BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//speaker//F3FZZK BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-W3ZLVP@talks.osgeo.org DTSTART;TZID=JST:20260902T113000 DTEND;TZID=JST:20260902T120000 DESCRIPTION:Coastal deltaic environments are highly dynamic landscapes wher e human activities interact with climate-related processes\, including sho reline change\, flooding\, and wetland degradation. The Niger Delta of sou thern Nigeria contains one of Africa’s largest mangrove and wetland syst ems\, supporting extensive estuarine networks and rapidly expanding coasta l settlements. Over recent decades\, increasing urbanization and agricultu ral expansion have significantly altered land use and land cover (LULC) pa tterns across the region. Despite numerous regional studies\, reproducible workflows that integrate multi-decadal change detection\, landscape fragm entation analysis\, spatial driver assessment\, and scenario modelling wit hin a unified open-source geospatial environment remain limited. This stud y\, 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 pos sible land-cover trajectories for 2050 to support climate-resilience plann ing. 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. Ima ge preprocessing procedures included cloud masking and band stacking of La ndsat imagery within the QGIS Semi-Automatic Classification Plugin (SCP) t o support supervised classification and improve thematic separability of l andcover classes. Supervised classification was implemented using the Rand om Forest algorithm with a stratified training and validation sampling str ategy. 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 public ly available datasets to ensure methodological transparency and reproducib ility consistent with FOSS4G principles. The classification results indica te 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 t he robustness of the classification workflow implemented within the open-s ource QGIS environment. Quantitative LULC analysis reveals significant tra nsformation across the coastal landscape over the 40-year study period. Ma ngrove extent declined from 2456 km² in 1986 to 1978 km² in 2026\, repre senting a loss of approximately 478 km² (−19.5%). Freshwater wetlands d ecreased 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\, co rresponding to a 56.7% increase. These trends indicate sustained conversio n of natural coastal ecosystems into urban and agricultural landscapes acr oss the Niger Delta. Landscape fragmentation analysis further reveals stru ctural degradation of mangrove and wetland ecosystems. Mangrove patch dens ity 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 w etlands exhibit similar fragmentation patterns\, with patch density increa sing 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 in dicate growing spatial fragmentation of coastal ecosystems against coastal flooding and environmental disturbance. Spatial driver analysis was cond ucted using terrain\, accessibility\, demographic\, and climatic variables derived from open datasets. Elevation and slope were derived from the Shu ttle Radar Topography Mission (SRTM) digital elevation model\, while acces sibility gradients were represented using Euclidean distance to roads\, sh oreline boundaries\, and urban centers derived from OpenStreetMap data. Po pulation density data from WorldPop and rainfall data from the CHIRPS data set were incorporated to evaluate potential socio-environmental influences on land conversion. Correlation analysis indicates that population densit y (r = 0.88)\, proximity to urban centers (r = 0.82)\, and accessibility t o roads (r = 0.78) exhibit the strongest positive associations with built- up expansion. Conversely\, mangrove and wetland losses show negative assoc iations with population density (−0.62 and −0.58 respectively)\, sugge sting strong anthropogenic pressure on coastal ecosystems. Future LULC sce narios for 2050 were simulated using the Cellular Automata–Markov (CA– Markov) model implemented through the MOLUSCE plugin within QGIS. Transiti on probabilities derived from the 1986–2026 period were combined with su itability surfaces generated from spatial driver variables. Under a busine ss-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% exp ansion. Concurrently\, mangrove and wetland ecosystems are projected to de cline by approximately 15.2% and 13.6%\, respectively. Alternative scenari o simulations indicate that conservation-oriented interventions could part ially 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\, landsca pe fragmentation assessment\, spatial driver modelling\, and land-use scen ario simulation can be implemented entirely within a reproducible open-sou rce geospatial framework. By integrating SCP\, GRASS GIS tools\, and the M OLUSCE 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 transform ation in the Niger Delta and emphasize the importance of integrating histo rical land-change analysis with forward-looking modelling to provide spati al evidence relevant to long-term coastal management and planning.\nKeywor ds: Coastal LULC change\, Niger Delta\, QGIS\, Random Forest classificatio n\, Landscape fragmentation\, CA–Markov modelling\, MOLUSCE plugin\, ope n-source GIS\, FOSS4G. DTSTAMP:20260717T220448Z LOCATION:Cosmos1 SUMMARY:Open-Source QGIS-Based Analysis of Coastal LULC Change and Drivers in the Niger Delta (1986–2026) with 2050 Scenario Projections - Dr.Victo r N.Sunday\, Grace Martins-Ateli URL:https://talks.osgeo.org/foss4g-2026/talk/W3ZLVP/ END:VEVENT END:VCALENDAR