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.

Contribution of Remote Sensing in The Study of The Spatio-Temporal Dynamics of Classified Forests: Case of The Classified Forest of Irobo (Southern Ivory Coast)

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This study was carried out in the south of the Ivory Coast as part of our master’s thesis. This study aims to highlight the improvement in knowledge on the phenomenon of degradation in the Irobo classified forest and to provide managers with essential elements for the establishment of a sustainable forest management policy. Concretely, it was a question of (1) characterizing the different types of land use of the Irobo classified forest, (2) mapping the vegetation cover of the classified forest of Irobo from the Landsat images of 1988, 2005 and 2020, (3) evaluating the forest dynamics between 1988 and 2020. To this end, the characterization of the types of land use, the mapping of the dynamics and the evaluation of the forest dynamics between 1988 and 2020 were carried out using cartographic methods on the one hand and area calculations on the other. The results indicate that there are nine types of land use. These are forests, reforestation plots, perennial crops, annual crops, fallows, bare soils and habitats. Regarding the assessment of forest dynamics, it appears that forest cover has lost 11,993.3 ha between 1988 and 2020, which means a decrease of 2.07% per year in favour of agricultural holdings (26,838.9 ha in 2020).

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.

Morphological Changes on Gungata River Watershed due to Anthropogenic Interferences, a part of the Upper Rihand Basin, Chhattisgarh

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The main aims of this research are to identify the morphological changes and development of the Gungata River watershed due to the rapid growth and effects of anthropogenic activities. The natural origin of rivers is sensitive to anthropogenic interference which causes a change in channel morphological characteristics. Human activities have revamped the river geomorphology and made limitless anthropogenic geomorphic features. These features have remarkable characteristics which have sometimes been misbalanced with landscapes produced by natural processes. Modern techniques like remote sensing and GIS were used to identification of morphological changes and their historical comparison etc. High-resolution satellite imagery (LISS-I 5m.), Digital Elevation Model (CARTO DEM 30m) were used to analyze the anthropogenic geomorphic features which provide different opportunities for a better understanding of landscape processes. This research paper has been shown how anthropogenic activities interference with the morphological changes of the Gungata river watershed.

Investigation of Groundwater Potential Using Remote Sensing and Geographical Information System (GIS) Techniques in Fakai Local Government of Kebbi State, Nigeria

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Groundwater is one of the most precious natural resource which supports human health, economic development and ecological diversity. Remote sensing and Geographical Information System (GIS) Techniques have been effectively used for the investigation of the potentiality of groundwater resource in Fakai local government area. The dataset for this research work are Landsat 8 Operational land imager (OLI), ASTER DEM, Topographical map and Geological map from which the essential criteria were obtained. The study used Weighted Linear Combination approach which involves mathematical weighing and ranking of the criteria. Multi-criteria evaluation was carried out on all the criteria using the Weighted Linear Combination approach in ArcGIS 10.4. Spatial analysis was carried out on the derived result using the Suitability Index (SI) value created from pairwise comparison analysis. The suitability map for groundwater recharge in the study area was hence produced using the suitability index. The result shows four classes for the study area. The classes include highly suitable, moderately suitable, less suitable and least suitable. Thus, the area most suitable for groundwater are found most towards the northern part, around the center and some regions in the northern part of the study area this serves as an indicator that most of the study area has good potential for groundwater recharge.