Change detection in land use and land cover using remote sensing data and gis - Free Essay Example

Sample details Pages: 25 Words: 7618 Downloads: 5 Date added: 2017/06/26 Category Geography Essay Type Essay any type Did you like this example? CHAPTER ONE INTRODUCTION 1.1 Background to the Study Studies have shown that there remains only few landscapes on the Earth that are still in there natural state. Due to anthropogenic activities, the Earth surface is being significantly altered in some manner and mans presence on the Earth and his use of land has had a profound effect upon the natural environment thus resulting into an observable pattern in the land use/land cover over time. The land use/land cover pattern of a region is an outcome of natural and socio economic factors and their utilization by man in time and space. Don’t waste time! Our writers will create an original "Change detection in land use and land cover using remote sensing data and gis" essay for you Create order Land is becoming a scarce resource due to immense agricultural and demographic pressure. Hence, information on land use / land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schemes to meet the increasing demands for basic human needs and welfare. This information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population. Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. The advancement in the concept of vegetation mapping has greatly increased research on land use land cover change thus providing an accurate evaluation of the spread and health of the worlds forest, grassland, and agricultural resources has become an important priority. Viewing the Earth from space is now crucial to the understanding of the influence of mans activities on his natural reso urce base over time. In situations of rapid and often unrecorded land use change, observations of the earth from space provide objective information of human utilization of the landscape. Over the past years, data from Earth sensing satellites has become vital in mapping the Earths features and infrastructures, managing natural resources and studying environmental change. Remote Sensing (RS) and Geographic Information System (GIS) are now providing new tools for advanced ecosystem management. The collection of remotely sensed data facilitates the synoptic analyses of Earth system function, patterning, and change at local, regional and global scales over time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity (Wilkie and Finn, 1996). Therefore, attempt will be made in this study to map out the status of land use land cover of Ilorin between 1972 and 2001 with a view to detecting the land consumption rate and the changes that has taken place in this status particularly in the built-up land so as to predict possible changes that might take place in this status in the next 14 years using both Geographic Information System and Remote Sensing data. 1.2 Statement of the Problem Ilorin, the Kwara State, capital has witnessed remarkable expansion, growth and developmental activities such as building, road construction, deforestation and many other anthropogenic activities since its inception in 1967 just like many other state capitals in Nigeria. This has therefore resulted in increased land consumption and a modification and alterations in the status of her land use land cover over time without any detailed and comprehensive attempt (as provided by a Remote Sensing data and GIS) 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 so that planners can have a basic tool for planning. It is therefore necessary for a study such as this to be carried out if Ilorin will avoid the associated problems of a growing and expanding city like many others in the world. 1.3 Justification for the Study Indeed, attempt has been made to document the growth of Ilorin in the past but that from an aerial photography (Olorunfemi, 1983). In recent times, the dynamics of Land use Land cover and particularly settlement expansion in the 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 than area photography 1.4Aim and Objectives 1.4.1 Aim The aim of this study is to produce a land use land cover map of Ilorin at different epochs in order to detect the changes that have taken place particularly in the built-up land and subsequently predict likely changes that might take place in the same over a given period. 1.4.2 Objectives The following specific objectives will be pursued in order to achieve the aim above. To create a land use land cover classification scheme To determine the trend, nature, rate, location and magnitude of land use land cover change. To forecast the future pattern of land use land cover in the area. To generate data on land consumption rate and land absorption coefficient since more emphasis is placed on built-up land. To evaluate the socio economic implications of predicted change. 1.5 The Study Area The study area (Ilorin) is the capital of Kwara State. It is located on latitude 80 31 N and 40 35 E with an Area of about 100km square (Kwara State Diary1997). Being situated in the transitional zone; between the forest and the savanna region of Nigeria i.e. the North and the West coastal region, it therefore serves as a melting point between the northern and southern culture.(Oyebanji, 1993). Her geology consists of pre-Cambrian basement complex with an elevation which ranges between 273m to 333m in the West and 200m to 364m in the East. The landscape of the region (Ilorin) is relatively flat, this means it is located on a plain and is crested by two large rivers, the river Asa and Oyun which flows in North South direction divides the plain into two; Western and Eastern part (Oyebanji, 1993). The climate is humid tropical type and is characterized by wet and dry seasons (Ilorin Atlas 1981). The wet season begins towards the end of March and ends in October. A dry season in the town begins with the onset of tropical continental air mass commonly referred to as harmattan. This wind is usually predominant between the months of November and February (Olaniran 2002). The temperature is uniformly high throughout the year. The mean monthly temperature of the town for the period of 1991 2000 varies between 250 C and 29.50 C with the month of March having about 300C. Ilorin falls into the southern savanna zone. This zone is a transition between the high forest in the southern part of the country and the far North with woodland properties. (Osoba, 1980). Her vegetation is characterized by scattered tall tree shrubs of between the height of ten and twelve feet. Oyegun in 1993 described the vegetation to be predominantly covered by derived savannah found in East and West and are noted for their dry lowland rainforest vegetal cover. As noted by Oyegun in 1983, Ilorin is one of the fastest growing urban centers in Nigeria. Her rate of population growt h is much higher than for other cities in the country (Oyegun, 1983). Ilorin city has grown in both population and areal extent at a fast pace since 1967 (Oyegun, 1983). The Enplan group (1977) puts the population at 400,000 which made it then the sixth largest town in Nigeria. The town had a population of 40, 990 in 1952 and 208, 546 in 1963 and was estimated as 474, 835 in 1982 (Oyegun, 1983). In 1984, the population was 480, 000 (Oyegun, 1985). This trend in population growth rate shows a rapid growth in population. The growth rate between 1952 and 1963 according to Oyebanji, 1983 is put at 16.0 which is higher than other cities in the country. The population as estimated by the 1991 population census was put at 570,000. 1.6 Definition of Terms (i) Remote sensing: Can be defined as any process whereby information is gathered about an object, area or phenomenon without being in contact with it. Given this rather general definition, the term has come to be associated more specifically with the gauging of interactions between earth surface materials and electromagnetic energy. (Idrisi 32 guide to GIS and Image processing, volume 1). (ii) Geographic Information system: A computer assisted system for the acquisition, storage, analysis and display of geographic data (Idrisi 32 guide to GIS and Image processing, volume 1). (iii) Land use: This is the manner in which human beings employ the land and its resources. (iv) Land cover: Implies the physical or natural state of the Eaths surface. CHAPTER TWO 2.1 LITERATURE REVIEW 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. While land cover categories could be cropland, forest, wetland, pasture, roads, urban areas among others. The term land cover originally referred to the kind and state of vegetation, such as forest or grass cover but it has broadened in subsequent usage to include other things such as human structures, soil type, biodiversity, surface and ground water (Meyer, 1995). Land use affects land cover and changes in land cover affect land use. A change in either however is not necessarily the product of the other. Changes in land cover by land use do not necessarily imply degradation of the land. However, 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, Meyer, and Turner, 1994). Land cover can be altered by forces other than anthropogenic. Natural events such as weather, flooding, fire, climate fluctuations, and ecosystem dynamics may also initiate modifications upon land cover. Globally, land cover today is altered principally by direct human use: by agriculture and livestock raising, forest harvesting and management and urban and suburban construction and development. There are also incidental impacts on land cover from other human activities such as forest and lakes damaged by acid rain from fossil fuel combustion and crops near cities damaged by tropospheric ozone resulting from automobile exhaust (Meyer, 1995). Hence, in order to use land optimally, 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 labor 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. A remote sensing device records response which is based on many characteristics of the land surfac e, including natural and artificial cover. An interpreter uses the element of tone, texture, pattern, shape, size, shadow, site and association to derive information about land cover. The generation of remotely sensed data/images by various types of sensor flown aboard different platforms at varying heights above the terrain and at different times of the day and the year does not lead to a simple classification system. It is often believed that no single classification could be used with all types of imagery and all scales. To date, the most successful attempt in developing a general purpose classification scheme compatible with remote sensing data has been by Anderson et al which is also referred to as USGS classification scheme. Other classification schemes available for use with remotely sensed data are basically modification of the above classification scheme. Ever since the launch of the first remote sensing satellite (Landsat-1) in 1972, land use land cover studies were carried out on different scales for different users. For instance, waste land mapping of India was carried out on 1:1 million scales by NRSA using 1980 82 landsat multi spectral scanner data. About 16.2% of waste lands were estimated based on the study. Xiaomei Y, and Rong Qing L.Q.Y in 1999 noted that information about change is necessary for updating land cover maps and the management of natural resources. The information may be obtained by visiting sites on the ground and or extracting it from remotely sensed data. 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 because it provides quantitative analysis of the spatial distribution of the population of interest. Macleod and Congation (1998) list four aspects of change detection which are important when monitoring na tural resources: i. Detecting the changes that have occurred ii. Identifying the nature of the change iii. Measuring the area extent of the change iv. Assessing the spatial pattern of the change The basis of using remote sensing data for change detection is that changes in land cover 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. A wide variety of digital change detection techniques have been developed over the last two decades. Singh (1989) and Coppin Bauer (1996) summarize eleven different change detection algorithms that were found to be documented in the literature by 1995. These include: 1. Mono-temporal change delineation. 2. Delta or post classification comparisons. 3. Multidimensional temporal feature space analysis. 4. Composite analysis. 5. I mage differencing. 6. Multitemporal linear data transformation. 7. Change vector analysis. 8. Image regression. 9. Multitemporal biomass index 10. Background subtraction. 11. Image ratioing In some instances, land use land cover change may result in environmental, social and economic impacts of greater damage than benefit to the area (Moshen A, 1999). Therefore data on land use change are of great importance to planners in monitoring the consequences of land use change on the area. Such data are of value to resources management and agencies that plan and assess land use patterns and in modeling and predicting future changes. Shosheng and Kutiel (1994) investigated the advantages of remote sensing techniques in relation to field surveys in providing a regional description of vegetation cover. The results of their research were used to produce four vegetation cover maps that provided new information on spatial and temporal distributions of vegetation in this ar ea and allowed regional quantitative assessment of the vegetation cover. Arvind C. Pandy and M. S. 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. It has been noted over time through series of studies that Landsat Thematic Mapper is adequate for general extensive synoptic coverage of large areas. As a result, this reduces the need for expensive and ti me consuming ground surveys conducted for validation of data. Generally, satellite imagery is able to provide more frequent data collection on a regular basis unlike aerial photographs which although may provide more geometrically accurate maps, is limited in respect to its extent of coverage and expensive; which means, it is not often used. In 1985, the U.S Geological Survey carried out a research program to produce 1:250,000 scale land cover maps for Alaska using Landsat MSS data (Fitz Patrick et al, 1987).The State of Maryland Health Resources Planning Commission also used Landsat TM data to create a land cover data set for inclusion in their Maryland Geographic Information (MAGI) database. All seven TM bands were used to produce a 21 class land cover map (EOSAT 1992). Also, in 1992, the Georgia Department of Natural Resources completed mapping the entire State of Georgia to identify and quantify wetlands and other land cover types using Landsat Thematic Mapper à ¢Ã¢â‚¬Å¾Ã‚ ¢ data (ERDAS, 1992). The State of southern Carolina Lands Resources Conservation Commission developed a detailed land cover map composed of 19 classes from TM data (EOSAT, 1994). This mapping effort employed multi-temporal imagery as well as multi-spectral data during classification. 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. Daniel et al, 2002 in their comparison of land use lan d 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. 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 us e 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. CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction The procedure adopted in this research work forms the basis for deriving statistics of land use dynamics and subsequently in the overall, the findings. 3.2 Data Acquired and Source For the study, Landsat satellite images of Kwara State were acquired for three Epochs; 1972, 1986 and 2001. Both 1972 and 1986 were obtained from Global Land Cover Facility (GLCF) an Earth Science Data Interface, while that of 2001 was obtained from National Space Research and Development Agency in Abuja (NASRDA). 0n both 2001 and 1986 images, a notable feature can be observed which is the Asa dam which was not yet constructed as of 1972. It is also important to state that Ilorin and its environs which were carved out using the local government boundary map and Nigerian Administrative map was also obtained from NASRDA. These were brought to Universal Transverse Marcator projection in zone 31. S/N DATA TYPE DATE OF PRODUCTION SCALE SOURCE 1. 2. 3. Landsat image Landsat image Landsat image 2001-11-03 1986-11-15 1972-11-07 30m à ¢Ã¢â‚¬Å¾Ã‚ ¢ 30m TM 80m TM NASRDA GLCF GLCF 4 FORMECU Land use/land cover Vegetation map. 1995 1:1,495, 389 (view scale) FORMECU 5 Administrative and local government Map of Nigeria. 2005 1:15,140,906 (view scale) NASRDA 6 Land use and infrastructure map of Ilorin. 1984 1:150, 000 Ilorin Agricultural Development Project Table 3.1 Data Source 3.2.1 Geo-referencing Properties of the Images The geo-referencing properties of both 1986 2001 are the same while image thinning was applied to the 1972 imagery which has a resolution of 80m using a factor of two to modify its properties and resolution to conform to the other two has given below; Data type: rgb8 File type: binary Columns: 535 Rows: 552 Referencing system: utm-31 Reference units: m Unit distance: 1 Minimum X: 657046.848948 Maximum X: 687541.848948 Minimum Y: 921714.403281 Maximum Y: 953178.403281 Min Value: 0 Max Value: 215 Display Minimum: 0 Display Maximum: 215 Image thinning was carried out through contract; contract generalizes an image by reducing the number of rows and columns while simultaneously decreasing the cell resolution. Contraction may take place by pixel thinning or pixel aggregation with the contracting factors in X and Y being independently defined. With pixel thinning, every nth pixel is kept while the remaining is thrown away. 3.3 Software Used Basically, five software were used for this project viz; (a) ArcView 3.2a this was used for displaying and subsequent processing and enhancement of the image. It was also used for the carving out of Ilorin region from the whole Kwara State imagery using both the admin and local government maps. (b) ArcGIS This was also used to compliment the display and processing of the data (c) Idrisi32 This was used for the development of land use land cover classes and subsequently for change detection analysis of the study area. (d) Microsoft word was used basically for the presentation of the research. (e) Microsoft Excel was used in producing the bar graph. 3.4 Development of a Classification Scheme Based on the priori knowledge of the study area for over 20 years and a brief reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area after Anderson et al (1967). The classification scheme developed gives a rather broad classification where the land use land cover was identified by a single digit. CODE LAND USE/LAND COVER CATEGORIES 1 Farmland 2 Wasteland 3 Built-up land 4 Forestland 5 Water bodies Table 3.2 Land use land cover classification scheme The classification scheme given in table 3.2 is a modification of Andersons in 1967 The definition of waste land as used in this research work denotes land without scrub, sandy areas, dry grasses, rocky areas and other human induced barren lands. 3.5 Limitation(s) in the Study There was a major limitation as a result of resolution difference. Landsat image of 1972 was acquired with the multi spectral scanner (MSS) which has a spatial resolution of 80 meters, whilst the images of 1986 and 2001 were acquired with Thematic Mapper à ¢Ã¢â‚¬Å¾Ã‚ ¢ and Enhanced Thematic Mapper (ETM) respectively. These both have a spatial resolution of 30 meters. Although this limitation was corrected for through image thinning of the 1972, it still prevented its use for projecting into the future so as to have a consistent result. Apart from this, it produced an arbitrary classification of water body for the 1972 classification. 3.6 Methods of Data Analysis Six main methods of data analysis were adopted in this study. (i) Calculation of the Area in hectares of the resulting land use/land cover types for each study year and subsequently comparing the results. (ii) Markov Chain and Cellular Automata Analysis for predicting change (iii) Overlay Operations (iv) Image thinning (v) Maximum Likelihood Classification (vi) Land Consumption Rate and Absorption Coefficient The fist three methods above were used for identifying change in the land use types. Therefore, they have been combined in this study. The comparison of the land use land cover statistics assisted in identifying the percentage change, trend and rate of change between 1972 and 2001. In achieving this, the first task was to develop a table showing the area in hectares and the percentage change for each year (1972, 1986 and 2001) measured against each land use land cover type. Percentage change to determine the trend of change can then be calculated by dividing observed change by sum of changes multiplied by 100 (trend) percentage change = observed change * 100 Sum of change In obtaining annual rate of change, the percentage change is divided by 100 and multiplied by the number of study year 1972 1986 (14years) 1986 2001 (15years) Going by the second method (Markov Chain Analysis and Cellular Automata Analysis), Markov Chain Analysis is a convenient tool for modeling land use change when changes and processes in the landscape are difficult to describe. A Markovian process is one in which the future state of a system can be modeled purely on the basis of the immediately preceding state. Markovian chain analysis will describe land use change from one period to another and use this as the basis to project future changes. This is achieved by developing a transition probability matrix of land use change from time one to time two, which shows the nature of change while still serving as the basis for projecting to a later t ime period .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 model. CA_Markov uses the output from the Markov Chain Analysis particularly Transition Area file to apply a contiguity filter to grow out land use from time two to a later time period. In essence, the CA will develop a spatially explicit weighting more heavily areas that proximate to existing land uses. This will ensure that land use change occurs proximate to existing like land use classes, and not wholly random. Overlay operations which is the last method of the three, identifies the actual location and magnitude of change although this was limited to the built-up land. Boolean logic was applied to the result through the reclass module of idrisi32 which assisted in mapping out separately areas of change for which magnitude was later calculated for. The Land consumption rate and absorption coefficient formula are give below; L.C.R = A P A = areal extent of the city in hectares P = population L.A.C = A2 A1 P2 P1 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) L.C.R = A measure of compactness which indicates a progressive spatial expansion of a city. L.A.C = A measure of change in consumption of new urban land by each unit increase in urban population Both the 2001 and 2015 population figures were estimated from the 1991 and the estimated 2001 population figures of Ilorin respectively using the recommended National Population Commission (NPC) 2.1% growth rate as obtained from the 1963/1991 censuses. The first task to estimating the population figures was to multiply the growth rate by the census figures of Ilorin in both years (1991, 2001) 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 (1991, 2001). This is represented in the formula below; n = r/100 * Po (1) Pn = Po + (n * t) (2) Pn = estimated population (2001, 2015) Po = base year population (1991 2001 population figure) r = growth rate (2.1%) n = annual population growth t = number of years projecting for *The formula given for the population estimate was developed by the researcher In evaluating the socio economic implications of change, the effect of observed changes in the land use and land cover between 1972 and 2001 were used as major criteria. CHAPTER FOUR DATA ANALYSIS 4.0 Introduction The objective of this study forms the basis of all the analysis 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 each class. 4.1 Land Use Land Cover Distribution The static land use land cover distribution for each study year as derived from the maps are presented in the table below LANDUSE/LAND COVER CATEGORIES 1972 1986 2001 AREA (Ha.) AREA (%) AREA (Ha.) AREA (%) AREA (Ha.) AREA (%) FARM LAND 2437.62723 25 7965.5733 8 14068.4949 15 WASTE LAND 41436.7713 43 55561.149 59 50317.263 52 BUILT-UP LAND 2198.2734 2 9702.8136 10 10815.921 11 FOREST LAND 11036.494 12 21393.0405 22 19960.2315 21 WATER BODY 16874.6562 18 1326.8916 1 787.5576 1 TOTAL 95949.468 100 95949.468 100 95949.468 100 Table 4.1 Land Use Land Cover Distribution (1972, 1986, 2001) The figures presented in table 4.1 above represents the static area of each land use land cover category for each study year. Built-up in 1972 occupies the least class with just 2% of the total classes. This may not be unconnected to the fact that the town (Ilorin) was made the state capital in MAP I. Derived from landsat image of Ilorin in 1972 1967 which is just five years old from the date of creation to the date the image was taken. Also, farming seems to be practiced moderately, occupying 25% of the total classes. This may be due to the fact that the city is just moving away from the rather traditional setting where farming seems to form the basis for living. Apart from this, the time of the year in which the area was imaged which happens to fall within the onset of hamattan could also be a major contributing factor to the observed classification, contributing to the high percentage of waste land an d the low percentage of forest land. Water body also seems to be arbitrarily exaggerated in the classification due to the aforementioned problem in section 3.5 In 1986, waste land still occupies the highest class with 59% of the total class, taking up more than half of the total classes. Furthermore, the high percentage may be due to the season of the year as mentioned in the last paragraph. Water body takes up the least percentage in the total class. The pattern of land use land cover distribution in 2001 also follows the pattern in 1986. Waste land still occupies a major part of the total land but there exist an increase by half in the total farm land. Still, water body maintains the least position in the classes whilst built-up occupies 11% of the total class. 4.2 Land Consumption Rate and Absorption Coefficient YEAR LAND CONSUMPTION RATE YEAR LAND ABSORPTION COEFFICIENT 1972 0.005 1972/86 0.09 1986 0.02 86/2001 0.005 2001 0.01 Table 4.2.1 YEAR POPULATION FIGURE SOURCE 1977 400,000 EPLAN GROUP 1977 1984 480,000 OYEGUN 1986 2001 689,700 RESEARCHERS ESTIMATE Table 4.2.2 Population figure of Ilorin in 1977, 1984 and 2001 It should be noted here that the closest year population available to each study year as shown above were used in generating both the Land Consumption Rates and the Land Absorption Coefficients as given in table 4.2.1 4.3 Land Use Land Cover Change: Trend, Rate and Magnitude LANDUSE/LAND COVER CATEGORIES 1972 1986 1986 2001 ANNUAL RATE OF CHANGE AREA (Ha.) PERCE TAGE CHANGE AREA (Ha.) PERCENT AGE CHA NGE 72 86 86 2001 FARM LAND -16410.699 -17 6102.9216 7 14068.4949 1.05 WASTE LAND 14124.3777 16 -5243886 -7 50317.263 -1.05 BUILT-UP LAND 7504.5402 8 1113.1074 1 10815.921 0.15 FOREST LAND 4518.3838 10 -1432.809 -1 19960.2315 -0.15 WATER BODY 16874.6562 -17 -539.334 0 787.5576 0 Table 4.3 Land use land cover change of Ilorin and its environs: 1972, 1986 and 2001 From table 4.3, there seems to be a negative change i.e. a reduction in farm land between 1972 and 1986. This may not be unconnected to the change in the economic base of the city from farming to other white collar jobs as a result of the creation of Kwara State in 1967 in which Ilorin was made the state capital. Subsequently, built-up land increased by 8% while both forest land and waste land both increased by 10% and 16% respectively. Many projects were embarked on after the creation of Kwara State which also falls within the oil boom era of the 1970s and this attracted a lot of people to the area thus contributing to the physical expansion of the city as evident in the increased land consumption rate from 0.005 to 0.02 and land absorption coefficient by 0.09 between 1972 and 1986. Many of these projects include the Army barracks at Sobi, Adewole Housing Estate, the International Airport, Niger River Basin Authority Headquarters, University of Ilorin among many others which all encouraged migration into the city. The period between 1986 and 2001 witnessed a drop in the rate at which the physical expansion of the city was going as against 1972 and 1986. For instance, the built-up land only increased by 1% as against the 8% increase between 1972 and 1986. This is also evident in the drop observed in the land absorption coefficient from 0.09 between 1972 and 1986. In deed, the austerity measure known as (SAP) introduced into the country at this period to restore the countrys economy could be a major factor to what was witnessed at this period. Also, there was a general increase of 7% in farm land which is evident in the 7% reduction of waste land and 1% reduction of forest land. This may be as a result of the shift back towards farming after the initial excitement of the oil boom which attracted many people from farming to white collar jobs. Furthermore, water body seems to remain at 1% though there are slight differences in the total hectare between this period. This was not so in 1972 because Asa river was not yet dammed which was the case in the period between 1986 and 2001 as shown in the maps. MAP II. Derived from landsat image of Ilorin in 1986 4.4 Nature and Location of Change in Land Use Land Cover An important aspect of change detection is to determine what is actually changing to what i.e. which land use class is changing to the other. This information will reveal both the desirable and undesirable changes and classes that are relatively stable overtime. This information will also serve as a vital tool in management decisions. This process involves a pixel to pixel comparison of the study year images through overlay. In terms of location of change, the emphasis is on built-up land. Map IV shows this change between 1972 and 1986. The observation here is that there seem to exist a growth away from the city center following the concentric theory of city growth postulated by Christaller (1933). Although the pattern seems to be uniform, there exist more growth MAP III. Derived from landsat image of Ilorin in 2001 towards the south western part of the city comprising of the Asa dam area, Adewole Estate and Airport. Between 1986 and 2001 as shown in Map V, there exist dra stic reductions in the spatial expansion of the city. The only noticeable growths are on the edges of the developed areas of 1986 built-up land. For the projected change as shown in Map VI, the edges of built-up land seems to have been filled up with developments by 2001 leaving the only noticeable developments to areas around the city center. These therefore suggest that there might be a high level of compactness in Ilorin by 2015. On the other hand, looking at the nature of change under stability i.e. areas with no change and instability- loss or gain by each class between 1972 and 1986 particularly in the change in hectares as observable in table 4.1, stability seems to be a relative term as no class is actually stable during this period except when observed from the percentage change. Thus, between 1972 and 1986, farm land has a loss of 17% but gained by 7% between 1986 and 2001. Waste land on the other hand gained by 16% between 1972 and 1986 but lost by 7% between 1986 and 2001. Built-up land increased i.e. gained by 8% between 1972 and 1986 which is incomparable with the reduced increase of 1% between 1986 and 2001. Forest land gained by 10% between 1972 and 1986 but lost by 1% between 1986 and 2001, while water body being arbitrarily exaggerated in 1972 could not be compared with 1986 but there exist a relative stability in this class between 1986 and 2001 as evident in the 0% increase shown in the table. MAP IV. Derived from the overlay of 1972 and 1986 Land use land cover map MAP V. Derived from the overlay of 1986 and 2001 Land use land cover map 4.5 Transition Probability Matrix 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 be the number of cells of corresponding land use in the later image. For the 5 by 5 matrix table presented below, the rows represent the older land cover categories and the column represents the newer categories. 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 predicting land use land cover of 2015. CLASSES FARM LAND WASTE LAND BUILT-UP LAND FOREST LAND WATER BODY FARM LAND 0.1495 0.5553 0.0885 0.1969 0.0097 WASTE LAND 0.1385 0.5132 0.1735 0.1692 0.0057 BUILT-UP LAND 0.0471 0.3902 0.5029 0.0507 0.0090 FOREST LAND 0.2163 0.4050 0.0501 0.3203 0.0083 WATER BODY 0.1682 0.4378 0.0633 0.3174 0.0133 Table 4.5: Transitional Probability table derived from the land use land cover map of 1986 and 2001 Row categories represent land use land cover classes in 2001 whilst column categories represent 2015 classes. As seen from the table, farm land has a 0.1495 probability of remaining farm land and a 0.5553 of changing to waste land in 2015. This therefore shows an undesirable change (reduction), with a probability of change which is much higher than stability. Waste land during this period will likely maintain its position as the highest class with a 0.5132 probability of remaining waste land in 2015.Built-up land also has a probability as high as 0.5029 to remain as built-up land in 2015 which signifies stability. On the other hand, the 0.4050 probability of change from forest land to waste land 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 0.0133 probability of remaining as water body and a 0.4378 probability of changing to waste land; which may not however be a true projection of this class except there is an occurrence of drought in the region. 4.6 Land Use Land Cover Projection for 2015 LAND USE LAND COVER CLASSES FARM LAND WASTE LAND BUIL-UP LAND FOREST LAND WATER BODY 2015 AREA IN HECTARES 16583.5458 47432.4759 11026.456 20397.8718 509.1183 AREA IN PERCENTAGE 17 50 11 21 1 Table 4.6: Projected Land use land cover for 2015 The table above shows the statistic of land use land cover projection for 2015. Comparing the percentage representations of this table and that of table 4.1, there exist similarities in the observed distribution particularly in 2001. This may tend to suggest no change in the classes between 2001 and 2015, but a careful look at the area in hectares between these two tables shows a change though meager. Thus in table 4.6, waste land still maintains the highest position in the class whilst water body retains its least position. Forest land takes up the next position, followed by built-up land and finally, farm land. As seen in Map VI, there is likely to be compactness in Ilorin by 2015 which signifies crowdedness. MAP VI. Derived from the 1986 and 2001 land use land cover map MAP VII. Derived from the overlay of 2001 and 2015 Land use land cover map CHAPTER FIVE 5.1 Findings, Implications and Recommendations ÃÆ'ËÅ" There is likely going to be crowdedness brought by compactness in Ilorin come 2015. This situation will have negative implications in the area because of the associated problems of crowdedness like crime and easy spread of diseases. It is therefore suggested that encouragement should be given to people to build towards the outskirts through the provision of incentives and forces of attraction that are available at the city center in these areas. ÃÆ'ËÅ" Indeed, between the period of 1986 and 2001, there has been a reduction in the spatial expansion of Ilorin compared to the period between 1972 and 1986. There is a possibility of continual reduction in this state over the next 14yrs. This may therefore suggest that the city has reduced in producing functions that attracted migration into the area. Indeed, there have been many defunct industries within this period. It is therefore suggested here that Kwara State government should encourage investors both local and foreig n and more importantly, see how the defunct industries will come up again. ÃÆ'ËÅ" After the initial reduction in farm land between 1972 and 1986, the city has witnessed a steady growth in this class and in deed, may continue in this trend in 2001/2015. For this projection to be realistic, it suggested here that a deliberate attempt should be made by the State government to achieve this since this will lead to food security and more importantly, it will be a source of revenue to the State. ÃÆ'ËÅ" Waste land seems to be reducing between 1986 and 2001 and between 2001 and 2015 thus signifying a desirable change. ÃÆ'ËÅ" Forest land has been steady in reduction between 1986 and 2001 and in deed; this may likely be the trend 2001/2015. It will be in the good of the State and in deed, the Nation as a whole if the moderate reduction in forest land observed in-between 1986 and 2001 which is also projected by 2015 is upheld. ÃÆ'ËÅ" Land consumption rate which is a measure of compactness which indicates a progressive spatial expansion of a city was high in 1972/86 but drop between 1986 and 2001 and this drop is also anticipated before 2015. ÃÆ'ËÅ" Also, land absorption coefficient being a measure of consumption of new urban land by each unit increase in urban population which was high between 1972 and 1986, reduced between 1986 and 2001. This therefore suggests that the rate at which new lands are acquired for development is low. This may also be the trend in 2001/2015 as there seems to be concentration of development at the city center rather than expanding towards the outskirts. This may be as a result of peoples reluctance to move away from the center of activities to the outskirts of the city. 5.2 Summary and Conclusion This research work demonstrates the ability of GIS and Remote Sensing in capturing spatial-temporal data. Attempt was made to capture as accurate as possible five land use land cover classes as they change through time. Except for the inability to accurately map out water body in 1972 due to the aforementioned limitation, the five classes were distinctly produced for each study year but with more emphasis on built-up land as it is a combination of anthropogenic activities that make up this class; and indeed, it is one that affects the other classes. In achieving this, Land Consumption Rate and Land Absorption Coefficient were introduced into the research work. An attempt was also made at generating a formula for estimating population growth using the recommended National Population Commission 2.1% growth rate. However, the result of the work shows a rapid growth in built-up land between 1972 and 1986 while the periods between 1986 and 2001 witnessed a reduction in this class. It was also observed that change by 2015 may likely follow the trend in 1986/2001 all things being equal. REFERENCES Adeniyi P.O and Omojola A. 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