazing effects that
cause deforestation in some African countries. Owubah et al. (2001) reported that “current statutes provide
few incentives for farmers to engage in sustainable practices since they perceive preservation of
indigenous, economically valuable trees and conservation of forests as having a net cost to them, especially
if compensation is not paid for damage to crops resulting from logging operations of concessionaires”.
Ashanti Mampong District is no exception when it comes to deforestation hence forest resources loss and
its causes. About 61% of the population are farmers and apply slash and burn method. Forests in this
district have been destroyed due to fast depletion of economic trees and medicinal herbs for charcoal
production, fuel wood, and timber operations. Also forest resources are destroyed by uncontrolled bush
fires from the activities of hunters, palm-wine tapers, cigarette smokers and unprofessional method of
charcoal burning. The vegetation has reduced from its original rain forest of the moist deciduous type in
most areas to secondary forests as a result of human activities resulting in loss of flora and fauna. High rate
of deforestation has consequently led to drying up of many rivers in the district as well as the depletion of
economic trees and wildlife (Government of Ghana).
1.2. Problem statement and justification
Ghana’s forestry sector is in crisis since the country suffers from rapid deforestation and destruction of
biodiversity (Forest Watch Ghana, 2006). Even sacred groves and community forests that contributed
immensely to biodiversity conservation are also now under serious threat (World Rainforest Movement,
2002). Ashanti Mampong District is one of the forested areas experiencing the above situation. The total
forested area in the district was 78,200 Ha forest reserve and 133,678 Ha forest outside the reserves in
1990. The rate of decrease in the forest ranges between 3% and 10% with an average of 7% per annum for
the past ten (10) years and the district is now left with about 16,107 Ha forest in reserves and 7,820 Ha
forest in outside forest reserves (Government of Ghana). “Communities and Forest Watch Ghana members

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working at the forest fringe continue to report rapid shrinkage of forests” (Forest Watch Ghana, 2006). It is
likely that the current rate of deforestation hence forest resources loss/decline have affected the living
conditions of the fringe communities in the district (Forest Watch Ghana, 2006). These communities might
have changed their way of life or might have implemented certain conservation practices for valuable
species in order to cope with the current loss/decline of forest resources on which they relied for their
livelihoods.
A review of literature over the past ten years shows that it is only recently that concerns have been raised
on the assessment and documentation of existing and potential prevention and mitigation conservation
strategies (Schwilch et al., 2007). Many studies have been carried out on the direct and underlying causes
of forest degradation and deforestation and its impacts in different parts of the world (Kanninen et al.,
2007, Tucker et al., 2001) and Ghana in particular (Tutu, 2008; Appiah et al., 2007; Asubonteng, 2007;
Sedego, 2007; Yiridoe and Nanang, 2001). The need for research to reflect fringe people’s interests has
also been stressed by Kanninen et al. (2007) while concerns on how to link environmental conservation
and poverty alleviation was also raised by Bandyopadhyay and Singhal (2005). Nkem et al. (2008) and
Studer and Liniger (2007) showed special attention over ongoing adaptations/innovations to climate
change while Maddison (2007) carried out a study on the perception and adaptation of farmers to climate
change in Africa.
Studies on local coping strategies of fringe communities to forest resources loss/decline are scarce. It is
therefore important to understand and consider local adaptive response strategies of fringe communities to
the loss of forest resources for long term success in forest resources management decisions. Thus this
study, which seeks to assess the loss of forest resources and to understand the coping adaptive strategies
which fringe communities have or would like to put in place with the loss/decline of forest resources, will
be important for viable management decisions towards forest resources conservation, poverty alleviation,
livelihood improvement initiatives and the ongoing climate adaptation strategies in Africa and the world at
large.
1.3. Research objectives and questions
The overall objective of this study is to assess the loss of forest resources in the southern part of Ashanti
Mampong District (SPAMD) in Ghana and to understand the coping adaptive strategies that fringe
communities have put in place.
1.3.1. Specific objectives
1. To map major land cover types in the SPAMD.
2. To identify and quantify areas where tree resources have been lost over the period 2002 to 2007 in
the SPAMD.
3. To identify the change in forest resources collection areas of fringe communities.
4. To analyse changes in forest resources and effects on fringe communities.
5. To investigate the adaptive strategies that fringe communities have or would like to put in place to
cope with the loss of forest resources.

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1.3.2. Research questions
1. What are the major land cover types in the SPAMD?
2. How has land cover changed between 2002 and 2007 in the study area and why?
3. What forest resources were and are collected by fringe communities?
4. Where are forest resources collection areas in 2002 and 2007?
5. What are the changes in availability and quantity of forest resources?
6. How has forest resources loss/decline affected fringe communities?
7. What coping strategies have the fringe communities put or plan putting in place as a result of the
loss/decline of forest resources?
1.4. Thesis outline
The report consists of eight (8) sections and include; the introduction, concepts and definitions, materials
and methods, results, discussions, conclusions and recommendations, references and appendices. The
introduction presents an overview of the research problem, formulation and definition of the research
objectives and questions. Following this is section two that clarified concepts and defines terms within the
field of research used in the report. The third section of the thesis deals with the materials and methods
employed in the implementation of the research to attain the objectives highlighted in the introduction. It
equally highlights on the description of the study area, the software and data used in the research. Section
four presents the results obtained following the implementation of the methods. The results are arranged
according to the specific objectives outlined in the introduction section. Discussions which put the results
in their proper perspective are dealt with in section five. Next is section six, the conclusions which provide
answers to the research questions asked and recommendations. References and appendices are addressed in
sections seven and eight respectively.
1.5. Research approach
The research started with extensive literature review of existing knowledge concerning natural resources
and its local management in order to identify existing knowledge gaps. This was followed subsequently by
identification of a research problem, formulation and definition of the research objectives and questions.
Then a suitable site for the study was selected. After, there was an assessment of the data needs and
availability. This was followed by a reconnaissance visit to gather more information on the study area. The
major steps taken to reach the research problem, research objectives and questions, implementation,
results, discussions, conclusion and recommendations are summarized in Figure 1.1 below.

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Figure 1.1: Research approach
Identification of research problem, formulation and
definition of research objectives and questions
Adaptations to
natural resources
loss
Socio-economic
approaches in natural
resources management
Literature review
Deforestation /
forest
resources loss
Land cover changes
& change detection
techniques
Selection of suitable study area
Logistical and data needs assessment
Reconnaissance survey
Image pre-processing
Land cover ground truthing
Visual image
interpretation
Image classification and analysis
Selection of study communities
Fieldwork
Data entering, processing and analysis
Results and discussion
Conclusion and recommendations

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2. CONCEPTS AND DEFINITIONS
2.1. Tree, forest and tree resources loss
A tree is “a woody perennial with a single main stem, or, in the case of coppice, with several stems, having
a more or less definite crown; includes bamboos, palms and other woody plants meeting the above criteria”
(FAO, 2005c). Tree resources can be considered a basic and integral part of forest resources. Several
definitions have been given for the term “forest” based on concerns and interests of different people. It is
worth noting that all definitions stress trees to constitute a forest depending on certain tree minimum value
of parameters like height, area and crown cover. Tucker et al. (2001) in their work did a review of different
definitions of the term forest and among the consensus arrived at, said that plantations and young forest
fallow re-growth are generally not counted as forests. Forest could be defined as “land of more than 0.5 Ha
with trees higher than 5 metres and a canopy cover of more than 10% or trees able to reach these thresholds
in situ” (Kanninen et al., 2007). This may include oil-palm plantations, bamboos, or fruit orchards if they
match the definition but banana plantations would not qualify as forests, though the constraints of
minimum height, area and crown cover might be satisfied (Neeff et al., 2006). This implies oil-palm
plantations, bamboos, or fruit orchards are considered as tree resources. In order to avoid the confusion of
what is actually termed forest, this research considered tree resources in the image classification rather
than forest. The defined parameters, implies that not every area with trees can be termed forest unless they
meet certain values for above mentioned criteria. Loss of tree resources will lead to loss of other forest
resources since the tree resources create favourable microclimate for the other resources as well as serve as
habitat for wildlife (animals). Loss of tree resources occur when areas previously covered by tree resources
are converted to other land cover types (adapted from definition of deforestation by Lambin et al., 2003).
2.2. Drivers/causes of land cover changes and forest resources loss
Bandyopadhyay and Singhal (2005) reported that about 350 million people in tropical regions, including
60 million indigenous people were estimated to live in or near forest areas and are directly dependent on
those forests for their subsistence and survival needs. Changes in land cover may have subsequent spatial
and temporal positive or negative impacts. Sunderlin et al. (2005) attributed decreasing forest density and
cover not only with growing population densities and higher market demands but also with changing types
of forest use by local population as they pass from hunting and gathering to sedentary agriculture. Rapid
human population growth leading to pressure on land resources has been noted to have negative
contribution by most literature on land use/cover change (Verburg et al., 2006, Wright, 2005).
The underlying causes of deforestation hence tree resources loss are generated outside the forestry sector,
and alternative land uses tend to be more profitable than conserving forests (Kanninen et al,. 2007).
Barbier and Burgress (2001) showed in their study that agricultural expansion is the main factor
determining forest resources loss in most areas of the tropical world. Hartemink et al. (2008) found that
forests hence trees are cleared for the expansion of cropland, wood extraction, or infrastructure expansion.
CHAPTER TWO

7
Lambin et al. (2001) concluded that population and poverty alone does not constitute the sole and
underlying causes of land cover change but people’s responses to economic opportunities as mediated by
institutional factors drive land cover changes and hence forest resources loss. Lambin et al. (2003)
attributed land-use change to be driven by synergetic factor combinations of resource scarcity leading to an
increase in the pressure of production on resources, changing opportunities created by markets, outside
policy intervention, loss of adaptive capacity and changes in social organization and attitudes.
In Ghana like most parts of Africa, land cover changes thus loss of forest resources are driven by excessive
logging, unsustainable agricultural practices, bush burning, mining and quarrying, settlement and related
infrastructure construction. Agyarko (2001) attributed the underlying causes of forest degradation in
Ghana to lack of stack holder participation in forest management, low forest taxes and fees regime, weak
institutional structures, lack of investment in the forestry sector, population pressure, failure in policy
interventions and poor institutional coordination. “Only the state has authority to issue permits for
harvesting of timber subject to the consent of the landowners” (Osafo, 2005). Farmers’ exclusion from
timber revenue (i.e. having no stake in trees that were to be sold to timber firms) made them view the
forest as an impediment to the expansion of cocoa farming (Boni, 2006). Farmers prepared and still
prepare their farms by slashing and burning all or most vegetation cover: they have had no interest in
preserving timber and have thus destroyed most trees growing spontaneously on their farm (Boni, 2005).
2.3. Effects of land cover changes and forest resources loss
Forests play an important role in the economy of many nations including Ghana. They provide ecosystem
goods and services such as fuel wood, wildlife (bush meat), timber, medicinal plants, shelter, furniture,
portable water supply sources and fruits thus providing livelihood for over 2.5 million people
(Agyarko, 2001). Apart from these they provide suitable microclimate and fertile soils for food and cash
crops as well as protection of watershed and rivers. As a result of land cover changes and forest resources
loss, most of these goods and services have been altered. Tropical deforestation leads to one-fifth of total
annual carbon emissions accounting for global warming (Kanninen et al., 2007). Negative changes in land
cover reduce the ability of the earth to continuously produce goods and services on which humans survive
in the long term (Dale et al., 2000). Dye (2003) reported that most land cover change lead to a net loss of
vegetation from the landscape and disturbance of the underlying soil resulting in the release of carbon
stored in vegetation and soils to the atmosphere adding up to atmospheric concentrations and potentially
leading to global warming. Kiamowitz and Angels (1998) ascertained that “factors affecting deforestation,
the interactions between them and the magnitude of their effects vary significantly from one location to the
other”. In Ghana, Boni (2006) indicated that deforestation has led to the decreased availability of non-
timber forest products (firewood; mushrooms; medicinal plants; pestles; wood and canes for craft) while
Tutu (2008) found out that the conversion of forests to other land cover types indirectly affected climatic
conditions. This adversely affects the frequency and periodicity of rainfall and hence a decline in crop
yields. Loss of forest resources has led to hardship on the local population especially the fringe
communities who derive direct benefits from the goods and services provided. The loss of tree resources
lead to lack of tree protection during potent thunderstorms, provokes devastation of houses in villages and
of crops on farms (Boni, 2006). Deforestation thus the loss of tree resources leads to the loss of tree
regulatory function of the environment, increasing floods and erosion hazards, reducing soil fertility,
contribute to loss of animals and jeopardize the sustainable provision of goods and services (FAO, 1999).

8
Several studies on the major causes and effects of land use/cover changes have been carried out on the
forest ecosystems of Ghana (Hawthorne and Abu-juam, 1995; Mensah, 2002). Recently, Tutu (2008) used
remote sensing techniques to assess the effects of land use /cover change on ecosystem services while
Asubonteng (2007) and Sedego (2007) used remote sensing techniques to assess causes and implications
of land use/cover changes in the Ashanti region of Ghana.
2.4. Responses/adaptations to deforestation and forest resources loss
Adaptation can be defined as a deliberate change in system design, function or behaviour either in
response to or anticipation of changing conditions and/or external event (Enquist, 2007). An adaptation
can either be reactive or autonomous when it is in response to changing conditions or
proactive/anticipatory when it is in anticipation of changing conditions. Adaptation is said to be successful
if the level of system services and functionality is appropriately maintained or restored (Enquist, 2007).
Soest and Lensink (1997) found that explicitly stated compensation function which closely follows
developments might be a better measure for long term forest conservation and improvement. In Ghana
attempts to address the issue of deforestation hence forest resources loss in southern Ghana since the mid
1990s has been tried through the Protected Areas Development Program and the Forest Resource Creation
Project sponsored by the Ghanaian government, NGOs and timber firms (Boni, 2006). Another important
strategy put in place was the Natural Resources Management Program (NRMP) aimed to “protect,
rehabilitate and sustainably manage national lands, forests and wildlife resources through collaborative
management and to sustainably increase the incomes of rural communities who own these resources”
(Agyarko, 2001). Some other measures put in place for sound management and restoration of degraded
forest reserves by the government of Ghana is the “modified taungya” system whereby community
members are given a piece of degraded or deforested lands to plant and care for trees at the early stage as
they cultivate their crops. Such members are given a percentage of the proceeds when the trees are mature
in addition to their crops they get from fertile soils during the first few years. This is also a means to
encourage the community member to guard the plantation until maturity. Also plantations of native tree
species or fast-growing exotic species have been established by the government. The domestication of
some animal species like the cane rat; snail rearing etc are other strategies put in place by Tropenbos
International and other environmental organisations (Tropenbos International, 2005). Despite these
strategies, research has not been carried out to understand fringe communities’ coping adaptive strategies
to the loss of forest resources which this research will address.

9
2.5. Image processing and classification
2.5.1. Image pre-processing and classification
Image pre-processing (or restoration and rectification) is the initial activity in image analysis essential for
correction of sensor and platform geometric and radiometric distortions present in image data in order to
derive accurate information (Kerle and Huurneman, 2004; Mather, 1999). Image pre-processing is
imperative in a case where the study has to compare image data sets from two or more dates so that
information derived are true representation of the real world situation. Geometric correction adjusts for
several distortions including perspective of sensor optics, terrain relief, curvature and rotation of the earth,
altitude, velocity and motion of the platform (Canada Centre for Remote Sensing, 2003; Lillesand and
Kiefer, 1994). The accuracy is usually conveyed in terms of root-mean-square error (RMSE) (Tardie and
Congalton, 2002?). Radiometric correction removes undesired influence of systems noise and atmospheric
interference due to variations in scene illumination, viewing geometry, atmospheric conditions, sensor
noise and response from the image brightness values (Lillesand and Kiefer, 1994).
Kerle and Huurneman (2004) defined image classification as the selection and preparation of the image
data, definition of clusters in the feature space, selection of classification algorithm and validation of the
results. Image classification assigns individual pixels in an image to a class based on its feature vector by
comparing it to predefined clusters in the feature space (Canada Centre for Remote Sensing, 2003;
Lillesand and Kiefer, 1994). Gelens et al. (2008) differentiated two commonly used classification methods.
These are the pixel-based and the object-based classifications. The pixel-based classification looks at the
spectral signatures on a pixel-by-pixel basis without incorporating any contextual information while the
object-based deals with objects which are groups of more or less homogenous pixels forming an object
with distinct boundaries. Depending on the level of operator/analyst intervention, pixel-based classification
can be supervised or unsupervised.
2.5.2. Change detection
This involves detection of change in the form of location, extent and sometimes identification (Muttitanon
and Tripathi, 2005). Lu et al. (2004) defined change detection as the process of identifying differences in
the state of an object or phenomenon at different times. Tardie and Congalton (2002?) defined it as “a
technique used in remote sensing to determine the changes in a particular object of study between two or
more time periods”. Change detection also called “delta classification” (Coppin et al., 2004) ranges from
visual comparison to detailed quantitative approaches (Wickware and Howarth, 1981). Stringa (2000)
concluded that pixel-based change detection techniques was more advantageous because it is simple by
making real-time application possible unlike region-based with more robust results to false alarms
introduced by noise. Lu et al. (2004) found post-classification and image differencing most common and
practically used for change detection. The post-classification comparison technique is based on classified
images meaning that classification accuracy is very crucial for real change detection. It is also time
consuming. Its advantages lie in its provision of a change matrix and the reduction of external impact from
atmospheric and environmental differences between multi-temporal images. Image differencing is simple

10
and results easy to interpret. It is however difficult to determine a suitable threshold (which is based on
complex statistical analysis or selected based on experience) to identify change areas. Another
disadvantage is that it does not provide a change matrix.
A comparison of change detection techniques by Lu et al. (2004); Tardie and Congalton (2002?);
Wickware and Howarth (1981) revealed the post-classification change detection method which involves a
per-pixel comparison of land cover classes for two successive dates to be most suitable for detecting land
cover change. This was used for this study.
2.6. Participatory rural appraisal in natural resources
Participatory Rural Appraisal (PRA) is a growing combination of approaches and methods that enable
rural people to share; enhance and analyze their knowledge of life and conditions, to plan, act, monitor and
evaluate (FAO, 2005b). PRA approaches among others include participatory mapping, focus group
interviews, household survey and uses one or a combination of structured questionnaires, semi-structured
and unstructured questionnaires.
Participatory mapping can be defined as a process in which local actors create representation of local
knowledge of space and engage in analysis of objects, relationships and issues (Minang, 2003).
Community-based maps provide a way of renewing local commitment to governing local exploitation of
resources. It further can help indigenous peoples to defend themselves, their territory, and their resources
(Alcorn, 2000). Blay et al. (2007) showed that involvement of local knowledge and people in activities
aimed at conservation and poverty alleviation are successful. Participatory mapping equally builds and
enhances confidence of the community members on the researcher and makes them willing to provide
information during interviews. Participatory mapping is appropriate in assessing changes in resources with
time, particularly where historical data are unavailable. It has equally been shown to be a better way of
mapping or assessing common pool resources which require consensus in determining their extent (Kalibo
and Medley, 2007 and Mapedza et al., 2003). Participatory mapping enhances communities’ interest in
involvement in development projects (Ericson, 2006; Abbot et al., 1998) and create greater awareness on
forest resources depletion.
PRA has been successfully used in many projects involving natural resources by several authors such as
Minang and McCall (2006); Aynekulu et al. (2006); Balram et al. (2004); McCall (2004); Anau et al.
(2003); Rambaldi and Callosa-Tarr (2002) and Barrow et al. (2000).

11
3. MATERIALS AND METHODS
The research combined satellite image analysis and GIS techniques with participatory rural appraisal
methods. The satellite image analysis and GIS techniques were used to locate communities which have
recently lost tree resources and to understand land cover changes. Then socio-economic surveys using
participatory mapping, focused group interviews with semi-structured questionnaires and household survey
with structured questionnaires were conducted to understand the coping strategies on selected communities.
Seidenberg and Kias (2003) successfully combined household surveys with remote sensing to analyse
changes in shifting cultivation practices and livelihood strategies and the impact on deforestation.
3.1. Location and description of the study area
The study was carried out in the off-reserved areas of the southern part of Ashanti Mampong District
(SPAMD) in Ghana. This site was selected because it has been found that forest fringe communities in the
district depended entirely on the forests and its resources for their livelihood as the district does not have
any major industry that employs the population. Also it used to be high forest which is now degraded and
deforested which certainly was accompanied with loss of other forest resources that are indispensable for
the communities’ livelihood. It is one of the districts in Ghana where not many studies have been carried
out and in particular studies on the loss of forest resources and societal coping strategies. The southern part
particularly has been considered for this study because it still had some tree resources in the recent period
considered. The study focused on off-reserved areas since fringe communities were willing to provide
information on them than the forest reserves where sometimes they collect forest resources illegally.
However, it is worth noting that deforestation in the forest reserves equally affect the fringe communities
both directly (i.e. availability of non timber forest products (NTFPs)) and indirectly (i.e. drying up of
streams, decreased soil fertility, increased erosion, longer dry season, shorter rainy season). The Ashanti
Mampong District is located in the northern part of the Ashanti region of Ghana. Specifically, the SPAMD
is located within longitudes 1°10’ and 1°35’ West and latitudes 6°55’ and 7°20’ North and covers a total
land area of 76,530 Ha. A map locating the study area within Ghana is given in Figure 3.1. Some
communities in this district are Mampong (the district capital), Kwamang, Nsuta, Damang, Sataso, Beposo,
Kofiase, Atonsu, Asam, Bimma, Birem, Benim, Atonsuagya, Amoamang, Yonso, Adidwan, Sekruwa,
Kyease, Bosomkyekye, Nkwanta and Abuontem.
CHAPTER THREE

12
3.1.1. Vegetation
The vegetation of the district used to be a vast forest with many different species of tropical hardwood
which has high economic value to the nation and the surrounding populations. The vegetation of the area
has been reduced from its original rain forest of the moist deciduous type in most areas to secondary forest
as a result of human activities like tree felling and farming. Vegetation of primary origin can only be found
within a Reserve known as the Kogyai Nature Reserve with a total land area of 11,500 Ha (Government of
Ghana). Other reserves in the district include the Kyrimfa, Ofin Head Water Reserve, Ogun and Adom
Reserves which are mainly made up of Tectonia grandis plantations and together occupy an area of 16,107
Ha. Hunting, logging and other human activities are not allowed in the forest reserves though these are
illegally done. The forest and farmlands have been destroyed due to fast depletion of trees for charcoal
production using unprofessional methods, poor farming practices, timber operations and bush fires. The
district is experiencing fast rate of depletion of economic trees and wildlife resources (Government of
Ghana).
3.1.2. Climate, topography and drainage
The climate lies within the west semi-equatorial zone and marked by a double maxima mode rainfall
between April and September with a mean annual rainfall of between 800 and 1500 mm. The highest point
Figure 3.1: Location of the study area
Landsat ETM+ 2007 (4, 3, 2)
/
0 5 10 15 202.5
Kilometers
Sataso
Adidwan
Atonsuagya
Damang

13
is 2,400 m whilst the lowest point is 135 m above sea level. The district is fairly drained by several streams
and rivers, notably Afram, Sene, Sasebonso and Kyirimfa.
3.1.3. Demographic characteristics and economic activities
About 70% of the population are rural in the Mampong Municipality and as at 2006 it was 155,755 as
against 143,206 of the 2000 Population and Housing Census figure. This gives a population increase of
8.06% over the six years period leading to increased pressure on forest resources. In general the land is
fertile for all farming activities because of its reserve nature and watery state (Government of Ghana). This
in addition to the rainfall pattern which supports two cropping seasons has attracted a lot of farming
immigrant population to the area with most settlers coming from the three northern regions of Ghana with
about 61% of the population being farmers by slash and burn methods. However, the fast depletion of the
forest resources has made the district suffer from decreased soil fertility and hence declining yields in
agricultural produce (Government of Ghana). The destruction of farmlands has caused a falling trend in
food production. The district used to be the main source of food supply.
3.1.4. Impact of human activities
Inappropriate and excessive use of chemicals in farming and fishing including farming along riverbanks are
some of the human activities, which impact negatively on the environment. Many rivers in the district are
drying up because of excessive evaporation due to the high rate of deforestation especially along the river
banks. The rate at which the vegetation is being degraded leaves much to be desired. Pragmatic measures
recommended include public education on modern farming practices, effects of bushfires, pollution of land
and water bodies through the use of chemicals. Reforestation was also encouraged in the degraded areas.
Trees are also to be planted along riverbanks to preserve them while farming along the riverbanks is
discouraged (Government of Ghana). The forestry commission has succeeded in sensitizing the
communities on the negative effects of bushfires that has improved the vegetation in the district in recent
years (Personal communication).
3.2. Data sources
Landsat satellite images (acquired in March 2002 and May 2007) were used because of its availability and
absence of cloud compared to other satellite images. The images were obtained from United States
Geological Survey (USGS) through the ITC geo-data source. Generated topographic maps (scale of
1:25,000) for the different communities were used for participatory mapping of forest resources collection
areas in 2002 and 2007 in the communities. Semi-structured questionnaires were used for focused group
discussions, village sketch map for household selection, structured questionnaires for household surveys
and information from other informal discussions.

14
3.3. Satellite image analysis
The recent five year interval (2002-2007) is selected because it was easier for community members to
remember things that happened. Coppin et al. (2004) reviewed that in most cases a minimum time interval
of three years was required to detect changes as well as a one-year interval to detect forest from non-forest.
Given the population pressure and rapid development taking place in the study area, changes in areas
covered by tree resources to other cover types were detected over the five year period.
3.3.1. Image pre-processing
Prior to using the Landsat ETM+ images of 2002 and 2007, they were pre-processed. The Landsat 2002
from the ITC archive was already geo-referenced to the Ghana projection system (given in Appendix 4).
The 2002 image was used to geo-reference and re-sample the 2007 image using the nearest neighbour re-
sampling algorithm to preserve the original image radiometry (Serra et al., 2003). The RMSE of 0.03
produced was less than the 0.5 pixels recommended for Landsat image (Yuan and Elvidge, 1998; Lunetta
and Elvidge, 1998; Townshend et al., 1992). The nearest neighbour re-sampling method assigned the
Digital Number (DN) value of the closest original pixel to the new pixel without changing it thus retaining
all spectral information, which makes the re-sampled image efficient in classification (Kerle and
Huurneman, 2004). Geo-referencing and re-sampling were aimed at correcting and adapting the 2007
image geometrically to make sure that it had the same spatial resolution (30m*30m) and projection as the
2002 image. This was necessary since the two images had to be compared and the 2007 image had to be
combined with other datasets like towns, roads, and rivers etc which were already in the local coordinate
system. They were also used to generate topographic maps used for participatory mapping exercise. A
subset of the study area was made from both the 2002 and 2007 images. Then the forest reserves within the
study area were masked to have only off-reserved areas. An overlay function was performed to verify that
the images overlapped exactly. Erdas Imagine 9.1 was used for processing of all remote sensing data while
ArcGIS 9 was used for all GIS processes and the production of maps.
3.3.2. Image classification
A visual interpretation was done using the band combination RGB 4, 3, and 2 on the 2007 image to
determine the different spectral signatures represented by the different colours identified on the image.
Points representing the identified spectral classes were made on unchanged areas from 2002 and codes
assigned making sure that equal samples of ground truth points were collected for all the spectral
signatures. These points were identified in the field using the go to function of the iPaq and Global
Positioning System (GPS).
Field survey was undertaken for ground truth data collection using an iPaq and GPS. This data were
divided into training sample data and validation data using random process in MS Excel by generating a
field of even and odd numbers beside the sample points. Sample points with even numbers were used for
training and the odd numbers used for accuracy assessment. The training sample data was used for
supervised classification and the validation data was used for accuracy assessment. The Gausian Maximum
Likelihood Classifier (GMLC) algorithm was used for the classification based on its advantages of

15
quantitatively evaluating both the variance and correlation of a category of spectral response patterns when
classifying an unknown pixel (Shrestha and Zinck, 2001; Lillesand and Keife, 1994) and the fact that
several studies have effectively used it to classify land cover from remotely sensed data (Munroe et al.,
2004; Braimoh and Vlek, 2003; Seidenberg et al., 2003). Supervised classification utilizes an analyst’s
prior knowledge of a landscape, gathered through a combination of fieldwork, aerial photography
interpretation, and personal experience (Mausel et al., 1990). This prior knowledge from fieldwork was
used to select homogenous areas (called training sites) within the image that has the thematic information to
be extracted. The classification was done using the false color composite band combination RGB 4, 3, and
2 which depicts vegetation in shades of red. Classification was done based on image pixel colors for areas
that have not changed for the 2002 and 2007 images. This is because it was noticed that a lot of changes in
land cover had taken place over the five years period from visual image comparison. Three cover classes as
described in Table 3.1 relevant for this study were used to simplify change analysis as suggested by Munroe
et al. (2004). This is further justified by the fact that it is mainly aimed at identifying communities which
have lost tree resources. The attribute tables for both classified images were exported to Microsoft (MS)
Excel for further analysis of area covered per class and changes.
Table 3.1: Description of land cover classes in SPAMD
Land cover class Description
Tree resource All areas dominated by trees and shrubs. It includes open canopy wood lots, degraded
wood lots, old fallow lands, palm/orange plantations, bamboo/raphia palm mostly
along waterways and marshy areas, teak/mango plantations and cocoa farms.
Farm/Grass lands All cultivated lands with annual crops and all forms of grasses from creeping to tall
elephant grasses. At times with sparsely distributed trees and shrubs. Also include
covered rocks.
Built-Up/Bare lands All forms of infrastructural development including buildings, roads and areas exposed
by human activities or natural factors. At times with sparsely distributed trees, shrubs
and grasses.
3.3.3. Accuracy assessment
Accuracy of the 2007 classified image was assessed using 103 ground truth points collected in November
2008. The accuracy of the classified 2002 image was not assessed since ground truth data was not
available for that year. However, its classification accuracy was assured by picking training samples from
unchanged areas and by verifying with people of the study area.
3.3.4. Change detection
Post classification change detection was carried out on the classified images to determine changes in the
classes over the study period of 2002-2007. This method was chosen because it is known to be the best for
detecting land cover change compared with other change detection methods (Lu et al., 2004; Mas, 1999;
Wickware and Howarth, 1981). Also, this method has successfully been applied by several tropical studies
which used satellite imageries for example Asubonteng (2007), Rona and Colfer (2006), Braimoh (2004),
Coppin et al. (2004), Braimoh and Vlek (2003), Zomer et al. (2001). This method is most preferred due to

16
its ability to provide a change matrix and reduction of external impact from atmospheric and
environmental differences between multi-temporal images.
The attribute table of the changed map was exported to MS Excel for further analysis; understand land
cover changes and generation of the change matrix.
3.4. Socio-economic surveys
Socio-economic survey was done by combining sample surveys with participatory methods (Malleson et
al., 2008) since it helps to achieve a more complete understanding of livelihood strategies (Ellis, 2000).
These are elaborated in the subsequent sections.
3.4.1. Community selection
Community maps of the SPAMD were overlaid on the change map generated. Those communities within
and/or in close proximity to the areas that have lost tree resources were identified. Together with a
prepared list of criteria (accessibility, safety, willingness of communities to corporate), and with the help
of the district forestry manager and forestry officers who were conversant with the area, communities
which fulfilled the set criteria and experienced loss of tree resources in the period 2002-2007 were
selected. Four communities namely Adidwan, Atonsuagya, Damang and Sataso with remarkable loss of
tree resources were finally selected. PRA techniques of participatory mapping and questionnaires
(Sample questionnaires in Appendix 1) were used to gather information from the selected communities.
3.4.2. Participatory mapping and focus group discussions
At the level of each community or village, the authorities were contacted to gather community members.
After the introduction of the researcher, the community members were told what the research involved. A
focus group questionnaire (Appendix 1a) was administered, followed by participatory village mapping to
illustrate the location of households and forest resources collection area mapping.
During the village mapping exercise, households were represented with square boxes with numbers and a
list of names of the corresponding household heads were written on a separate sheet in order to make sure
all the houses were indicated and the households were easily identifiable during the surveys. School,
church, mosque and other buildings not inhabited by people were indicated with square boxes as well and
the initials like ‘S’ for school, ‘C’ for church, ‘M’ for mosque were used in place of numbers. At the end,
households which could not be interviewed like those that have lived in the community for less than five
years, bereaved, and travelled as well as sacred houses etc were indicated and their numbers not
considered for the random sample draw. After the village mapping, a forest resources collection area map
was generated.
The community members were asked what forest resources they got in 2002 and then currently. They
were asked to indicate where they got the resources they listed for 2002 and currently on the generated
topographic map with easily identifiable bench marks like roads, rivers, neighbouring communities as

17
well as community itself with the landsat ETM+ satellite image of 2007 as back drop. This participatory
mapping exercise gave a sketch map of forest resources collection areas. The sketch map of forest
resources collection area map was validated at the level of the household during the questionnaires
administration where household members were asked to indicate on the same generated topographic map
where they got and are still getting their forest resources as well as the approximate distance they walked
to get the resource in 2002 and currently. Based on the distances walked, the average maximum distances
to forest resources were estimated. A buffer of the average maximum distance from the community was
then generated for 2002 and 2007. An overlay of the buffers and the participatory sketch map per
community was used to produce the forest resources collection area map of 2002 and 2007.
3.4.3. Selection of households and questionnaire administration
Numbers of all considered households were written on pieces of papers and a 33% (giving 50, 109, 10 and
13 households in Adidwan, Atonsuogya, Damang and Sataso respectively) sample was randomly drawn
and administered structured questionnaires. At the household level, only household members above 15
years of age were interviewed and such persons were those who grew up with the household or in cases
where other members of the household above 15 years were available they jointly provided answers to
questions. The questionnaires investigated issues like what and where forest resources they collected and
how the resources have changed in terms of availability, quantity, how the changes affected them and how
they cope with the situation. The quantity was estimated by use of 10 stones which the respondent divided
into 2002 and 2007 quantities on the ground and was counted and recorded accordingly by the researcher.
This was done for each forest resource listed.
For this research, the household was considered as “a group of people living together in the same house
who regularly cook and eat from the same pot” (Malleson et al., 2008). However, as Guyer (1981) pointed
out there are many problems related to defining what constitutes a household in Africa. The above
definition of a household is problematic in the case of Ghana where Fortes (1970) cited by Malleson et al.
(2008) describes that household structure may be strongly influenced by matrilineal kinship ties and
cooked food may be taken by children from the houses in which their mothers live to those in which their
fathers live.
While doing these activities, the researcher walked around for direct observations and had informal
discussions with community members to gather and complete information gathered by participatory
mapping and questionnaires. The questionnaires were coded and entered using the Statistical Package for
Social Sciences (SPSS) and descriptive statistical analysis was conducted. The results were presented in the
form of tables, histogram and pie charts.
A flow of the methods is given in Figure 3.2 below as follows:

18
Landsat ETM+
Image 2002
Landsat ETM+
Image 2007
Image Pre-processing
Supervised Classification
Land Cover
Map 2002
Land Cover
Map 2007
Post Classification
change Detection
Change Map highlighting
Recent tree resources
loss areas
Overlay of community map
Visit to District Forest Manager with
selection criteria
Selection of village
communities
In depth study using PRA Tools (Questionnaires &
Participatory mapping)
List of
forest
resources
Effects of forest
resources loss on
fringe communities
AOI Image of
2002
Identification of communities near /
within tree resources loss area
Local
Coping
Strategies
and Effects
AOI Image of
2007
Study Area
Shapefile
Subset of Area of
Interest (AOI)
Rectified Images
Ground Truth Data
Collection
Training
samples
Accuracy Assessment
Validation
samples
Maps Indicating
Forest Resources
Collection Areas
Change
Marix
Process
Data
Key
Figure 3.2: Flow of methods

19
4. RESULTS
4.1. Land cover classification in SPAMD
4.1.1. Classified land cover maps of 2002 and 2007 in SPAMD
The supervised classification gave two land cover maps with three classes each. These are tree resource,
built-up/bare and grass/farm lands. The land cover maps of Landsat ETM+ 2002 and 2007 are presented in
Figures 4.1 and 4.2 respectively.
CHAPTER FOUR
Figure 4.1: Land cover map of Landsat ETM+ 2002 in SPAMD
/
LEGEND
TREE RESOURCE
GRASS/FARM LANDS
BUILT-UP/BARE LANDS
0 5 10 15 202.5
Kilometers

20
In the 2002 land cover map in Figure 4.1, the tree resources covered the south western and north eastern
portions of the study area while the grass/farm lands were more to the central part and the built-up/bare
lands were found in clusters but scattered almost on every portion of the study area.
It can be seen on the land cover map of 2007 in Figure 4.2 that, the tree resources concentrated more to the
south western parts with some along water bodies in the northern part of the study area. The built–up/bare
lands concentrated more to the northern portions of the study area. The grass/farm lands were more to the
central part, then to the south eastern part followed by the northern part and least in the south western part.
There has been a substantial loss of trees around the water bodies especially in the north western part.
It should be noted that the area had many large rocks which could be clearly seen on the 2007 image due to
exposure from frequent bush fires that removed the vegetation that covered it in the earlier years including
2002. However, the rocks were added to the farm/grass class since they were mostly located within the
farmlands and the spectral reflectance was close to the farm/grass lands class.
Figure 4.2: Land cover map of Landsat ETM+ 2007 in SPAMD
0 5 10 15 202.5
Kilometers
LEGEND
TREE RESOURCE
GRASS/FARM LANDS
BUILT-UP/BARE LANDS
/

21
The classified Landsat ETM+ image of 2007 yielded an overall accuracy of 92.23% when compared on a
pixel by pixel basis with 103 ground truth points from the field. Also the Kappa statistics yielded 84.60%
overall. The accuracy assessment table is given in Appendix 3. Since the accuracy of the classified Landsat
ETM+ image of 2002 could not be assessed statistically, its classification accuracy was ascertained using
local knowledge and validated with information on “no change areas” in the Landsat ETM+ images of
2007 and 2002.
4.1.3. Land cover change between 2002 and 2007 in SPAMD
The change map highlighting the tree resources lost areas from 2002 to 2007 is given in Figure 4.3 below.
A total of 28 communities were found in areas where tree resources were lost between 2002 and 2007.
Four communities with marked tree resources loss based on both visual and pixel-based image analysis
which fulfilled the set criteria of easy accessibility, safety and willingness to cooperate were selected for
Figure 4.3: Land cover change map of Landsat ETM+ 2002-2007 in SPAMD
0 5 10 15 202.5
Kilometers
/

22
further study with PRA approaches. These included Adidwan, Atonsuagya, Damang and Sataso
communities. The sketch maps of these communities are given in Appendix 2. The change matrix which
records both spatial and temporal differences is given in Table 4.1 as follows:
Table 4.1: Change matrix
Land cover 2002
Land cover 2007
Tree Resource Farm/Grass Built-up/Bare Total Area 2002
Tree Resource 16,595.82 18,023.76 1,374.75 35,994.33
Farm/Grass 4,152.60 31,823.10 2,416.23 38,391.93
Built-up//Bare 61.38 1,105.92 979.38 2,146.68
Total Area 2007 20,809.80 50,952.78 4,770.36 *76,532.94
*Is the total area (Ha) of the study area
From the land cover change map, it can be seen that a great part of the areas covered by tree resources in
2002 was converted to farm/grass than to built-up/bare in 2007 as can be seen in Figure 4.4. The total area
covered by tree resources decreased from 47.03% in 2002 to 27.19% in 2007. The built-up/bare lands
increased from 2.80% in 2002 to 6.23% in 2007 while the farm/grass areas increased from 50.16% in 2002
to 66.58% in 2007. This is illustrated in Figure 4.4 as follows:
Figure 4.4: Area covered (Ha) by the different land cover classes in 2002 and 2007 in SPAMD
The study covered a total area of 76,532.9 Ha. A total area of 64.55% (49,398.3 Ha) did not change within
the study period while a total area of 5.51% (4,214 Ha) gained tree resources in 2007. The total area
changed per cover class between 2002 and 2007 with respect to total area changed is illustrated in Figure
4.5 below. Only the area covered by tree resources decreased while the farm/grass and built-up/bare areas
increased over the study period.
Land cover

23
Figure 4.5: Area changed (%) per land cover class between 2002 and 2007 in SPAMD
The total area that lost tree resources could be explained by Figure 4.6 below.
Figure 4.6: Percent respondents who expanded and located new farms in SPAMD
More respondents increased the size of existing farms and located new farms as can be seen in figure 4.6.
A further explanation for the loss of tree resources is shown in Figure 4.7 below. The areas covered by
trees were highest for both farm expansion and new farm location followed by grass and lastly farmlands.
Land cover

24
Figure 4.7: Land cover where farms expanded and new farms were located in SPAMD
4.2. Forest resources collected and collection areas of fringe communities
A total of 150, 330, 26 and 40 households were found in Adidwan, Atonsuagya, Damang and Sataso
respectively. This gave a total of 182 households which were administered questionnaires in four
communities applying the 33% random sample draw. Village maps and a display of the total number of
households with respect to that sampled per community are given in Appendix 2 and Figure 4.8
respectively.
Figure 4.8: No. of households sampled with respect to total households per community in SPAMD
The percent respondents with trees on their farms were more than twice those who did not have trees on
their farms in all four communities. 90% of respondents own trees on farms in Damang while less than
40% do so in the other 3 communities. The tree resources presence and ownership per community are
given in Figure 4.9 below.
Community
Land cover

25
In Sataso, less than 10% respondent did not have trees on their farms while in Damang every respondent
had trees on their farm. Of those respondents who had trees on their farms in all the communities, the
proportion who found the trees exceeded those who planted the trees in all four communities as can be
seen in Figure 4.10 below.
Figure 4.10: Percent respondents with trees on farms per community in SPAMD
Figure 4.9: Tree resources presence and ownership per studied community in SPAMD
Community
Tree presence
Community
Tree ownership

26
4.2.1. Forest resources collected by fringe communities per location in SPAMD
The list of forest resources collected with an indication of average unit quantity (%) per location in 2002
and 2007 is presented in Table 4.2 below.
Table 4.2: Average quantity (%) of forest resources collected in 2002 and 2007 per location in SPAMD
Forest resources
Quantity (%) per forest resource
Forest Farm Fallow
2002 2007 2002 2007 2002 2007
Fuel wood 40 41 27 29 33 29
Wildlife 38 39 31 28 31 33
Mushroom 33 47 33 27 33 27
Snail 33 54 25 31 42 15
Charcoal 43 43 21 29 36 29
Marantaceae Leaf 40 100 20 0 40 0
Cola nuts 25 0 25 0 50 0
Poles 47 62 20 0 33 38
Oil Palm 0 25 45 58 55 17
Avocado pear 0 47 36 24 64 29
Pawpaw 0 47 40 29 60 24
Wild cocoyam 0 0 40 0 60 0
As can be seen from the table, cola nuts and wild cocoyam which existed in 2002 are not easily found in
all the four communities in 2007. There is a general decrease in the quantity of forest resources collected
from fallow lands. All four communities studied got their forest resources from their farms, fallows and
forests which were located not too far from their settlements in 2002 as shown on the maps in Figures 4.11
below. Also the quantity of farm produce they get these days compared to the past has reduced greatly
probably due to soil fertility decline.
4.2.2. Forest resources collection areas per community in SPAMD
There was no clear cut boundary of where particular forest resources were found since every forest
resource that still exist was and are still located almost everywhere though in diminished quantities. The
estimated average maximum distance from settlement to forest resources per community in 2002 and 2007
is given in Figures 4.11 below. The distance to forest resources increased in all the studied communities. It
increased in all directions in Adidwan, Atonsuagya and Sataso communities whereas it increased more to
the south eastern direction in Damang community.

27
Figure 4.11: Forest resources collection areas around 4 communities in SPAMD
Atonsuagya
Sataso
Adidwan & Damang
0 5 10 15 202.5
Kilometers
/

28
4.3. Changes in forest resources to fringe communities and effects in SPAMD
4.3.1. Changes in availability of forest resources in SPAMD
Of the sampled population a greater proportion said some forest resources were extinct as presented in
Figure 4.12 below.
Among the respondents that indicated forest resources to be extinct, a higher proportion indicated some
species of wildlife (bush meat), followed by timber species to be extinct, then cola nuts, snails, mushroom,
tortoise, wild palms and avocado pear, wild pepper, chewing stick, mango and Marantaceae leaf to be
extinct in that order in the study area. Some of these forest resources such as tortoise and wild pepper got
extinct even before 2002. This is why they are not found in the list in Table 4.2 above. This is represented
in Figure 4.13 as follows.
Figure 4.13: Percent respondents against forest resources that are extinct in SPAMD
Forest resources
Figure 4.12: Proportion of respondents who said forest resources are and are not extinct

29
4.3.2. Changes in quantity of forest resources in SPAMD
When the average quantities of forest resources got in the past was compared to the present, the quantity of
forest resources available currently is less than half of what was obtained in the past as can be seen in
Figure 4.14 below. The past and present estimated quantities by respondents were consistent in all four
communities.
4.3.3. Effects of forest resources loss/decline on fringe communities in SPAMD
The following are the direct and indirect effects of the loss/decline of forest resources mentioned by the
fringe communities:
- Extinction of some forest resources especially wildlife species
- Scarcity of forest resources
- Decreased quantity of available forest resources like timber and NTFPs
- Drying up of some water bodies and seasonal shortage of water
- Decreased soil fertility
- Late start and irregularities of rainfall
- Increased poverty level
- Increased unemployment
- Declined agricultural yields
- Increased strong winds and its devastating effects
Figure 4.14: Comparison of past and present quantities of forest resources in SPAMD
Forest resources

30
4.4. Adaptive strategies to forest resources loss/decline and effects in SPAMD
Different adaptive strategies within their best means were practiced and these have their effects both on the
forest resources base as well as on the fringe community. These adaptive strategies practice within the
communities and their effects are highlighted in Table 4.3 as follows:
Table 4.3: Adaptive strategies and effects in SPAMD
Adaptive strategy Effects
1. Walk further to new sites where the forest
resources are available.
More depletion of the forest resources especially
tree resources as can be clearly seen on the change
map in Figure 4.3 above.
2. Change to forest resources substitutes which
they now buy from the market and other places
where the resources still exist.
Hardship, hunger and poverty especially to those
who cannot afford to buy.
3. Shorten fallow period or in some cases
abandonment of fallowing practice as an adaptation
to land scarcity.
Soil fertility and forest resources not well
recovered. This leads to buying of fertilizer and
forest resources substitutes.
4. Use of fertilizers, increasing farm sizes and the
location of new farm sites to overcome soil fertility
decline and decreased farm yields.
Modification of the natural micro-climate by
fertilizer, more depletion of the forest resources as
organisms unable to adapt to new environment die.
5. Turn to other activities as a result of late start
and irregularities of rains that damage crops on the
farm.
Hardship, poverty and hunger especially those who
cannot afford the means to start up new activities.
6. Bore holes have been dug in all the communities
to overcome seasonal water shortages due to
seasonal drying streams
Communities now walk shorter distances to get
water for their daily activities.

31
5. DISCUSSIONS
5.1. Land cover classification in SPAMD
5.1.1. Land cover maps of 2002 and 2007 and change map between 2002-2007
The images used were not captured at anniversary dates as was normally supposed to be in order to avoid
or minimise the influence of sun angle and seasonal differences (Coppin et al., 2004, Lillesand and Kiefer,
1994) and for better results of change detection using multi-temporal images. Contrary to the expectations
from the seasonal discrepancy which should have favoured more vegetation in 2007 and since the image
was taken during the rainy season (May), there was rather less vegetation on the 2007 image. This is
because there was a lot of tree resource loss, probably even more serious than estimated based on the
classification. This means that the vegetation loss noticed is actually tree resources because grasses would
usually have low reflectance in the dry season due to decrease in chlorophyll content. However, the
seasonal discrepancy caused some misleading gain in tree resources at the west of the central part of the
study area as shown. This is due to the presence of teak and cocoa trees which were leafless during dry
season, as in 2002 image but regained their leaves during the rainy season, as in 2007 image. This gave a
false tree resource increase in that part. The other tree resources increase can be attributed to the absence
of bush fires for about three successive years now in the area due to sensitization and bush fire control
measures put in place by the district office of the Forestry Commission.
There was a decrease of 19.84% in the total area covered by tree resources between 2002 and 2007 while
the grass/farm and built-up/bare lands increased by 16.42% and 3.43% respectively. This implies most of
the areas covered by tree resources became farm/grass which confirms the assertion of Hartemink et al.
(2008), Mbatu (2006) and Barbier and Burgress (2001) that agricultural expansion is the main factor
determining forest and tree resource loss in most areas of Ghana and the tropical world (Ampofo, 2008;
Braimoh, 2003). Also Agyarko (2001) affirmed that agricultural expansion is a threat to forest and tree
resources when he found that agricultural lands increased by 9% every two years.
The total area that lost tree resources i.e. 25.35% was more than four times the area which gained tree
resources i.e. 5.51% over the period 2002-2007. There was a substantial loss of tree resources around the
water bodies as their shapes neatly come out on the 2007 land cover map in Figure 4.2. The loss of tree
resources is attributed to the establishment of new farms and expansion of existing farms as illustrated in
Figure 4.6 above. Increasing farming population leads to shortage of land, driving farmers to farm up to the
banks of rivers and streams. This accounted for the loss of tree resources observed over the five year period
around the water bodies as can be clearly seen on the land cover map of 2007 in Figure 4.2. This result is
similar to what was obtained by Gelens et al. (2008) who affirmed increased farming along water bodies
due to land scarcity in their study area.
Also, more farmers expanded existing farms and located new ones as a result of decreased crop yields. The
expanded existing farms and newly located farms did so in areas that initially were covered by tree
CHAPTER FIVE

32
resources as illustrated in Figure 4.7 above. In addition to land scarcity and decreased crop yields, farmers
believe that such areas covered by tree resources are more fertile. Tree resources loss can also be attributed
to the fact that farming is the main and only income source to 98.90% of the total population with the
remaining 1.10% involved in charcoal burning and other businesses as their main income source. This
confirms Perrings’ assertion that deforestation process and thus forest resources loss are associated with
areas where a high proportion of output and/or employment derives from agriculture.
In Adidwan, Atonsuagya, and Sataso communities, most of the people found and protected the trees on
their farms. These trees belong to the landlord because most of them are settlers from different countries
like Togo, Côte d’Ivoire, Mali and other parts of Ghana especially from the three Northern regions. They
came to settle as farmers and have thus contributed to the conversion of areas covered by tree resources to
farms. Most trees found on the farms in Adidwan, Atonsuagya and Sataso communities belonged to the
landlord while the tree resources found on farms in Damang community belonged to the farmers because
they inherited it from their parents. This is because in Damang community the inhabitants were all
indigenes of the community while in the other three communities, most of the inhabitants were settlers. A
small proportion of the respondents in Adidwan and Atonsuagya found and planted some trees on their
farms. Most of the respondents who planted trees on their farms were the land owners of the farms and the
rest were those who were engaged in the “taungya system” put in place by the government.
Atonsuagya is one of the highest maize suppliers from the Ashanti region with trucks of maize leaving the
community to other parts of Ghana every now and then. This also accounted for the great loss of tree
resources found around this community. Furthermore, mechanised maize farming is implicated in the
absence of trees on farms. Thus the trees are completely cleared in order to ease tractor movement on the
farm. Tractor farming of maize is also practiced in Adidwan and Sataso and thus the loss of tree resources
found around these communities. Furthermore, the farmers are not encouraged to live or protect trees
especially timber species on their farms since they do not have full control over the tree resources and do
not derive any direct benefit from them. This was ascertained by Osafo (2005) that farmers and even land
lords do not have right to commercial timber exploitation and that only the state had right to issue timber
exploitation permits.
There were some misclassifications between the farm/grass class with the tree resources and built-up/bare
classes which reduced the classification accuracy. These results, however, generally suggest good
conformity between the classification and the actual land cover categories observed in the field. The high
accuracy result of 92.23% could be attributed to good training areas and reference data as reported by
Lillesand and Kiefer (1994) that a good accuracy is as good as the training area and reference data. The
results are close to that of Zhoua et al. (2004?) who obtained accuracies of 92.3% and 93.7% in their land
cover classifications.

33
5.2. Forest resources collected and collection areas of fringe communities
The participatory mapping of forest resources collection areas would have been best done using aerial
photographs or higher resolution images unlike the medium resolution landsat image used. These were
however not available for the studied communities. However, the generated topographic map used was
good as the communities members could easily identify land marks presented since it was at a reasonable
scale (1:25,000).
The exercise showed that the location of the forest resources had changed as well as the quantities they got
for all the forest resources that still existed. Mushroom, snail and wildlife were still obtained from where
they got them in 2002 though in smaller quantities. Likewise, further away when they go for the other
resources like fuel wood and charcoal. There was a general decrease in the quantity of forest resources
collected from fallow lands as a result of abandoning the practice of allowing farmlands to fallow over
long time period before re-farming due to land scarcity. Thus the fallow period is too short to produce
forest resources.
As can be seen on table 4.2 above, oil palm, avocado pear and pawpaw did not exist in the forest in 2002.
As at 2007 they existed meaning that these resources were introduced in the community in order to make
up for the resources they cannot get any more.
The distances to forest resources increased over the years. In 2002 the forest resources were located
around the communities at a circumference of 2km averagely while in 2007 the circumference increased to
4km averagely. This result is similar with those of Miah et al. (2003) who reported increasing distances
travelled to fuel wood collection sites with years. The communities walked longer distances to secure
substantial quantity of forest resources for the household. The circumference around Adidwan community
was tilted to the south eastern direction because of urban expansion of neighbouring Mampong and the
New Damang communities located in the north western side. There was overlap in forest resources
collection areas of Adidwan and Atonsuagya communities, however there were no conflicting cases
reported. Apart from the “taungya system” which legally permits community members to farm and collect
forest resources within the forest reserves, some community members have “illegal” farms within forest
reserves and thus collect forest resources from within the forest reserves.
5.3. Changes in forest resources to fringe communities and effects in SPAMD
5.3.1. Changes in availability and quantity of forest resources
The result presented in Figure 4.14 represents the people’s opinions and interests in the forest resources.
Those who indicated the extinction of specific forest resources either traded in or relied on them for their
subsistence. On the other hand those who said that no forest resource was extinct or had no idea were those
who collect the forest resource when they came across the resource but did not entirely rely on it for
subsistence or as income source.

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