Prediction of Forest Structural Parameters Using Airborne Full-Waveform LiDAR and Hyperspectral Data in Subtropical Forests

Accurate acquisition of forest structural parameters, which is essential for the parameterization of forest growth models and understanding forest ecosystems, is also crucial for forest inventories and sustainable forest management. In this study, simultaneously acquired airborne full-waveform (FWF) LiDAR and hyperspectral data were used to predict forest structural parameters in subtropical forests of southeast China. The pulse amplitude and waveform shape of airborne FWF LiDAR data were calibrated using a physical process-driven and a voxel-based approach, respectively. Different suites of FWF LiDAR and hyperspectral metrics, i.e., point cloud (derived from LiDAR-waveforms) metrics (DPC), full-waveform (geometric and radiometric features) metrics (FW) and hyperspectral (original reflectance bands, vegetation indices and statistical indices) metrics (HS), were extracted and assessed using correlation analysis and principal component analysis (PCA). The selected metrics of DPC, FW and HS were used to fit regression models individually and in combination to predict diameter at breast height (DBH), Lorey's mean height (H-L), stem number (N), basal area (G), volume (V) and above ground biomass (AGB), and the capability of the predictive models and synergetic effects of metrics were assessed using leave-one-out cross validation. The results showed that: among the metrics selected from three groups divided by the PCA analysis, twelve DPC, eight FW and ten HS were highly correlated with the first and second principal component (r > 0.7); most of the metrics selected from DPC, FW and HS had weak relationships between each other (r < 0.7); the prediction of H-L had a relatively higher accuracy (Adjusted-R-2 = 0.88, relative RMSE = 10.68%), followed by the prediction of AGB (Adjusted-R-2 = 0.84, relative RMSE = 15.14%), and the prediction of V had a relatively lower accuracy (Adjusted-R-2 = 0.81, relative RMSE = 16.37%); and the models including only DPC had the capability to predict forest structural parameters with relatively high accuracies (Adjusted-R-2 = 0.52-0.81, relative RMSE = 15.70-40.87%) whereas the usage of DPC and FW resulted in higher accuracies (Adjusted-R-2 = 0.62-0.87, relative RMSE = 11.01-31.30%). Moreover, the integration of DPC, FW and HS can further improve the accuracies of forest structural parameters prediction (Adjusted-R-2 = 0.68-0.88, relative RMSE = 10.68-28.67%).