Canopy classification using LiDAR: a generalizable machine learning approach

The integration of machine learning algorithms with LiDAR-derived datasets has long been used in the development of canopy classification models with the main objective of differentiating between tree canopies and other types of land cover. However, these integrated canopy classification models often require investigators to provide training reference data, use vendor classification codes that vary in quality and availability, and/or rely on site-specific information to achieve the required accuracy. In this study, a generalizable canopy classification model based solely on LiDAR-derived datasets is proposed and evaluated. Ten watersheds that are located in different regions in the continental USA were selected to represent a wide range of physiography, topography, climate, tree diversity, and LiDAR data characteristics, ensuring model applicability to different environments. Three canopy classification model development strategies were considered: general, specific, and single. The final decision tree-based general canopy classification model contains five datasets with the roughness of the filtered DHM yielding the highest normalized feature importance of 0.9. The developed general canopy detection model accuracy was comparable to of the specific/single models and it generated an average testing kappa statistic of 0.90 and 0.96 when applied to training/testing and testing datasets, respectively. This study demonstrated the existence of a consistent canopy signal in LiDAR datasets across the contiguous US that can be used to create a general canopy classification models that are functional regardless of the study area or LiDAR quality.