Articles

Geospatial Assessment of Three Decades of Shoreline Shifts and Two Decades of Vegetation Change in the Grand Saloum Transboundary Wetland Complex, Senegal-The Gambia

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Coastal wetlands at the land–sea interface are on the frontline of climate change, yet integrated evidence on geomorphic and ecological responses remains limited in West Africa. We quantified shoreline trajectories (1990–2020) and land-cover dynamics (2000–2020) across the transboundary Grand Saloum complex (Senegal–The Gambia) using Landsat surface-reflectance time series, spectral indices (NDVI, NDWI, NDBI), and the Digital Shoreline Analysis System (DSAS). Shorelines were extracted from NDWI-based water masks, filtered and vectorized, then analyzed in DSAS with End Point Rate statistics. Vegetation was mapped in Google Earth Engine with a Random Forest classifier (mangrove, other vegetation, built/bare, water). The coastline is dominated by erosion (mean −2.44 m·yr⁻¹) interspersed with localized accretion (mean +1.84 m·yr⁻¹). Erosion hotspots concentrate in central sectors, whereas mixed erosion–accretion patterns occur near the northern and southern mouths. Concurrently, mangrove cover expanded from 57,867.61 ha in 2000 to 66,840.17 ha in 2020 (~+15.5%), while other vegetation declined from 23,483.18 ha to 16,146.11 ha (~−31.3%). Within a 1-km coastal buffer, mangroves remained broadly stable to slightly increasing (16.43%→16.81%). These findings depict a dynamic yet resilient system where mangrove gains coexist with heterogeneous shoreline retreat and conversion of non-mangrove covers to bare substrates and water. Management should safeguard landward migration corridors, target erosion-prone reaches with nature-based measures, and institutionalize a transboundary monitoring, reporting, and verification framework that updates DSAS and satellite products at 2–3-year intervals while integrating in-situ elevation, salinity, and sediment data. Our workflow provides transferable, decision-relevant evidence for coastal adaptation and blue-carbon planning in data-limited deltas and policy design.

Predictive Modeling in Remote Sensing Using Machine Learning Algorithms

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Predictive modeling in remote sensing using machine learning (ML) algorithms has emerged as a powerful approach for addressing various environmental and climatic challenges. This paper explores the integration of advanced ML techniques with remote sensing data to enhance predictive capabilities for applications such as land cover classification, crop yield prediction, climate change monitoring, and disaster management. We review related works and existing systems, highlighting platforms like Google Earth Engine (GEE), NASA Earth Exchange (NEX), and Sentinel Hub, which leverage cloud computing to handle large-scale data processing and model deployment. The proposed system incorporates data acquisition, preprocessing, feature extraction, model selection and training, and prediction and visualization to provide accurate and timely predictions. Future enhancements, including deep learning integration, real-time data processing, enhanced user interfaces, and collaboration with Internet of Things (IoT) devices, are discussed to further strengthen the system’s capabilities. The paper concludes by emphasizing the potential of ML algorithms in transforming remote sensing applications, supporting informed decision-making, and improving the management of Earth’s resources.