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

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.

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.

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.

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.

• 
  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.
  

• 
  Waste land seems to be reducing between 1986 and 2001 and between 2001 and 2015 thus signifying a desirable change.
  

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  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.
  

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 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.

REFERENCES

Adeniyi P.O and Omojola A. (1999) Landuse landcover change evaluation in

Christaller (1933), Central Place Theory – Wilkipedia Free Encyclopedia

Coppin, P. & Bauer, M. 1996. Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote Sensing Reviews. Vol. 13. p. 207-234.

Daniel, et al, 2002 A comparison of Landuse and Landcover Change Detection Methods. ASPRS-ACSM Annual Conference and FIG XXII Congress pg.2.

Dimyati, et al.(1995). An Analysis of Land Use/Land Cover Change Using the Combination of MSS Landsat and Land Use Map- A case study of Yogyakarta, Indonesia, International Journal of Remote Sensing 17(5): 931 – 944.

ERDAS, Inc. 1992. ERDAS Production Services Map State for Georgia DNR in the Monitor, Vol. 4, No 1, ERDAS, Inc, Atlanta, GA.

EOSAT 1992. Landsat TM Classification International Georgia Wetlands in EOSAT Data User Notes, Vol. 7, No 1, EOSAT Company, Lanham, MD.

EOSAT 1994. EOSAT,s Statewide Purchase Plan Keeps South Carolina Residents in the know, in EOSAT Notes, Vol. 9, No 1, EOSAT Company Lanham, MD.

Fitzpatric-lins et al (1987). Producing Alaska Interim Land Cover Maps from Landsat Digital and Ancillary Data, in Proceedings of the 11^th Annual William T. Pecora Memorial Symposium: Satellite Land Remote Sensing: current programs and a look into the future American Society of Photogrammetry and Remote Sensing, Pp. 339 – 347.

Idrisi 32 guide to GIS and Image processing, volume 1, Release 2. Pp. 17

Kwara State of Nigeria (1997) Kwara State Diary, Government press Ilorin.

Macleod & Congalton. 1998. A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data. Photogrammetric Engineering & Remote Sensing. Vol. 64. No. 3. p. 207 - 216.

Meyer, W.B. 1995. Past and Present Land-use and Land-cover in the U.S.A. Consequences. p.24-33.

Moshen A, (1999). Environmental Land Use Change Detection and Assessment Using with Multi – temporal Satellite Imagery. Zanjan University.

Olaniran, J.O (2002). Rainfall Anomalies in Nigeria: The contemporary Understanding. 55^th inaugural lecture, University press Ilorin.

Olorunfemi J.F (1983). Monitoring Urban Land – Use in Developed Countries – An aerial photographic approach, Environmental Int.9, 27 – 32.

Oyebanji, J. O. (1993),”Kwara State” in Udo, R.K and Mamman, A.D (Eds), Nigeria: Giant in the Tropics, Vol. 2, State Survey, Gabumo Publishing Co. Ltd. Lagos.

Oyegun, R.O (1983). Water Resources in Kwara State. Matanmi and Sons printing and publishing Co. Ltd. Ilorin.

Oyegun R.O (1985), “The Use and Waste of Water in a Third World City” GeoJornal, Reidel Publishing Company, 10.2,205 – 210.

Riebsame, W.E., Meyer, W.B., and Turner, B.L. II. 1994. Modeling Land-use and Cover as Part of Global Environmental Change. Climate Change. Vol. 28. p. 45.

Shoshany, M, et al (1994). Monitoring Temporal Vegetation Cover Changes in Mediterranean and Arid Ecosystems Using a Remote Sensing Technique: case study of the Judean Mountain and the Judean Desert. Journal of Arid Environments, 33: 9 – 21.

Singh, A. 1989. Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing. Vol. 10, No. 6, p. 989-1003.

U.S. Geological Survey, 1999. The Landsat Satellite System Link, USGS on the World Wide Web. URL: http://landsat7.usgs.gov/landsat_sat.html. 11/10/99.

University of Ilorin, Department of Geography. (1981) Ilorin Atlas; Ilorin University press

Wilkie, D.S., and Finn, J.T. 1996. Remote Sensing Imagery for Natural Resources Monitoring. Columbia University Press, New York. p. 295.

Xiaomei Y and Ronqing L.Q. Y, (1999). Change Detection Based on Remote Sensing Information Model and its Application to Coastal Line of Yellow River Delta – Earth Observation Center, NASDA, China.

Yeates, M and Garner, B. (1976). The North American City, Harper and Row Pub. New York.

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

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