CHANGE DETECTION IN LAND USE AND LAND COVER USING REMOTE SENSING DATA AND GIS

(A case study of Ilorin and its environs in Kwara State.)

BYZUBAIR, AYODEJI OPEYEMI

MATRIC NO. 131025

A PROJECT SUBMITTED TO THE DEPARTMENT OF GEOGRAPHY, UNIVERSITY OF IBADAN IN PARTIAL FULFILMENT FOR THE

AWARD OF MASTER OF SCIENCE (MSc) DEGREE IN GEOGRAPHICAL INFORMATION SYSTEMS

OCTOBER, 2006

CERTIFICATION

This project has been read and approved as meeting the requirements for the

award of Master of Science (MSc) degree in Geographic Information Systems (GIS) in

the Department of Geography, University of Ibadan, Ibadan.

………………………………...................

SURVEYOR R.K YUSUF

SUPERVISOR

………………………………………….

PROF. A.O AWETO

HEAD OF DEPARTMENT

………………………………………….

EXTERNAL SUPERVISOR

ii

DEDICATION

This project is dedicated to God and my loving, caring and industrious mother

whose effort and sacrifice has made my dream of having this degree a reality. Words

cannot adequately express my deep gratitude to you. I pray you will live long to reap the

fruits of your labor.

iii

ACKNOWLEDGEMENT

To the most High God be glory great things He has done. I acknowledge Your great

provisions, protections and support throughout the duration of this course.

My appreciation also goes to my Dad, Dr. S.D Zubair for his effort and suggestions

towards my progress in life

I cannot but appreciate the constructive suggestions, criticisms and encouragement of my

supervisor in person of Surveyor R.K Yusuf who allowed in particular, the use of internet in

communicating over the long distance.

I remain indebted to the entire staff of Space Applications, National Space Research and

Development Agency, Abuja particularly, Mr. Bayo Omoyajowo, My Folusho Fagbeja Mr.

Mustapha Aliu, Mr. John Nwagwu, Mr. Ibilewa, Ms. Rakia Abdullahi and other colleagues;

Bayo Ogundele, Folaranmi Olujuyigbe, Tukur, Tomi and particularly Oloojo Bamiji who has

been a good friend and confidant; you have all been there for me.

To my siblings, Toyin, Ibukun, Oluwaseun, and particularly, Rotimi for his financial and

moral support throughout the duration of the course; I say God will reward you greatly.

To my uncle, Michael Bamidele I say you will always be remembered for your support

and interest in my progress.

My thanks also go to Ehiwuogwu Uche and Ikena who encouraged me in the first place

to put in for the course.

I cannot but remember my roommate and friends, Akinola Akinwumiju and Meshach

Ijagbemi for their moral support during this course.

The efforts of my lecturers in the department in persons of Dr Fabiyi, Prof Ayeni, Mr.

Lekan Taiwo, Dr Dada, Mr. Adeleye and Prof Abumere (of blessed memory) at equipping me

for the challenges ahead is well acknowledged.

I also acknowledge the Global Land Cover Facility (University of Maryland) for the

provision free Landsat data which was used for this project.

Finally, deep gratitude goes to the entire students of GIS, University of Ibadan

particularly those who have both served as valuable classmates and close friends in persons of

Paul Azogor, Kemi Agboola, Meenakshi Singh, Itimi Victoria, Tope Adelaja, Adewuyi Pelumi,

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Oyebade Niyi, Agbi Sunday, Oyedeji Adeoye, Samuel Afolayan, Abba Ottowo, Julian Uanhoro,

Adepetu Olubukola and others I am not able mention; I will miss you all.

TABLE OF CONTENTS PAGE

Title page…………………………………………………………………………… i

Certification………………………………………………………………………… ii

Dedication…………………………………………………………………………... iii

Acknowledgments……………………………………………………………………iv

Table of contents……………………………………………………………………...v

List of tables………………………………………………………………………….vi

CHAPTER ONE: INTRODUCTION

1.1 Background to the study……………………………………………………… 2

1.2 Statement of the problem………………………………………………………2

1.3 Justification for the study………………………………………………………3

1.4 Aim and objectives……………………………………………………………..3

1.4.1 Aim……………………………………………………………………………. 3

1.4.2 Objectives………………………………………………………………………3

1.5 The study area…………………………………………………………………. 5

1.6 Definition of terms……………………………………………………………. 5

CHAPTER TWO: LITERATURE REVIEW

2.1 Literature review……………………………………………………………... 11

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction and Cartographic Model………………………………………… 12

3.2 Data Acquired and Source……………………………………………………. 14

3.2.1 Geo-referencing properties…………………………………………………… 15

v

3.3 Software used………………………………………………………………….. 15

3.4 Development of a Classification Scheme………………………………………16

3.5 Limitation (s) in the study……………………………………………………... 17

3.6 Methods of Data Analysis………………………………………………………19

CHAPTER FOUR

4.0 Introduction…………………………………………………………………….. 21

4.1 Land Use Land Cover Distribution……………………………………………...22

4.2 Land Consumption Rate and Land Absorption Coefficient…………………… 23

4.3 Land Use Land Cover Change: Trend, Rate, Magnitude……………………… 24

4.4 Nature and location of change in Land Use Land Cover………………………. 27

4.5 Transition Probability Matrix………………………………………………….. 30

4.6 Land Use Land Cover Projection for 2015……………………………………. .30

CHAPTER FIVE

5.1 Findings, Implications and Recommendations………………………………. 34

5.2 Summary and Conclusion……………………………………………………. 35

REFERENCES………………………………………………………………………..39

vi

LIST OF TABLES

TABLE PAGES

3.1 Data Source……………………………………………………..14

3.2 Land Use Land Cover Classification Scheme…………………..16

4.1 Land Use Land Cover Distribution in 1972, 1986, 2001..............20

4.2.1 Land Consumption Rate and Absorption Coefficient...................22

4.2.2 Population figure of Ilorin in 1977, 1984 and 2001……………..22

4.3 Land Use Land Cover Change of Ilorin and its

Environs (1972, 1986 and 2001)…………………………………23

4.5 Transition Probability table………………………………………29

4.6 Projected Land Use Land Cover for 2015………………………..30

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LIST OF MAPS

MAPS PAGE

I 1972 Land Use Land Cover map of Ilorin…………………………21

II 1986 Land Use Land Cover map of Ilorin………………………....25

III 2001 Land Use Land Cover map of Ilorin…………………………26

V 1972/86 Overlay of Built-up Land…………………………………27

VI 86/2001 Overlay of Built-up Land…………………………………28

IV 2015 Land Use Land Cover map of Ilorin…………………………31

VII 2001/15 Overlay of Built-up Land…………………………………32

viii

LIST OF FIGURES

FIGURES PAGE

I 1972 Land Use Land Cover Categories of Ilorin 40

II 1986 Land Use Land Cover Categories of Ilorin 41

III 2001 Land Use Land Cover Categories of Ilorin 42

IV 2015 Land Use Land Cover Categories of Ilorin 43

ix

x

ABSTRACT

This project examines the use of GIS and Remote Sensing in mapping Land Use

Land Cover in Ilorin between 1972 and 2001 so as to detect the changes that has taken

place in this status between these periods. Subsequently, an attempt was made at

projecting the observed land use land cover in the next 14 years. In achieving this, Land

Consumption Rate and Land Absorption Coefficient were introduced to aid in the

quantitative assessment of the change. 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.

Suggestions were therefore made at the end of the work on ways to use the information as

contained therein optimally.

xi

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.

1

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

2

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).

3

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.

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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.

5

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

6

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.

7

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.

8

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

9

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

10

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.

11

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.

Fig 1. Cartographic Model

OBJECTIVES OF LAND USE/LAND COVER

DATA ACQUISITION RECONNAISANCE SURVEY

DATA ENHANCEMENT, PROCESSING AND INTEGRATION

INITIAL LAND USE /LAND COVER CLASSIFICATION

DEVELOPMENT OF A CLASSIFICATION SCHEME

GROUND TRUTHING

EDITING OF INITIAL LAND USE/LAND COVER MAPS

FINAL PRODUCTION OF LAND USE/LAND COVER MAPS

CHANGE DETECTION ANALYSIS BASED ON LAND USE/LAND COVER MAP FOR EACH YEAR

CHANGE PREDICTION FOR PLANNING

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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 ™

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

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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

14

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

15

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.

16

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

17

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)

18

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

(%)

19

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

20

LE G E N DN O C L A S SFA R M L A N DW A S TE LA N DB U ILT U P L A N DFO R E S T LA N DW A T E R B O D Y

N

LA N D U S E L A N D C O V E R M A P O F IL O R IN IN 1 97 2655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

1000 000 000 0 1000 000 000 2000 000 000 M e te rs

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 and the low percentage of forest land.

21

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 RESEARCHER’S ESTIMATE

Table 4.2.2 Population figure of Ilorin in 1977, 1984 and 2001

22

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

23

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 country’s 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.

24

LE G E N DN O C LA SSFAR M LAN DW AS TE LAN DB U ILT U P LAN DFO R ES T L AN DW ATER B O D Y

800000000 0 800000000 160000000 0 M eter s

N

LA N D U S E L A N D C O V E R M A P O F IL O R IN IN 1 98 6

655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

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

25

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

LE G E N DN O C L AS SFA R M L AN DW A S TE LA N DB U ILT U P L A N DFO R E S T LA N DW A TE R B O D Y

900000000 0 900000000 M eter s

N

LA N D U S E L A N D C O V E R M A P O F IL O R IN IN 2 0 0 1655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

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 drastic

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

26

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.

27

LE G E N DO T H E R C LA S SE SB U ILT U P IN 19 86B U ILT U P IN 19 72

80 000000 0 0 8 000000 00 160 000000 0 M e te r s

N

O V E R L AY O F B U IL T U P L AN D T O S H O W T H E L O C A T IO N O F C H A N G E IN 1 9 7 2 /8 6655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

MAP IV. Derived from the overlay of 1972 and 1986 Land use land cover map

28

LE G E N DN O C L A S SB U ILT U P IN 20 01B U ILT U P IN 19 86

80 0000000 0 800000 000 160000 000 0 M eter s

N

O V E R L AY O F B U IL T U P L AN D T O S H O W T H E L O C A T IO N O F C H A N G E IN 8 6 /2 0 0 1

655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

MAP V. Derived from the overlay of 1986 and 2001 Land use land cover map

29

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

30

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.

31

LE G E N DN O C L A S SFA R M L A N DW A S TE LA N DB U ILT U P L A N DFO R E S T LA N DW A TE R B O D Y

80 0000000 0 800000 000 160000 000 0 M eter s

N

P R O J E C T E D L A N D U S E L A N D C O V E R O F IL O R IN IN 2 0 1 5655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

MAP VI. Derived from the 1986 and 2001 land use land cover map

32

LE G E N DNO CL ASSBU ILT U P IN 20 15BU ILT U P IN 20 01

N

O V ER L AY O F B U IL T U P L AN D T O S H O W T H E L O C AT IO N O F C H A N G E IN 20 0 1 /1 5

655000

655000

660000

660000

665000

665000

670000

670000

675000

675000

680000

680000

685000

685000

690000

690000

925000 925000

930000 930000

935000 935000

940000 940000

945000 945000

950000 950000

7000 000 00 0 7000 000 00 1400 000 000 M e te rs

MAP VII. Derived from the overlay of 2001 and 2015 Land use land cover map

33

CHAPTER FIVE

5.1 Findings, Implications and Recommendations

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.

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 foreign and more importantly, see how the defunct

industries will come up again.

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.

34

Waste land seems to be reducing between 1986 and 2001 and between

2001 and 2015 thus signifying a desirable change.

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.

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.

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 people’s

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

35

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.

36

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40

24376.2723

41436.7713

2198.2734

11063.4948

16874.6562

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

CATEGORIES

HEC

TAR

ES

FARM LANDWASTE LANDBUILT_UP LANDFOREST LANDWATER BODY

FIGURE I: LAND USE LAND COVER CATEGORIES OF ILORIN IN 1972

41

7965.5733

55561.149

9702.8136

21393.0405

1326.89160

10000

20000

30000

40000

50000

60000

CATEGORIES

HEC

TAR

ES

FARM LANDWASTE LANDBUILT_UP LANDFOREST LANDWATER BODIES

FIGURE II: LAND USE LAND COVER CATEGORIES OF ILORIN IN 1986

42

14068.4949

50317.263

10815.921

19960.2315

787.55760

10000

20000

30000

40000

50000

60000

CATEGORIES

HEC

TAR

ES

FARM LANDWASTE LANDBUILT_UP LANDFOREST LANDWATER BODIES

FIGURE III: LAND USE LAND COVER CATEGORIES OF ILORIN IN 2001

43

16583.5458

47432.4759

11026.4562

20397.8718

509.11830

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

CATEGORIES

HEC

TAR

ES

FARM LANDWASTE LANDBUILT_UP LANDFOREST LANDWATER BODY

FIGURE IV: LAND USE LAND COVER CATEGORIES OF ILORIN IN 2015

44