Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach

Forest canopy height (FCH) is a crucial indicator in the calculation of forest biomass and carbon sinks. There are various methods to measure FCH, such as space-borne light detection and ranging (LiDAR), but their data are spatially discrete and do not provide continuous FCH maps. Therefore, an FCH estimation method that associates sparse LiDAR data with spatially continuous variables is required. The traditional approach of constructing a single model overlooks the spatial variability in forest growth, which will limit the FCH accuracy. Considering the distinct nature of forest in different ecological zones, the following hold. First, we proposed an ecological zoning random forest (EZRF) model in 33 ecological zones in China. Compared with the total zone RF (TZRF) model, the EZRF model showed a greater potential, which was 21.5%–36.5% more accurate than the TZRF model. Second, we analyzed a total of 62 variables related to forest growth, including Landsat variables and ancillary variables (forest canopy cover, bioclimatic, topographic, and hillshade factors). An insight into variable selection in FCH modeling was provided by analyzing the prediction accuracy of FCH under different categorical variables and analyzing the importance of variables in different ecological zones. Third, finally, we produced a 30-m continuous FCH map by the EZRF model. Compared with the airborne LiDAR data, the FCH prediction results produced a root mean square error (RMSE) of 2.50–5.35 m, which were 41%–72% more precisely than the Global FCH, 2019. The results demonstrate the effectiveness of our proposed method and contribute to the study of forest carbon sinks.