Landuse Landcover Change In Traditional Urban Settlement: A Case Study of Ibadan Metropolis, Nigeria

Landuse Landcover Change in Traditional Urban Settlement: A Case Study of Ibadan Metropolis, Nigeria.

CHAPTER ONE

1.0 Introduction

Landuse/Landcover encompasses the biosphere and it includes biota, soil, topography water

body habitat and exposed rock surfaces (Oyinloye et al, 2004). While landcover could be

described as the biophysical state of the earth’s surface and immediate subsurface, Landuse can

be described to include both manner in which the biophysical attributes of the land are

manipulated and the purpose for which the land is used (Briassoulis, 2000). Lillesand et al,

(2004) assume that landcover relates to the type of feature present on the surface of the earth

which may include urban buildings, lakes and maple trees while landuse relates to the human

activity that is associated with a specific piece of land. Alternatively landuse can be described as

an abstraction that is not always directly observable by even the closest inspection (Campbell,

2002). A detailed and a thorough set of land planning and management activities would require a

concise knowledge and understanding of both landuse and landcover. Campbell (2002) posits

that landuse is abstract while landcover is discrete; he also deduced that the distinction between

the two concepts (landuse and landcover) is that landcover lacks the emphasis upon the

economic function that is essential to the concept of landuse.

The challenges to monitor spatial changes in urban land use have been a general concern to

researchers, urban planners and other actors in urban management. The concern relies on

appropriate technology, and techniques to achieve accurate urban spatial changes in order to

predict the future trend for urban planning purposes. Remote Sensing data and the use of

Geographical Information Systems (GIS) techniques have proved efficient in recent times. It has

thus been possible to monitor spatial changes in urban landuse and model the pattern of future

changes. This study will examine the spatial growth of Ibadan metropolis, an indigenous urban

settlement in Nigeria.

M. Sc. Thesis Oyadiran Ola1


1.1 Landuse Change in Urban Areas

Human use of land has altered the structure and functioning of the ecosystem (Vitonset et al,

1997). The most spatially and economically important human use of land globally includes

cultivation in various ways, construction, reserves, protected lands and timber extraction (Turner

et al 1994). The pattern of landuse can provide insight into the factors that have caused the land

cover to change. The urban population in Africa is growing faster than any other continent. It is

predicted that by 2030 about 5 billion people, approximately the population size of the entire

continent today will be in urban areas and that these figures will be absorbed by the urban areas

of the less developed regions (United Nation Population Revision, 2001). The driving forces

behind the rapid urbanization in Africa today are the combination of rural-urban migration and

natural increase within the towns and cities themselves. This is worsened in some regions by

forced migration precipitated by various stresses including ethnic conflict wars, drought, famine

and the stringent measures imposed on the developing Nations in the early 1970’s due to a

decline in the Official Development Assistance (ODA), (Economic Commission for Africa,

1996).

The rapid rate of urbanization brought with it intractable urban problems, such as poor

environmental sanitation, pollution, crime, unemployment and overcrowding among others.

Other writers noted that Nigerian urban centres are faced with rapid growth and development,

which contribute to landuse change.

Yang and Lo (2003) investigated the modeling of urban growth and landscape changes in the

Atlanta metropolitan area, using an urban model closely coupled with a land transition model to

simulate the future of urban growth, Nagai et al (2002) developed a methodology to reconstruct

long term land cover changes from fragmentary observational data and knowledge of changes

using a genetic algorithm. Turner et al (1993) states that Human alterations of the terrestrial

surface of the earth are unprecedented in their pace, magnitude and spatial reach, of these, none

are more important than changes in land cover and landuse. Despite the improvement in land

cover characterization made possible by remote sensing data, especially those obtainable from

satellite sensors, global, regional and local land cover uses are poorly enumerated. It is



recognized that the magnitude of change is generally high. For example, it is estimated that the

global expansion of croplands since 1850 has consumed some 6 million square kilometers of

world land and 4.7 million square kilometres of savanna/grassland of which respectively, 1.5

million sq km and 0.6 million sq km of cropland by category has been abandoned (Ramankutty

and Foley 1999).

The United Nations Centre for Human Settlements, (2001) observed that sustainable

development is an integral component of human settlement development giving full

consideration to the needs and requirement of achieving economic growth and development,

social development and social progress employment opportunities that are in harmony with the

environment. With this concept, efforts should be made to see that proper planning and

implementation are given to landuse policies so that whatever the impact of urbanization, little

effects will be noticed on land use changes. Moreover, change detectability is a function of the

"from" and "to" classes, the spatial extent, and the context of the change (Khorram et al, 1994).

Laymon, (2003) noted that landuse changes are a consequence of national growth, regional

assessments of historical and contemporary landuse change are needed to anticipate the impacts

associated with change and contribute to an understanding of productive environmental

sustainability. Adeniyi and Omojola, (1999) believe that in Nigeria all land development

programmes and projects have evolved without an appreciation of landuse and landcover

information.

1.2 Aim

The aim of this study is to examine the spatial pattern of urban landuse changes in the Ibadan

region using remotely sensed data and GIS techniques. The study will also attempt a predictive

model of future urban growth.

1.2.1 Objectives

Examination of the spatial and temporal dynamics of landuse changes in Ibadan.

To analyze the specific changes in the landuse types.



To identify physical changes of urban landuse and landcover, map them and draw

inferences as to the underlying social and economic reasons for the changes seen.

To calculate the areal extent of each land use type and show the direction of growth of

the study area with a view to predicting changes in landuse in the future.

1.3 The Study Area

Ibadan city is located approximately on latitude 7o 23’ North and on longitude 3o 5’ East of the

Greenwich Meridian (Ayeni 1994). The name ‘Ibadan’ emanated from “Eba Odan” literarily

meaning ‘near the grassland’ (Ikporukpo, 1994). This reflects its location on the fringe of the

forest zone near the savanna. Udo, (1994) points out that Ibadan is located near the forest

grassland of South Western Nigeria.

Ibadan Metropolis

(The Study Area)

Fig 1a: Map showing the study area (Ibadan Metropolis).



1.3.1 Historical and Spatial Growth of Ibadan

Areola (1994) noted that Ibadan was founded in the 1830’s as a camp for refugees seeking

protection from the Yoruba wars that followed the collapse of the Oyo - Empire. Ibadan, apart

from being a forest site, also had several ranges of hills varying in elevation from 160 to 275

metres surrounding it (Fourchard, 2002), which made the city easily defensible from the wars.

The city grew very rapidly under the protection of a series of warlords and by 1890, when British

rule was imposed, Milson, (1891) noted that Ibadan had extended over an area of about 40

square kilometres.

The establishment of British rule brought peace over the city and the surrounding areas. This

peaceful atmosphere encouraged the development and expansion of the city. Between 1946 -

1952, Ibadan witnessed rapid growth following the designation of the Western province in 1946.

Ibadan has since then remained the capital city of Oyo State of Nigeria (fig. 1b).

The establishment of Nigeria’s premier university college in 1948 and the university college

hospital in 1957 (Mabogunje, 1968) as well as the semi autonomous western region in 1946 all

contributed to the astronomical growth of the city (Areola, 1994).

Oyelese, (1970) in Areola (1994) estimated the total area of the city to be approximately 103.8

kilometres square. This represented a 260% increase in area since the 1890’s. However, only

about 36.2 sq. km. (or 34.9% of the land area) was actually developed for urban land uses

(Areola 1994).

Udo, (1994) attributed the growth of the Ibadan cities to strong commercial activities, while

Ayeni, (1994) surmised the growth being due to virile educational, industrial, administrative and

commercial activities. However Areola concluded that the disappearance of non urban land uses

especially at the fringe of the city immensely contributed to the city’s growth.

Gbadegesin, (1981) showed clearly that by 1981 residential buildings had taken over most of the

fallowed lands and farmlands existing in 1973. Areola (1994) opined that the 1991 provincial

census results showed the built up area of Ibadan to be approximately 240 sq. km. Since then the

city has witnessed further rapid growth.

Ibadan is a rapidly changing urban area and ranked 78 in the urban area rank by the United

Nations Human Development Index Report (Human Development Index Report, 2004). Though



an indigenous population it is also the home of many tribes within and outside Nigeria. The

population growth accounts for the rapid changes that Ibadan has witnessed to date.

Author: Archer R., (2000). Source: http://www.maptown.com/geos/nigeria.html

Fig 1b: Map showing digitized map of Ibadan region



Ibadan metropolis is a typical traditional settlement without proper planning. Ibadan thus

comprises of core traditional settlements (Fig 2) mostly occupied by the indigenous population,

representing the traditional centres of the city.

Fig 2: Photo showing core traditional settlement. Source: Photograph taken (by the author - Ola, 2005) in

the field.

According to Mabogunje, (1968) Ibadan has two central business districts which are the

traditional centre (Fig 3a and 3b) where the Ibas palace and the traditional open market are

located.



Fig 3a: photo showing traditional open market (the oldest market in Ibadan - Oja Oba). Adapted from Fourchard, (2002).

Fig 3b: Photos showing traditional business district centre. Source: photograph taken (by the author – Ola, 2005) in the field.



The commercial centres (Figures 4a and 4b) are the product of the incursion of the European

colonialists and their business conglomerates. They were given lands outside the traditional core

in what was originally called Gbagi, the local appellation for pegging which describes how the

land surveys were carried out in the new city centre (Areola, 1994).

Fig 4a: Photo showing commercial area. Source: Photograph taken (by the author – Ola, 2005) in the field.



Fig 4b: Photo showing commercial area. Source: Photograph taken (by the author – Ola. 2005) in the field.

This research topic will be limited to Ibadan metropolis, which is the major urban and semi -

urban part of the capital city of Oyo State of Nigeria.

1.4 Geology, Climate and Vegetation of Ibadan City

Akintola, (1994) in his article “geology and hydrogeology of Ibadan” believes that the basement

complex rocks in Ibadan are mainly the metamorphic types of Pre-Cambrian age, but with a few

intrusions of granites and porphyries of Jurassic age. Oyegun, (1980, 1984), and Faniran, (1994)

ascertain the landform units of Ibadan to consist of hills, plains and river valleys.

Ibadan is characterised by two climatic conditions – the rainy and the dry season , Oguntoyinbo,

(1994) attributes this to the latitudinal location of Ibadan. The dry season occurs from November

to February and the rainy season runs from March to October. A study between the years



1953 -1998 (Oguntoyinbo, 1994) shows Ibadan to have a mean annual temperature of 26.6

degree centigrade with seasonal variation occurring in consonance with the seasonal variations in

radiation, sunshine and cloud cover. Similarly another study by Oguntoyinbo, (1994) between

1911- 1988 also showed Ibadan to have a mean annual rainfall of 1258.9mm.

The forest zones are found mainly in the southern part of Nigeria where humid tropical

conditions favour tree growth, whereas the savannah zones are located in the middle of the

country to the northern part of the country. The savannah zone consists of the Guinea, Sudan and

Sahel savannah. However, Ibadan is found in the Guinea savannah, thus it is naturally a belt of a

mixture of trees and tall grasses in the south, with shorter grasses and less trees in the north. The

vegetation pattern in Ibadan is a patchwork of broken forest, savannah woodland, dense thickets

and large tracts of forbs vegetation dominated by chromolaena (Eupatorium) and odorata (Siam

weed), (Fagbami, 1976).



CHAPTER TWO

2.0 Literature Review

2.1 Satellite Remote Sensing In Urban Problems

Urban and regional planners require timely and accurate information on land use and land cover.

The most commonly used approach to qualify these changes has been the acquisition of aerial

photographs, visual interpretation and the comparison with existing photographs and landuse

map data. Santos et al (1981) and Welch (1982) in Sankaran and Chandrasekaran (2001)

demonstrated the analysis and interpretation of aerial photography and its application to urban

areas. Adeniyi and Bullock (1978), Areola (1998) and Fabiyi, (1999) among others have used

aerial photographs and other remote sensing products to explain and map the spatial relationship

between different land uses in urban areas over period of time.

Satellite remote sensing has been demonstrated as a useful tool to capture data that are relevant

for the analysis of urban landuse patterns, for example Adeniyi (1981), Prentice et al, (1993),

Areola (1998), and Fabiyi (1999) have demonstrated the use of satellite remote sensing as a

useful tool to capture data that are relevant for the analysis of urban land use patterns. Campbell

(2002) believes that remotely sensed images lend themselves to accurate landcover and landuse

mapping due to ease of interpretation of landcover information from evidence available in aerial

photographs and images. Thus remotely sensed satellite imagery provides a source of reliable

data for landcover and landuse change analysis.

Generally remote sensing images do not record activity or the way land is being used (i.e. forest,

agricultural, residential or industrial) directly but acquire responses based on characteristics of

land surface. Rapid technological development has aided man’s study of the earth and its

surroundings in the short time with satellite remote sensing providing consistent, repetitive

measurements of earth surface and numerous means for monitoring landuse, landcover change.

Weng, (2002) states that satellite remote sensing is effective in providing multi-temporal and

multi-spectral data along with the required information for understanding and monitoring land



development patterns and processes for building landuse and landcover datasets. Ramandan et al,

(2004) employed the use of satellite remote sensing for urban growth assessment in Shaoxing

city, Zhejiang province. The study incorporated the use of Landsat Thematic Mapper (TM)

images from 1984, 1997 and 2000 to qualitatively and quantitatively estimate the growth of the

city, and the study concluded that the spectral ranges of the images were able to discriminate

landcover changes from urbanisation.

Belaid, (2003) used GIS and RS to detect and analyse urban-rural landuse changes for the cities

of Ksar El kabir, Khemisse and Beni Mellal in Morocco and Al Alsa oasis in Saudi Arabia. It

was discerned that the application of time series econometrics and artificial neural networks

improved the monitoring of change and time of change in a series of images.

Ghribi, (2004) in the pilot case study on the use of GIS for monitoring environmental changes in

the gulf of Tunis, Tunisia, used remotely sensed satellite images to model landuse/landcover

changes in the coastal area of the Gulf. Li et al (2004) also investigated landuse change

dynamics through the use of combined satellite remote sensing and GIS in Yulin Prefecture;

Northwestern China, with the objective of determining landuse transition rates among landuse

types over 14 years from 1986 – 2000. The outcome of their study indicates that integration of

satellite remote sensing and GIS was an effective approach in analysing the direction, rate and

spatial pattern of landuse change.

Mass, (1998) attributes the basic premise in using remote sensing data for change detection for

changes in radiance values while Gibson and Power (2000) attribute this to changes in spectral

reflectance of the surface. Whichever way it is looked at, it is clear that interpretations of

remotely sensed images lend credence to the solution of environmental issues and problems

using the power of its multi-temporal analyses to provide key information on specific landuse

changes. It also provides multi-spectral, multi-source and multi-temporal information for

accurate landcover classification. Robinove, (1981) in Brandon and Bottomley, (2000) concludes

that the utilisation of remotely sensed data enables surrogate mapping due to the impracticality of

direct measurement of the landscape. Also Riebsame et al, (1994) in Brandon and Bottomley



(2000) defined Landuse and landcover change as the conversion from one land cover category to

another, emphasizing that the type of phenomena results in a change of reflected electromagnetic

radiation (EMR) values representative as a surrogate of the earth’s surface which can be

remotely sensed.

2.2 Landuse Landcover Systems in Nigeria

Landuse and landcover changes play a pivotal role in global environmental change. It contributes

significantly to earth-atmosphere interactions and biodiversity loss, and it is a major factor in

sustainable development and human responses to global change (United Nations Agenda 21,

2000). Mathew (1999) posits that land has been going through tremendous transformations due

to sprawls in agriculturalisation, industrialisation and urbanisation. Human activities sometimes

produce changes in nature, some of which accumulate to become globally pervasive. Changes

due to human activities are often referred to as the terrestrial ecosystem and can be broadly

divided into landcover and landuse changes. Meyer & Turner, (1994) in Holman (2002) states

that landcover denotes the physical state of the land including the quantity and type of surface

vegetation, water, and earth materials. Brandon and Bottomley, (2000) also believe that changes

in landcover driven by landuse can be categorized into modification and conversion; with

modification being a change from one of condition within a cover type and conversion a change

from one cover type to another. Meyer et al, (1994) noted that landuse describes the human

employment of the land, including settlement, cultivation, pasture, rangeland, and recreation,

among others.

Briassoulis’s, (2000) review of literature enabled the writer to infer that landcover conversion

involves a change from one cover type to another while land cover modification involves

alterations of the structure or function without a wholesale change from one type to another.

Similarly Turner et al (1994) in Briassoulis, (2000) also noted that landuse change is likely to

cause landcover change, but landcover may change even if the landuse remains unaltered.

Meyer, (1995) in Briassoulis (2000) supports this view by noting that changes in landcover by

landuse do not necessarily imply a degradation of the land. Rajan and Shibasaki, (2000) observed

that landuse can be looked upon as a multi-dimensional process, which consequently poses many



difficulties for proper description and classification. Singh, (1989) also noted that change

detection is the process of identifying differences in the state of an object or phenomenon by

observing it at different times.

Nigeria has complex land systems where chiefs, families, individuals and government own land.

In 1990 (The Library of Congress, 1991) estimates indicated that 82 million hectares out of

Nigeria's total land area of about 91 million hectares were arable. Traditional land tenure

throughout Nigeria was based on customary laws under which land was considered community

property, but the customary law did little or nothing to avail crisis emanating from the use and

distribution of the land. In response to a potential crisis in land distribution, the Federal Military

Government promulgated the Land Use Decree of March 1978, thus establishing a uniform

tenure system for all of Nigeria with a view to opening the land to development by individuals,

corporations, institutions, and governments. The decree gave state and local governments’

authority to take over and assign any undeveloped land.

Landuse and landcover sequences express change in the use of land, as a result of changes in

technology, the socioeconomic and biophysical environment landuse practices are often

subjected to complex uses in the two broad belts of vegetation types (i.e. the forest and the

savannah).

Quite a lot of farming activities occur in these areas which along with the tenure systems have

resulted in the complexity of elements and patterns of the use of cover types. A lot of the

modification of the environment has also occurred due to human activities, urbanization and

industrialization being the most significant in terms of areas. Summarily the changes in landuse

affect the ecosystem in terms of landcover, land quality and capability, weather and climate,

quantity of land that can be sustained and in short the whole population and socio-economic

determinants. All these are important factors in the development of a classification scheme and

methodology for the landuse and landcover change analysis.



2. 3 Developing a Classification Scheme

Remote sensing provides tremendous means and ways of classifying landuse landcover change

due to the synoptic and repetitive coverage capability that can be used to identify and monitor

changes at regional and global scales. Mispan and Mather, (1997) are of the opinion that the

spatio-temporal patterns of change in surface radiance offer reliable information sources on the

state and nature of the surface features and the process of changes that has taken place over a

period of time. Additionally Briassoulis, (2000) posits that the analysis of landuse change

depends critically on the chosen system of landuse and landcover classification, while the

magnitude and quality of landuse change is expressed in terms of specific landuse or landcover

types. In developing a classification scheme much emphasis is placed on the concept of what is

and what should be where. Wolman (1987) in Briassoulis (2000) noted that the "what is"

encompasses the land available on earth and its characteristics as described by a given

technology at a given point in time while the "what should be" relates to values placed on the

land and its characteristics and the resulting choices made by people about uses for land. It is

imperative to understand the dynamics behind landuse changes so as to fully understand the

underlying changes in landcover. Therefore landuse landcover classifications are demarcated

with reference to the spatial scale of analysis. Researches have shown that the first consideration

in landuse landcover classification entails considering the use to which land is being put to

agriculturally and forest wise thereafter urbanization and industrialization aspects of the land is

then considered.

Aspinall and Justice, (2003) argue that improved scientific knowledge of historic and current

landuse and landcover changes is required as a basis for understanding the dynamics and trends

in landuse and landcover change, and for increasing our understanding of the processes by

which changes occur and the impacts of land management and decision-making on change.

Existing studies are synthesized in order to improve generalisation, conceptualisation and theory

of landuse change.

Marsh (1991) in Briassoulis (2000) believes that the United States Conservation Service, the

Canada Soils Directorate and the Food and Agricultural Organisation (FAO) were the first to



produce conformal soil classification systems. Landuse landcover classification is often place

specific however numerous countries have diverse classification systems. For conformity and

analytical purposes, the landuse classification system developed by Anderson et al (1976) is

widely used today for this purpose (see Appendix A i.e. Anderson et al Classification System).

Anderson, (1971) believes that a landuse and landcover classification system which can

effectively employ orbital and high-altitude remote sensor data should meet each criterion stated

below:

1. The minimum level of interpretation accuracy in the identification of land use and land

cover categories from remote sensor data should be at least 85 percent.

2. The accuracy of interpretation for the several categories should be about equal.

3. Repeatable or repetitive results should be obtainable from one interpreter to another and

from one time of sensing to another.

4. The classification system should be applicable over extensive areas.

5. The categorization should permit vegetation and other types of land cover to be used as

surrogates for activity.

6. The classification system should be suitable for use with remote sensor data obtained at

different times of the year.

7. Effective use of subcategories that can be obtained from ground surveys or from the use

of larger scale or enhanced remote sensor data should be possible.

8. Aggregation of categories must be possible.

9. Comparison with future land use data should be possible.

10. Multiple uses of land should be recognized when possible.

Giri and Shrestha, (1995) posit that the classification system for remotely sensed data varies

primarily with the kind of the satellite data used and the objective of the classification. For the

purpose of this study however, landuse and landcover patterns will be delineated into different

landuse categories using the modified version of the Anderson scheme of landuse/landcover

classification to suit the existing landuse/landcover classifications already in use in Ibadan

Metropolis.



Though it is expected that some of these criteria should apply to landuse and landcover

classification in general, most criteria often apply primarily to landuse and landcover data

interpreted from remote sensing data.



CHAPTER THREE

3.0 Methodology

3.1 Overview

This project will first map and analyse the 2000 satellite imagery, then look back in time to

examine, map and analyse the past (1984 and 1972) satellite imageries to assess for change

detection. Data preparation will be carried out using ERDAS Imagine Software. The Landsat

images (1972, and 1984) as well as the ETM (2000) image will be geocorrected in ERDAS

Imagine interface. Radiometric correction of these images will also be performed to destripe the

image and remove noise and haze. The images will be reprojected in ERDAS Imagine to a

common projection with other data. Hardcopy maps showing landuse landcover for the years

(1972, 1984, and 2000) under consideration will be produced using ArcGIS software.

Subsequently final maps showing landuse landcover changes between 1972 to 1984 and 1984 to

2000 will also be generated in ArcGIS software while Idrisi Kilimanjaro GIS software will be

used to generate the predictive map.

3.2 Data Acquisition

The sources of data for this study will basically be secondary – data acquired from other sources

other than in the field. The study will incorporate the use of the following data:

Table 1: Table showing data types and sources

M. Sc. Thesis Oyadiran Ola

Data Type Date Acquisition Sources

Landsat TM 18 Dec 1984 EarthSat.

Landsat MSS 08 Nov 1972 GeoTIFF

ETM 02 June 2000 USGS/GLCF

Landuse maps 1973 & 1983

Federal surveys, Lagos

19


3.2.1 Selection of Software

The major consideration in selecting the software for use in the project is the nature of the

analogue data. The analogue data consists largely of maps, which are more amenable to

digitizing using vector-based software. ArcGIS (version 9.0) is the major software used for the

study. It possesses the required capabilities for spatial and attribute database creation and spatial

analysis. It also possesses adequate product generation capabilities, given its ability to produce

high quality maps and charts. ERDAS Imagine and Idrisi Kilimanjaro were also used in the

study.

3.2.2 Data Preparation

Data preparation was done using ERDAS Imagine Software. The satellite images were obtained

in Unwrapped Hierarchical Data (HDF) L1G format. This enables the user to either load the data

as either HDF or individual band files. All files in this format have .L1G (signifying that the

image has been pre-processed to level 1 and is radiometrically and geometrically corrected

ensuring that it is correct to within 3-4 pixels). However the pixels in the image are

georeferenced. The compressed ("gzipped") data were first unzipped using WinZip software

thereafter ERDAS Imagine software was used to unwrap the HDF imagery. The *_MTL.L1G

File (metadata file) that was printed from the image metadata shows the number of Rows and

Columns as follows:

Table 2: Table showing Rows and Columns of the satellite images

Images Pixels per Line

(Rows)

Lines Per Data File

(Columns)

Landsat MSS 1972 3715 3329

Landsat TM 1984 6939 6389

ETM+ 2000 7512 8525



The individual bands were subsequently imported to ERDAS Imagine and the resulting Imagine

(.img) file layers were then stacked to obtain a single image, using the stack layer function. This

procedure was repeated for all the images used and the resulting images were named

accordingly, as shown overleaf).



Fig 5: Figure showing Landsat MSS 1972 image



3.3 Image Preprocessing

Preprocessing of satellite images prior to data analysis, image classification and change detection

is usually essential. Image preprocessing comprises a series of sequential operations.

Radiometric and geometric errors are corrected for in image preprocessing. Coppin & Bauer,

(1996) posit that the main operations in image preprocessing include atmospheric correction or

normalization, image registration, geometric correction, and masking (for clouds, water and

irrelevant features). Fakhar, (2005) noted that the initial processing on the raw data is usually

carried out to correct for any distortion due to the characteristics of the imaging system and

imaging conditions.

3.3.1 Image Restoration

Image restoration includes correction of data received from the satellite before classification can

be made. The following corrections were done:

3.3.2 Geometric Correction

Neameh, (2003) observed that remote sensing data is affected by geometric distortions due to

sensor geometry, scanner and platform instabilities, earth rotation, high altitude etc. The images

in raw format are not georeferenced even though they (images) are said to have been processed

to level one (1), therefore; in order to integrate these data with other data in GIS, it is necessary

to georeference the image, using existing an map(s) of the study area. A Universal Transverse

Mercator Projection (UTM) and affine transformation were equally used for correcting and

georeferencing the image.

3.3.3 Haze Correction

Haze computes the fourth Tasselled Cap Haze component for Thematic Maps and removes it

from the image. According to Neameh, (2003), the occurrence of water vapour and suspended

particles in the atmosphere results in a low image contrast and affects visible and infrared ETM

bands Haze correction was carried out successfully to enhance the images.



3.3.4 Image Enhancement

Image enhancement techniques are used to make raw images visibly better and therefore more

interpretable. It improves image quality and the visual impact for the human eye. There are many

techniques and methods of image enhancements used for visual interpretation; however

Histogram Equalization was employed in this study.

3.4 Creating Subset of the Study Area

Subsetting is the process of delineating out a portion of a large image file into one or more

smaller files. The satellite images obtained from the Earth Science Data Interface (ESDI) at the

Global Land Cover Facility (GLCF) covers a wider area, and in order to consider only the

project area (Ibadan Metropolis), it was imperative to create a subset of the satellite images. This

was done using the Data Preparation function of ERDAS Imagine. The process of creating a

subset image involves using either two corner’s co-ordinates or four corner’s co-ordinate, For

this project the following co-ordinates were used to create subsets of the Landsat MSS 1972,

Landsat TM 1984 and ETM+ 2000;

Table 3: Table showing co-ordinates of subsets

ULX 587361.72 LRX 613011.72

ULY 834078.22 LRY 807003.22

URX 613011.72 LLX 587361.72

URY 834078.22 LLY 807003.22

The unsigned 8 bit data type was chosen as the output data type. The images overleaf show the

subsets of the satellite images created (figures 6a, 6b and 6c).



Fig 6a: figure showing subset of Landsat MSS 1972 image



Fig 6b: figure showing subset of Landsat TM 1984 image



Fig 6c: figure showing subset of ETM+ 2000 image



3.5 Map Compilation

Landuse and landcover patterns for 1973 and 1983 were first digitized and then mapped from

their various sources using ArcGIS (see Appendix A). They were then delineated into different

landuse categories using the modified version of the Anderson scheme (Appendix B) of

landuse/landcover classification (Anderson et al, 1976). Thereafter a GIS and statistical analysis

was carried out.

3.5.1 Digitizing the Features

Digitizing can be defined as the process of converting the geographic features on an analog map

into digital format using a digitizing tablet, or digitizer, which is connected to a computer

(ArcGIS Desktop Help).

The Ibadan Metropolis boundary map and the landuse map were first scanned and then

georeferenced using the ArcView GIS interface. They were subsequently digitized with the

subset of the satellite images to derive the relative landuse maps. Thereafter the feature attributes

were created by adding a new field of data to the attribute table and this was subsequently

named.

3.6 Spatial Analysis

The 1973 and 1983 landuse maps were digitized in the ArcGIS environment and the spatial

analysis functions that relates to the geo-processing was activated in order to create image

subsets while using the overlay function (intersect and union) to perform analysis. The Editor

function of ArcMap was used for areal calculation of landuse and landcover themes. Two major

map analyses were then carried in the GIS to accomplish the objectives of the study. These are:

Area calculation of the landuse and land cover themes.

Overlays for change detection.

Thus, two change detection methods were used in this study. The first is the comparison of the

landuse and landcover statistics derived from the area analysis in consonance with the

comparative landcover change detection method developed by Mongkolsawat and Thirangoon,



(1990) in Yasothon province, Thailand. This comparison is to highlight the trend and rate of the

landuse and landcover changes over the period of analysis 1972-1984 and 1984 -2000.

The second method is the area-specific change detection procedure. This involves the actual

topological overlay of the classified landuse and land cover maps to generate the nature, location

and magnitude of the changes (see Table 7, table 8 and table 12).

3.7 Geo-Statistical Analysis

3.7.1 Markov Modeling of Landuse and Landcover Change

Markov chains are stochastic methods for studying how the states of a system change over time.

The method presumes that we can define the initial state of a system as well as probabilities of

change from one state to another. Micheal, (1994) noted that stochastic models have been used to

simulate and explore the entire gamut of dynamic systems including that of landuse changes.

Weng, (2002) applied Markov chains to landuse change analysis in the Zhujiang Delta of China

using satellite remote sensing. Logsdon et al. (1996) in Briassoulis (2000) observed that Markov

analysis of landuse change has been combined with GIS to create a tool for visualizing and

projecting the probabilities of landuse change among categories of landuse.

Briassoulis (2000) concludes that Markov analysis of landuse change is an aggregate,

macroscopic modelling approach as it does not account for any of the drivers of landuse change;

instead, it assumes that all forces work to produce the observed patterns. This study therefore

considers the Markov process to be in discrete states with about fifteen classes of landcover and

transitions occurring at discrete times. The Markov module from GIS Analysis – Change/Time

series of Idrisi software was used to perform the statistical analysis and to predict the changes

into the future (2020) while employing the use of Cellular Automata (CA) to add spatial

character to the Markov model.

Ban et al, (2005) noted that Cellular Automata, effectively models proximity, one of the basic

spatial elements that underlie the dynamics of many change events, i.e., areas will have a higher

tendency to change to a landuse class when they are near existing areas of the same class.



The landuse landcover (1972-1984 & 1984–2000) shapefile (.shp) data were first imported into

the Idrisi environment. A Virtual image was subsequently created to enable rasterisation of the

imported landcover vector shapefile. This was then rasterised. Eastman, (2003) noted that

Markov operation requires that the two land cover images to be compared have the same number

of matching classes and be numbered from 1 with no intermediate gaps. For this requirement to

be met the images were first reclassified and the landuse landcover classes were reclassed as

shown in the table below:

Table 4: Table showing reclassified Landuse landcover classes

Landuse Number Sequence

Airport 1

High Density Urban 2

High Density Sprawl 3

Lake/Water Body 4

Low Density Sprawl 5

Low Density Urban 6

Medium Density Sprawl 7

Medium Density Urban 8

Open Space 9

Peri –Urban Development 10

Recreational parks 11

Rock Outcrop 12

Scattered Sprawl 13

Urban Core 14

Vegetation 15

Source: Image Classification

The reclassified images were then used as input in the Markov transition estimator. In this

instance the 1972 – 1984 image was used as the earlier image while the 1984 – 2000 image was

used as the later image. The number of time periods between the first and the second image was

taken as 16 while 20 was used as the number of time periods to project forward from the second



image (i.e. form year 2000 to year 2020). The background cell option was assigned a value of 0

to keep the areas as background and a proportional error of 0.15 (15%).

This produces a transition probabilities matrix, a transition areas matrix and a set of conditional

probability images.

Thereafter CA_Markov was used to add spatial entity to the Markov transition estimator. This

was done via GIS Analysis > Change/Time Series > CA_Markov. The 1984/2000 image was

used as the basis landcover image, the output transition areas matrix was used as the transition

area files while the probability image maps created by the Markov method was used as the

Transition suitability image collection. A value of twenty (20) was used as the number for

Cellular Automata iterations. The standard 5 x 5 contiguity filter was employed as the Cellular

Automata Filter type.

3.8 Image Classification Techniques

3.8.1 Supervised Classification

Usually supervised classification is based on the area shown in the image (Mather, 2004) and

supervised classification allows the user to predefine spectral classes while it generally relies on

statistical or neural algorithms. Statistical algorithms use parameter derived sample data in the

form of training classes, such as the minimum and maximum values on the features, or the mean

and variance-covariance matrices for each of the classes. Statistical methods are parametric.

Neural methods are non parametric and do not rely on statistical information derived from the

sample data but are trained on the sample data directly – the method makes no assumption

concerning the frequency distribution of the data. Training samples are selected, where the

known pixels are assigned. The landcover in some case are homogeneous and in other cases are

mixed, however it was ensured that the training areas selected for each category provides a total

of at least 100 pixels per category. The table below shows the training data used in the study:



Table 5: Table showing training dataset

The more pixels that can be used in training, the better the statistical representation of each

spectral class (Lillesand et al, 2004). After construction of the training samples, the Landsat

images (1972, 1984 and 2000) were classified using the Maximum likelihood classification. This

considers the mean and average values in assigning classification as well as the variability of

brightness values in each class. Mather, (2004) noted that the accuracy of a supervised

classification analysis depends on:

The representativeness of the estimates of both the number and the statistical nature of

the information classes present in the image data, and

The degree of departure from the assumption upon which the classification technique is

based.

Also Dobbertin & Biging, (1996) in Mather 2004 show that classification accuracy tends to

improve as sample size increases.


ID TYPE X_COORD Y_COORD1 Lake/Water Body 598913.55236 827984.836822 Scattered Sprawl 594795.12822 828311.261063 Urban Core 598719.53040 815785.259345 Low Density Urban 590137.98840 816498.274466 High Density Urban 603784.38299 817249.298027 Peri-Urban Development 595363.56338 820024.044678 Vegetation/Open Space 607385.27586 823788.738849 Medium Density Urban 608998.71622 816512.4391810 Rock Outcrop 591360.27370 826209.2758211 High Density Sprawl 587497.80062 830523.2067812 Medium Density Sprawl 587425.17868 817991.82123

32


Fig 7: Map of ETM+ 2000 showing training areas



3.10 Methods of Landuse Classification

The Landsat satellite data of 1972, 1984 and 2000 were obtained through the use of supervised

classification techniques. After the initial image processing stages, the images were enhanced in

order to visually understand the tonal presentations, the nature and characteristics of the landuse

landcover discernable from the images. Landuse classification schemes were developed by

careful refinement of the Anderson classification scheme (Appendix B) and what are discernable

from the images.

The refined landuse classification scheme was used as the input in the selection of the training

sites that were used in the supervised classification using ERDAS Imagine. With the aid of on

screen digitizing the classified images were vectorised and the maps for each year period were

captured.

The classification scheme developed has the following landuse landcover types

1. Urban Core Areas

2. High Density Urban

3. Medium Density Urban

4. Low Density Urban

5. Peri-Urban Development

6. High Density Sprawl

7. Medium Density Sprawl

8. Low Density Sprawl

9. Scattered Sprawl

10. Vegetation Cover

11. Rock Outcrop

12. Lake and Water Body

The twelve landuse and landcover types were used to classify the data into classes using the

same templates (classification). The landuse/landcover types for each period of consideration are

presented in what follows:



Fig 8a: Map showing 1972 landuse classes



Fig 8b: Map showing 1984 landuse classes



Fig 8c: Map showing 2000 landuse classes



The flowchart below (Fig 9a & Fig 9b) shows the sequence of operation

Unzipping compressed data

Data Import

Fig 9a: Figure showing flowchart of operations used in phase one


Digitization

WinZip ERDAS Imagine

Satellite Images (1972, 1984 & 2000

Image Enhancement 1972, 1984 & 2000

Editing

Georeferencing & Rectification TM1972

Georeferencing & Rectification TM 1984

Georeferencing & Rectification ETM+ 2000

Reprojection

Image Subset 1972, 1984 & 2000

Supervised Classification1972, 1984 & 2000

ClassificationLanduse Mapping 1972

ClassificationLanduse mapping 1984

ClassificationLanduse Mapping 2000

Landuse/Landcover Change Detection 1972 – 1984 – Step 1

Landuse/Landcover Change Detection 1984-2000 – Step 2

B (Operation B)

Satellite Images downloaded from GCLF website

Landuse 1973/1983


Fig 9b: Figure showing flowchart of operations used in phase two

CHAPTER FOUR


Data Import from Step 2

Data Import from Step 1

Rasterization(7284) & (842000)

Virtual Image Creation (Image in Step 1 - 7284)

Image Reclassification

(7284)

Markov Operation

CA _Markov Operation

(2020 Projection)

Virtual Image Creation (image in

Step 2- 842000)

Image Reclassification

(842000)

B(Operation B)


4.0 Results

4.1 Ibadan Landuse Landcover in 1972.

In 1972 Nigeria was just emerging from the four years of civil war (1966-1970) shortly after the

collapse of colonial rule. Most cities including Ibadan were rather small and the Table 6 shows

the area covered by different landuse classes in Ibadan region during the year 1972.

Table 6: Landuse landcover of Ibadan metropolis in 1972

Source: Image classification The table above shows vegetation to be the dominant landuse landcover class in 1972, with

about 92 percent of the total area. This is followed by Peri urban development which covers

an area of about 25.64 Km2 which is about 3.68 percent of the study area. Urban core

representing the traditional parts and the oldest parts of the city, covers an area of about

20.70 Km2 or about 2.98 %. Urban core is in high density areas and are not large space users.

The Peri-Urban development prevalent at this period is principally residential zones with low


1972 Landuse

Landcover Class

Areas in km

Sq.

Percentages

Occupied

Lake/Water Body 3.00 0.43

Low Density

Urban 4.33 0.62

Peri – Urban

Development 25.64 3.68

Urban Core 20.70 2.98

Vegetation/Open

Space 642.30 92.29

Total 695.97 100.00

40


density urban occupying an area of about 4.33 Km2 or 0.4% with lake/water body accounting

for 3.00 Km2 (0.43%).

4.2 Ibadan Landuse Landcover in 1984


Source: Image

classification

A critical look and examination of the 1984 landuse landcover class shows that the vegetation

cover in this area has reduced giving way to urban development, the total vegetal cover/open

space left in the study area as at March 1984 is 436.56 km2 as opposed to 642.30 km2 in 1972. It

covered about 63.12 % of the total study area in 1984 whereas in 1972 it covered about 92.3%.

Thus in a period of twelve years there has been a reduction of about 205.74 km2 in areas covered


1984 Landuse landcover

Class

Areas in km.

Sq.

Percentages

Occupied

High Density Sprawl 0.94 0.14

High Density Urban 11.42 1.65


Low Density Sprawl 6.04 0.87

Low Density Urban 82.48 11.93

Medium Density Sprawl 23.58 3.41

Medium Density Urban 21.47 3.10

Peri – Urban Development 20.35 2.94

Rock Outcrop 10.90 1.56

Scattered Sprawl 45.37 6.56


Vegetation/Open Space 436.56 63.12

Total 691. 69 100.00

41


by vegetation, similarly the Lake/Waterbody and the Peri-Urban development also witnessed a

gradual decrease. Lake/Waterbody decreased from 3.0 km2 in 1972 to 2.3 km2 in 1984 while the

Peri- urban development decreased from 25.6 km2 (1972) to 20.3 km2 (1984). However, there

was a sharp increase in the landmass occupied by low density urban and urban-core. In 1972 low

density urban occupied an area of about 4.3 km2 (less than 1% of the total landmass) while in

1984 it occupied a large area of about 82.5 km2 (about 12% of the total landmass). Urban core

occupied an area of about 20.7 km2 in 1972 (about 3%) and in 1984 it occupied an area of about

30.3 km2 (about 4.4%). Thus as population densities increased as communities mature more and

more landmass are being occupied by other uses accounting for the landmass used by high

density urban – about 11.2 km2 (2%) and medium density urban - about 21.5 km2 (3%) in 1984.

Changes in the economic fortunes of the city meant more conversion of land for ancillary uses

with scattered sprawl using about 45.4km sq of the landmass (about 6.6%), medium density

sprawl 23.5km sq(about 3.5%), and low density sprawl occupying an area of about 6km sq

(about 1%).

Statistics from the table above suggest that Ibadan city has a central growth with peripheral

development fast springing up and spatial entities reducing vegetal cover. This accounts for the

figures accruing from scattered sprawl, peri-urban development, medium density sprawl, low

density sprawl and high density sprawl.

4.3 Ibadan Landuse Landcover in 2000

Table 8 shows the landuse landcover for 2000. It is interesting to note many new developments

had taken place since 1984 to generate an even distribution in areas covered by each land use

type. It clearly show much reduced vegetation cover suggesting that in later years there might be

little or no vegetal cover. The table also shows a remarkable increase in space occupied by high

and medium density urban as well as the scattered sprawl. The absence of low scattered sprawl

coupled with the increase in scattered sprawl and Peri-urban development also suggest that the

city is now growing both inwards and outwards. The growth of the city in this time period shows

that there is a difference between the spatial growth of the city and that of the past decades.

Whereas the previous growth entailed a lot of filling in of the city by the build - up of vegetation



lands (e.g. change in vegetation/open space from 1972 to 1984), the present growth is virtually

an outward expansion of the urban fringe.


Source: Image classification


2000 Landuse

Landcover Class

Areas in Km

Sq.

Percentages

Occupied

High Density Sprawl 0.224 0.03

High Density Urban 64.175 9.26


Low Density Urban 115.052 16.61

Medium Density sprawl 1.166 0.17

Medium Density Urban 98.388 14.20

Peri-Urban Development 41.152 5.94

Rock Outcrop 8.577 1.24

Scattered Sprawl 69.750 10.07


Vegetation/Open Space 269.736 38.93

Total 692.848 100

43


The various changes that have taken place between the time periods (1972 to 2000) is

summarised in table 9 below

Lulc_Ibadan

_1972

Area in

Km Sq

%

Occupied

Lulc Ibadan

1984

Area in

Km Sq

%

Occupied

Lulc Ibadan

_2000

Area in

Km Sq

%

Occupied


High Density

Sprawl 0.94 0.14

High Density

Sprawl 0.23 0.03

Low Density

Urban 4.33 0.62

High Density

Urban 11.43 1.65

High Density

Urban 64.18 9.26

Peri – Urban


Lake/Water

Body 2.32 0.34 Lake/Water Body 1.99 0.29


Low Density

Sprawl 6.04 0.87 Low Density Urban 115.05 16.61

Vegetation/

Open Space 642.30 92.32

Low Density

Urban 82.48 11.92

Medium Density

Sprawl 1.17 0.17

Total 695.77 100.00

Medium Density

Sprawl 23.58 3.41

Medium Density

Urban 98.389 14.20

Medium Density

Urban 21.47 3.10

Peri – Urban


Peri – Urban

Development 20.35 2.94 Rock Outcrop 8.58 1.24

Rock Outcrop 10.89 1.57 Scattered Sprawl 69.75 10.07

Scattered Sprawl 45.37 6.56 Urban Core 22.64 3.27


Vegetation/

Open Space 269.74 38.93

Vegetation/Open

Space 436.56 63.12 Total 692.85 100.00

Total 691.69 100.00

Table 9: Table showing Landuse landcover of Ibadan metropolis from 1972–2000.

Source: Image classification



Table 10: Matrix table showing total areas of landuse landcover changes in Ibadan between 1972

and 1984.

Land

use

UC HDU MDU LDU HDS PUD MDS LDS SSP VGO LWB ROC

UC 19.32 0.00 0.00 0.00 0.00 5.68 0.00 0.00 0.00 3.84 0.00 0.00

HDU 1.37 0.00 0.00 0.28 0.00 1.26 0.00 0.00 0.00 9.35 0.00 0.00

MDU 0.05 0.00 0.00 1.95 0.00 2.99 0.00 0.00 0.00 17.85 0.00 0.00

LDU 0.00 0.00 0.00 2.01 0.00 12.82 0.00 0.00 0.00 65.99 0.87 0.00

HDS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.91 0.00 0.00

PUD 0.01 0.00 0.00 0.06 0.00 0.29 0.00 0.00 0.00 19.93 0.00 0.00

MDS 0.00 0.00 0.00 0.00 0.00 1.98 0.00 0.00 0.00 21.59 0.00 0.00

LDS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.04 0.00 0.00

SSP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.82 0.64 0.00

VGO 0.00 0.00 0.00 0.01 0.00 0.40 0.00 0.00 0.00 433.56 0.00 0.00

LWB 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.74 1.46 0.00

ROC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.87 0.00 0.00

UC: Urban Core HDU: High Density UrbanMDU: Medium Density Urban LDU: Low Density UrbanHDS: High Density Sprawl PUD: Peripheral Urban DevelopmentMDS: Medium Density Sprawl LDS: Low Density SprawlSSP: Scattered Sprawl VGO: Vegetation/Open SpaceLWB: Lake/Waterbody ROC: Rock Outcrop

Land

use

UC HDU MDU LDU HDS PUD MDS LDS SSP VGO LWB ROC

UC 30.25 2.87 0.12 0.18 0.00 0.06 0.00 0.72 0.00 0.00 0.00 0.00

HDU 0.00 8.47 13.73 18.50 0.07 3.39 2.26 0.00 6.78 14.53 0.00 0.00

MDU 0.00 0.09 8.94 22.21 0.13 12.07 9.17 5.18 4.63 31.50 0.00 0.00

LDU 0.00 0.00 0.00 39.52 0.06 2.86 8.42 0.00 25.78 28.61 0.47 0.00



HDS 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PUD 0.00 0.00 0.00 1.34 0.96 1.97 2.20 0.00 0.81 34.13 0.00 3.79

MDS 0.00 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.00 0.58 0.00 0.00

LDS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.00 0.00

SSP 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00 7.36 63.26 0.00 0.00

VGO 0.00 0.00 0.00 0.59 0.00 0.00 0.00 0.00 0.00 258.32 0.22 0.00

LWB 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 1.63 0.00

ROC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.74 0.00 7.10

Table 11: Matrix table showing total areas of landuse landcover changes in Ibadan from 1984-2000.

UC: Urban Core HDU: High Density UrbanMDU: Medium Density Urban LDU: Low Density UrbanHDS: High Density Sprawl PUD: Peripheral Urban DevelopmentMDS: Medium Density Sprawl LDS: Low Density SprawlSSP: Scattered Sprawl VGO: Vegetation/Open SpaceLWB: Lake/Waterbody ROC: Rock Outcrop

The above matrix table (Table 10) shows the epoch of change between 1972 and 1984. Column 2

row 2 shows the total unchanged area in the Urban Core between the two time periods to be

19.32 km2, and change from Urban Core to High Density Urban, Medium Density Urban, Low

Density Urban, High Density Sprawl, Medium Density Sprawl, Low Density Sprawl, Scattered

Sprawl, Lake/Waterbody and Rock Outcrop to be zero meaning that in the two time periods

Urban Core has totally changed to other landcover types while Peripherial Urban Density and

Vegetation/Open Space retains an unchanged area of about 5.68 km2 and 3.84 km2. However it

is interesting to note that the matrix shows Vegetation/Open Space to retain fairly small amount

of unchanged areas in the two time periods. The biggest of these is 433.56 km2.

Similarly Table 11 reflects the total changes that have taken place between 1984 to 2000. The

most significant changes occur in Vegetation/Open Space, Low Density Urban and Urban Core.

The table shows Vegetation/Open Space to now occupy an area of about 258.32 km2 against

433.56 km2 when compared to the changes that have taken place between 1972 to 1984. This

represents a downward trend and a decrease of 175.24 km2 in area, likewise Urban Core to



Urban Core now occupies an area of about 30.25 km2 showing an upward trend with an

increased value in area of about 10.94 km2, Low Density Urban (1984) to Low Density Urban

(2000) also increase in area to 39.52 km2, an increase of 37.51 km2 when compared with the

changes that took place from 1972 to 1984. There are other changes in the table that reflects both

upward and downward trends meaning that in some cases the landuse landcover occupies greater

areas and in some cases a reduced area when compared to the previous landuse landcover matrix

table (Table 10).

4.4 Markov Transition Probability Matrix

The table below shows the probability matrix table of changing from one state to another that

was used to project for the future 2020. In the matrix table the rows represent the older landcover

categories and the columns represent the newer categories. Thus a count is made of the number

of times that landuse/landcover changes from one state (2000) to another (2020). The probability

table generates a value between 0.0 and 1.0 with the summation of rows and columns also

tending to 1. Wood et al, (1997) noted that the diagonals of the transition probability represent

the self replacement probabilities and the off diagonals values indicate the probability of change.

Thomas & Laurence, (2005) also noted that the process considers each landcover as a host

category and all other landuse/landcover classes as claimant classes and complete with the host

class for land. It can be clearly seen that the tables have not remained constant and that quite a

number of transitions have taken place, for instance the transition table shows the probability of

class 4 (in 2000) remaining class 4 (in 2020) is 49% and the probability of same class 4 transiting

to class 6 is 18% while the probability of class 11 remaining class 11 is 0% and the probability of

class 4 transiting to class 2 is 65%. This logic can then be used to explain the transition tables

however the resultant iteration produces a new landuse/landcover map (fig 10) indicative of the

projected years (2020).



Table 12: Table showing Probability CA_Markov Matrix.

Class 1 = Airport. Class 2 = High Density Urban. Class 3 = High Density Sprawl. Class 4 = Lake/Water Body.

Class 5 = Low Density Sprawl. Class 6 = Low Density Urban. Class 7 = Medium Density Sprawl.

Class 8 = Medium Density Urban. Class 9 = Open Space. Class 10 = Peri –Urban Development

Class 11 = Recreational Parks. Class 12 = Rock Outcrop. Class 13 = Scattered Sprawl. Class 14 = Urban Core.

Class 15 = Vegetation.


Class 1

Class 2

Class 3

Class 4

Class 5

Class 6

Class 7

Class 8

Class 9

Class 10

Class 11

Class 12

Class 13

Class 14

Class 15

Class 1

0.5599 0.0318 0.0000 0.0000 0.0003 0.0000 0.0032 0.1492 0.0009 0.0200 0.0000 0.0000 0.0135 0.0000 0.2211

Class 2

0.0000 0.2524 0.0000 0.0002 0.0000 0.3073 0.0000 0.1181 0.0000 0.0037 0.0000 0.0000 0.0000 0.3183 0.0000

Class 3

0.0056 0.0056 0.0143 0.0000 0.0006 0.1386 0.0433 0.4322 0.0019 0.1011 0.0000 0.0005 0.0766 0.0000 0.1796

Class 4

0.1128 0.0198 0.0000 0.4947 0.0001 0.1812 0.0003 0.0344 0.0003 0.0231 0.0000 0.0000 0.0326 0.0027 0.0980

Class 5

0.0000 0.6490 0.0000 0.0000 0.0001 0.1483 0.0000 0.1635 0.0002 0.0133 0.0000 0.0000 0.0015 0.0000 0.0241

Class 6

0.0002 0.3912 0.0000 0.0020 0.0000 0.3366 0.0000 0.2415 0.0000 0.0154 0.0000 0.0000 0.0019 0.0000 0.0112

Class 7

0.0000 0.3083 0.0000 0.0009 0.0000 0.3318 0.0008 0.2926 0.0001 0.0138 0.0000 0.0001 0.0202 0.0000 0.0314

Class 8

0.0000 0.5208 0.0000 0.0000 0.0000 0.3411 0.0000 0.0357 0.0001 0.0067 0.0000 0.0000 0.0021 0.0834 0.0100

Class 9

0.0000 0.6648 0.0000 0.0000 0.0000 0.2368 0.0000 0.0000 0.0000 0.0011 0.0000 0.0000 0.0000 0.0958 0.0016

Class 10

0.0003 0.5112 0.0000 0.0018 0.0002 0.2484 0.0007 0.1551 0.0005 0.0156 0.0000 0.0008 0.0149 0.0000 0.0505

Class 11

0.0000 0.6509 0.0000 0.0000 0.0000 0.2015 0.0000 0.1309 0.0000 0.0101 0.0000 0.0000 0.0000 0.0000 0.0066

Class 12

0.0017 0.0182 0.0000 0.0002 0.0000 0.0000 0.0000 0.2990 0.0000 0.1768 0.0000 0.4527 0.0284 0.0000 0.0231

Class 13

0.0010 0.1742 0.0000 0.0075 0.0003 0.4620 0.0012 0.2303 0.0009 0.0242 0.0000 0.0002 0.0251 0.0000 0.0731

Class 14

0.0000 0.1288 0.0000 0.0000 0.0000 0.0443 0.0000 0.3694 0.0000 0.0029 0.0000 0.0000 0.0000 0.4533 0.0014

Class 15

0.0141 0.0398 0.0003 0.0006 0.0027 0.1499 0.0028 0.1425 0.0083 0.0741 0.0000 0.0058 0.1542 0.0000 0.4048

48


Fig 10: Map showing 2020 Projected Landuse Landcover



CHAPTER FIVE

5.0 Discussion

The goal of this remote sensing project exercise was to examine the spatial pattern of urban

landuse changes in Ibadan region using remotely sensed data and GIS techniques, while

attempting a predictive model of future urban growth. This goal was achieved through a 2-phase

process. The first instance employs maximum likelihood supervised classification using training

areas chosen during field study to produce relative maps. The output and results of the

supervised classification were subsequently used to produce the landuse/landcover class changes

from 1974 -1984 and 1984 -2000. Examination of the matrix table of these respective changes

gave an indication of the changes that has taken place in the time periods. These two

landuse/landcover classes were then reclassified (fig11a & fig 11b) to have common classes

(table 4) and was later subjected to Markov analysis in the second phase. Results of the second

phase indicate that there have been notable changes in the landuse/landcover in Ibadan

Metropolis. The rates of the changes in some cases are dynamic while in others they are static.

Markov Analysis outputs a number of maps indicative of suitability of each of the landuse

classes (15 in this case) which is then stretched to have a range of 0- 255 before being used as

suitability images for the CA_Markov operation. The output of the Markov predictive map for

the year 2020 appears to be a map with interlocking cells. This is so due to the fact that the

projected years are farther into the future which seems to have overstretched the probability

matrix of projection.



Fig 11a: Map showing reclassified 1972-1984 landuse/landcover class

Fig 11b: Map showing reclassified 1984 - 2000 landuse/landcover class



Chapter four shows the results of analysis obtained in both phase one and phase two operations.

However table 6 (reflecting changes in Ibadan Landuse Landcover in 1972) shows vegetation to

be the dominant landuse landcover class in 1972 occupying 92% of the total area. But over the

years it was noted to have considerably decreased (occupying 63.1% in 1984 and 38.9% in

2000). Other landuse landcover classes such as lake/Waterbody and Open Space were also noted

to have decreased over the years. Similarly it was also noted that as the year progresses the areas

occupied by medium density urban, high density urban, scattered sprawl and Peri-Urban

development were note to have increased suggesting that new developments had taken place to

generate an even distribution in areas covered by each land uses.

The matrix table (table 10 - table showing total areas of landuse landcover changes in Ibadan

between 1972 and 1984) shows that about 0.28 km2 of High Density Urban changed to Low

Density Urban. In reality this should not be so (as High Density does not change to Low Density

Urban). The only possible explanation for this could be that the classes are not discreet but are

continuous while the computer techniques employed in the supervised classification is discreet,

therefore there are bound to be some error(s) of misclassification.

Areola, (1994) noted that Ibadan is a traditional settlement whose growth can be explained by the

concept of central core growth theory that is, its growth radiates from inward to outwards with

the buildings closely located (Fig. 2 and fig. 3a). There are hardly any gaps between the

buildings. Table 4. 7 (predicted future growth) predicts 45% probability of urban core will still

remain urban core in 2020 - in 16 years time. Mabogunje (1968) and Ayeni (1994) offer 2

possible explanation of this. While Mabogunje, (1968) ascribes the urban core growth trend to

growth by fission – disintegration of the compound system, Ayeni (1994) relates it to socio-

economic status, family status and ethnic status. Ayeni’s central point of view is based on the

fact that Ibadan being a traditional city has large family ties with emerging new families sticking

to their base for easy identification and association. This kind of association is similar to that,

found in Britain where royal and other status families are easily identified by staying and

sticking together as the case with Buckingham palace. However Harvey, (1975) argues that



residential differentiation is produced by forces emanating from the capitalist production process

thus creating distinctive zones such as peripheral urban development, sprawls (scattered, low and

high medium) which eventually shapes a city. The diverse creation of distinctive zones/areas in

Ibadan metropolis still shows growth in a directional way as evident in Table 12. This reflects

open space, recreational parks, peripheral urban density, low density sprawl and medium density

urban to posses high capacity of changing to high density urban in the near future (2020), with

low density sprawl possessing a capacity of 65%, medium density urban 52%, open space 66%,

peripheral urban density 51% and recreational parks 65% .

5.1 Constraints

In executing this project the following constraints were encountered:

Technical

lack of appropriate digital base maps of Ibadan region

difficulty in correlating Remote Sensing data with corresponding cadastre information

limitation on availability of data i.e. population data

inability to procure digital data

Software & Others

the CA_Markov Module used of Idrisi Software is computationally intensive

the module takes too long a time execute.

time Consuming

financial constraint.



CHAPTER SIX

6.0 Conclusion

This project used a two phase approach to predict the future growth and to examine the spatial

pattern of urban landuse landcover changes in Ibadan region using remotely sensed data and GIS

techniques.

The first phase of the project exercise involve careful refinement of Anderson landuse

classification in line with the available landuse landcover information classes to categorize the

landuse landcover classes into twelve different classes (i.e. urban core, high density urban,

medium density urban, low density urban etc). These classes were then used along with the

remotely sensed data to generate differential landuse landcover classes for the year 1972, 1984

and 2000. The second phase of the project exercise involves further reclassification of the

landuse landcover classes into fifteen classes to project for the future employing both Markov

analysis and Cellular Automata Markov (CA_Markov).

Though the landuse landcover classifications did not include such broad areas as industrial or

commercial classifications evidence from tables 9, 10, 11 and 12 suggest that many new

developments had taken place enabling the changes in the landuse landcover to be dynamic and

to generate outward growth of the city in all directions. These developments led to a sharp

reduction in the proportion of land devoted to vegetation. The degradation of vegetation cover

may be a factor as well as a reflection increased pressure on land by man’s activity and desire for

more housing culminating in increased urban core. Findings also suggest that by the year 2020

most landuse landcover classes in Ibadan metropolis would be changing to high density urban.

A full understanding of the spatial pattern of landuse landcover changes and growth of Ibadan

metropolis can be gained through detailed studies of the region. This study only used the

available landuse landcover information to draw inference and generate growth prediction.



6.1 Recommendation

Based on this study it is recommended that further studies make use of make use of quantitative

data (i.e. census data) and qualitative data. This will help provide insight to various factors that

have caused or aided the transitions of each landuse landcover classes from one state to another.

Other physical factors that normally aid urban growth such as, employment, family ties,

technological advancement e.t.c. should also be considered.

Finally it is recommended that further studies consider the use of a wider area so as to be able to

draw conclusive inference representative of Ibadan Region.



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Appendix A

Fig 12: Map Showing 1973 Landuse Map



Appendix BLand-Based Classification Standards Citywide or Countywide Classification ExampleAnderson Land-Use Land Cover Classification System: Levels I and II

Classification SystemCodes Description1 Urban or build-up land11 Urban or build-up land -- Residential12 Urban or build-up land -- Commercial and services13 Urban or build-up land -- Industrial14 Urban or build-up land -- Transportation, communications, and utilities15 Urban or build-up land -- Industrial and commercial complexes16 Urban or build-up land -- Mixed urban or built-up land17 Urban or build-up land -- Other urban or built-up land2 Agricultural21 Agricultural -- Cropland and pasture22 Agricultural -- Orchards, groves, vineyards, nurseries, and

ornamental horticultural 23 Agricultural -- Confined feeding operations24 Agricultural -- Other agricultural land3 Rangeland31 Rangeland -- Herbaceous rangeland32 Rangeland -- Shrub and brush rangeland33 Rangeland -- Mixed rangeland4 Forest land41 Forest land -- Deciduous forest land42 Forest land -- Evergreen forest land43 Forest land -- Mixed forest land5 Water51 Water -- Streams and canals52 Water -- Lakes53 Water -- Reservoirs54 Water -- Bays and estuaries6 Wetland61 Wetland -- Forested and wetland62 Wetland -- Nonforested wetland7 Barren land71 Barren land -- Dry salt flats72 Barren land -- Beaches74 Barren land -- Bare exposed rock75 Barren land -- Strip mines, quarries, and gravel pits76 Barren land -- Transitional areas77 Barren land -- Mixed barren land8 Tundra81 Tundra -- Shrub and brush tundra82 Tundra -- Herbaceous tundra83 Tundra -- Bare ground tundra



84 Tundra -- Wet tundra85 Tundra -- Mixed tundra9 Perennial snow on ice92 Perennial snow on ice -- Glaciers

Source: http://landcover.usgs.gov/pdf/anderson.pdf - Last Accessed 04/07/2005
