Tree Extraction of Airborne LiDAR Data Based on Coordinates of Deep Learning Object Detection from Orthophoto over Complex Mangrove Forest

Abstract

Knowing rainforest environments is rendered challenging by distance, vegetation intensity, and coverage; however, knowing the complexity and sustainability of these landscapes is important for ecologists and conservationists. The airborne light detection and ranging (LiDAR) system has made dramatic improvements to forest data collection and management especially on the forest inventory aspect. LiDAR can reliably calculate tree-level characteristics such as crown scale and tree height as well as derived measures such as breast height diameter (DBH). To do this, an exact tree extraction method is needed inside LiDAR data. Within LiDAR data, tree extraction often starts by locating the treetops via local maxima (LM). Wide-ranging efforts have been developed to extract individual trees from LiDAR data by starting to localize treetops through LM within LiDAR data. Throughout this research, a demonstration of a new tree extraction framework inside LiDAR Point Cloud by incorporating a new tree extraction method using the bounding-box coordinates provided by deep learning-based object detection. Tree extraction inside the LiDAR point cloud using the bounding-box coordinates was successful and feasible.