%0 Journal Article %T Estimation of Canopy Height in Zambia through Integration of GEDI, Sentinel-1 and Sentinel-2 Measurements %A Chimwemwe M’ %A tonga %J Journal of Geoscience and Environment Protection %P 138-156 %@ 2327-4344 %D 2025 %I Scientific Research Publishing %R 10.4236/gep.2025.135010 %X Accurate canopy height estimation is critical for forest management and carbon monitoring in Zambia’s ecologically diverse landscapes. This study developed a high-resolution canopy height model by integrating multi-sensor remote sensing data—NASA’s GEDI LiDAR, ESA’s Sentinel-1 SAR, and Sentinel-2 optical imagery—using a Random Forest algorithm. The approach addressed key limitations of sparse GEDI sampling (25 m footprints) through fusion with continuous 10 m-resolution Sentinel-1/2 data and SRTM elevation metrics, processed via Google Earth Engine. The model achieved robust performance, with training accuracy of r2 = 0.76 (RMSE = 2.1 m) and validation accuracy of r2 = 0.71 (RMSE = 2.3 m), representing relative errors of 13.1–14.3%. Analysis revealed a bimodal height distribution (Hartigan’s dip test: p < 0.01), with peaks at 6.2 m (southern savannas, 41.7% of areas) and 15.8 m (miombo woodlands, 53.3%), plus rare tall forests (>30 m, 5.0%) in protected highlands. Variable importance analysis ranked GEDI’s RH98 metric (38%) as most influential, followed by Sentinel-2’s NIR band (22%) and Sentinel-1’s VH polarization (17%). Topographic correction using SRTM reduced errors by 23% in escarpment regions. These results demonstrate the synergy of LiDAR, SAR, and optical data for national-scale canopy mapping, particularly in heterogeneous tropical ecosystems. The 2-m height discrimination capability supports Zambia’s REDD+ monitoring, enabling targeted conservation of carbon-rich miombo woodlands and biodiversity refugia. Future work should integrate ICESat-2 and wet-season SAR data to address dry-season bias and fragmented canopy limitations. %K Canopy Height %K Aboveground Biomass %K GEDI %K Sentinel-1 %K Sentinel-2 %K Random Forest %K Multi-Sensor Integration %K Zambia %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=142912