Mapping Canopy Base Height Through Integration of GEDI and Sentinel-2 Data

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R2 = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes.