Individual tree crown delineation from high-resolution UAV images in broadleaf forest

Abstract

Unmanned aerial vehicles (UAVs) paired with a structure from motion (SfM) algorithm (UAV-SfM) can be used to derive canopy height models (CHMs) for individual tree crown delineation (ITCD). ITCD algorithms normally perform well in coniferous forests, but their capabilities in broadleaf or mixed forests are still challenging. In this study, we investigated the application of three ITCD algorithms using UAV-based high-resolution imagery in a broadleaf Hyrcanian forest. Three uneven-aged sites including a high-density (HD), a medium-density (MD), and a low-density (LD) stand were selected located in Noor city in Mazandaran province (Iran). Three marker-controlled segmentation algorithms, i.e., inverse watershed segmentation (IWS), local maxima (LM), and region growing (RG) were tested for a series of CHMs generated from point clouds derived by a structure from motion algorithm, across a range of spatial resolutions and a Gaussian filter with varying sigma. The delineation results were validated using field inventory data. False positives outnumbered false negatives for fine resolution CHMs. The highest overall accuracy was achieved for a spatial resolution of 100 cm using the RG algorithm and the IWS algorithm. Also, the effect of different forest structures, CHM filtering, and different tree species on the accuracy of tree delineation algorithms were evaluated. Overall, the selected delineation algorithms influenced the success of ITCD in a way that the RG algorithm generated significantly more accurate results than the other two algorithms. The RG algorithm was the most appropriate approach for the individual tree crown delineation.