Browsing by Author "Idrees, M.O"

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    Flood Susceptibility Mapping in Asa Local Government Area, Kwara State, Using Remote Sensing and GIS Approach
    (Journal of Environmental Issue Department of Geography and Environmental Management, Faculty of Social Sciences, University of Ilorin, Ilorin, Nigeria, 2022) Lawal, F.O; Idrees, M.O; Ifechukwu, O.M.; Babalola, A.; Omar, D.M; Azeez, K.A.; Salami, I.B
    The increasing incidences of flood events in recent times have necessitated the need to come up with an early warning information for flood risk preparedness and reduction. The study aimed at mapping flood susceptibility in Asa Local Government Area, Kwara State, using Geographic Information System (GIS) based multi criteria evaluation approach. The data required for the study include: rainfall amount, soil texture, digital elevation model, distance from stream (euclidean distance) and land use land cover of the study area. Using Analytic Hierarchical Process, flood criteria weights were calculated for the different variables according to their influence on flooding and overlay analysis was employed in analysing the data for the study. The result of the study show there is spatial variation in the level of susceptibility to flood risks in the study area. Thus, the low-risk area covers 16.1% (~222.24 km2), moderate class 28.8% (~398.08 km2), high risk zone occupies 39.5% (~408.70 km2) while the very high-risk zone takes up 25.6% (~354.15 km2) of the area coverage of the study area. Furthermore, the accuracy assessment produced area under the curve (AUC) value of 0.795 (~80%). From the study, it was also discovered that Owode, parts of Afon, Bode, Onikangu, Abule-Alagbede fall within the very high and high-risk flood zones. The map could be a useful tool to decision makers in Asa local government for effective flood management plans.
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    Urban Land Use Land Cover Mapping in Tropical Savannah using Landsat-8 derived Normalized Difference Vegetation Index (NDVI) Threshold
    (South African Journal of Geomatics, Published by CONSAS Conference, South Africa., 2022) Idrees, M.O; Omar, D.M; Babalola, A.; Ahmadu, H.A; Yusuf, A.; Lawal, F.O
    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.