International Journal of Advanced Multidisciplinary Research and Studies
Volume 4, Issue 4, 2024
Mapping and Analysis of Seasonal Flooding in Benue State Nigeria using Remote Sensing and GIS Approach
Author(s): Ekebuike AN, Oliha AO, Ojo PE
Abstract:
This study aimed at assessing the impact of seasonal flooding in parts of Benue State using Remote Sensing and GIS. Its objectives were to delineate different risk levels of flooding in the study area, to determine the effect or impact of flooding on different land cover types and to produce flood risk map of the study area. The methodology involves data acquisition, data processing and reclassification and overlay analysis. This study has been able display the usefulness of remote sensing and GIS technologies in classifying and in identifying areas with high, moderate, low risk of flooding within the study area. The landuse/landcover distribution of Benue State in 2021 as shown in figure 4.5 and figure 4.6 indicate that forested area, accounted for the largest land cover/use of about 65.13% and an area of about 2039157 hectares. Grassland had 5.73% and a coverage area of 239423.9 hectares, farmland had 5.73% and a coverage area of 179424.5 hectares. Bare surface had 6.47% and a coverage area of 202653.6 hectares, rocky area had 4.98% and a coverage area of 153274.9 hectares, settlement had 9.19% with area coverage of 287964.3 hectares, while waterbody had 0.92 with area coverage of 28864.94 hectares. The results of the overlay analysis produced a layer showing three hazard zones; namely high risk, moderate risk and low risk in the study area. The results indicated that high-risk zone occupied 34.68% of the entire study area, covering an area of 10857.48km2, while moderate risk zone occupied 47.05%, covered an area of 14730.24km2. Low risk zone occupied 18.25% covering 5713. It is recommended that the results achieved in this research can be used as a base to help identify areas at risk of being flooded in the study area.
Keywords: Benue State, Flooding, GIS, Landcover/Landuse, Remote Sensing
Pages: 597-602