Coastal deltaic environments are highly dynamic landscapes where human activities interact with climate-related processes, including shoreline change, flooding, and wetland degradation. The Niger Delta of southern Nigeria contains one of Africa’s largest mangrove and wetland systems, supporting extensive estuarine networks and rapidly expanding coastal settlements. Over recent decades, increasing urbanization and agricultural expansion have significantly altered land use and land cover (LULC) patterns across the region. Despite numerous regional studies, reproducible workflows that integrate multi-decadal change detection, landscape fragmentation analysis, spatial driver assessment, and scenario modelling within a unified open-source geospatial environment remain limited. This study, therefore, applies a QGIS-based open-source remote sensing framework to analyze long-term coastal LULC dynamics in the Niger Delta between 1986 and 2026, identify spatial drivers of land conversion, and simulate possible land-cover trajectories for 2050 to support climate-resilience planning. Multi-temporal Landsat surface reflectance imagery from Landsat 5 TM (1986), Landsat 7 ETM+ (2000), Landsat 8 OLI (2013), and Landsat 9 OLI-2 (2026 composite) was obtained from the USGS Earth Explorer archive. Image preprocessing procedures included cloud masking and band stacking of Landsat imagery within the QGIS Semi-Automatic Classification Plugin (SCP) to support supervised classification and improve thematic separability of landcover classes. Supervised classification was implemented using the Random Forest algorithm with a stratified training and validation sampling strategy. Six LULC classes were mapped: mangrove forest, freshwater wetlands, built-up areas, agricultural land, bare surfaces, and water bodies. Classification accuracy was evaluated using overall accuracy and the Kappa coefficient to ensure the reliability of multi-temporal change detection. All analyses were performed using open-source geospatial tools and publicly available datasets to ensure methodological transparency and reproducibility consistent with FOSS4G principles. The classification results indicate strong model performance across all epochs. Overall accuracy increased from 86.2% (κ = 0.83) in 1986 to 90.2% (κ = 0.88) in 2026, confirming the robustness of the classification workflow implemented within the open-source QGIS environment. Quantitative LULC analysis reveals significant transformation across the coastal landscape over the 40-year study period. Mangrove extent declined from 2456 km² in 1986 to 1978 km² in 2026, representing a loss of approximately 478 km² (−19.5%). Freshwater wetlands decreased from 3789 km² to 3098 km², corresponding to a loss of 691 km² (−18.2%). In contrast, built-up areas expanded substantially from 457 km² in 1986 to 1290 km² in 2026, representing an increase of 833 km² (+182.3%). Agricultural land also expanded from 1235 km² to 1935 km², corresponding to a 56.7% increase. These trends indicate sustained conversion of natural coastal ecosystems into urban and agricultural landscapes across the Niger Delta. Landscape fragmentation analysis further reveals structural degradation of mangrove and wetland ecosystems. Mangrove patch density increased from 0.51 patches/km² in 1986 to 1.13 patches/km² in 2026, while mean patch size declined from 1.97 km² to 0.89 km². Freshwater wetlands exhibit similar fragmentation patterns, with patch density increasing from 0.62 to 1.12 patches/km² and mean patch size decreasing from 1.62 km² to 0.90 km². Increasing edge density and decreasing patch size indicate growing spatial fragmentation of coastal ecosystems against coastal flooding and environmental disturbance. Spatial driver analysis was conducted using terrain, accessibility, demographic, and climatic variables derived from open datasets. Elevation and slope were derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model, while accessibility gradients were represented using Euclidean distance to roads, shoreline boundaries, and urban centers derived from OpenStreetMap data. Population density data from WorldPop and rainfall data from the CHIRPS dataset were incorporated to evaluate potential socio-environmental influences on land conversion. Correlation analysis indicates that population density (r = 0.88), proximity to urban centers (r = 0.82), and accessibility to roads (r = 0.78) exhibit the strongest positive associations with built-up expansion. Conversely, mangrove and wetland losses show negative associations with population density (−0.62 and −0.58 respectively), suggesting strong anthropogenic pressure on coastal ecosystems. Future LULC scenarios for 2050 were simulated using the Cellular Automata–Markov (CA–Markov) model implemented through the MOLUSCE plugin within QGIS. Transition probabilities derived from the 1986–2026 period were combined with suitability surfaces generated from spatial driver variables. Under a business-as-usual scenario, built-up areas are projected to increase from 1289 km² in 2026 to approximately 2157 km² by 2050, representing a 67.2% expansion. Concurrently, mangrove and wetland ecosystems are projected to decline by approximately 15.2% and 13.6%, respectively. Alternative scenario simulations indicate that conservation-oriented interventions could partially stabilize mangrove and wetland systems, while development-intensive trajectories could accelerate ecosystem loss. Beyond regional findings, this study demonstrates that multi-decadal LULC change detection, landscape fragmentation assessment, spatial driver modelling, and land-use scenario simulation can be implemented entirely within a reproducible open-source geospatial framework. By integrating SCP, GRASS GIS tools, and the MOLUSCE plugin within QGIS, the research provides a transparent analytical workflow aligned with the principles of Free and Open-Source Software for Geospatial (FOSS4G). The results highlight accelerating coastal transformation in the Niger Delta and emphasize the importance of integrating historical land-change analysis with forward-looking modelling to provide spatial evidence relevant to long-term coastal management and planning.
Keywords: Coastal LULC change, Niger Delta, QGIS, Random Forest classification, Landscape fragmentation, CA–Markov modelling, MOLUSCE plugin, open-source GIS, FOSS4G.