Search

Tropical forests are widely regarded as the Earth’s most important ecosystems, yet they are severely threatened by anthropogenic disturbances. Rapid and extensive monitoring of forest structure and biodiversity is crucial for developing ecologically sound conservation and restoration strategies. Airborne light detection and ranging (LiDAR) can effectively monitor three-dimensional forest canopy structures. Traditionally, LiDAR-based plant diversity estimation has relied on individual tree-based and area-based approaches. However, these approaches face significant challenges in tropical forests due to their complex canopy structures and diverse plant compositions. Therefore, by proposing a novel community-based approach, this study aims to examine the relationship between field-derived biodiversity indices and LiDAR-derived canopy structural metrics of plant communities in a species-rich ForestGEO forest dynamic plot in tropical Hong Kong. Our goal is to determine whether canopy structural metrics extracted from airborne LiDAR data can serve as a robust and efficient alternative for expediting plant diversity monitoring in highly dense and diverse tropical forests. Our results indicate that an integration watershed segmentation technique (for automatic patch-scale plant community delineation), LiDAR-derived canopy structural metrics, and machine learning-based random forest regression analysis can provide accurate predictions of community-based species diversity indices. Among various diversity indices, species richness and the Shannon-Wiener index are most accurately estimated using LiDAR-derived metrics. This study reveals that species richness is predominantly influenced by the existence of multi-layered canopy structures, whereas the Shannon-Wiener index is associated with both multi-layered structures and canopy morphologies. Overall, our findings showcase the immense potential of airborne LiDAR data in advancing the monitoring of structure and biodiversity in dense-canopy and species-rich tropical forests in a spatially explicit manner.