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 man’s 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. 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 world’s 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 man’s activities on his natural resource 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 Earth’s 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

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

1.4 Aim 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 growth 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 Eath’s surface.

CHAPTER TWO

2.1 LITERATURE REVIEW

According to Meyer, 1999 every parcel of land on the Earth’s surface is unique in the cover it possesses. Land use and land cover are distinct yet closely linked characteristics of the Earth’s 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 surface, 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 natural 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. Image 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 area 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 time 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 ™ 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 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.

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.

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.

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.

Table 3.2 Land use land cover classification scheme

The classification scheme given in table 3.2 is a modification of Anderson’s 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 ™ 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 time 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