ABSTRACT Accurate forest-height estimation is essential for ecological monitoring and forest management. This study applies an adaptive stacking-based fusion framework that screens combinations of out-of-fold predictions from three regression models and three existing canopy-height products within the training data. Applied to the Guangdong–Hong Kong–Macao Greater Bay Area, nested random five-fold cross-validation yielded r = 0.54, RMSE = 7.54 m and MAE = 5.61 m.
