Conclusion
In conclusion, remote sensing, AI, and multi-sensor fusion have significantly advanced tropical forest carbon monitoring, biodiversity assessment, and disturbance detection. However, their application inWest Africa remains limited by key gaps in accuracy, equity, and ecological relevance. The heavy research focus on humid forests has marginalized dry forests and savanna-parkland systems, leaving species of high socio-economic and ecological value, such as Tamarindus indica and Psittacus erithacus, poorly represented. This bias weakens estimates of regional carbon stocks and overlooks landscapes that sustain rural livelihoods. Technically, the lack of species-specific allometric equations for dominant West African trees introduces systematic errors into GEDI-derived biomass estimates. This compromises the reliability of national REDD+ reporting and results-based climate finance. Institutionally, near real-time remote sensing alerts remain disconnected from indigenous and local knowledge systems. Communities that manage most of the landscape are often excluded from alert design, validation, and response, which undermines governance and effectiveness. Addressing these challenges requires targeted investment in dryland ecology, expanded ground plot networks with destructive sampling for local species, and co-developed monitoring frameworks that integrate satellite data with community knowledge. Closing these gaps could improve biomass accuracy by 20–40% and enable species-level conservation. Only by aligning cutting-edge geospatial science with West Africa’s ecological and social realities can remote sensing deliver on its promise for climate mitigation, biodiversity protection, and sustainable development.BOOK CHAPTER AUGUST 2026.Salami, K. D. and 2Falade, L. O 8
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