Mapping and Quantifying Coastal Blue Carbon Ecosystems in the Nigerian Coastline: Methodological Innovation and Climate Mitigation Implications

Blue carbon ecosystems encompassing mangroves, seagrasses, and salt marshes store disproportionately large quantities of organic carbon relative to their aerial extent, yet Nigerian coastal blue carbon stocks have never been comprehensively mapped or quantified at a national scale. This research develops a methodological framework for national-scale blue carbon ecosystem mapping and carbon stock quantification for the Nigerian coastline, combining remote sensing innovation with extensive field validation and addressing critical methodological gaps in tropical blue carbon science. National-scale habitat mapping was performed using a multi-sensor fusion approach integrating Sentinel-1 SAR, Sentinel-2 multispectral, and GEDI LiDAR data processed through a random forest classification algorithm. Field campaigns at 43 stratified validation sites provided carbon stock measurements for above-ground biomass (allometric equations), below-ground biomass (core excavation), and soil organic carbon (loss-on-ignition to 1 metre depth). Total organic carbon was computed using Tier 2 IPCC blue carbon accounting methods. The remote sensing classification achieved an overall accuracy of 91.3 percent for five habitat classes. Total national mangrove extent was mapped at 6,840 square kilometres, 18 percent lower than the last national estimate due to loss between 2000 and 2024. Total blue carbon ecosystem stock was estimated at 218 teragrams of carbon, with mangrove soil carbon contributing 76 percent of the total. Annual greenhouse gas emissions from blue carbon habitat loss were estimated at 4.2 teragrams of carbon dioxide equivalent per year. A novel spatial uncertainty propagation method developed for this study reduced stock estimate confidence intervals by 35 percent compared to standard methods. Keywords: blue carbon, mangrove carbon stocks, remote sensing, Nigeria, climate mitigation.