Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges

Abstract

Individual mangrove tree parameters are necessary for the efficient management and protection of this unique ecosystem, but to measure them using remote sensing (RS) is still a new and challenging task due to the high clumping density of mangrove crowns and the relatively low spatial resolution of RS data. Unmanned aerial vehicles (UAVs), as an emerging RS technique, significantly improves the spatial resolution, but has not been used for individual mangrove analysis. This study presents the first investigation into the possibility of individual tree detection and delineation (ITDD) for mangroves using light detection and ranging (LiDAR) data (91 pt./m2) collected from UAV. Specifically, we aim to detect and measure tree height (TH) and crown diameter (CD) of each mangrove tree, and analyze the impact of crown clumping density and spatial resolution on mangrove ITDD. To this end, we combined the variable window filtering method and marker controlled watershed segmentation algorithm, and successfully delineated 46.0% of the 126 field measured mangroves. This was promising considering the complexity of mangrove forest. TH and CD were estimated with higher accuracies than previous studies. The isolated trees, with the lowest clumping density, were delineated with the highest accuracy. To identify the optimal spatial resolution of canopy height model (CHM), we defined four spatial resolutions (0.1 m, 0.25 m, 0.5 m, and 1 m) and conducted a simulation. Based on the results, we propose a rule-of-thumb that the spatial resolution should be finer than one-fourth of CD for ITDD, which is also applicable to other forest types. The main difficulty for mangrove ITDD is an overall under-detection of trees, which is caused by the high clumping density and limited height difference between adjacent mangroves. We recommend combining UAV LiDAR with imagery and terrestrial LiDAR to improve the mangrove ITDD performance.