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.)
BY
ZUBAIR, 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,
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.
iv
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
3.3
Software used………………………………………………………………….. 15
3.4
Development of a Classification Scheme………………………………………16
v
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
vii
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
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.
x
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.
4
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.
OBJECTIVES OF LAND USE/LAND COVER
DATA ACQUISITION
RECONNAISANCE SURVEY
DATA ENHANCEMENT,
PROCESSING AND
INTEGRATION
DEVELOPMENT OF A
CLASSIFICATION SCHEME
INITIAL LAND USE /LAND
COVER CLASSIFICATION
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
Fig 1. Cartographic Model
12
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.
13
S/N
DATA TYPE
DATE
SCALE
OF
SOURCE
PRODUCTION
1.
Landsat image
2001-11-03
30m ™
NASRDA
2.
Landsat image
1986-11-15
30m TM
GLCF
3.
Landsat image
1972-11-07
80m TM
GLCF
4
FORMECU Land use/land cover
1995
1:1,495, 389
5
6
Vegetation map.
(view scale) FORMECU
Administrative and local government 2005
1:15,140,906 NASRDA
Map of Nigeria.
(view scale)
Land use and infrastructure map
1984
of Ilorin.
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
14
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.
15
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 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.
16
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
17
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)
18
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.
19
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
1972
1986
2001
LANDUSE/LAND COVER AREA
AREA AREA
AREA AREA
AREA
CATEGORIES
(Ha.)
(%)
(Ha.)
(%)
(Ha.)
(%)
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
95949.468
100
95949.468
100
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
L A 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 9 7 2
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
N
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
1000 000 000
665 00 0
670 00 0
0
675 00 0
680 00 0
1000 000 000
685 00 0
LE G E N D
N O C LASS
FAR M L AN D
W A S TE LA N D
B U IL T U P L A N D
FO R E S T LA N D
W A TE R B O D Y
690 00 0
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
1986 – 2001
1972 - 1986
LANDUSE/LAND
ANNUAL RATE
OF CHANGE
COVER
AREA
PERCE
AREA
PERCENT
CATEGORIES
(Ha.)
TAGE
(Ha.)
AGE CHA
CHANGE
72 - 86
86 - 2001
NGE
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
L A 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 9 8 6
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
N
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
800000000
660 00 0
665 00 0
0
670 00 0
675 00 0
800000000
680 00 0
685 00 0
LEGEN D
NO CLASS
F AR M L AN D
W A S TE L A N D
B U IL T U P L A N D
F O R E S T L AN D
W AT E R B O D Y
690 00 0
160000000 0 M eters
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
25
L A 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 1
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
900000000
665 00 0
0
670 00 0
675 00 0
680 00 0
685 00 0
N
LE G E N D
N O C LAS S
FAR M L AN D
W A S TE LA N D
B U IL T U P L A N D
FO R E S T LA N D
W A TE R B O D Y
690 00 0
900000000 M eters
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
city center. These therefore suggest that there might be a high level of compactness
in Ilorin by 2015.
26
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
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 6
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
950 00 0
950 00 0
N
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
800000000
665 00 0
670 00 0
0
675 00 0
800000000
680 00 0
685 00 0
LE G E N D
O T HE R C LAS SE S
B U IL T U P IN 1 9 8 6
B U IL T U P IN 1 9 7 2
690 00 0
160000000 0 M eters
MAP IV. Derived from the overlay of 1972 and 1986 Land use land cover map
28
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
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
N
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
800000000
665 00 0
0
670 00 0
675 00 0
800000000
680 00 0
685 00 0
LE G E N D
NO CL ASS
B U IL T U P IN 2 0 0 1
B U IL T U P IN 1 9 8 6
690 00 0
160000000 0 M eters
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 WASTE BUILT-UP FOREST WATER
LAND LAND
LAND
LAND
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 FARM
COVER CLASSES
LAND
WASTE
BUIL-UP FOREST
WATER
LAND
LAND
BODY
LAND
16583.5458 47432.4759 11026.456 20397.8718 509.1183
AREA IN
2015 HECTARES
AREA
IN 17
50
11
21
1
PERCENTAGE
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
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 5
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
800000000
665 00 0
0
670 00 0
675 00 0
800000000
680 00 0
685 00 0
MAP VI. Derived from the 1986 and 2001 land use land cover map
32
690 00 0
160000000 0 M eters
N
LE G E N D
N O C LASS
FAR M L AN D
W A S TE LA N D
B U IL T U P L A N D
FO R E S T LA N D
W A TE R B O D Y
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 2 0 0 1 /1 5
655 00 0
660 00 0
665 00 0
670 00 0
675 00 0
680 00 0
685 00 0
690 00 0
950 00 0
950 00 0
945 00 0
945 00 0
940 00 0
940 00 0
935 00 0
935 00 0
930 00 0
930 00 0
925 00 0
925 00 0
655 00 0
660 00 0
7000 000 00
665 00 0
670 00 0
0
675 00 0
7000 000 00
680 00 0
685 00 0
N
LE G E N D
NO CL ASS
B U IL T U P IN 20 15
B U IL T U P IN 20 01
690 00 0
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
the inability to accurately map out water body in 1972 due to the aforementioned
35
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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45000
41436.7713
40000
35000
HECTARES
30000
FARM LAND
25000
24376.2723
WASTE LAND
BUILT_UP LAND
20000
16874.6562
FOREST LAND
WATER BODY
15000
11063.4948
10000
5000
2198.2734
0
CATEGORIES
FIGURE I: LAND USE LAND COVER CATEGORIES OF ILORIN IN 1972
41
60000
55561.149
50000
HECTARES
40000
FARM LAND
WASTE LAND
BUILT_UP LAND
30000
21393.0405
20000
10000
7965.5733
FOREST LAND
WATER BODIES
9702.8136
1326.8916
0
CATEGORIES
FIGURE II: LAND USE LAND COVER CATEGORIES OF ILORIN IN 1986
42
60000
50317.263
HECTARES
50000
40000
FARM LAND
30000
WASTE LAND
BUILT_UP LAND
FOREST LAND
19960.2315
20000
WATER BODIES
14068.4949
10815.921
10000
787.5576
0
CATEGORIES
FIGURE III: LAND USE LAND COVER CATEGORIES OF ILORIN IN 2001
43
50000
47432.4759
45000
40000
HECTARES
35000
FARM LAND
30000
WASTE LAND
BUILT_UP LAND
25000
20397.8718
20000
15000
16583.5458
FOREST LAND
WATER BODY
11026.4562
10000
5000
509.1183
0
CATEGORIES
FIGURE IV: LAND USE LAND COVER CATEGORIES OF ILORIN IN 2015
44