Developing a Novel Method for Vegetation Mapping in Temperate Forests Using Airborne LiDAR and Hyperspectral Imaging

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach integrates these structural metrics with hyperspectral spectral information, alongside detailed remote sensing data extraction. Through machine learning-based clustering, which combines both structural and spectral features, we successfully classified eight specific tree species, community boundaries, identified dominant species, and quantified their abundance, contributing to precise vegetation and forest type mapping based on predominant species and detailed attributes such as diameter at breast height, age, and canopy density. Field validation indicated the methodology’s high mapping precision, achieving overall accuracies of approximately 98.0% for individual species identification and 93.1% for community-level mapping. Demonstrating robust performance compared to conventional methods, this novel approach offers a valuable foundation for National Forest Ecology Inventory development and significantly enhances ecological research and forest management practices by providing new insights for improving our understanding and management of forest ecosystems and various forestry applications.