Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR

Automatic, wall-to-wall monitoring of forests from remote sensing data is a dream slowly becoming a reality. One barrier to development is the laborious task of developing quality ground reference data. Structural information captured by terrestrial laser scanners (TLS) or unmanned aerial systems (UAS) would expedite the collection of ground reference data if tree species could be automatically determined from point clouds. This study aims to improve species classification from point clouds by identifying detectable structural features useful for classifying species. We compare the effectiveness of multiple feature design strategies for classifying dominant hardwood species (oaks and sugar maples) from single-scan TLS and UAS data of natural hardwood forests, and analyze the separability of these species within and across the canopy layers. We find that oaks and sugar maples have distinct profile shapes that both single-scan TLS data and UAS LiDAR data can capture. TLS captures species-specific profile shapes through direct measurement of canopy width. UAS LiDAR, with its characteristically occluded understory, relies more on canopy density features. Our results emphasize the importance of tailoring data processing and feature extraction for capturing understory structure and highlight the need for modality-specific feature design. Implementing these insights will improve the accuracy and efficiency of automated tree-level inventories in hardwood forests, ultimately supporting more robust forest monitoring and management practices.