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Evaluation of Land Use Change in Akinyele Local Government Area of Oyo State
Tunji Akinlotan
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AKINLOTAN, PETER ADETUNJI B.Sc (Hons) Ife
SSP09/10/H/1084
THESIS SUBMITTED TO THE DEPARTMENT OF GEOGRAPHY, FACULTY OF SOCIAL SCIENCES, OBAFEMI AWOLOWO UNIVERSITY, ILE-IFE OSUN STATE, NIGERIA.
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.Sc) DEGREE IN REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM
AUTHORISATION TO COPY OBAFEMI AWOLOWO UNIVERSITY HEZEKIAH OLUWASANMI LIBRARY POST GRADUATE THESIS
AUTHOR: TITLE:
AKINLOTAN, Peter Adetunji EVALUATION OF LAND USE CHANGE IN AKINYELE LOCAL GOVERNMENT AREA OF OYO STATE
DEGREE: YEAR:
M.Sc. (Remote Sensing and Geographic Information System) 2012
I, AKINLOTAN Peter Adetunji hereby authorised the Hezekiah Oluwasanmi Library to copy my thesis, in whole or in part in response to request from individual researcher and organisation for the purpose of private study or research.
Signature......................................................
Date............................................
CERTIFICATION This is to certify that this research work was carried out by AKINLOTAN Peter Adetunji in the Department of Geography, Obafemi Awolowo University Ile-Ife.
_______________ Prof. O. Aloba Supervisor
________________ Date
_______________ Dr. N. O. Adeoye Co-Supervisor
________________ Dr. O. A. Ajala Head of Department
to the blessed memory of
Chief M A Idowu (mnitp, rtp) a worthy mentor
ACKNOWLEDGMENT To God be the glory, great things He has done for me. I am most grateful to the Lord God of Host for the knowledge, understanding, wisdom and strength bestowed on me to survive this second purposeful stride in OAU, Ife. I would like to express my deepest appreciation to my supervisors, Professor Oluwole Aloba and Dr. N. O. Adeoye, and to all members of staff for their utmost encouragement and support, constructive criticism and supervision on this research work. It is on this premise that I wish to recognize the selfless effort of and the professional guidance I got from you all, without their guidance and timely readings, I could not have completed this thesis. A number of ideas presented in the thesis come from their invaluable advice and comments. I am also indebted to my beloved parents, Pa Siji and Chief (Mrs.) Moradeyo Adegoke-Akinlotan, and all my wonderful family members; brothers, sisters, nephews, nieces, cousins and in-laws, for their numerous and adequate contributions financially, morally and spiritually towards the completion of this program. I really appreciate all my colleagues in Geography and Urban and Regional Planning departments and to all my special friends at home and in Diaspora, who stood by me in the need and needless times during the course of study. I would just but mention a person, with whom I held the assertion to head-on and achieve my goal as far as this program is concerned, Jide Akinlotan-thank you for your immeasurable contributions to the successes since, the bond will continue to grow stronger. I cannot explicitly list names of the people that have numerously contributed in no small measures to an outstanding success of this research work, without whom my study and my dissertation would be impossible, thank you all.
Peter Adetunji AKINLOTAN September, 2012
TABLE OF CONTENTS TITLE PAGE AUTHORISATION TO COPY CERTIFICATION DEDICATION ACKNOWLEDGEMENT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ABSTRACT i ii iii iv v vi ix x xi
CHAPTER ONE 1.0 INTRODUCTION 1 1 5 7 7 8 8 10 12 13 13 14 14
1.1 Background to the Study 1.2 Statement of the Problem 1.3 Aim and Objectives 1.3.1 Aim 1.3.2 Objectives 1.4 Justification for the Study 1.5 The Study Area 1.6 Scope of Study 1.7 Definition of terms 1.7.1 Land 1.7.2 Land-cover 1.7.3 Land-use
1.7.4 Land-cover and Land-use changes 1.7.5 Remote sensing 1.7.6 Geographic Information system CHAPTER TWO 2.0 THEORETICAL FRAMEWORK AND LITERATURE REVIEW
2.1 Theoretical Framework 2.1.1 Micro-Economic Theoretical Approaches Agricultural Land Rent Theory Urban Land Market Theory Agent-Based Theories of Urban and Regional Spatial Structure 2.2 Literature Review 2.2.1 Empirical Evidence on the Causes of Land-Use Change 2.2.2 General Insights on Sectoral Causes of Land-Use Change
CHAPTER THREE 3.0 RESEARCH METHODOLOGY 3.1 Introduction 3.2 Methodology Workflow 3.3 Nature and Sources of Data Required 3.3.1 Spatial Data and Source 3.3.2 Primary Data 3.4 Methods of Data Analysis 3.4.1 Pre-Processing and Processing 3.4.2 Maximum Likelihood Classification 3.4.3 Markov Chain Analysis 3.4.4 Quantitative Analysis 46 46 47 48 48 52 53 53 56 56 57
3.4.5 Urban Land Consumption Index 3.5 Software Used
CHAPTER FOUR DATA ANALYSIS AND DISCUSSION OF RESULT 4.1 Introduction 4.2 Land Use/Cover between 1990 and 2010 4.2.1 Land Use/Cover in 1990 4.2.2 Land Use/Cover in 2000 4.2.3 Land Use/Cover in 2010 4.3 Rate of Land Use/Cover Change between 1990 and 2010 4.3.1: Land Consumption Index 4.4 Land Use/Cover Prediction 4.4.1: Transition Probability Matrix and CA_Markov Operation 60 60 60 61 63 65 67 73 74 75
CHAPTER FIVE SUMMARY, CONCLUSION AND RECOMMENDATIONS 5.1 5.2 5.3 Summary of Findings Conclusion Recommendations 79 79 80 82 83
LIST OF TABLES Table 3.1: attributes of datasets Table 3.2: The selected training sites Table 3.3: the software used and their relevancies Table 4.1: Land Use/Cover in 1990 Table 4.2: Land Use/Cover in 2000 Table 4.3: Land Use/Cover in 2010 Table 4.4: land use distribution during the period of study (ha and % of total land area) Table 4.5: Area/Percentage and Annual Rate of Decrease/Increase of Land Use/Cover Changes 1990-2010 Table 4.6: Population figures for the study area Table 4.7: Results of Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) for the study area Table 4.8: Transitional Probability table derived from land use/cover map of 1990 and 2010 Table 4.9: Projected Land Use/Cover in 2030 Table 4.10: Area/Percentage and Annual Rate of Decrease/Increase of Land Use/Cover 2030 78 75 78 74 72 74 70 53 55 59 62 64 66
LIST OF FIGURES Figure 1.1: Map of Nigeria showing Oyo State Figure 1.2: Map of Oyo State showing Akinyele LGA (Study Area) Figure 3.1: methodology flowchart Figure 3.2: Enhanced Landsat TM imagery of 1990 Figure 3.3: Enhanced Landsat TM imagery of 2000 Figure 3.4: Enhanced Landsat TM imagery of 2010 Figure 4.1: land uses in hectares for year 1990 Figure 4.2: Land use/cover map of Akinyele LGA in 1990 Figure 4.3: land uses in hectares for year 2000 Figure 4.4: Land use/cover map of Akinyele LGA in 2000 Figure 4.5: land uses in hectares for year 2010 Figure 4.6: Land use/cover map of Akinyele LGA in 2010 Figure 4.7: % area cover by the land uses between 1990 and 2010 Figure 4.8: Projected Land use/cover map of Akinyele LGA in 2030 11 12 47 49 50 51 62 63 64 65 66 67 70 77
ABSTRACT The study identified the different land uses and their extent; evaluated the rate of change in the land uses between 1990 and 2010 and predicted the future patterns of land use in Akinyele Local Government Area of Oyo State. This was with a view to examining the land use pattern at different periods and evaluating the changes that have taken place over time and predict future changes over a given period. Both primary and secondary data were used for the research. The acquisition of primary data was done with the use of Global Positioning System (GPS) receiver to collect geographic coordinates of all relevant features in the study area. The secondary data included Landsat TM and ETM+ imageries (1990, 2000 and 2010); the administrative map of Nigeria obtained from Akinyele Local Government Planning Authority, and population data gotten from National Population Commission. The study used Maximum Likelihood Classification algorithm to classify the images to reveal different land uses in the area for the purpose of arriving at a land use/land cover maps. Land consumption rate and absorption co-efficient aided the determination of the extent of land consumed at intervals of years under study as well as the rate of land use changes between 1990 and 2010; the data collected were analysed using geospatial techniques. The results showed that there were four different classes of land use namely; forest, farmland, built up and water body. Forest area covered 81.09%, 64.28% and 58.85% of the study area in 1990, 2000 and 2010 respectively; farmland area covered 14.56% in 1990, 27.85% in 2000 and 22.14% in 2010, while built up covered 4.24% of the study area in 1990, 7.78% in 2000 and 18.89% in 2010, water-body covered 0.1% in 1990, 0.09% in 2000 and 0.12% in 2010. Also, built up area increased by 345% between 1990 and 2010 and farmland increased by 52.2%; conversely, forest area decreased by 27.4% while water-body also decreased by 3.45%. The Markov Chain and Cellular Automated predictive model indicated that by the year 2030, built up area would have increased by 22.15% points, while forest area,
farmland and water-body would have decreased by 20.27%, 1.88% and 0.09% points respectively, given that the factors responsible for the observed changes remained. The study concluded that Akinyele Local Government Area was in transition in terms of land use as a result of its contact with the urban fringe of Ibadan city, and that further ecological loss was predicted due to the rate of development.
CHAPTER ONE INTRODUCTION 1.1 Background to the Study The history of urban growth and urbanisation reveals that urban areas belong to the most dynamic land use and land cover types on earth. The trend of urban growth is usually towards the urban rural fringe where there are less built areas, irrigation and other water management systems. Regardless of the regional economic importance, urban growth, particularly the expansion of residential and commercial land use towards the periphery of urban areas, has an impact on the ecosystem (Yuan et al. 2005). It is evident that such trend of urban growth has an impact on natural resources and on land use change dynamics at large. The issues of land management and land-use change (rural or urban) dominate the development agenda of all countries of the world and, over the years, have remained highly political and contentious. This is expected in the context in which optimal land-use planning is perceived as an indispensable factor for ensuring food security, environmental sustainability and economic development. One of the recent studies on the impact of increased pressure on land and the effects of land-use and land management practices on the dynamic character of rural ecosystems show that a strong correlation exists between balanced, sustainable land development and, human, food and
environmental security (Houet, T,. et al, 2006). Other recent studies on land management indicate that, in many parts of the world, the rural landscape is experiencing rapid land-use/land- cover (LULC) changes (Kamusoko, C. et al, 2008). It is not surprising therefore that the issue of land use change occupies the frontburner of the development initiatives of responsible governments the world over. The desire of planning authorities and municipal governments is to articulate policies and
programmes capable of maintaining a balanced ecosystem or to mitigate or prevent, on a sustainable basis, the devastating consequences of severe land-use change when they occur, and achieving this goal requires that such authorities must adopt responsible, holistic and sustainable development strategies which must axiomatically be carried out using spatial decision support tools and methodologies. Studying land use dynamics and change detection is essential in order to examine various ecological and developmental consequences of land use change over a space and or time. This makes land use mapping and change detection relevant inputs into decision-making for implementing appropriate policy responses (Fasona and Omojola et al., 2005). Evaluation of Land use changes allow for the identification of major processes of change and, by inference, the characterization of land use dynamics. The reason for such consequence is as a result of over-dependence on primary resources with direct effect on biodiversity; land use and land cover dynamics, terrestrial ecosystem and climate (atmospheric composition, vegetation, temperature changes and occurrence of extreme climatic events). With rapid urbanization and a finite land area, the available land per individual shrinks drastically. The result is an urgent need for proper geo-management of land that is dependent upon the availability of a detailed, accurate and up-to-date data. During the 20th century, land use changes have emerged as a global phenomenon (Ramankutty et al., 2005). Land cover changes at global, national and local levels have characterized human-environment relationship. Indeed the growing human population has a huge potential for changing the face of the planet earth. Information on land use and land cover is the basis on which the past and present human interactions and the impacts of such interactions with natural resources
and the environment can be understood. One major consequence of the globally recognized rapid land cover change is the loss of biodiversity and ecosystem functioning. Although, it is now widely accepted that multiple factors in synergetic interactions dominate land-change processes and that these causal clusters vary across regions and time (Geist et al., 2005), agricultural expansion is one of the proximate causes of land use/land cover change (Geist et al., 2005; Reid et al., 2000). Today, roughly a third of the world landscapes are being used for growing crops or grazing cattle (Ramankutty, et al., 2005). Major changes in human activities, particularly through large scale agriculture have been identified as the major cause of the dramatic changes in land cover and land use patterns globally. These dramatic land cover and land use changes that would have once required centuries now take a few decades to manifest. The increasing concern for the management of natural resources in recent times has been necessitated by the increasing demographic pressures and its associated man-made activities which have led to serious environmental stress and ecological instability. In the last 300 years, the impacts of land use change have increasingly assumed significant to threatening proportions (Briassoullis, 1999). Man is the principal agent that brings about these changes and which is responsible for their magnitude and severity. Of course these changes have been found to be more profound in developing countries, due to high population growth rate and the subsequent resource over-exploitation. The impacts of these environmental problems are serious both in the short and in the long term. In the short term, food security, human vulnerability, health and safety are at stake; in the longer term the viability of the earth is being threatened (Briassoullis, 1999).
The analysis of land use change is essentially the analysis of the relationship between people and land. Why, when, how and where does land use change happen? To provide answers to these closely interrelated questions, theories have been advanced and models have been built in the past years. The rural-urban fringe zone is an area where various rural and urban characteristics are mixed together. Around major urban centres the physical expansion of built up areas beyond their municipal boundaries has been very conspicuous. As one moves out of a major city along one the roads, one observes new residential colonies and a considerable amount of vacant land with partially developed residential land use. An important problem in the rural urban fringe area is the problem of land use. The pattern of land use in the area is dynamic and changes from rural land use to urban land use over short periods of time and distance. The spatio-temporal dynamics of rural-urban ecosystems is an invariably complex phenomenon that involves a complex nexus of interacting forces of causal agents. It has been demonstrated that, in order to disentangle the complex suite of socio-economic and bio-physical forces that influence the rate, spatial pattern and distribution of land use change and to estimate the impacts of such changes,
spatially-explicit land use models are indispensable (Costanza, R. et al, 1998). As reproducible tools, such spatial simulation models have the potential to expand the planners knowledge domain and to support the exploration of future land use changes under different scenario conditions, thus supplementing his existing mental capabilities to analyze land use change and to make more informed decisions. Such tools can help to predict ecological responses to changing landscape heterogeneity and to gain insights into the variability of landscape patterns and processes over time.
1.2 Statement of the Problem Land use reflects the dynamic processes between the Earth's bio-physical characteristics and human activities. While major parts of the global economy are getting increasingly independent from land-based resources, our global society becomes more knowledge, technology and information driven. Growing personal mobility and expanding transport networks provide new spatial opportunities for living, work, production and recreation. As a result, land use changes are occurring at much higher rates and in unprecedented dimensions. Land use types such as agriculture, forestry, mining, settlements, infrastructure, tourism, and nature conservation are affected by international policy decisions, demographic phenomena and global environmental impacts such as climate change. Understanding the driving forces, dynamics and directions of land use changes and assessing its impact on regional sustainable development has therefore become a key challenge for scientists. City is a social, economic and natural compound ecosystem with human activity at the centre. With the speeding of up the development of modern industry and urbanization, as the core of regional system, the population of city increase quickly and urban size also growing. The internal structure reorganization and the peripheral regional expansion has become the main way of complex urban structure and spatial changes. For the complex urban system, how to timely and accurately grasp the evolution of urban spatial form status and trend has become an urgent problem. Since the remote sensing techniques can obtain the surface changes information quickly, timely and accurately, it is widely used in land use change evaluation and land expansion change study. Land use change analysis at home and abroad regularly researches are weak and scattered in recent years, including mainly
urban morphology research, extensive research, dynamic model research and space structure research, and so on. For the dynamic mechanism of urban land use change, many scholars with different methods and from different perspectives have carried out several studies; however their studies are mainly concentrated on the economic developed area, such as the major cities, while for the urban fringe research is less. In addition, different regions of the urban spatial shape have different cities structure and development patterns. Therefore, the continued analysis and research for the characteristics of space and time of urban land use change are particularly important. Land use changes can occur through the direct and indirect consequences of human activities to secure essential resources. Land use/cover change is known as a complex process which is caused by the mutual interactions between environmental and social factors at different spatial and temporal scales (Valbuena et al. 2008; Rindfuss et al. 2004). More recently, industrial activities and developments, the so-called industrialisation, has encouraged the concentration of population within urban areas. This is called urbanization, which includes depopulation of rural regions along with intensive farming in the most productive lands and the abandonment of marginal lands (Ellis and Pontius 2006). Land use changes are increasingly known as the consequence of actors and factors interactions (Bakker and van Doorn 2009). These conversions and their consequences are obvious around the world and it has been becoming a disaster around the metropolitan areas in developing countries. The study area lies in the fringe of a traditional urban settlement i.e Ibadan, in South-western part of Nigeria, and the area has witnessed remarkable expansion, growth and developmental activities such as building, road construction, deforestation
and many other anthropogenic activities in the recent years. This has therefore resulted in increasing land consumption, modification and alterations in the land use status over time. It was however viewed that less detailed and comprehensive attempt (as provided by a Remote Sensing data and GIS) has been adopted to evaluate this status as it changes over time with a view to detecting the land consumption rate and also make attempt to predict same and the possible changes that may occur in this status, for planners to have a basic tool for planning. It is therefore necessary for a study such as this to be carried out on regular basis if the study area will avoid the associated problems of an unprecedented growth. Thus, the fundamental science questions associated with this research from the defined problems are: 1. What are the types of land use/cover change identifiable in the study area? 2. What are the geographic distributions of these changes? 3. What are the overall rates of change (e.g., rates of conversion from agricultural to urban land cover)? 4. What are the driving agents of this land use/land cover changes and the consequences it poses now or in the nearest future? 1.3 Aim and Objectives 1.3.1 Aim The aim of this research is to examine the land use pattern in Akinyele Local Government Area at different periods with a view to evaluating the changes that have taken place over time and predict future changes over a given period.
1.3.2 Objectives The following specific objectives will be pursued in order to achieve the aim above: i. identify different land uses and their extent in the study area; ii. evaluate the rate of change in the land uses between 1990 and 2010; and iii. predict the future patterns of land use in the study area. 1.4 Justification for the Study Changes in the use of land are an inevitable phenomenon occurring globally due to both temporary and or permanent interest of the inhabitants in a particular area. The phenomenon could be revealed either in a small or large scale but the most interesting and fundamental observation is that change occurs over time in a particular place. Land-use and land-cover changes are local and place specific, occurring incrementally in ways that often escape our attention (De-Sherbinin, 2002). In the past two centuries the impact of human activities on the land has grown enormously, altering entire landscapes, and ultimately impacting the earth's nutrient and hydrological cycles as well as climate. Most major metropolitan cities face the growing problems of urban sprawl, loss of natural vegetation and open space and a general decline in wildlife habitat. This problem is observed when residential and commercial development replaces undeveloped land around them. Cities have changed from small, isolated population centers to large, interconnected economic physical and environmental features. The results of the pressure are numerous and they include intensified agriculture, decreasing amount of forestland, loss of biodiversity, intensified land degradation and soil erosion (Pellika et al, 2004). Despite this, the strong interest in land-use and land cover results from their direct relationship to many of the planets fundamental
characteristics and processes, including the productivity of the land, the diversity of plant and animal species, and the biochemical and hydrological cycles. Land cover is continually transformed by land-use changes, suggesting that land use is the cause of land cover change and the underlying driving forces remain economic, technological, institutional and demographic factors (De Sherbinin, 2002). Land use and land cover form the basis from which past and present human interaction and their impacts on natural resources and the environment can be understood. The diversity of real world situations casts doubt on whether a general theory of land use change will be able to provide, besides broad explanatory driving factors, patterns and processes of change, those details which may be critical in explaining land use change in particular contexts and circumstances. In recent times, the dynamics of Land use and particularly settlement expansion in the study area requires a more powerful and sophisticated system such as GIS and Remote Sensing data which provides a general extensive synoptic coverage of large areas. The main goal of this research is the evaluation of the changes in urban land use using the Landsat TM and ETM+ images of 1990, 2000 and 2010, which achieved the quantity and spatial distribution characteristics of the study area in such a way that the different land use types transformation each other are realized. At last, this research discusses the possible reasons for the change detected from the economic progress and population, terrain and administrative factors, especially the comprehensive effect of these factors, so as to provide scientific basis for sustainable development and urban planning.
1.5 The Study Area
Akinyele Local Government is one of the eleven local governments that make up Ibadan metropolis. It headquarters are at Moniya. It is geographically located within latitude 7250N, longitude 3394E; and latitude 74230N, longitude 40700E. The local government area was created in 1976 and it shares boundaries with Afijio Local Government to the north, Lagelu Local Government area to the east, Ido Local Government Area to the west and Ibadan North Local Government to the south. It occupies a land area of 464.892 square kilometers with a population density of 516 persons per square kilometer. Using 3.2% growth rate from 2006 census figures, the 2010 estimated population for the local government is 239,745.
Akinyele Local Government Area is located in Ibadan, South-western Nigeria in the south-eastern part of Oyo State about 120 km east of the border with the Republic of Benin, in the forest zone close to the boundary between the forest and the savannah. The area ranges in elevation from 150 m in the valley area, to 275 m above sea level on the major north-south ridge which crosses the central part of the city of Ibadan. The Local Government total area is 910.196 square kilometers.
The Local Government Area has a tropical wet and dry climate (Koppen Climate classification) with a lengthy wet season and relatively constant temperatures throughout the course of the year. The wet season runs from March through October, though August sees somewhat of a lull in precipitation. This lull nearly divides the wet season into two different seasons. November to February forms the areas dry
season, during which the area experiences the typical West African Harmattan. The mean total rainfall is 1420.06 mm, falling in approximately 109 days. There are two peaks for rainfall, June and September. The mean maximum temperature is 26.46 C, minimum 21.42 C and the relative humidity is 74.55%.
The local Government could be classified to be majorly agrarian and it is a major center for trade in cassava, cocoa, cotton, timber, rubber and palm oil. The main industries in the area include the processing of agricultural products; Tobacco processing and Cigarette (Manufacture); flour milling, leather-working and furniture making. There is abundance of clay, kaolin and aquamarine in its environs, and there are several cattle ranches, dairy farm as well as a commercial abattoir in the area.
Fig. 1.1: Map of Nigeria showing Oyo State Source: COPINE, 2011
Fig.1.2:Map of Oyo State showing Akinyele LGA (Study Area) Source: COPINE, 2011
1.6 Scope of Study Increasing population in the already overpopulated developing countries forces a considerable shift of population concentration from rural to the urban areas. Helped by a total array of twentieth century technology, such a shift is accompanied by the rapid expansion of cities, especially the metropolitan areas. Since resource constraint restricts vertical expansion of the city to a considerable extent, cities in developing countries experience more horizontal growth swallowing vast tracts of rural lands in this process. Changes in land use are perhaps the most dynamic aspect in such areas. Even these are not uniform all along the fringe. Transport and physical conditions bring their own influence. To this effect, it should be noted that this study had only emphasised on horizontal development, although there has been some studies that combines both horizontal and vertical expansion in this kind of research. The time
frame and level of technology advancement in the area of land use changes in Nigeria had restricted the researcher from considering the two forms of development in the area.
1.7 Definition of terms This section gives some important definitions and clarifications of basic terminologies often used in land-use change studies. The definitions are important since they facilitate information exchange among scientists (Braimoh, 2004), improve understanding of existing classifications and their legends (Di Gregorio, 2005) and finally the land-use/cover change studies by a broad readership. The selected terms are: Land, land-cover, land-cover change, land-use, land-use change, deforestation, driving forces, and modelling.
1.7.1 Land As stated by Briassoulis (2000) and the Food and Agriculture Organization (FAO) in the documentation for the Convention to Combat Desertification (FAO, 1995; cited UN 1994). Land is defined in a holistic way as "a delineable area of the earths terrestrial surface, encompassing all attributes of the biosphere immediately above or below this surface, including those of the near-surface climate, the soil and terrain forms, the surface hydrology (including shallow lakes, rivers, marshes, and swamps), the near-surface sedimentary layers and associated groundwater reserve, the plant and animal populations, the human settlement pattern and physical results of past and present human activity (terracing, water storage or drainage structures, roads, buildings, etc.)". The same author concluded on a variety of definitions of land by arguing that it
is worth noting that all definitions of land, although in general similar, differ as to the priority given to the attributes that characterize land.
1.7.2 Land-cover Land-cover refers to the observed (bio) physical cover on the earths surface and immediate subsurface (Turner et al. 1995). It includes vegetation, water (surface water, ground water), desert, ice, soil, topography and human-made structures such as mine exposure and settlement (IGBP/IHDP-LUCC and IGBP-DIS, 1997; Di Gregorio, 2005). When focusing on a very pure and strict sense, land-cover consists exclusively in the description of vegetation and man-made features.
1.7.3 Land-use Land-use is the intended employment and management underlying human exploitation of a land-cover. It is characterized by the arrangements, activities and input people undertake in a certain land-cover type to produce, change or maintain it (IGBP/IHDP-LUCC and IGBP-DIS, 1997; Di Gregorio, 2005). Consequently, there is a link between land-cover and human activities in the environment because contemporary land-cover is changed mostly by human use (Allen and Barnes 1985; Turner II et al., 1990; Whitby, 1992 quoted by Turner et al., 1995).
1.7.4 Land-cover and Land-use changes Numerous researches conducted over the last decades helped to improve the understanding, the causes, and the predictive model of the land-use and land-cover changes (LUCC) concepts as they have been adopted under the auspices of the Land-
Use and Land-Cover Change (LUCC) project of the International GeosphereBiosphere Programme (IGBP) and International Human Dimensions Programme on Global Environmental Change (IHDP) (Lambin, Geist, and Lepers, 2003; LUCCIGBP-IHDP, 1997). In the case of land-cover changes, two types of change can be distinguished from the literature: conversion and modification (Lambin, Geist, and Lepers, 2003; Turner et al., 1995). Land-cover conversion consists of change from one cover type to another (i.e. the complete replacement of one cover type by another) while Land-cover modification involves alterations of structure or function without a total change from one type to another; it could involve changes in productivity, biomass, or phenology (Skole, 1994). In a similar way, land-use change may involve either conversion from one type of use to another i.e. changes in the pattern of land uses in an area or modification of a certain type of land-use. Modification of a particular land-use may involve changes in the intensity of this use as well as alterations of its characteristic qualities/attributes for instance changes of suburban forests from their natural state to recreation uses (the area of land remaining unchanged). Land-cover/Land-use conversion takes place through many pathways such as deforestation, desertification, agricultural intensification, etc. Below more attention is given to the two first terminologies: desertification and deforestation.
1.7.5 Remote sensing Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites (space borne) or from the air using aircrafts (airborne). Remote sensing uses a part or several parts of the electromagnetic
spectrum. It records the electromagnetic energy reflected or emitted by the earths surface. The amount of radiation from an object (called radiance) is influenced by both the properties of the object and the radiation hitting the object (irradiance). Remote sensing imagery has many applications in mapping land-use and cover, agriculture, soils mapping, forestry, city planning, archaeological investigations, military observation, and geomorphological surveying, land cover changes, deforestation, vegetation dynamics, water quality dynamics, urban growth, etc.
1.7.6 Geographic Information system A geographic information system (GIS) integrates hardware, software and data for capturing, managing, analysing, and displaying all forms of geographically referenced data. GIS allows the viewing, understanding, questioning, interpreting, and visualising data in many ways that reveals relationships, patterns, and trends in the form of maps, globes, reports, and charts. It answers questions and solve problems by looking at data in a way that is quickly understood and easily shared. GIS technology can be integrated into any enterprise information system framework. GIS can be used to map the changes in an area to anticipate future conditions, decide on a course of action, or to evaluate the results of an action or policy. By mapping where and how things move over a period of time, one can gain insight into how they behave.
CHAPTER TWO THEORETICAL FRAMEWORK AND LITERATURE REVIEW 2.1 Theoretical Framework A number of authors have provided useful reviews on the subject and summarized most of the work to date. The present effort has its own specific interest, however, which stems from the recognition that land cover reflects land use and that land use has a significant theoretical foundation. Be this as it may, the theoreticians of land use la Alonso, Mills, Muth, Fujita, Krugman, and others, have had little to say about land-cover and land-use change despite the relevance of their conceptual frameworks. For its part, land cover and land-use change research often addresses highly empirical questions using statistical methods and case studies, and it has generally avoided abstract theoretical formulations. But what is a theory of land use change? Simply stated, it is a set of propositions used to understand the "what" of land use change and the "why" of this change. In other words, a theory of land use change describes the structure of the changes in the uses of land from one type to another and explains why these changes occur, what causes these changes, what are the mechanisms of change. The "what" and the "why" of land use change are closely related although extant theories rarely address both; they refer either to the "what" or to the "why". As regards the latter, it is important to cite Sack (1990): "describing what. can be accomplished up to a point without considering social and individual motivations. These motivations, however, must be included in the why of human behavior, and knowing the why is essential if we expect to change what we do.. Much of the why in human-nature relations can be understood only through the social side of the
equation that is, through understanding the nature of individuals and societies that create the what" (Sack 1990).
Getting to the second issue, which theories of land use change are included in this chapter, it is noted that the majority of theories of land use change have to be sought in the more general theoretical frameworks of disciplines studying economic, environmental and spatial change (or, transformation). Andersson and Kuenne (1986) state that (static) spatial analysis is concerned with four reasonably distinct bodies of spatial phenomena: (a) locations, (b) interaction flows, (c) changes in availability of factors of productions and (d) spatial structures. The latter are defined as "including areal or curvilinear patterns of economic activities such as land use patterns, urban structure, transportation networks, and market or supply areas" (Andersson and Kuenne 1986, 201). Hence, for the present purposes, a broad distinction is drawn between those theoretical schemata which treat land, land use and, more importantly, land use change explicitly and those where reference to land use change is more or less indirect and implied by the broader discussion. In other words, an attempt is made to cover those theories where land is defined, at a minimum, as "a delineable area of the earths terrestrial surface".
A broader set of theories which deal with the dynamics of urban and regional spatial structure are given consideration in this contribution. These theories treat land and land use as points in space mostly (but not always) but their significance lies in that they analyze the broader spatial processes that ultimately result in land use change. The majority of these theories are agent-based; in other words, they deduce changes in spatial structure starting from the behaviour of the individual household or firm. One of the reasons these theories may prove important in the future for the
analysis of land use change is that they can support the building of spatially-explicit models which focus on the level of the individual decision making unit (farm, firm, household).
Finally, the role of theories of land use change in the study of the subject needs to be stressed. Common to all analytical tasks is the need to have a vehicle to structure the conception and explanation of reality i.e. a theory. The analysis of land use change is no exception. Idealist and theories adopting similar epistemologies aside, theories of land use change guide thinking about land use change, indicate conceptual and operational expressions of change, its determinants and the relations between them, and suggest explanatory schemata for making sense of available empirical evidence; i.e. they support model building. Moreover, to reiterate, Sack (1990) mentioned above: "knowing the why is essential if we expect to change what we do"; in other words, theory is a guide to policy on land use change a strong and critical demand of the contemporary times. Inappropriate and inadequate theories of land use change may misguide policy and produce more ills than those policies are assumed to cure.
2.1.1 Micro-Economic Theoretical Approaches
Three main micro-economic theory-based theoretical schemata for the analysis of land use patterns and their changes are discussed below: J. F. von Thunens agricultural land rent theory, W. Alonsos urban land market theory, and agent-based theories of urban and regional spatial structure. It should be noted that all three are considered theories as well as models because their developers proposed a theoretical structure which they translated then into a mathematical, form; i.e. a symbolic model (not necessarily operational, however).
Agricultural Land Rent Theory
The analysis of land use patterns and their changes in the micro-economic theorization tradition (but also in the macro-economic) has been influenced in fundamental ways by the agricultural land rent theory developed in 1826 by the North German estate owner, J. H. von Thunen (1966). The purpose of von Thunens exercise was to prescribe the optimum (most economical) distribution of rural land uses around a market town (Hoover and Giarratani 1984, 1999). The basic concept he used was that of the land rent which is defined as the "price for the use of a piece of land" (Hoover and Giarratani 1984, 132) or, equivalently, "the price of the services yielded by land during a specific time period" (Romanos 1976, 32). At the regional level of analysis, to which von Thunens formulation refers mostly, the land use types considered are various types of agricultural land primarily and, secondarily, forest land. The analysis concerns land which is devoted to growing different types of crops (and forestry). Land was assumed to be a uniform, isotropic (of equal fertility) flat plain with movement possible in all directions around a market town located at the center of the region of interest. Land rent varies only with distance from the center. Each crop has an associated rent gradient (or, rent curve) that extends in all directions from the center (Figure 3.2a in Hoover and Giarratani, 1999) as well as the same delivered price and unit transport cost irrespective of location or rent. Moreover, the intensity of land use for each crop and the yield per acre are fixed regardless of the relative prices of land (rents), the other inputs, and the output. Perfectly competitive markets are assumed.
The rule of determining the location of a particular activity (land use) with respect to the market center is that each activity (land use) occupies the zone in which
the user can pay the highest rent than any one of the other users. And the rent the user of a particular land use can afford to pay depends on the value of the products produced on a parcel of land. Hence, in the jargon of land economics, the user of an activity (land use) associated with high value products can bid higher land rents and, thus, outbids other users that cannot pay the same rent. In von Thunens formulation, the activity (land use) with the largest amount of output per acre (highest value of output) has the steepest rent gradient and, hence, locates closest to the market center. The other activities (land uses) follow in decreasing order of the slope of their rent gradients. The resulting land use pattern is a set of concentric rings around the market center with each ring devoted to growing a particular crop. The envelope of the individual crop rent gradients (formed by their uppermost parts) is the bid rent curve (for the study region). A last remark on this formulation: the optimum solution to the land use pattern produced following the above procedure is independent of "whether: (1) one individual owns and farms all the land, seeking maximum returns; (2) one individual owns all the land but rents it out to tenant farmers, charging the highest rents he or she can get; or (3) there are many independent land owners and farmers, each seeking his or her own advantage" (Hoover and Giarratani 1984, 143). A mathematical exposition of von Thunens theory can be found in Hoover and Giarratani (1984, 1999). An exposition of von Thunens theory with several extensions can be found in De la Barra (1989).
The von Thunen formulation makes no explicit reference to mechanisms of land use change because it is a static theory where the optimum land use pattern is assumed to be produced instantaneously. However, it is not difficult to see an implicit mechanism even under all the restrictive assumptions of the theory. If the relative prices of the crops change exogenously, this will change the relative ability of farmers
(the users of land) to bid for particular locations making, thus, possible a change in location (the land use pattern preserving its circular form). The very restrictive and unrealistic assumptions of the original formulation of the agricultural land rent theory were relaxed by von Thunen himself and by researchers who used it in subsequent applications (see, for example, Alonso 1964, Romanos 1976, Wheeler and Muller 1981, Hoover and Giarratani 1984, 1999, Stahl 1986). These applications covered a wide range of spatial scales from the global (Peet 1969 cited in Johnston et al. 1994, 673) to the individual village and farm holding (Blaikie 1971 and Chisholm 1979 cited in Johnston et al. 1994, 673) as well as other land uses such as residential and commercial. More importantly, perhaps, this theory provided the foundations for Alonsos (1964) urban land market theory (discussed below). In general, there is no doubt that von Thunens theory is the predecessor of both location theory and the analysis of urban and regional spatial structure.
Urban Land Market Theory
In the years that followed von Thunens seminal approach to a theory of land use, several attempts were made at analyzing various components of the urban and regional system (see, for example, Romanos 1976). However, it was only after almost 140 years that W. Alonso would present his celebrated urban land market theory that applied and refined von Thunens original ideas (Alonso 1964). This theory aims to describe and explain the residential location behaviour of individual households and the resulting spatial structure of an urban area. The focus is on residential location; the behaviour of firms is treated more briefly and abstractly. The central concept of this theory is the bid-rent function for each household and/or firm. The bid rent of a household is defined as the "maximum rent that can be paid for a unit of land (e.g. per
acre) some distance from the city center if the household is to maintain a given level of utility " (Hoover and Giarratani 1984) The bid rent curve R of the actual land rents in the city reflects the outcome of a bidding process by which land is allocated to competing uses (residential demanded by households and commercial/ industrial demanded by firms).
As in von Thunens theory, a monocentric, flat, continuous and uniform urban area is assumed. The city center is the central business district (CBD) where households work and shop. A households utility (or satisfaction) is assumed to depend on: housing (of a given lot size), distance from the city center (reflected in the transportation cost) and all other goods (Chapin and Kaiser 1979, Romanos 1976). The household allocates its fixed budget among these three components with the aim to maximize its utility. Its preferences determine the trade-offs it is willing to make among the above three items. The price of housing and of other goods is independent of the quantities purchased. The price of housing and of commuting depends on distance from the city center. There is a distance decay relationship between land rent and distance from the CBD. The further a household lives from the city center, the more it will have to spend on commuting and the less it will be able to spend on housing. Based on these assumptions, the bid-rent curves are downward sloping (rent decreases with distance from the city center to offset transport costs) and singlevalued; i.e. for a given distance from the CBD only one rent bid is associated with a given level of utility. The steepness of the slope of the bid rent curve depends on transport costs and the households (or the firms) demand for space. Steeper curves are associated with higher transport costs and/or less demand for space (hence, higher value attached to accessibility). Flatter curves are associated with lower transfer costs and/or higher demand for space (and, hence, preference for more outlying locations).
Finally, lower bid-rent curves are associated with greater utility as, assuming fixed budgets, at any given distance from the CBD, if a lower rent bid is accepted, more goods can be consumed (Hoover and Giarratani 1984, 154).
Alonsos theory distinguishes two stages in the residential location process. In the first stage, the theory derives individual equilibria for households (and firms) on the basis of their bid-rent functions (one for each level of utility/satisfaction). Households, possessing perfect knowledge of the actual land rent structure in the city and of the transport costs, choose a location that maximizes their utility subject to their budget constraint; this is the point where the lowest bid-rent curve touches the actual rent curve. At a second stage, the equilibrium for the entire urban market is derived through a market clearing mechanism that starts from the CBD and involves potential users bidding for land and landlords selling or renting the land to the highest bidder. The most central locations go to the highest bidders (steepest bid-rent functions). Remaining available land goes to the next bidder and the process continues until the last user is located at the edge of the city. The price of land at the edge of the city is adjusted to agree with the actual price there (basically, the value of agricultural land close to the edge of the city). However, as Romanos (1976, 71) notes, because Alonso does not assume a perfectly competitive market and bid-rents are not unique but members of families of bid-rent curves, the theory cannot provide an equilibrium market solution as it was the case with von Thunens theory. In order to derive equilibrium land use patterns, additional assumptions must be made about the level of utility of the bidders or the number and types of bidders. In the simplest case, equilibrium in the land market results if all bidders have identical incomes and preferences; hence, a common set of bid rent curves and land rents within the city coincide with this set.
Alonso urban land rent theory provides a static description and explanation of urban (mainly residential) land use. In the present context, its significance lies in its explicit treatment of the actual amount of housing consumed; hence, it is a theory of (residential) land use. The bidding process is a realistic account of the way land is allocated to various competing users and it has been used in the theoretical and modeling exercises that ensued Alonso original contribution (see, for example, Romanos 1976, Brueckner 1986). However, the mechanism of land use change is implicit; it has to be elicited from the factors which the model assumes to determine the steepness and the height of the bid-rent curves. These depend on preferences for various locations within the city (measured as distances from the city center) and income. Hence, when preferences and income change, the land use system will move to another equilibrium position. Other potentially important influences on the bid-rent curves such as socio-cultural and political forces are not accounted for directly by the theory.
While Alonsos theory has been used extensively in analyses of urban spatial structure as well as in impact analysis of urban policies (see, for example, Bockstael and Irwin 2000), it suffers from several restrictive assumptions which limit its usefulness in approximating observed land use patterns as well as in analyzing land use change. The two most important perhaps are the assumption of a single, exogenously-given center (the monocentric city assumption) and the importance assigned to accessibility to this single center in explaining urban spatial structure. The theory does not consider a number of interrelated factors which, on the one hand, capture the particular forms that characterize modern urban agglomerations and, on the other, account for the dynamics of urban land use change. The most important of them include: the existence of more than one centers in metropolitan areas,
externalities (e.g. traffic congestion, air pollution), increasing returns to scale, imperfect markets, the durability and inflexibility of the housing stock, technological change (see, among others, Romanos 1976, Quigley 1985, Arnott 1986, Krugman 1995, Bockstael and Irwin 2000).
The monocentric city assumption has several implications for the land use patterns derived from this theory. First, as the size of an urban area increases, employment can by no means be concentrated in the CBD. Real world evidence reveals the continuous decentralization of employment centers in large metropolitan areas and the development of polycentric cities coupled with a declining or minimal growth of the CBD. At the same time, the role of land developers in determining the location and timing of ex-urban development is critical in the evolution of urban spatial structure. Hence, the urban land market theory assigns to the CBD greater importance than it actually deserves. Secondly, the analysis of land uses in the rest of the urban area is not adequate as other nonresidential uses are present (Romanos 1976). The heterogeneity of the natural environment in which the city is embedded is also ignored as a factor which may distort the analytical tractability of the bid-rent functions. Thirdly, the monocentric city assumption is associated with another assumption; that of constant returns to scale in the production of goods and services in the CBD. However, increasing demand for these goods and services, implies increased demand for transportation to the CBD, hence, increasing diseconomies associated with congestion, pollution, etc. that lead to decreasing returns of central city production. Therefore, the assumption of constant returns is at variance with the importance attributed to the city center.
Several attempts have been made to modify the original theory to account for the presence of more than one centers, more than one places of employment, and the existence of externalities, among others, whose discussion is beyond the intent of this contribution (see, for example, Solow 1973, Romanos 1976, Shieh 1987). These modifications, however, have aimed mostly at providing improved operational versions of the theory i.e. of the urban land market model while leaving its basic tenets intact. Therefore, the basic limitations of the theory, the absence of a dynamic explanatory schema of land use change, still remain. A brief discussion of more recent theoretical attempts at analyzing the evolution of urban spatial structure is undertaken in the next section.
Agent-Based Theories of Urban and Regional Spatial Structure
A broader set of theoretical schemata have been proposed for the description and explanation of the evolution of urban spatial structure which focus on the agents operating in urban contexts and the interactions among them which influence the resulting spatial patterns. This group of "agent-based" theories does not always treat explicitly land use as in Alonsos theory (i.e. as amount of space consumed by an agent) and their emphasis is mostly on agents characteristics as well as on the processes through which and the conditions under which agents interact in space. In other words, they are indirect theories of land use change for the present purposes. The following is a schematic presentation of certain of their particular features and the gist of their approach to the analysis of the evolution of urban spatial structure which render them more realistic frameworks for the study of land use change.
The agent-based theoretical approaches differ from the micro-economic approach of the urban land rent theory in that they stress particular features of these
agents which relate to their linkages and interactions in space; broadly speaking, they take into account the market structure of the urban setting. Several of the ideas contained in these approaches can be found in earlier theories of urban and regional spatial structure and development (e.g. Christaller 1966, Pred 1966, Myrdal 1957, Henderson 1974) as well as in broader theories stressing the role of human agency in general in the evolution of spatial form (for a collection of references see, Pred 1985). However, it seems that the synthesis of these ideas into more rigorous theoretical schemata as well as their use in building models of change began in the early 1980s. Agents are assumed to operate in the context of markets mostly. Imperfect competition is allowed in several approaches (see Krugman 1995). To explain the clustering or the dispersion of certain uses in space which is observed in the real world, the concepts of external economies, externalities, backward and forward linkages between activities, and durability of urban development are employed in the broader schema of centripetal and centrifugal forces impinging on agents behaviour.
Centripetal forces account for the cohesion and clustering of certain activities in space (Hoover and Giarratani 1984, 1999; Krugman 1995). These forces derive from the existence of economies of scale (increasing returns to scale) and agglomeration economies in certain locations. Activities linked by means of forward (being suppliers of goods and services to other activities) and backward (being buyers of goods and services) linkages are held together in a given location. Forward and backward linkages among activities are called also vertical linkages (Hoover and Giarratani 1984, 1999). A circular relationship develops also between the location of the market and the location of an activity; i.e. activities concentrate where the market is large and the market is large because it contains a large number of activities (see Krugman 1995).
At the same time, however, centrifugal forces work against the clustering of activities in space and cause their dispersion. These concern the horizontal relationships among activities (Hoover and Giarratani 1984, 1999) and involve the competition among activities for markets and/or inputs as well as the cost of transport to the sources of inputs or to the markets. Increased competition for certain locations having locational advantages for certain activities or containing scarce resources drive land rents up. Some activities are driven away to sites where land rents are lower. Some others may remain in the same location; however, because of the presence of other advantages it offers which render it profitable for its user. Other centrifugal forces are associated with various kinds of diseconomies or negative externalities caused by either the clustering of activities or owing to the particular nature of certain activities which reduce the potential benefits to be reaped from being at a specific location.
Several other factors enter the decision making calculus of individual agents which may function either as centripetal or centrifugal forces that impact on their utility function and, thus, affect their locational choices. These include speculation, in particular land speculation, the durability of the physical infrastructure associated with particular activities, the cost of land conversion to other uses (the land use inertia), other local conditions, and "historical chance" (Arthur 1989). The spatial result of the interaction of centrifugal and centripetal forces on individual but interdependent agents is the generation of monocentric, polycentric, dispersed, linear, etc. urban land use patterns. In other words, the resulting spatial structure is characterized by multiple instead of a single equilibrium pattern.
The main point put forward by agent-based theories is that the decisions and actions of agents are influenced by past locational decisions and they influence future location decisions. Thus, the resulting spatial patterns (the spatial distribution of agents and of the associated activities) are endogenously determined. Variations and changes in the factors underlying these patterns mentioned before give rise to land use change; or, in broader terms, they explain the evolution of the spatial system over time. Agent-based theories have been used in building corresponding models of locational behaviour but several of the interactions these theories postulate have not received empirical testing yet. Finally, most theories focus on equilibrium patterns while real world experience shows that most of the time the process of land use change is out of equilibrium (Bockstael and Irwin 1999). Nevertheless, these theories represent a considerable improvement over the monocentric model of the 1960s and exhibit a flexibility which permits the consideration of many more factors even idiosyncratic which account for land use change as well as for the impacts of this change.
2.2 Literature Review Land use and land cover is dynamic in nature and is an important factor for the comprehension of the interaction and relationship of anthropogenic activities with the environment. Knowledge of the nature of land use and land cover change and their configuration across spatial and temporal scales is consequently indispensable for sustainable environmental management and development (Turner et al 1994). According to Long et al stress that urban landscapes are exemplified by the large concentration of population, and fast expansion of urban zones which lead to alteration in the land use and land cover configuration that consequently impacts the landscape environment (Long et al, 2008). Remote sensing technology is principally
appropriate for mapping environmental phenomena such as land use and land cover as field-based mapping is practically difficult, remote sensing observations provide continuous monitoring across varied spatial and temporal scales (Gibson and Power, 2000). The spatial, temporal and spectral characteristics of the remote sensing data are effectively used in land use and land cover change mapping, hence helping in decision making for sustainable land resource management (Berlanga-Robles and Ruiz-Luna 2002). Change detection in land use and land cover can be performed on a temporal scale such as a decade to assess landscape change caused due to anthropogenic activities on the land (Gibson and Power, 2000). These anthropogenic activities on land are as a result of rapid urbanisation and industrialisation. Land use and land cover change have been recognised as important drivers of global environment change (Turner et al., 1994). Land use and land cover are two separate terminologies which are often used interchangeably (Dimyati et al., 1996). According to Longley (2001), land cover refers to the physical materials on the surface of a given parcel of land, while land use refers to the human activities that takes place on or make use of land e.g. residential, commercial, industrial etc. Jensen (2007) in his investigation of urban landscape perceived land use as a way by which human beings utilise land while land cover exists as a natural environmental system. Remote sensing and Geographic Information Science (GIS) technologies have been utilised productively to detect and quantify changes in the landscape and the consequential environmental impacts. Studies have utilised remote sensed data to examine urban land changes in modern times with conclusions showing varying degree of different patterns of urban expansion and development in which could be associated with specific environmental factors (Long et al., 2008).
Prakasam (2010) studied the land use and land cover change in Kodaikanal region of Western Ghats in Tamilnadu State of India to observe changes during a span of 40 years from 1969 to 2008, using Landsat satellite data and performing supervised classification techniques, he found that 70% of the region was covered in forests in 1969 but has decreased to 33% in 2008, The built-up lands have increased from 3% to 21% showing that the region is affected by rapid urbanisation which is leading to adverse environmental effects for the identified bio-diversity rich region of Kodaikanal. Samant and Subramanyam (1998) utilised Landsat TM imageries to study the land use change in Bombay (Mumbai), India which is the highest populated metropolis of India and found a remarkable increase in built-up land by 300% and a reduction in forests by 55%, due to the increasing pressure of urban expansion to cope up with the population rise. He carried out a study using land use maps for 1925 and 1967 and compared them with Landsat imagery in 1994 to quantify a change spanning from 1925 to 1994. Zubair (2006) utilised remote sensing and GIS technologies to detect the land use and land cover changes in Ilorin, Nigeria from 1972 to 2001 through Landsat TM images of 1972, 1986, and 2001, using Maximum likelihood algorithm of supervised classification method was to delineate five land use and land cover classes for the study, namely: farmland, wasteland, forest, built-up and water-bodies from images. The results attained demonstrated that from 1972 through to 1986, the farmland declined by 7% while the built-up land increased by 8%, the decline in the farmland was recognised as the transition from farming to urban activities due to the creation of Kwara State and with Ilorin being the capital city. The augmentation in the built-up land was attributed to the rapid urbanisation and redevelopment projects as a result of state creation.
Thus land use change in terms of urbanisation and industrialisation have a critical impact on the environment, the changes quantified using remote sensing technologies provide observations which may show critical adverse and undesirable environmental impacts, hence requiring crucial sustainable land management policies and practices to avoid the endangering of the environment. In recent times, efforts on settlement studies is being directed towards its growth i.e. increase in the rate of land use consumption. According to Meyer (1999) every parcel of land on the Earths surface is unique in the cover it possesses. Land use and land cover are distinct yet closely linked characteristics of the Earths surface. The use to which we put land could be grazing, agriculture, urban development, logging, and mining among many others all which culminate to form what we know as settlement. An essential ingredient in the study of settlement which is growth is its ability to affect biodiversity. Many shifting land use patterns driven by a variety of social causes, result in land cover changes that affects biodiversity, water and radiation budgets, trace gas emissions and other processes that come together to affect climate and biosphere (Riebsame et al, 1994). In order to effectively monitor settlement growth, it is not only necessary to have the information on existing land use land cover but also the capability to monitor the dynamics of land use resulting out of both changing demands of increasing population and forces of nature acting to shape the landscape. Conventional ground methods of land use mapping are labour intensive, time consuming and are done relatively infrequently. These maps soon become outdated with the passage of time, particularly in a rapid changing environment. In fact according to Olorunfemi (1983), monitoring changes and time series analysis is quite difficult with traditional method of surveying. In recent years, satellite remote sensing
techniques have been developed, which have proved to be of immense value for preparing accurate land use land cover maps and monitoring changes at regular intervals of time. In case of inaccessible region, this technique is perhaps the only method of obtaining the required data on a cost and time effective basis. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Change detection is an important process in monitoring and managing natural resources and urban development (settlement growth) because it provides quantitative analysis of the spatial distribution of the population of interest. The basis of using remote sensing data for change detection in settlement is that changes in land use result in changes in radiance values which can be remotely sensed. Techniques to perform change detection with satellite imagery have become numerous as a result of increasing versatility in manipulating digital data and increasing computer power. An analysis of land use and land cover changes using the combination of MSS Landsat and land use map of Indonesia (Dimyati, 1995) reveals that land use land cover change were evaluated by using remote sensing to calculate the index of changes which was done by the superimposition of land use land cover images of 1972, 1984 and land use maps of 1990. This was done to analyze the pattern of change in the area, which was rather difficult with the traditional method of surveying as noted by Olorunfemi in 1983 when he was using aerial photographic approach to monitor urban land use in developing countries with Ilorin in Nigeria as the case study. In a similar study, Fabiyi (2006) opined that urban land uses had been increasingly subjected to changes of different forms, sorts and types since the urban explosion of the 1970s after adopting the technique of remote sensing to evaluate land use land cover changes in the traditional urban settlement of Ibadan. It was observed
that there are considerable dynamic changes in Ibadan metropolis and the major contributor to changes is the vegetal cover, low density and sprawl development. There are growths by fission within and even the high density areas and the catchments area for rivers are also location for urban spatial growth or change. Also, Adeniyi and Omojola, (1999) in their land use land cover change evaluation in Sokoto Rima Basin of North Western Nigeria based on Archival Remote Sensing and GIS techniques, used aerial photographs, Landsat MSS, SPOT XS/ Panchromatic image Transparency and Topographic map sheets to study changes in the two dams (Sokoto and Guronyo) between 1962 and 1986. The work revealed that land use land cover of both areas was unchanged before the construction while settlement alone covered most part of the area. However, during the post - dam era, land use /land cover classes changed but with settlement still remaining the largest. Daniel et al, (2002) in their comparison of land use land cover change detection methods, made use of 5 methods viz; traditional post classification cross tabulation, cross correlation analysis, neural networks, knowledge based expert systems, and image segmentation and object oriented classification. A combination of direct T1 and T2 change detection as well as post classification analysis was employed. Nine land use land cover classes were selected for analysis. They observed that there are merits to each of the five methods examined, and that, at the point of their research, no single approach can solve the land use change detection problem. Arvind and Nathawat (2006) carried out a study on land use land cover mapping of Panchkula, Ambala and Yamunanger districts, Hangana State in India. They observed that the heterogeneous climate and physiographic conditions in these districts has resulted in the development of different land use land cover in these districts, an evaluation by digital analysis of satellite data indicates that majority of
areas in these districts are used for agricultural purpose. The hilly regions exhibit fair development of reserved forests. It is inferred that land use land cover pattern in the area are generally controlled by agro climatic conditions, ground water potential and a host of other factors. From the few studies that have been reviewed above, it is revealed that a lot still need to be done in the area of land use and land cover change mapping particularly because of the vast size of the study area and the rapidity at which changes are taking place. This calls for an urgent need for the use of some modern methods of digital image interpretation methods for proper management of land in Nigeria. The diversity is decreasing and the need to balance human well-being and environmental sustainability involves adjusting the way we use ecosystem goods and services produced by the land (Reid et al., 2005). Of course land use is at the centre of these trade-offs. Much of global land-change science has improved our understanding of connections between land use and environment (Reid, et al., 2005).
2.2.1 Empirical Evidence on the Causes of Land-Use Change Land use is defined by the purposes for which humans exploit the land cover. There is high variability in time and space in biophysical environments, socioeconomic activities, and cultural contexts that are associated with land-use change. Identifying the causes of land-use change requires an understanding of how people make land-use decisions and how various factors interact in specific contexts to influence decision making on land use. Decision making is influenced by factors at the local, regional, or global scale. Proximate (or direct) causes of land-use change constitute human activities or immediate actions that originate from intended land use and directly affect land cover (Ojima et. al, 1994). They involve a physical action on land cover. Underlying (or indirect or root) causes are fundamental forces that
underpin the more proximate causes of land-cover change. They operate more diffusely (i.e., from a distance), often by altering one or more proximate causes (Leemans et. al, 2003). Underlying causes are formed by a complex of social, political, economic, demographic, technological, cultural, and biophysical variables that constitute initial conditions in the human-environment relations and are structural (or systemic) in nature. Proximate causes generally operate at the local level (individual farms, households, or communities). By contrast, underlying causes may originate from the regional (districts, provinces, or country) or even global levels, with complex interplays between levels of organization. Underlying causes are often exogenous to the local communities managing land and are thus uncontrollable by these communities. Only some local-scale factors are endogenous to decision makers. An important system property associated with changes in land use is feedback that can either accentuate or amplify the speed, intensity, or mode of land change, or constitute human mitigating forces, for example via institutional actions that dampen, impede, or counteract factors or their impacts. Examples are the direct regulation of access to land resources, market adjustments, or informal social regulations (e.g., shared norms and values that give rise to shared land management practices). Place-based research followed by systematic comparative analyses of case studies of land-use dynamics have helped to improve understanding of the causes of land-use change. These syntheses produced general insights on sectoral causes of land-use change and on the mode of interaction between various causes. They identified dominant pathwaysalso referred to as spirals, trajectories, or syndromesleading to specific types of change. What has been lacking so far is the
development of an integrative framework that would provide a unifying theory for these insights and pathways of land-use change and a more process oriented understanding of how multiple macro-structural variables interact to affect micro agency with respect to land. 2.2.2 General Insights on Sectoral Causes of Land-Use Change i. Multiple Causes- land-use change is always caused by multiple interacting factors originating from different levels of organization of the coupled human environment systems. The mix of driving forces of land-use change varies in time and space, according to specific human-environment conditions. Driving forces can be slow variables, with long turnover times, which determine the boundaries of sustainability and collectively govern the land use trajectory (such as the spread of salinity in irrigation schemes or declining infant mortality), or fast variables, with short turnover times (such as food aid or climatic variability associated with El Nino oscillation). Biophysical drivers may be as important as human drivers. The former define the natural capacity or predisposing conditions for land-use changes. The set of abiotic and biotic factors that determine this natural capacity varies among localities and regions. Trigger events, whether these are biophysical (a drought or hurricane) or socioeconomic (a war or economic crisis), also drive landuse changes. Changes are generally driven by a combination of factors that work gradually and factors that happen intermittently (Lambin et. al, 2001). ii. Natural Variability- natural environmental change and variability interact with human causes of land-use change. Highly variable ecosystem conditions driven by climatic variations amplify the pressures arising from high demands on land resources, especially under dry to sub-humid climatic conditions. Natural and socioeconomic changes may operate as synchronous but independent events. In the
Iberian Peninsula during the sixteenth and seventeenth centuries, the peak of the Little Ice Age occurred almost simultaneously with large-scale clearing for cultivated land following the consolidation of Christian rule over the region. This cultivation triggered changes in surface hydrology and significant soil erosion (Puidgefabregas, 1998). Natural variability may also lead to socioeconomic unsustainability, for example when unusually wet conditions alter the perception of drought risks and generate overstocking on rangelands. When drier conditions return, the livestock management practices are ill adapted and cause land degradation. This overstocking happened several times in Australia and, in the 1970s, in the African Sahel (Puidgefabregas, 1998). Land-use change, such as cropland expansion in dry lands, may also increase the vulnerability of human-environment systems to climatic fluctuations and thereby trigger land degradation. iii. Economic and Technological Factors- available case studies highlight that, at the timescale of a couple of decades or less, land-use changes mostly result from individual and social responses to changing economic conditions, which are mediated by institutional factors. Opportunities and constraints for new land uses are created by markets and policies and are increasingly influenced by global factors. Economic factors and policies define a range of variables that have a direct impact on the decision making by land managers, e.g., input and output prices, taxes, subsidies, production and transportation costs, capital flows and investments, credit access, trade, and technology (Barbier, 1997). Internal consumption affects land less than external demand, so subsistence croplands consequently decrease while land under crops for markets increases with a parallel increase in agricultural intensity. Market
access is largely conditioned by state investments in transportation infrastructure. The unequal distribution of wealth between households, countries, and regions determines geographic differences in economic opportunities and constraints. It affects, for example, who is able to develop, use, and profit from new technologies that increase efficiency in land management. Improving agricultural technologyas much as providing secure land tenure and giving farmers better access to credit and marketscan potentially encourage more deforestation rather than relieving pressure on the forests. The differing impact of agricultural development on forest conversion depends on how the new technologies affect the labour market and migration, whether the crops are sold locally or globally, how profitable farming is at the forest frontier, as well as on the capital and labour intensity of the new technologies (Angelsen et. al, 2001). iv. Demographic Factors- at longer timescales, both increases and decreases of a given population also have a large impact on land use. Demographic change does not only imply the shift from high to low rates of fertility and mortality (as suggested by the demographic transition), but it is also associated with the development of households and features of their life cycle. The family or life-cycle features relate mainly to labour availability at the level of households, which is linked to migration, urbanization, and the breakdown of extended families into several nuclear families. As an example of the latter phenomenon, the splintering of family herds in the West African Sudan-Sahel zone over the past 25 yearsdue to increases in nuclear households and the transfer of livestock wealth from herding families to merchants, agriculturalists, and government officialsled to increased investment in crop production, reduced labour availability among pastoral households, lower energy and
skills applied to livestock husbandry, and reduced livestock mobility, which increased the risk of land degradation (Turner, 1999). Fuel-wood demand by households in Africa differs between nuclear family units and larger consuming units; the latter are generally more energy efficient. Small consuming units thus cause more forest degradation, especially in peri-urban environments (Cline-Cole et. al, 1990). The internal dynamics of traditional and colonist families in humid forest frontiers in South America, which are mainly related to households capital and labour constraints, explain the micro level dynamics of land-cover modification by forest types, land quality, and gender division, as well as the changing social context of deforestation in the Amazon Basin. Forest clearing is caused by a variety of actors, with differing effects: recent in-migrants practice slashand burn agriculture; their childrens families shift to fallow agriculture; long-settled families have diversified production; small families have crop/livestock combinations (associated with high rates of forest losses); large families have perennial production modes (associated with low rates of forest losses); and small ranchers are displaced by large ranchers, and upland croppers are displaced by lowland ranchers. Life-cycle features arise from and affect rural as well as urban environments. They result from households strategic responses to both economic opportunities (for example, market signals indicating higher crop profitability) and constraints (due to economic crisis conditions, for example). They shape the trajectory of land-use change, which itself affects the households economic status. Therefore, a population analysis of great nuances is required. Migration in its various forms is the most important demographic factor causing land-use change at timescales of a couple of decades. Migration operates as a significant driver with other non-demographic factors, such as government policies, changes in consumption
patterns, economic integration, and globalization. Some policies resulting in land-use change either provoke or are intricately linked with increased migration. The growth of urban aspirations, the urban-rural population distribution, and the impact of rapidly growing cities on ecosystem goods and services are likely to become dominant factors in land-use change in the decades to come, be it in major urban or peri-urban areas or in remote hinterland or watershed areas. Many new urban dwellers in developing countries still own rural landholdings. Although the growth of urban areas creates new local and regional markets for livestock, timber, and agricultural products, it also increases urban remittances to the countryside (Lambin et. al, 2001). v. Institutional Factors- to explain land-use changes, it is also important to understand institutions (political, legal, economic, and traditional) and their interactions with individual decision making. Access to land, labour, capital, technology, and information is structured (and is frequently constrained) by local and national policies and institutions (Batterbury et. al, 1999). Land managers have varying capabilities to participate in and to define these institutions. Relevant nonmarket institutions include: property-rights regimes; environmental policies; decision making systems on resource management (e.g., decentralization, democratization, and the role of the public, of civil society, and of local communities in decision making); information systems related to environmental indicators as they determine perception of changes in ecosystems; social networks representing specific interests related to resource management; conflict resolution systems concerning access to resources; and institutions that govern the distribution of resources and thus control economic differentiation.
There is often a mismatch between environmental signals reaching local populations and the macro level institutions. Therefore, the rules used for making policies are important to ensure that local users are able to influence resourcemanagement institutions (Poteete et. al, 2004). Institutions need to be considered at various scales, to identify the local mediating factors and adaptive strategies and to understand their interactions with national- and international-level institutions. Many land-use changes are due to ill-defined policies and weak institutional enforcement, as exemplified by the widespread illegal logging in Indonesia linked to corruption and to the devolving of forest management responsibilities to the district level (Jepson et. al, 2001). On the other hand, recovery or restoration of land is also possible with appropriate land-use policies. Consolidation of landholdings and the shift from communal, traditional systems to formal, state-sanctioned regimes is a trend observed throughout the developing world (McConnell et. al, 2003). Examples of policies that influence land-use change are state policies to attain self-sufficiency in food; taxation, fiscal incentives, subsidies, and credits; price controls on agricultural inputs and outputs; decentralization; infrastructure support; investments in monitoring and formally guarding natural resources; resource commodification; land consolidation, nationalization, and collectivization; structural adjustment measures; and international environmental agreements. With increasingly interconnected market forces and the rise of international environmental conventions, the impact of institutional drivers moves from the local to the global level. Land degradation is more prominent when macro-policies, either capitalist or socialist, undermine local adaptation strategies. In particular, perverse subsidies for road construction, agricultural production, forestry, and so forth are
thought to be one of the biggest impediments to environmental sustainability (Myers, Kent. 2001) vi. Cultural Factors- numerous cultural factors also influence decision making on land use. Land managers have various motivations, collective memories, and personal histories. Their attitudes, values, beliefs, and individual perceptions influence landuse decisionsfor instance through their perception of and attitude toward risk. Landuse decisions have intended and unintended consequences on ecosystems; these depend on the knowledge, information, and management skills available to land managers. Culture is often linked with political and economic inequalities, e.g., the status of women or ethnic minorities (Leemans et. al, 2003) that affect resource access and land use. Understanding the controlling models of various factors may thus explain the management of resources, adaptive strategies, compliance or resistance to policies, or social learning and therefore social resilience in the face of land-use change. vii. Globalization- researchers have recently argued that cross-cutting the local and national pathways of land-use/cover change are the many processes of globalization that amplify or attenuate the driving forces by removing regional barriers, weakening national connections, and increasing the interdependency among people and between nations. Globalization as such is not a driver of land-use change but is a process that underlies the other driving forces discussed above. Globalization accelerates or buffers the impact of these drivers on land use. For example, Barbier (2000) identified land-use change as the immediate and principal environmental impact of economic liberalization and globalizationmostly trade liberalization and reforms to open up the agro-industrial sectorin Ghana and Mexico.
Directly, increased agricultural productivity triggered forest conversion and increased land degradation from unsustainable production methods. Indirectly, agroindustrial development displaced the landless and rural poor, who were then pushed to marginal agricultural lands or to the forest frontier. Although the environmental effects of macroeconomic policies and trade liberalization are particularly important in countries with fragile ecosystems (e.g., semiarid lands and mangrove forests), international trade and other forms of globalization can also improve environmental conditions through green certification and ecolabeling, wider and more rapid spread of technologies, better media coverage allowing international pressure on states that degrade their resources, and free circulation of people, which provides better educational and employment opportunities. Naylor et al. (2002) showed, for example, that in a small island of Micronesia, international migration, foreign aid, and monetary remittances from family members living overseas have relieved the pressures of economic crowding on mangrove forests, despite an increase in population and a decline in local government jobs. International institutions (including organizations within the U. N. system and nongovernmental organizations) can be instrumental in promoting and funding policies aimed at combating environmental degradation, setting political agendas, building consensus, and creating constraints and incentives for sustainable land management (Lambin et. al, 2002).
CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter gives an overview of data requirements, data collection and the methods applied for data processing as well as the modelling approach, geographical dataset creation and analytical techniques adopted for the study. The procedure adopted in this research work forms the basis for deriving statistics of land use changes, and subsequently in the overall, the findings. The methodology workflow to achieve the objectives as shown below is divided into four steps: Step I: Data acquisition: acquisition of satellite image, base map, literatures and population figures; Step II: Image data processing; GPS survey for Ground Control Points (GCPs); geo-referencing; ground truthing and collection of field-based data; data entry and processing; Step III: Data integration and analysis: Image pixel sampling training and classification with a view to identifying the land uses in the study area Step IV: Evaluation of land use change through overlay analysis and projection of future land use through markov and cellular automata model based on the results in Stage III
3.2 Methodology Workflow
Step I
Landsat TM 1990 Georeferenced
Landsat ETM+ 2000 Georeferenced
Nigeria-Sat1 2010 Georeferenced
Image Caliberation
Step II
Pixel Sampling Training
Supervised Classification (Maximum Likelihood)
Step III
Classified Image 1990 Classified Image 2000 Classified Image 2010
Land Use/Land Cover Change
Change Evaluation
Step IV
Future Projection
Figure 3.1: methodology flowchart
3.3 Nature and Sources of Data Required 3.3.1 Spatial Data and Source The study employed some spatial data which were secondarily sourced. The following are the datasets that formed the spatial data for the study, Landsat TM of 1990, and Landsat ETM+ of 2010, NigeriaSat-1 imagery of 2010 and Nigeria Administrative map. For the study, Landsat TM 1990, ETM+ 2000 and NigeriaSat-1 2010 satellite images of Akinyele Local Government Area were acquired. Both 1990 and 2000 were obtained from Global Land Cover Facility (GLCF), an Earth Science Data Interface, while that of 2010 was obtained from Cooperative Network Information (COPINE) OAU, an arm of National Space Research and Development Agency (NASRDA) Abuja. It is important to state that the local government area which was carved out using the local government boundary map and Nigerian Administrative map was also obtained from COPINE.
Figure 3.2: Enhanced Landsat TM imagery of 1990
Figure 3.3: Enhanced Landsat ETM+ imagery of 2000
Figure 3.4: Enhanced NigeriaSat-1 imagery of 2010
The figures above represent the Landsat TM & ETM+, and NigeriaSat-1 imageries of 1990, 2000 and 2010 respectively, with the boundary of the study area super imposed on each of the imageries. All the analyses done in this research with respect to image classification, identification of different land uses, extraction of land use/cover map and future projection map were carried out from these set of enhanced imageries. The scenes for digital imageries usually comes in rectangular shape as was depicted by the plates above, and to avoid or limit the level of errors in the analyses and inferences made from the digital imageries, the scenes were clipped in ArcGIS environment with the local government shape-file extracted from the boundary of the study area. Thus, the resulting maps (to be seen in the latter chapter) created from the result of the analyses will be in the shape of the study area clipped from the imageries.
3.3.2 Primary Data The research identified and examined land use and pattern of change in the study area, in achieving this, an attempt was made to develop a methodology helpful to identify and analyse the dynamics of changes. During the field survey, coordinate information was collected with the use of GPS (Global Positioning System) device to take ground control points (GCPs) for georeferencing and ground-truthing. An outline of the secondary and primary set of data used for the research was presented in Table 3.1 below. The bulk of the data used are secondary, the acquired images for the three years understudy for the research, Population figures for the years- which were projected from the data made available by the Nigerian population commission, and the boundary for the study area was extracted from the administrative map, while ground control points (GCPs) acquired through the use of global positioning system (GPS) device were used to compliment the knowledge of the author about the study area in georeferencing and classification of the images according to what was obtainable in the study area.
Table 3.1: attributes of datasets Source Year S/n Data 1 Landsat TM& http;//glcf.umiacs.umd.edu/ 1990, 2000 ETM+/ & COPINE, NARSDA 2010 NigeriaSat-1 2 COPINE, NASRDA Nigerian Administrative map Population data GPS Coordinate National Commission Field Survey 2011
Scale/Path&Row Resolution 190/055 30m
32m 1:150,000 -------------
Population 1991 & 2006 2011
3.4 Methods of Data Analysis The following methods of data analysis were adopted in order to achieve the goal set for the research work, it is imperative to note that the decision to adopt these methods was informed by the review of relevant works done in the past in relation to the subject matter and to the peculiarity of the study area.
3.4.1 Pre-Processing and Processing After the acquisition of the data, the standard image pre-processing and processing techniques of extraction, layer stacking, geometric correction/ georeferencing, classification and change evaluation were performed on the Landsat TM 1990, ETM+ 2000 imageries, and NigeriaSat-1 2010 imagery obtained on different dates. The rationale for adopting the years was to have a wide span of period between the first and the last year in order to obtain a meaningful inference from the research. The 1990, 2000 and 2010 imageries were geometrically corrected to a common Universal Transverse Mercator coordinate system, Datum WGS 1984; Zone 31N.
Steps Involved in the Processing i. The three set of imageries in tif format were imported via Geogateway into the ILWIS environment, this is done to allow for further processing, ii. The next step involved the colour composite; this process was done to bring the imagery to conventional colours which will aid in the identification of the different land covers present in the study area. A false colour composite that combined Bands 4, 3 and 2 in that order was obtained from the Landsat imageries, while Bands 1, 2 and 3 in that order was also obtained from the NigeriaSat-1 imagery, iii. Since imageries normally comes in scenes, and these scenes cover a vast area of land which in some cases could combine up to 5 states within a country. Its thus necessary to reduce the imageries to the work area. The imageries were submapped using the coordinates generated from the study area shape-file. The submapped imageries were shown in Plates 3.1 3.3, iv. Colour separation was carried out on the sub-mapped imageries to prepare for the enhancement of the imageries, v. Filtering process was then carried out to make correction for all atmospheric errors in the imageries, so that the real situation on the earth surface will be portrayed by the imageries and thus minimise errors from the resulting analyses to be further carried out, vi. Then map-list was created from the combination of the bands that gave the false colour composite and subsequently a sample set was created to allow for pixel sampling, which in turn trained the system on the classification processs. The assessment of land use and land cover was done by adopting a classification scheme for the Landsat images for years 1990, 2000 and 2010 and a Supervised classification (Maximum likelihood) was carried out based on ancillary data from thematic maps and other
information as well as literature sources. An effective land use management planning is represented by a classification scheme as a necessary component (Jensen, 1996). The satellite imageries were classified into land use classes. Anderson et al (1976) was adopted and modified to classify the images into four land use classes: built-up (impervious layers such as residential and commercial services, office blocks, educational centres, hospitals, manufacturing industries, motor roads etc); water-body (Rivers, lakes, ponds, lagoons, dams, marsh wetlands); farmland (all cultivated areas such as farmlands, crop fields including vegetable gardens, plantations, fallow plots) and forest (protective forests, timber forest, economic forest, firewood forest and forests of special use like reserves etc). Training sites were generated on the images by on-screen digitizing for each land cover classes derived from image of different band combination. A supervised maximum likelihood classification was implemented for the three images. This was due to the fact that the author has familiarized himself with the study area through dedicated field observation, whereby the spectra characteristics of the classes in the sampled area has been identified. Ground truth information was used to assess the accuracy of the classification. The training site was shown in Table 3.2 below. Based on the prior knowledge of the study area for over 10 years and a reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area. The classification scheme developed gives a rather broad classification where the land use land cover was identified and presented for further analyses. Table 3.2: The selected training sites S/N 1 2 3 4 Training Sample Forest Farmland Built-up Water-body Description forest area, firewood forest and special use like reserves etc area occupied by farming activities developed area occupied by people for habitation area occupied by stream, pond or river, dam
3.4.2 Maximum Likelihood Classification The image classification process was carried out using maximum likelihood classification, the intent of this process was to categorise all pixels in a digital image into one of several land cover classes, or themes. As explained earlier on, on-screen digitizing was done based on the pixel training sample shown in Table 3.2 above, to arrive at a categorised layers of identified land uses in the study area. This categorised data were then used to produce thematic maps of the land cover. Maximum Likelihood Classification is a statistical decision criterion to assist in the classification of overlapping signatures; pixels are assigned to the class of highest probability according to the pixel sampling training done earlier on in the analysis. This method of classification was adopted because it was adjudged to give the most accurate result amongst the other types, it assumed that classes in the input data have Gaussian distribution and the signatures were well selected.
3.4.3 Markov Chain Analysis The Markovian process is used to model the future state of the study area, purely on the basis of immediately preceding state(s). The Markov module in Idrisi analyzes a pair of already classified land cover images and outputs a transition probability matrix, a transition area matrix, and a set of conditional probability images. The transition probability matrix is a text file that records the probability that each land cover category will change to every other category. The transition probability matrix of land use change from time one to time two extracted from the initial analysis was then further processed to give the projected land cover images for the later periods. The difference between the understudied years is 20 years, and based on this presumption, 20 years was also considered for the projection that was carried out. In the research, the land cover image of 1990 and later image of 2010 was combined to have a projection for year 2030. Simply put, the first image (1990) as the earlier image and the second image (2010) as the latter image was inputted. The number of time period between
the two images and the number of the projected year from the second image was also inputted, 20 years (1990-2010) and 20 years (2010-2030) were imputed for the projection to year 2030, as stipulated in the module for the analysis. The transition probability may be accurate on a per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. Hence, Cellular Automata (CA) was used to add spatial character to the prediction model and thus a projected land use/cover map for 2030 was presented as shown in the latter part of the research.
3.4.4 Quantitative Analysis The comparison of the land use land cover statistics assisted in identifying the percentage change, trend and rate of change between the three periods that were considered. Calculation of the Area in hectares of the resulting land use/land cover types for each study year and subsequently comparing the results was carried out. In achieving this, the first task was the development of a table showing the area in hectares and the percentage change for each year (1990, 2000 and 2010 measured against each land use land cover type). Percentage change and Annual rate of change to determine the rate and trend of changes were calculated by the formula given below: i. (trend) percentage change = Vpr - Vin* 100 Vin ii. where Vpr=present value, Vin=initial value
annual rate of change = percentage change divided by diff in the number of study year 1990 2000 (10years) 2000 2010 (10years)
3.4.5 Urban Land Consumption Index A method of evaluating urban land use change is to analyze how much urban land, on average, is consumed per person for a given area. Analyzing the trend in urban land consumption over time reveals whether the pattern of urban development has become more
or less concentrated. The urban land consumption index is defined as the amount of urban land within an area for a given year divided by the population of that area for the same year. It is measured with Land Consumption Rate and Absorption Coefficient. This method quantifies the rate of conversion of land for development with respect to the population growth of the study area. The Land consumption rate and absorption coefficient formula are given below; i. L.C.R = A P ii. where A = areal extent of the area in hectares, P = population
L.A.C = A2 A1 P2 P1 where A1 and A2 are the areal extents (in hectares) for the
early and later years, and P1 and P2 are population figure for the early and later years respectively (Yeates and Garner, 1976). Land Consumption Rate is a measure of compactness which indicates a progressive spatial expansion of a city, while Land Absorption Coefficient is a measure of change in consumption of new urban land by each unit increase in urban population. The population figures for the years under consideration were estimated from the 1991 and 2006 census using the recommended National Population Commission (NPC) 2.1% growth rate. The first task to estimating the population figures was to multiply the growth rate by the census figures of the study area while subsequently dividing same by 100. The result was then multiplied by the number of years being projected for, the result of which was then added to the base year population. This was represented in the formula below; i. n = r/100 * Po ii. Pn = Po + (n * t)
Where: Pn = estimated population (2000, 2010, 2030), Po = base year population (2000 & 2010 population figure), r = growth rate (2.1%), n = annual population growth and t = number of years projecting for.
3.5 SOFTWARE USED The data (imageries) acquired were first processed in ILWIS environment, for image rectification from various distortions and subsequently for the classification processes. The embedded predictive model in Idrisi Taiga was adopted for the projections made for the latter years. For proper display and visualisation, ArcGIS was used to present the resulting maps and images from the analyses carried out on the research. The table below was presented to outline these softwares and their respective usages.
Table 3.3: the softwares used and their relevancies S/N 1 2 Software ILWIS 3.3 Relevance it was used for the development of land use/cover classes
IDRISI TAIGA 16 this was used for change evaluation and analysis of the study area and subsequently for the projected future land cover
ARCGIS 9.3
ArcGIS was used for displaying and subsequent processing and enhancement of the images as well as the resulting maps
Microsoft Office: MS word & Excel
Excel was used in producing the bar chart, while Microsoft word was used generally for the presentation of the research in text, chart and map formats
DATA ANALYSIS AND DISCUSSION OF RESULT 4.1 Introduction The objectives of this study form the basis of all the analyses carried out in this chapter. The results are presented inform of maps, charts and statistical tables. They include the static, change and projected land use land cover of the identified land uses for the study area. 4.2 Land Use/Cover between 1990 and 2010 Urban growth in Nigeria is occurring at unprecedented rates, creating extensive urban landscapes at the expense of converting farmlands, wetlands, forests, and in other uses. In addition, growth in urban areas is outpacing growth in urban and suburban areas, causing substantial changes in the rural landscape. The growth pressure is particularly high at the rural-urban fringe. These development trends have changed not only the composition of the rural landscape and rural population, but also the perception of the rural setting from one that provides economic livelihood through agricultural production, to one that provides rural amenities for urban and suburban residents. The continuous expansion of urban areas necessitates a comprehensive analysis of changes in land-use patterns. Human-induced changes of the landscape have raised not only social and economic concerns, but are also responsible for a growing number of environmental concerns. The static land use/cover distribution for each evaluated year as derived from the analysis carried out are presented in the figures and tables below to show the pattern and extent of the identified land uses, measured in hectares.
4.2.1 Land Use/Cover in 1990 The table below depicts the result of analysis for year 1990, been the first of the three years understudy for this research. With reference to Table 4.1, it was discovered that forest
area with a total land mass of 73810.56 ha has the highest coverage amongst the identified land uses and it covers 81.1% of the entire study area. This could be as a result of the fact that the study area is partly rural and urban in nature, having being located in the fringe of the metropolitan city of Ibadan. The forested area covers part of Eleyele forest reserve and embodies the reserve or undeveloped area within the International Institute of Tropical Agriculture (IITA) Ibadan. Meanwhile, areas cultivated for farmland amounted to 13244.20 ha, which represented 14.6% of the total land area in the study area. The availability of a vast area of land as shown by the result could have been a major boost for the people of the study area to be agrarian in nature, thereby making their source of livelihood solely depending on forestry and agricultural practices ranging from arable farming to grazing and the likes. The built-up within the study area in 1990 stood at 3865.18 ha, covering about 4.2% of the entire area with a corresponding population of about 140,118 people (projected by the author), it can be inferred here that development was just springing up in the area and the rate of urbanisation was not that high as of that period partly due to the fact many of the inhabitants were agrarian in nature and the major source of their livelihood is based on agricultural practices. The water-body identified within the study area covers a meagre 0.1% and a total of 99.61 ha, a little part of Eleyele dam and the irrigation dam within the International Institute of Tropical Agriculture (IITA) form the basic component of the water body in the study area. The Figure 4.1 depicts the variation in the areas covered by the identified land uses within the study area, measured in hectares. The highest covered area been the forest land use, followed by the cultivated farmland, and built up and water body in descending order respectively. The Figure 4.2 shows the distribution of the classes of land use in the study area for the year 1990, the most spread land use been the forest cover and a little stint of water body. There is a cluster of farmland and built up areas around the southern part of the area which
indicates the bond between the people of the study area and agricultural practices. It can be inferred from the map that farming activities were carried out in no much distance to residential areas. Development as at this time can be considered to be linear in nature, as it can be deduced that built up areas follows a linear pattern probably along the major road that spans the local government area. Table 4.1: Land Use/Cover in 1990 Land Uses Area (Ha) 73810.56 Forest Farmland Built-up Water-body Total Source: Authors field work
80000 70000 60000 Hectares 50000 40000 30000 20000 10000 0 Forest Farmland Built-up Water-body 13244.2 3865.18 99.61 73810.56
% Coverage 81.1 14.6 4.2 0.1 100
13244.20 3865.18 99.61 91019.55
Figure 4.1: land uses in hectares for year 1990
Figure 4.2: Land use/cover map of Akinyele LGA in 1990
4.2.2 Land Use/Cover in 2000 In Table 4.2, the static land use/cover of the study area in year 2000 was presented; forest area has the largest percentage of 64.3 and covers 58513.55 ha of land in the study area, but it is worthy of note that there is an obvious reduction in the total area covered by forest land use when compared with what was obtainable in the previous year. The forest area is partly giving way for the increasing farming activities and urbanisation in the study area. A larger part of the forest area would have been cleared off for farming, road construction and majorly urban development to meet up with the growing population in the study area. In the same vein, the table indicates that farmland now covers 27.9 %, totalling 25352.67 ha of the entire area. While the area was losing forest cover, agricultural or cultivated area was on the increasing side, purposely to guarantee food security for the teeming population of the study area. With reference to the table above built up area covers 7062.88 ha of land, measuring to
7.7 % of the entire study area. Despite the fact that the study area is along the fringe part of Ibadan, there is a notable development at this period, as evident in the rate of change compared to the previous year. The area coverage of water body stood at 90.44 and a percentage area of 0.1%. There is a little shrink in the size of the area covered by water as compared with 1990. Table 4.2: Land Use/Cover in 2000 Land Uses Forest Farmland Built-up Water-body Total Source: Authors field work
70000 60000 50000 Hectares 40000 30000 20000 10000 0 Forest Farmland Built-up Water-body 7062.88 90.44 25352.67 58513.55
Area (Ha) 58513.55 25352.67 7062.88 90.44 91019.55
Figure 4.3: land uses in hectares for year 2000 The Figure 4.3 above depicts the variation in the coverage of the identified land uses for year 2000, forest areas remain the highest land use with 58513.55 ha, while farmland, built up and water body, with 25352.67 ha, 7062.88 ha and 90.44 ha respectively followed forest in ascending order of magnitude.
4.2.3 Land Use/Cover in 2010 The table below displays the result of analysis for 2010, the third and the last year understudied for the research. Forest area with a total of 53570.44 ha remains the highest land use in the study area, and covers 58.85% of the entire area. There is a little reduction in forest cover within this period, this probably can be attributed to a public awareness through widespread campaign to discourage people from deforestation and avoid its probable consequences. Farmland or cultivated area remains at 22.1% and covers 20152.53 ha of the entire study area, while built up hit the highest record of 18.89%, totalling 17200.41 ha of land. Water body stood at 96.17 ha representing 0.12% of the study area.
Table 4.3: Land Use/Cover in 2010 Land Uses Area (Ha) % Coverage
Forest Farmland Built-up Water-body Total Source: Authors field work
70000 60000 50000 Hectares 40000 30000 58370.44
58370.44 20152.53 12400.41 96.17 91019.55
64.1 22.1 13.7 0.1 100
20152.53 20000 10000 96.17 0 Forest Farmland Built-up Water-body 12400.41
Figure 4.5: land uses in hectares for year 2010 The rate of urbanisation is really evident in the study area by the year 2010, from the figure below built up area has widely expanded and was pushing off farmland and subsequently encroaching into the forest. Development that seems to be confined to the southern part of the study area in 1990 has really spread although linearly towards the northern part of the local government area. The forested area is fast losing its grip to the ever increasing urban expansion; this can only result in loss of biodiversity within the study area. The figure also revealed a reduction in the level of agricultural practices as area covered with cultivation has somewhat reduced, this may be unconnected with urbanisation, which in turn tends to shift the attention of people from agricultural to non agricultural form of occupation, thereby reducing the level of food security for the populace.
4.3 Rate of Land Use/Cover Change between 1990 and 2010 The results from Tables 4.4 and 4.5 respectively evidently presented the changes that occurred in the study area within the years considered for the research. In 1990 forest land use was the highest amongst the land uses in the study area covering 81.1% with a total land area of 73810.56 ha, this may be unconnected with the fact that the study area is located at the fringe of Ibadan metropolis and in this area; larger percentages of the inhabitants are agrarian in nature. After about 10 years, the forest cover began to reduce; Table 4.4 revealed that forested area stood at 64.28%, 58513.55 ha in year 2000 at an annual decrease rate of 2.07, this reduction however can probably be attributed to the farming activities engaged by the inhabitants of the study area and a growing expansion in urban development. It further reduced to 53570.44 ha and covered 58.85% in 2010; this invariably implies that
deforestation has been the bane of the study area through many means ranging from clearing of land for cultivation, road construction and more especially for urban expansion. The dense forest has reduced by 42.05% in approximately 20 years as a result of the increased deforestation rate, land degradation, farming and encroachment of people for building purposes. The advantageous roles of these depleted forests cannot be underestimated in terms of carbon management and implication for regional climate change, and by and large the loss of biodiversity as well as other valuable trees and plants that should have been exploited for medicinal and health care purposes. Area of land cultivated for agriculture in 1990 was 13244.2 ha, covering 14.55% of the area, and by 2000 it rose to 27.85 % with land coverage of 25352.67 ha. An agricultural practise was on the increase between 1990 and 2000, the arable land appears to have increased in this study probably because of the vast devastation done to the forested lands at the outlying extremes of the city for subsistence agricultural purposes, having lost out the existing arable lands to building and other construction purposes but an overturn was experienced in year 2010 with an annual decrease rate of -2.05. This overturn largely may be linked to the fact that people have significantly turned away from farming and allied practices in the recent years. The level of urbanisation coupled with some other factors has greatly reduced the interest of people in cultivation of land. For instance, the boom experienced with the resurgence of democracy has taken many youths and the like away from farming because it (politics) fetches more money easily and with less stress when compared to farming. Politics is being practiced by majority of the people now either by contesting for political offices or by attaching themselves to the political office holders for dividends of democracy, and this is very common among the youths who are supposed to be agile in farming activities in order to guarantee food security. The most obvious change within the study area was evident in the rate of built up between 1990 and 2010; the level of development was on a high increase from the very
former year till the recent time. It was indicated by Table 4.4 that built up area covers 3865.182 ha on an average of 4.24%, there continues to be an increase and by 2000, built up area had risen to 7062.88 ha covering 7.78% of the study area, this high rate of increase may not be unconnected to the level of urbanisation being experienced by the study area as well as exponential growth of population. The rapidly increasing built up area rose to cover a total land mass of 17200.41 ha in 2010, this accounted for 18.89% coverage of the entire area. With reference to table 4.5, the percentage increase between 1990 and 2000 was 82.73% at an annual growth rate of 8.27; meanwhile the percentage increase rose to an alarming 143.53% between 2000 and 2010 with a corresponding annual growth rate of 14.35. Urban area increased from 3865.18 ha to 17200.41 ha between 1990 and 2010, this represents an alarming increase of 81.65% of urban growth within just 20 years. If this change persists, there is likely going to be a development of slum in the study area in the nearest future, and haphazard development will be inevitable if a check is not put in place to stop the menace of uncontrolled city expansion. The area of the study covered with water in 1990 stood at 99.60 ha with a meagre 0.1%, representing the least coverage of the land uses, a reduction at an annual rate of -9.2 was then recorded in 2000 when the area coverage shrunk to 90.45, thus covering 0.09% of the area, a little upsurge was later recorded in 2010 with the water body risen to 96.17 ha for a 0.12% coverage.
Table 4.4: land use distribution during the period of study (ha and % of total land area) land-use 1990 Area(Ha) forest 73810.56 Area(%) 81.09 2000 Area(Ha) 58513.55 Area(%) 64.28 2010 Area(Ha) 53570.44 Area(%) 58.85
farmland built up water body
13244.2 3865.17 99.60 91019.55
14.56 6 4.24 0.1 100
27.85 7.78 0.09 100
20152.53 17200.41 96.17 91019.55
22.14 1 18.89 0.1 0.12 100
Source: Authors field work
Area Covered (%)
1990 27.85 2000 22.14 18.89 2010
40 14.56 30 20 10 0 Forest
Built up
Land Use Categories
Figure 4.7: % area cover by the land uses between 1990 and 2010
It was discovered that built up areas and farmland increased tremendously in size from 1990 to 2010, whereby a biodiversity loss was experienced as a result of the increasing encroachment into the forest area. Invariably, it means within twenty years a lot of buildings were constructed in the study y area; at the same period of time farming activities werent that stable due to some factors that contributed to loss of participation in agricultural practice by the people of the study area. The results further confirmed that agricultural land is fast losing l its extent to the growing demand for urban expansion, it can be inferred thus that in the foreseeable future, land in the fringe will automatically would have been converted from agricultural to urban use if adequate measures are not put in place to check the menace menace.
Table 4.5: Area/Percentage and Annual Rate of Decrease/Increase of Land Use/Cover Changes 1990-2010 Land Uses Area Covered (Ha) Diff (Ha) % Incr/ Decr Annual rate Incr/Decr % Area Covered of (Ha) Diff (Ha) % Incr/Decr Annual Incr/Decr % rate of
Forest Farmland Built-up Water-body total
73810.56 58513.55 -15297.01 13244.2 3865.18 99.61 25352.67 12108.47 7062.88 90.45 3197.7 -9.16
-20.72 -2.07 91.42 82.73 9.14 8.27
58513.55 53570.44 -4943.11 25352.67 20152.53 -5200.14 7062.88 90.45
-5.8 -2.05 14.35 0.63
17200.41 10137.53 143.53 96.1716 5.72 6.32
-91.96 -9.2
91019.55 91019.55
Source: Authors field work percentage change = present-initial/initial * 100 annual rate of change = percentage change/10(diff in yrs)
In terms of pattern of change, the emphasis is on built-up land. It was observed that there exists a growth away from the edge of the city of Ibadan and into the hinterlands of the study area. Although the pattern seems to be uniform, but the growth has been remarkable towards the northern part of the study area, and it can be seen to be following a linear pattern which further expresses the role of major transportation networks within the area. In the most recent time, an express road was constructed from Ibadan to Ilorin, part of this new road span across the study area, and has thus opened up a vast area of land for development within the study area. The massive increase in urban land could also have a close relationship with the rapid urban development process that has taken place in the study area during the last two decades; this combined with high level of immigration from areas less developed around the city has caused to a certain extent an uncontrolled urban growth and reduction in forest land area.
4.3.1: Land Consumption Index Land Consumption is increasing rapidly with the exponential growth of population; Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) were introduced to aid in the quantitative assessment of changes in relation to urban growth and expansion in the study area. The result depicted by table 4.7 has shown a rapid growth in land consumption rate till 2010. Table 4.6: Population figures for the study area from Year 1990 2000 2010 Population Figure 140118 162146 233839 Source Projected by the Researcher Projected by the Researcher Projected by the Researcher
Source: Authors field work Table 4.7: Results of Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) for the study area
Population fig 140118 162146 233839
Areal extent 3865.18 7062.88 17200.41
LCR 0.02 0.04 0.07
1990/2000 0.3 2000/2010 0.14
Source: Authors field work It should be noted here that the projected population figures for each study year as shown above were used in generating both the Land Consumption Rates and the Land Absorption Coefficients as given in Table 4.7. Increases in this index over time indicate an increasing amount of urban land is being consumed per person, which implies a less concentrated and more sprawling pattern of development. A growing amount of urban land was consumed per person from 1990 to 2010 and it shows (Table 4.7) a higher index across the twenty-year time period in the study area. In 1990, the study area average urban land consumption index was 0.02 ha per person. In 2000 it increased by 50% to 0.04 urban acres per person and further increased to 0.07 in year 2010, representing a further increase of about 57%.
4.4 Land Use/Cover Prediction In arriving at the study areas predicted land use for the future, the Markov Chain and Cellular Automata models were employed. The basis for the model is to predict the future state of an area based on the immediate preceding state(s). Therefore, the static land use/cover conditions of the study area for years 1990 and 2010 were adopted as the basis for the projection that was made. Although, the model operates base on the assumption that all factors acting on the study area resulting to the identified changes remain the same. Baring this in mind, the difference between the understudied years 1990-2010 i.e 20 years, was also adopted for the future projection, thus projection was made for year 2030. The following process was adopted for the projection.
4.4.1: Transition Probability Matrix and CA_Markov Operation The transition probability matrix records the probability that each land cover category will change to the other category. This matrix is produced by the multiplication of each column in the transition probability matrix by the number of cells of corresponding land use in the later image. For the 4 by 4 matrix table presented below, the rows represent the older land cover categories (1990) and the column represents the newer categories (2010). Although this matrix can be used as a direct input for specification of the prior probabilities in maximum likelihood classification of the remotely sensed imagery, it was however used in this research for predicting land use land cover for 2030.
Table 4.8: Transitional Probability table derived from land use/cover map of 1990 and 2010 Land Uses Forest Farmland Built up Water body Forest 0.40 0.18 0.12 0.22 Farmland 0.48 0.43 0.09 0.38 Built up 0.57 0.61 0.78 0.03 Water body 0.01 0.0004 0.0000 0.42
Source: Authors field work Row categories represent land use land cover classes in 1990 whilst column categories represent 2010 classes. As seen from the table, farmland has a 0.43 probability of remaining farm land and a 0.61 of changing to built up. This shows an undesirable change (reduction), with a probability of change which is much higher than stability. Built up during this period will likely maintain its position as the highest class with a 0.78 probability of remaining built up in the nearest future which signifies a strong stability. On the other hand, the 0.57 probability of change from forest land to built up shows that there might likely be a
high level of instability in forest land during this period. Water body which is the last class has a 0.42 probability of remaining as water body and a 0.38 probability of changing to farmland. The probability matrix generated above, and the base year (1990 & 2010) land use/cover outputs were used to produce the resultant projected map for the study area. From the output of the land use projection for year 2030, the Table 4.9 below provided the static land use/cover that will be obtainable for the year. With reference to the table below, forest area that should be the mainstay of the area by protecting the ecosystem and providing diverse resources for human survival continues to reduce drastically. In the next 20 years forest would cover 43.83% of the area with a total of 39897.94 ha, invariably it implies that by 2030 the study area would have lost a considerable 13672.5 ha compared with what was obtainable in 2010. The percentage decrease of forest land use stands at a pathetic 15.02, reducing at an annual rate of change of -3 as depicted by Table 4.10. It could be inferred that pressure mounted by the consequences of burgeoning population on forest cover within the study area would lead to unabated deforestation, which has been recognized by researchers as one of the major drivers of biodiversity loss as well as a threat to the existence of the global ecological lung. Exploitation of the forest resources taking place consistently for various purposes which varies from commercial to non commercial, need for space in road construction, shifting agriculture, firewood harvesting, construction of residential building, sand excavation etc., can only contribute to the ever changing climate we have started experiencing all over the world with Nigeria and particularly the study area inclusive. The area that will be covered by farmland in 2030 stands at 18400.83 ha as shown by the table below, this would have accounted for 47.7% reduction in farmland when compared with that of 2010, the rapid urban growth already has had far-reaching effects on agriculture and will continue thus at it was implied by the result from the projection. Not only does urban growth require additional land, but it has effects on the use of land that remains in agriculture.
Figure 4.8: Projected Land use/cover map of Akinyele LGA in 2030 In addition to the direct physical takeover of land, urban growth had affected the use of land remaining in agriculture. And these effects can be seen to some extent by the changes depicted in the figures above. This in turn will have adverse effect on the intensity of land use and the values of the crops produced within this area. Arguably, the rate of urban expansion in the study area has been following an alarming increase.
Table 4.9: Projected Land Use/Cover in 2030 Land Uses Forest Farmland Area (Ha) 39897.94 18400.83 % Coverage 43.83 20.22
Built-up Water-body Total Source: Authors field work
32636.06 84.72 91019.55
35.85 0.1 100
Within 2010 and 2030, the built area would have appreciated in size to 32636.06 ha representing 35.85%, closing in on the highest land use which is forest cover. A total of 15435.65 ha would have been acquired by urban expansion from every other land uses within the range of 20 years in the study area resulting from a significant percentage increase of 16.95 with a very high annual rate of change of 3.39 as shown by Table 4.10 below. It can be inferred thus that urban development in the study area has enlarged the modification of natural resources and has changed land use and land cover patterns. Due to the proximate and underlying causes of urban expansion, land use/cover change will become the main challenge of the area because much attention is not been given to these changes occurring at an alarming rate at the present. The study area impliedly will be compelled to face the looming severe threats due to the huge course of urbanization. Table 4.10: Area/Percentage and Annual Rate of Decrease/Increase of Land Use/Cover 2030 Land use Area(Ha) 2010 Forest Farmland Built up 53570.44 20152.53 17200.41 Area(Ha) 2030 39897.94 18400.83 32636.06 84.72 91019.55 -13672.5 -1751.7 15435.65 -11.45 -15.02 -1.92 16.95 -0.01 -3.00 -0.38 3.39 -0.002 Diff(Ha) %Incr/Decr Annual rate of change
Waterbody 96.17 91019.55 Source: Authors field work
CHAPTER FIVE SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS 5.1 Summary of Findings
There has been an increasing interest in the phenomenon of urban rural fringe development to determine the pattern and rate of land use land cover changes in the recent time. In response to this research interest therefore, the study area was selected. Land use changes were evaluated with the aid of geospatial techniques to provide a clear understanding about the rate and pattern of changes within this area. The study revealed an exponential horizontal urban expansion towards the fringe as depicted by the land use/cover map of 2010; the rate of urban expansion was evidently shown by the figures presented in the analytical tables. It was indicated that built up area covers 3865.182 ha on an average of 4.24% in 1990, there continues to be an increase and by 2000, developed area had risen to 7062.88 ha covering 7.78% of the study area, this high rate of increase may not be unconnected to the level of urbanisation being experienced by the study area as well as exponential growth of population. Urban area increased from 3865.18 ha to 17200.41 ha between 1990 and 2010, this represents an alarming percentage increase of 345.01% of urban growth within just 20 years. It could then be inferred that the development did not follow a specific and aligned pattern because much of this expansion arose from little or no development control, this implies that over crowdedness and slum development is imminent in the study area as a result of neglect of development control measures. Also, the rate at which the forest is being converted for various other uses in the study area calls for an urgent attention, the study can reveal the changes that have occurred in forest cover within the years understudied. Development of different kinds coupled with other anthropogenic activities carried out by the people had greatly reduced forest cover from 81.1% obtainable in 1990 to 58.85% in 2010, and a loss of 20240 ha was recorded within just twenty years. The changes in land use in the study area with emphasis on forest cover will only generate many environmental problems on a local scale. Release of greenhouse gases, loss of biodiversity, and sedimentation of lakes and streams are but a sampling of the sorts of impacts that arise by virtue of modifications in vegetative cover due to changes in land use.
Impliedly, deforestation, along with urban sprawl, agriculture, and other human activities which is substantially altering and fragmenting the vegetative cover of the study area can change the atmospheric concentration of carbon dioxide, the principal heattrapping gas, as well as affect local climate. Forests provide many ecosystem services, they support biodiversity, providing critical habitat for wildlife, remove carbon dioxide from the atmosphere, intercept precipitation, slow down surface runoff, and reduce soil erosion and flooding. These important ecosystem services will be reduced or destroyed when forests are converted to agriculture or urban development. It is also obvious that the study area is growing at such an alarming rate using up all the green and other forms of soft landscape in the area. The area is growing by succession and invasion and expanding at an unprecedented rate due to rapid urbanization and this could lead to various urban problems such as slums, obsolescence, traffic congestion, blight and overcrowding in the absence of a well coordinated state control of the use of land and building activities.
5.2 Conclusion This research has been able to effectively apply the use of geospatial techniques embedded in Geographic Information System (GIS) to the evaluation of land use changes in Akinyele Local Government Area of Oyo State. The analytical tools of GIS with its powerful capacity for spatial data management, spatial analysis, and visualization were adopted for the research to come up with meaningful inferences about the rate and pattern of land use changes in the study area. It was however discovered that the study area located within the Ibadan fringe has been affected by its contact with the urban edge of the city; this area has shown that its in transition in terms of land use as well as social and demographic characteristics. In compendium, the research has provided intangible information on the extent and dynamics of land use changes and the rate of ecological or biodiversity loss in the study area. Meanwhile, the main conclusion to be drawn from the review of literature on land
use changes is that urban areas are complex entities presenting features that are specific to particular urban regions in affecting the rate at which the area is subjected to various form of changes. With reference to the existing literature reviewed, there is an opinion that land-use change leads to rapid loss of open space, increases in land conversion at rates that far exceed population increases, decentralization of urban areas accompanied by increasingly lowdensity new development, and substantial fragmentation of the landscape, particularly in urban fringe areas, the study has been able to validate this perception. It therefore becomes imperative that a broad understanding of the trends taking place in such an area like this be developed in order to better anticipate major factors affecting land use land cover changes peculiar to urban fringe areas. The understanding therefore will expand the planners knowledge domain and to support the exploration of future land use changes under different conditions and this will eventually supplement planners existing mental capabilities to analyze land use change and to make more informed decisions. An adequate knowledge of these changes therefore is a prerequisite for an efficient and effective urban planning with a view to creating an aesthetic, pleasing and comfortable environment for living, working and recreating in the study area.
5.3 Recommendations There is a veritable need for an institutional control of urban development and this in essence requires a sound knowledge of urban growth and a versatile tool for modelling and simulating such process in order to afford urban planners the necessary tool and skill for
monitoring urban land use change. With regards to the findings and inferences made from this research, the following recommendations were put up: i. an urgent response to the increasing need for residential and urban expansion should be pursued through the implementation of relevant portions of the 1992 Urban and Regional Planning Decree No. 88, with particular reference to the establishment and more importantly empowerment of development control agencies at the local government level and effective implementation of the control measures. This is expected to eradicate haphazard development in order to avert the looming sprawls and slum settlement spreading into the hinterlands of the study area; ii. in order to reduce the effects of deforestation in the study area, Government by way of policy should be strict in preserving the forest from illegal occupation. Conservatively, there should be promotion of alternative energy source for fire wood in order to reduce the pressure on the forest through tree felling. Development and promotion of trade in non timber forest product to reduce the pressure on timber resources and to enhance rural livelihood will be a good riddance to deforestation. iii. it will be imperative for the local governments to realize that agricultural land is a limited natural resource and thus specific policies and programs need to be implemented to develop farm schemes in order to ensure food security, reduce the influx of urban migration and more importantly discourage further encroachment into the forests. REFERENCES Adeniyi P.O and Omojola A. (1999): Land-use land-cover change evaluation in Sokoto Rima Basin of North Western Nigeria based on Archival of the Environment (AARSE) on Geoinformation Technology Applications for Resource and
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