Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging. It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems. Yet current regional to national scale forest height maps were mainly produced at coarse-scale. Such maps lack spatial details for decision-making at local scales. Recent advances in remote sensing provide great opportunities to fill this gap.

In this study, we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China. A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models. Specifically, the relationships between three types of in-situ measured tree heights (maximum-, averaged-, and basal area-weighted- tree heights) and plot-level remote sensing metrics (multispectral, radar, and topo variables from Landsat, Sentinel-1/PALSAR-2, and SRTM) were analyzed. Three types of models (multilinear regression, random forest, and support vector regression) were evaluated. Feature variables were selected by two types of variable selection approaches (stepwise regression and random forest). Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach. Then, tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.

The best estimation of plot-level tree heights (R² ranged from 0.47 to 0.52, RMSE ranged from 3.8 to 5.3 ​m, and rRMSE ranged from 28% to 31%) was achieved using the random forest model. A comparison with existing forest height maps showed similar estimates of mean height, however, the ranges varied under different definitions of forest and types of tree height.

Primary results indicate that there are small biases in estimated heights at the province scale. This study provides a framework toward establishing regional to national scale maps of vertical forest structure.