The use of fixed-wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer-broadleaf forest

Remote sensing (RS) data are often used as a complementary data source to acquire accurate quantitative estimations of merchantable volume (V) and carbon stock in living biomass (CST), which are critical for the sustainable use of forest resources. In this study, we investigated the utility of unmanned aerial vehicles (UAVs) and the structure from motion (SfM) technique for estimating and mapping the spatial distributions of V and CST of an uneven-aged mixed conifer-broadleaf forest that had experienced major disturbances (e.g., wind damage and selection harvesting) over time. In addition to the commonly used RS structural metrics, we also calculated an image metric (broadleaf vegetation cover percentage) using a UAV-SfM orthomosaic to use as an explanatory variable. Plot level validation of UAV-SfM-estimated V revealed a root mean square error (RMSE) of 39.8 m3 ha-1 and a relative RMSE of 16.7%, whereas the RMSE and relative RMSE vales for UAV-SfM-estimated CST were 14.3 Mg C ha-1 and 17.4% respectively. Our image metric had a statistically significant association with V and CST, providing additional explanatory power in the regression analysis. Nevertheless, RMSE values did not significantly change after adding the image metric into the regression analysis, e.g., %RMSE was reduced by 1.9% for V estimation, and 1.5% for CST estimation. Furthermore, the UAV-SfM estimates we obtained were comparable to light detection and ranging (LiDAR) estimates (relative RMSE of 16.4% and 16.7% for V and CST, respectively). We also successfully mapped the spatial distributions of V and CST and identified their stand- and landscape-level variations. Therefore, we confirmed the potential of UAV imagery when combined with LiDAR digital terrain model to capture the fine scale spatial variation of V and CST in uneven-aged forests subjected to silvicultural practices and natural disturbances over time.