The Niger Delta region of Nigeria faces escalating coastal erosion and shoreline retreat, threatening ecosystems, infrastructure, and livelihoods. This study presents an integrated geospatial methodology combining deep learning-based shoreline detection, multi-temporal satellite imagery, and statistical modeling to assess coastal vulnerability and shoreline evolution. Landsat imagery from 2002, 2015, and 2023 was processed using the CoastSat toolkit, which employs a U-Net convolutional neural network for semantic segmentation of land-water boundaries. Shoreline positions were extracted with sub-pixel accuracy using the Modified Normalized Difference Water Index (MNDWI). The Digital Shoreline Analysis System (DSAS) quantified shoreline dynamics, revealing that 75.3% of transects experienced erosion, with maximum retreat exceeding 8,000 m. High Shoreline Change Envelope values reflect barrier island migration and tidal channel dynamics characteristic of deltaic systems. A Coastal Vulnerability Index (CVI) was computed by integrating elevation, slope, distance to shoreline, tidal height, and tidal range through an AHP-weighted multi-criteria framework. Statistical validation using multiple regression, following Principal Component Analysis to address multicollinearity between slope and elevation (VIF > 14), confirmed that topographic factors are the dominant predictors of coastal vulnerability (Adjusted R² = 0.716, p < 0.001). The Topographic Component emerged as the primary predictor (β = -0.795), followed by distance to shoreline (β = -0.273), while tidal variables contributed significantly but with smaller effect sizes. Despite temporal gaps in Landsat coverage and moderate spatial resolution (30 m), the methodology demonstrates robust performance for regional-scale assessments. This framework provides a scalable, data-driven approach for coastal monitoring and highlights the urgent need for adaptive management in the Niger Delta.
Nnam et al. (Sun,) studied this question.