Zubair, ayodeji opeyemi

The procedure adopted in this research work forms the basis for deriving statistics of land use dynamics and subsequently in the overall, the findings.

Fig 1. Cartographic Model

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.

thinning was applied to the 1972 imagery which has a resolution of 80m using a factor of two to modify its properties and resolution to conform to the other two has given below;

Data type: rgb8

File type: binary

Columns: 535

Rows: 552

Referencing system: utm-31

Reference units: m

Unit distance: 1

Minimum X: 657046.848948

Maximum X: 687541.848948

Minimum Y: 921714.403281

Maximum Y: 953178.403281

Min Value: 0

Max Value: 215

Display Minimum: 0

Display Maximum: 215

Image thinning was carried out through contract; contract generalizes an image by reducing the number of rows and columns while simultaneously decreasing the cell resolution. Contraction may take place by pixel thinning or pixel aggregation with the contracting factors in X and Y being independently defined. With pixel thinning, every n^th pixel is kept while the remaining is thrown away.

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.

5. 
   Microsoft Excel was used in producing the bar graph.
   

Based on the priori knowledge of the study area for over 20 years and a brief reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area after Anderson et al (1967). The classification scheme developed gives a rather broad classification where the land use land cover was identified by a single digit.

Table 3.2 Land use land cover classification scheme

The classification scheme given in table 3.2 is a modification of Anderson’s in 1967

The definition of waste land as used in this research work denotes land without scrub, sandy areas, dry grasses, rocky areas and other human induced barren lands.

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.

Six main methods of data analysis were adopted in this study.

1. 
   Calculation of the Area in hectares of the resulting land use/land cover types for each study year and subsequently comparing the results.
   
2. 
   Markov Chain and Cellular Automata Analysis for predicting change
   
3. 
   Overlay Operations
   
4. 
   Image thinning
   
5. 
   Maximum Likelihood Classification
   
6. 
   Land Consumption Rate and Absorption Coefficient
   

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

Going by the second method (Markov Chain Analysis and Cellular Automata Analysis), Markov Chain Analysis is a convenient tool for modeling land use change when changes and processes in the landscape are difficult to describe. A Markovian process is one in which the future state of a system can be modeled purely on the basis of the immediately preceding state. Markovian chain analysis will describe land use change from one period to another and use this as the basis to project future changes. This is achieved by developing a transition probability matrix of land use change from time one to time two, which shows the nature of change while still serving as the basis for projecting to a later time period .The transition probability may be accurate on a per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. Hence, Cellular Automata (CA) was used to add spatial character to the model.

CA_Markov uses the output from the Markov Chain Analysis particularly Transition Area file to apply a contiguity filter to “grow out” land use from time two to a later time period. In essence, the CA will develop a spatially explicit weighting more heavily areas that proximate to existing land uses. This will ensure that land use change occurs proximate to existing like land use classes, and not wholly random.

Overlay operations which is the last method of the three, identifies the actual location and magnitude of change although this was limited to the built-up land. Boolean logic was applied to the result through the reclass module of idrisi32 which assisted in mapping out separately areas of change for which magnitude was later calculated for.

The Land consumption rate and absorption coefficient formula are give below;
L.C.R = A

P A = areal extent of the city in hectares

L.A.C = A₂ – A₁

L.C.R = A measure of compactness which indicates a progressive spatial expansion of a city.

L.A.C = A measure of change in consumption of new urban land by each unit increase in urban population

Both the 2001 and 2015 population figures were estimated from the 1991 and the estimated 2001 population figures of Ilorin respectively using the recommended National Population Commission (NPC) 2.1% growth rate as obtained from the 1963/1991 censuses.

The first task to estimating the population figures was to multiply the growth rate by the census figures of Ilorin in both years (1991, 2001) while subsequently dividing same by 100. The result was then multiplied by the number of years being projected for, the result of which was then added to the base year population (1991, 2001). This is represented in the formula below;
n = r/100 * Po (1)

Pn = Po + (n * t) (2)

Pn = estimated population (2001, 2015) Po = base year population (1991 & 2001 population figure)

r = growth rate (2.1%) n = annual population growth

t = number of years projecting for

*The formula given for the population estimate was developed by the researcher

In evaluating the socio – economic implications of change, the effect of observed changes in the land use and land cover between 1972 and 2001 were used as major criteria.


CHAPTER FOUR
DATA ANALYSIS

4.0 Introduction

The objective of this study forms the basis of all the analysis carried out in this chapter. The results are presented inform of maps, charts and statistical tables. They include the static, change and projected land use land cover of each class.

The static land use land cover distribution for each study year as derived from the maps are presented in the table below

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

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.

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.

Many projects were embarked on after the creation of Kwara State which also falls within the oil boom era of the 1970s and this attracted a lot of people to the area thus contributing to the physical expansion of the city as evident in the increased land consumption rate from 0.005 to 0.02 and land absorption coefficient by 0.09 between 1972 and 1986. Many of these projects include the Army barracks at Sobi, Adewole Housing Estate, the International Airport, Niger River Basin Authority Headquarters, University of Ilorin among many others which all encouraged migration into the city.

The period between 1986 and 2001 witnessed a drop in the rate at which the physical expansion of the city was going as against 1972 and 1986. For instance, the built-up land only increased by 1% as against the 8% increase between 1972 and 1986. This is also evident in the drop observed in the land absorption coefficient from 0.09 between 1972 and 1986. In deed, the austerity measure known as (SAP) introduced into the country at this period to restore the 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.

MAP II. Derived from landsat image of Ilorin in 1986