A Tree-Shrub Layer Separation Method for MLS LiDAR Point Clouds of Typical Tropical Seasonal Rainforests

As the most complex community structure in terrestrial ecosystems, tropical rainforests still employ traditional manual surveys to obtain spatial information on trees, which are time-consuming and laborious with significant errors. Light detection and ranging (LiDAR) can provide high-quality 3-D point cloud data, but using it to extract structural information of trees and shrubs in complex forests remains a technical challenge for point cloud mapping. Therefore, this article proposes a solution for realizing the accurate separation of LiDAR point cloud data for trees and shrubs within complex forests such as tropical rainforests. The method first preprocesses the data to obtain understory point cloud data. The local curvature (L-curvature) features are then utilized to perform preliminary separation. Then, the growth segmentation is performed based on the normal vector and the curvature magnitude to obtain the clustered objects. Accordingly, trees and shrubs are identified according to the features of the segmented clustered objects. Finally, eight tropical rainforest plots were selected to evaluate and analyze the performance of the method. The research results indicate that the accuracy of tree extraction in tropical rainforests using this method can reach over 91%. The accuracy and efficiency of this method for tree-shrub separation are superior to other methods. This study provides essential support for investigating forest understory vegetation characteristics and the spatial growth distribution of tropical rainforest trees and shrubs. It lays a foundation for the subsequent application and promotion of terrestrial LiDAR when investigating vegetation information in complex forest scenarios.