FDH-30C：Forest dominant height dataset for China with 30 m resolution

The dataset is calibrated using 1,117 km² of high-density UAV LiDAR data distributed across all eight major forest ecozones across China as reference data, representing diverse stand ages, structures, and species compositions. To produce spatially continuous estimates, the model used in this dataset integrates 30 geospatial predictors derived from multi-source remote sensing products, including climatic, edaphic, topographic, vegetation, and SAR-based variables.

A machine-learning–based mixed-effects modeling (MLME) framework combines the UAV LiDAR reference data with these predictors to generate spatially coherent estimates while preserving local accuracy and reducing ecozone boundary effects. The resulting map provides a consistent national baseline for applications such as site-index mapping, growth-and-yield parameterization, biomass and carbon estimation, vertical structure analysis, and the evaluation of spaceborne LiDAR missions including GEDI and MOLI.