Remote Sensing and GIS Applications in Land Use Studies

Adeniyi (1985) observed that computer assisted classification of Landsat MSS image can provide rapidly, basic up-to-date location-specific as well as quantitative data on broad categorization of land use and land cover for the semi-arid area of Bakolori, Nigeria. He added that the best period for Landsat MSS acquisition for the classification of land use and land

cover of the study area is January and February for the dry season inventory and August and September for the wet season inventory.

Using Remote Sensing and GIS for land use and land cover mapping along the Sokoto River, in the north-western part of Nigeria, Omojola and Soneye (1993) demonstrated through visual analysis the usefulness of Landsat MSS in the generation of land use/land cover information in the area. They also demonstrated the relevance of the input of such data for map analysis and presentation in a way that can make the results of resource analysis useful for planning and management process. Thirdly, they demonstrate the capability of interactive Remote Sensing and GIS techniques in the inventory; presentation and analysis of land use and land cover information of the area. The visual interpretation of the enhanced Landsat MSS data provided adequate spectral information required for the mapping of land use and land cover in the study area while the GIS subsystem used afforded easy analysis and presentation of the maps generated through remote sensing techniques.

Using the NigeriaSat-1 data of December 2003 for land use and land cover mapping of an area within Osun State, Oyinloye et al (2004) observed that land use and land cover are dynamic phenomena that are characterized by seasonal changes, particularly in south-western Nigeria where farming is both intensive and extensive. They added that in order to effectively manage these phenomena, it is necessary to map the different themes from time to time, as this will provide a good understanding of the land use and land cover pattern of the area. The result of the study as compared to the images of LANDSAT TM of the same are showed that the satellite data is very relevant for land use and land cover mapping.

Raji (2004) also used data from NigeriaSat-1 and appropriate GIS software packages to assess the current and potential land use in the Kadawa sub-sector of the Kano

River Irrigation Project. Existing soil map of Kadawa at 1:25,000 and topographic map at 1:50,000 were digitized and used to obtain the land suitability/potential land use map.

Supervised algorithm was employed in the classification of the December 2003 NigeriaSat-1 image of Kadawa area. He noted on the whole that rainfed agriculture remained the predominant land use, accounting for about 50% of the total land area. The different land utilization types within the irrigated areas could not be discriminated at the scale of the image which is32m. Irrigated agriculture accounted for only 5% of the total land area. Assessment of the adequacy of current land use management shows that only 36% of the total land area is properly managed while 41% of the land area is categorized as over-utilized. Within this over-utilized land, the most severe environmental problems such as rising groundwater table and salinity are envisaged to be caused by intensive-use established on soils of moderate and marginal suitability.

Olowolafe (2004) also carried out a study to evaluate the usefulness of NigeriaSat-1 imagery for soil mapping in some parts of the Jos Plateau, Nigeria. The false colour composite print (Bands 1, 2 & 3) was selected since it shows more boundaries and feature differentiation. Visual interpretation was made at a scale of 1:100,000 followed by ground truth verification, while the final map was compiled at a scale of 1: 250,000.

Monoscopic interpretation of Landsat ETM and the conventional method involving air-photo interpretation were also employed for comparison. Four main physiographic units were identified and mapped. These were further subdivided into fourteen soil mapping units. The main physiographic units’ boundaries are much more deciphered than the soil mapping units’

boundaries. The result shows that feature differentiation and soil mapping unit delineation on the NigeriaSat-1 are good. The soil boundaries produced from it shows considerable coincidence with those derived from the conventional air-photo interpretation method. He

concluded that, NigeriaSat-1 imagery has a good discriminating capability between different soil types. To some extent, some mapping units are similar in areal coverage on both maps.

The differences however, are attributed to the finer resolution of the black and white panchromatic photographs. The 3-band NigeriaSat-1 product compares favourably with the 7-band Landsat ETM imagery in soil mapping. A key issue is that NigeriaSat-1 product has considerable potential for aiding small-scale soil mapping.

The most common conventional methods of collecting data in the research work involving farming systems analysis include the following: surveys (field measurements, interviews and questionnaires); field observation and measurements direct rural rapid appraisal and participatory rural appraisal. Table 10 contained the comparison of the general characteristics of formal and informal field surveys for farming systems (Norman, 1993).

Table 10. Comparing General Characteristics of Formal and Informal Field Surveys for Farming Systems

Characteristic Informal Formal

Background information required Minimal Substantial

Time allocation by researchers:

Preparation Less More

Implementation More Less

Analysis and writing Less More

Total time Less More

Hypotheses: Required beforehand Not essential Essential

Created during Yes No

Likely discipline interaction More likely Less likely

Implementation:

Questionnaire used? No Yes

Interviewers Field Assistants Mainly enumerators

Potential for creativity/literation Maximum Minimal

Potential for learning/verification Mainly learning Mainly verification Potential for representative sample Less likely More likely

Potential quality of information:

Attitudinal Better Poorer

Qualitative Better Poorer

Quantitative Poorer Better

Probability of high: Sampling errors Higher Lower

Measurement errors No difference No difference

Value of statistical techniques in analysis Little Great Source: Norman (1993)

As contained in the Integrated Land Resource Survey (ILRS) which was evolved in Australia in the 1950’s, identification of settlements, arable farming and grazing sites using Photographic Remote Sensing technique can enable the interpretation of base images so as to identify different tracts of land with respect to setting intended use. The result of the survey shows that if such imageries are subjected to rigorous interpretation, we could identify boundaries that will show departure from one tract of land and another for different use types. The ILRS employ the use of panchromatic photographs on the scale of 1:500000, which were interpreted using the fundamentals of API and stereoscope to identify and delineate particularly arable farming and grazing sites in Australia. In Nigeria, the ILRS methodology was applied together with the Land capability system of the United States Department of Agriculture (USDA) in the northern Nigeria between 1972 and 1978 to identify suitable land for agricultural development. In addition, it was applied in Britain for the planning of routes (tracks) of their rail system of transportation. It was also used successfully in Tanzania to assess the surface soil erosion to determine the appropriate management and control measures.

Furthermore, using IRS - IA (Indian Remote Sensing) application for land use and land cover mapping in India, Rao (1996) stressed that land use and land cover inventories are needed for the optimal utilization and management of land Resources of the country. They concluded that Remote Sensing application with IRS - IA data helped generation of district wise land use and land cover maps of the whole country on 1:250,000 scale to serve the requirement of agro-climatic zonal planning, initiated under the planning commission of the Government of India.

Jenabfer (2001) reiterated that land use and land cover data is one of the most important data layer in any Geographic Information System for agriculture resource

management. The lack of real-time and reliable data for preparation of land use and land cover maps has made satellite imagery, the only practical source of up-to-date data for such studies .The study area Gilan province, located in north of Iran was selected because of it variable land cover and diverse agricultural activities. Three different seasonal TM data sets acquired between 1991 and 1994, were utilized in the preparation of land use and land cover maps, which comprised seven major classes (24 sub-classes) and four mixed classes. In addition some specific crops like tea and olive were also mapped. The information derived from these maps together with other data such as topography, transportation network, international and provincial boundaries, etc were compiled for use in the envisaged GIS that will eventually have more than 25 different information layers.

Balaselvakumar (2002) employed remote sensing techniques for agriculture survey in India. He maintained that the use of remote sensing technology has rapidly expanded for the development of key sectors in India as such the remote sensing techniques will continue to be very important factor in the improvement of present system of acquiring agricultural data considering the fact that it provides various platforms for agricultural survey. He concluded that satellite imagery has unique ability to provide the actual synoptic views of large area at a time, which is not possible for conventional survey methods and also the process of data acquisition and analysis are very fast through GIS as compared to the conventional methods. The different features of agriculture were acquired by characteristic, spectral reflectance, spectral signature of agriculture and associated phenomena through EMR. In general, the study emphasized the utmost need of timeliness and accuracy of the output generated by remote sensing techniques and its calibration with ground-truth and other information systems like aerial photography and satellite imagery etc. Furthermore, the importance of remote sensing with special reference to agricultural sector involving crop

acreage, crop production, rangeland and livestock were discussed in detail.

Satoshi (2003) examined Indian Remote Sensing Satellite (IRS) data to analyze temporal and spatial characteristics of agricultural land use in the semi-arid tropics of India and one of the major cropping season, Rabi (post rainy), was selected to monitor its agricultural activity. In the Rabi season NDVI value of cropped area showed higher amount than that of forest and also those of other categories. The pattern of temporal change of NDVI of each land use category could be approximated as a linear decrease in the latter period of Rabi season. This formation was applicable to correct the difference of date of observation of satellite data to discriminate the cropped area from other land use by NDVI value. The results of application of this method showed that the cropped area in Rabi season had increased double in the period of 1999 to 2002 and the higher rate of increase was indicated at the part of higher land suitability for agricultural purpose estimated by IRS data.