REMOTE SENSING

This research endeavored to uncover the yet unexplored importance of Geographic Information System (GIS) and Remote Sensing in the teaching of physical geography, specifically in secondary schools in Ovia North East Local Government Area of Edo State, Nigeria. The integration of GIS and remote sensing technology offers students a comprehensive understanding of spatial relationships, enabling them to analyze and interpret physical geography phenomena with greater depth. GIS and remote sensing allow students to apply their knowledge to real-world scenarios, preparing them for future careers in geography and related fields. Secondary schools in certain regions lack the necessary technological infrastructure for the effective implementation of GIS and remote sensing. Incorporating GIS and remote sensing into the curriculum requires training for educators which many teachers lack the expertise needed to effectively teach these technologies. The study aspires to offer valuable insights that can influence educational policies and practices, with the overarching goal of elevating the standards of geography education within the local government area.

Flooding remains one of the most pressing environmental challenges in the Niger Delta region of Nigeria, where rapid urbanization, land degradation, and climate variability interact to intensify hydrological extremes. This study evaluates the combined impact of land use/land cover (LULC) changes and climate variability on flooding in the Lower Niger Region, focusing on Edo, Delta, Bayelsa, and Rivers States. An integrated methodological framework was developed using remote sensing (RS) and machine learning (ML) techniques implemented on the Google Earth Engine (GEE) platform. Rainfall and temperature datasets were obtained from multiple satellite products, CHIRPS, PERSIANN-CDR, PERSIANN-CCS, ERA5, and CPC, and validated against observations from the Nigerian Meteorological Agency (NiMET). LULC classification was conducted using Random Forest (RF) and Support Vector Machine (SVM) algorithms applied to Sentinel-2 and Landsat-8 imagery, generating five major land cover classes: water bodies, forest, barren land, vegetation, and built-up areas. Flood dynamics were assessed using multiple spectral indices, including AWEI, FWI, NDWI, MNDWI, WRI, and NDVI, evaluated across different temporal scales to capture hydrological variations. The results reveal significant interannual and seasonal rainfall variability, with the Standardized Precipitation Index (SPI) effectively identifying alternating wet and dry cycles, although long-term annual rainfall trends were largely non-significant. There is, however, evidence of increasing frequency of extreme rainfall events, particularly in Delta and Rivers States. Temperature analysis (1971–2023) indicates a statistically significant warming trend, with minimum temperatures rising faster than maximum temperatures—most notably during the dry season (DJF). LULC assessment confirmed the superior performance of the RF classifier over SVM and showed a consistent pattern of urban expansion at the expense of vegetation and wetlands. Flood analysis revealed a persistent hotspot occurring along river corridors, low-lying floodplains, and southeastern basins. Overall, the findings demonstrate that while climate variability influences hydrological extremes, LULC transformations, particularly urban encroachment into natural flood buffers, have been a more decisive factor driving flood vulnerability in the Lower Niger Region.

Flooding remains one of the most pressing environmental challenges in the Niger Delta region of Nigeria, where rapid urbanization, land degradation, and climate variability interact to intensify hydrological extremes. This study evaluates the combined impact of land use/land cover (LULC) changes and climate variability on flooding in the Lower Niger Region, focusing on Edo, Delta, Bayelsa, and Rivers States.An integrated methodological framework was developed using remote sensing (RS) and machine earning (ML) techniques implemented on the Google Earth Engine (GEE) platform. Rainfall and temperature datasets were obtained from multiple satellite products, CHIRPS, PERSIANN-CDR, PERSIANN-CCS, ERA5, and CPC, and validated against observations om the Nigerian Meteorological Agency (NiMET). LULC classification was conducted using Random Forest (RF) and Support ector achine (SVM) algorithms applied to Sentinel-2 and Landsat-8 imagery, generating five major land cover classes: water bodies, orest, barren land, vegetation, and built-up areas. Flood dynamics were assessed using multiple spectral indices, including EI, FWI, NDWI, MNDWI, WRI, and NDVI, evaluated across different temporal scales to capture hydrological variations.The results reveal significant interannual and seasonal rainfall variability, with the Standardized Precipitation Index (SPI) effectively identifying lternating wet and dry cycles, although long-term annual rainfall trends were largely non-significant. There is, however, evidence of increasing frequency of extreme rainfall events, particularly in Delta and Rivers States. Temperature analysis (1971–2023) indicates a statistically significant warming trend, with minimum temperatures rising faster than maximum temperatures—most notably during the dry season (DJF). LULC assessment confirmed the superior performance of the RF classifier over SVM and howed a consistent pattern of urban expansion at the expense of vegetation and wetlands. Flood analysis revealed a persistent hotspot occurring along river corridors, low-lying floodplains, and southeastern basins. Overall, the findings demonstrate that while climate variability influences hydrological extremes, LULC transformations, particularly urban encroachment into natural flood buffers, have been a more decisive factor driving flood vulnerability in the Lower Niger Region.