This study aimed at flood modeling and vulnerability analysis of Abia State using Remote Sensing and Flood modeler. The methodology involved acquisition of Sentinel-2 imagery covering Abia State, Rainfall data and ALOS PALSAR. Image subsetting was done to extract the area of study from the acquired dataset, this was followed by analysis of DEM accuracy using root mean square error, image classification to extract the landuse/ landcover of the study area, surface runoff modelling to determine surface runoff potential in the study area and flood modelling. The flood frequency return as modeled by Flood Modeler indicated a 25.04km 2 inundation extent for 2-year return period, 28.10km 2 inundation extent for a 5-year period and 26.04km 2 inundation extent for a 10-year return period. Increasing to its peak extent by 3.67% by the 5-year return period, and then decreased by 2.24% by the 10-year return period. The surface runoff potential revealed that 35.99% of the study area with an area coverage of 1630.19 km 2 had low infiltration potential, 32.51% with an area of 1472.56 km 2 had moderate infiltration while 31.50% with an area of 1426.82 km 2 had high infiltration. This indicated that the study area had a high extent of low surface infiltration which will lead to flooding during heavy or frequent rainfalls. This study recommends flood modeler as it is reliable for flood modeling, having been proven with correlation results of 0.8196that it fits to the ground flood points gotten during field validation.
Heavy floods in Nigeria have shown increasing trend in recent years. Ajeokuta is one of the areas affected annually by flood due to its location along the river Niger basin. Flood risk mapping and analysis are vital elements for appropriate land use planning in flood prone areas. The aim of this paper is to demarcate flood risk potential areas and determine the spatial impact of the recent major flood event in Ajeokuta using Remote Sensing and GIS techniques. Identified flood inducing factors in the study area, such as slope, elevation, drainage density, proximity to the river and land use were reclassified and combined to delineate flood risk zones using multi-criteria approach in a GIS environment. The idea was to identify the areas with the highest number of flood inducing factors and assess its proximity to the inundated areas during the recent flood events as a criteria for determination of locations for future flood events. Moderate resolution imaging spectroradiometre (MODIS) data of NASA terra satellite, SRTM, Landsat image with resolution of 30m, geographical map of the study area and geographical information system (GIS) were used for this purpose. Each of the flood indicators was reclassified into four which included high risk, moderately risk, low risk, and no risk through ranking process. Flood risk map (FRM) was later generated by overlaying the reclassified maps of all the parameters using addition operator and validated with a view to assisting decision makers on the menace posed by the disaster. The flood risk map revealed that the very high risky places covered area of 376.31 square kilometers (27.63%) while high risky covered 322.88 square kilometers (23.71%), The low risky areas covered 151.76 square kilometers (11.14%) and areas free from risk covers 511.040 square kilometers (37.52%). This analysis further revealed that 56 settlements are within the very high risk zone these includes Geregu,
Hydrocarbon micro – seepages are light hydrocarbon that cause oxidation – reduction reaction on the earth’s surface, resulting in alterations and anomalies such as red bed bleaching, ferrous iron enrichment and increase in the concentration of clay minerals and carbonate in overlying soils and sediments. Remote sensing has become a valuable tool in hydrocarbon micro – seepage studies and have been successfully used to interpret surface alterations and anomalies of minerals. In this study, Landsat 7 ETM+ remotely sensed data was utilized for interpreting the onshore hydrocarbon micro – seepage induced alterations zone in Ugwueme. Spectral enhancements techniques such as the principal component analysis (PCA), band ratio (BR) and false color composite (FCC) were adopted for delineating alteration zones. With Landsat 7 ETM+ band selection, and for PCA, the 1457PC3, 1345PC2 and 3457PC4 are the most suitable PC image for spectral enhancement of ferric iron, ferrous iron and clay minerals. Band ratio index such as (3/1), (7/5) and (2+5)/(3+4) also yields better enhancement for anomalous micro – seepage. The study shows that PCA, BR, FCC are good spectral enhancement techniques for interpreting hydrocarbon micro – seepage alterations in overlying soils and sediments.
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