Browsing by Author "Mohammed, Oludare Idrees"

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    LAND SUITABILITY FOR RICE CROP FARMING IN KWARA STATE USING GIS-BASED MULTI-CRITERIA DECISION ANALYSIS
    (Faculty of Agricultural Sciences and Food, ScCyril and Methodius University in Skopje, Republic of North Macedonia., 2022-03-23) Ayo, Babalola; Mohammed, Oludare Idrees; Ruth, K. Aniyikaye; Hussein, A. Ahmadu; Oyedapo, A. Ipadeola
    This study employs GIS-based multi-criteria decision approach to identify suitable areas for cultivating rice crop in Kwara State, Nigeria, using essential climatic, soil, terrain and environmental variables selected based on FAO framework for land evaluation. Weights indicating the relative importance of each variable was determined using Analytical Hierarchical Process (AHP). The criteria, their weights and constraints were integrated in GIS environment to produce suitability map, classified into five levels of suitability (Very highly suitable, highly suitable, moderately suitable, low suitable and not suitable) using weighted overlay operation. The result indicates that 9.7% (343803.75 ha) of the total land area is unsuitable for cultivating rice while 14.6% (516169.46 ha) is classified as low suitable area. The moderately suitable, highly suitable and very highly suitable classes occupy 30.8% (1091145.20 ha), 40.56% (1436504.55 ha) and 4.4% (154408.94 ha), respectively. Quantitative assessment of the work yields overall accuracy (area under the ROC curve) of 0.97 (97%). Based on the findings of this study, we recommend that the state land use planning agency review zoning mechanism, incorporates grassroots participatory land use planning policy and evaluate suitable land for other essential crops by incorporating GIS in order to sufficiently allocate lands for optimal utilization.
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    Urban land use land cover mapping in tropical savannah using Landsat-8 derived normalized difference vegetation index (NDVI) threshold
    (University of Cape Town, South Africa., 2022-02) Mohammed, Oludare Idrees; Dahir, Muazu Omar; Ayo, Babalola; Hussein, Adomu Ahmadu; Abdulganiyu, Yusuf; Falilat, O. Lawal
    Generation of land use/land cover map at different spatial scales using satellite remote sensing data has been in practice as far back as early 1970s. Since then, research focus has been on the development of classification steps and improving the quality of the resulting maps. In recent times, the demand for detailed high accuracy land-use and land-cover (LULC) data has been on the increase due to the growing complexity of earth processes, while, at the same time, processing step is becoming more complex. This paper explores Landsat 8 derived normalized difference vegetation index (NDVI) threshold for the purpose of simplifying land cover classification process. NDVI images of January, May and December, 2018, representing dry, wet and harmattan seasons were generated. Thereafter, NDVI values corresponding to the location of a set of training data representing the target urban land covers (water, built-up area, soil, grassland and shrub) were extracted. Using the statistics of the extracted values, NDVI threshold for the respective land cover type were determined for the classification process. Finally, the classification accuracy was evaluated using the unbiased matrix coefficient technique which produced overall accuracy of 71.3%, 46.4% and 75.6% at 95% confidence limit for the months of January, May and December of the year review respectively. The result has shown that NDVI threshold is a simple and practical alternative to obtain LULC map at a reasonable time with a few data.