DIGITAL FOREST INVENTORY BASED ON UAV IMAGERY

Abstract

This study explores the application of unoccupied aerial vehicles (UAVs) and structure from motion (SfM) techniques for digital forest inventories, addressing key challenges in sustainable and cost-effective forest monitoring. UAV-SfM data products such as 3D point clouds and orthomosaics were generated from a study site in the Hainich National Park, Germany, to create a comprehensive digital representation of the forest structure, encompassing canopy, stems and ground components. Subsequently, this data was leveraged for the automated derivation of forest parameters such as tree stem position, individual tree crown delineation (ITCD), diameter at breast height (DBH), and coarse wood debris (CWD). The employed algorithms involve deep learning models (U-Nets), clustering techniques, and object-based methods.