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<p>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&#xa0;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 &gt; 14), confirmed that topographic factors are the dominant predictors of coastal vulnerability (Adjusted R² = 0.716, <i>p</i> &lt; 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&#xa0;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.</p>

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Deep learning-enhanced shoreline dynamics and vulnerability assessment in Niger Delta area of Nigeria

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月8日

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  • Victor Chukwuemeka Nnam,
  • Joseph O. Odumosu,
  • Ikwueze Uche,
  • Souleman Lamidi

摘要

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.