Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Abstract Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.