Estimation of Complex Heterogeneous Stand Canopy Height Using a Bi-Directional Stacking Model Framework with Multi-Forest-Type Feature Fusion Based on GEDI and Multi-Source Remote Sensing Data

Forest canopy height (FCH) is a fundamental parameter for carbon assessment and ecological monitoring. The Global Ecosystem Dynamics Investigation (GEDI) mission provides full-waveform LiDAR for FCH estimation, yet discontinuous sampling and heterogeneous forests increase uncertainty. Conventional ensemble models rarely account for forest-type-specific structures and remote sensing responses, reducing accuracy and stability. We propose a regional framework combining MBF-Contrast feature selection and a bi-directional stacked model with multi-forest-type feature fusion (BS-MFTF). MBF-Contrast integrates model-based importance with feature-distribution diagnostics to remove redundant and multicollinear variables. BS-MFTF leverages complementarity and structural differences among forest types to improve modeling in heterogeneous canopies. MBF-Contrast reduces feature dimensionality by ~35% versus Mutual Information, Boruta, and RFECV, and improves performance across forest types. BS-MFTF overall achieves R2 = 0.68 (RMSE = 3.22 m; MAE = 2.34 m). Airborne LiDAR validation shows high consistency (R2 = 0.59; RMSE = 1.31 m; MAE = 1.00 m) and a 15–25% R2 gain over conventional ensembles. The framework offers a scalable solution for large-scale FCH estimation in structurally diverse forests.