Stem and root assessment in mangrove forests using a low-cost, rapid-scan terrestrial laser scanner

Abstract

Accurate assessment of forest structure and biomass is hampered by extensive field measurements that are time-consuming, costly, and inefficient. This is especially true in mangrove forests that have developed complex above-ground root structures for stability and survival in the harsh, anaerobic, and reducing conditions of water-logged sediments. These diverse structures can differ even among similar species, providing complex three dimensional structures and making them difficult to accurately assess using traditional allometric methods. Terrestrial laser scanners (TLS) have been used widely in collecting forest inventory information in recent years, mainly due to their fine-scale, detailed spatial measurements and rapid sampling. In this work we detected stems and roots in TLS data from three mangrove forests on Pohnpei Island in Micronesia using 3D classification techniques. After removing noise from the point cloud, the training set was acquired by filtering the facets of the point cloud based on angular orientation. However, many mangrove trees contain above-ground roots, which can incorrectly be classified as stems. We consequently trained a supporting classifier on the roots to detect omitted root returns (i.e., those classified as stems). Consistency was assessed by comparing TLS results to concurrent field measurements made in the same plots. The accuracy and precision for TLS stem classification was 82% and 77%, respectively. The same values for TLS root detection were 76% and 68%. Finally, we simulated the stems using alpha shapes for volume estimation. The average consistency of the TLS volume assessment was 85%. This was obtained by comparing the plot-level mean stem volume (m3/ha) between field and TLS data. Additionally, field-measured diameter-at-breast-heights (DBH) were compared to the lidar-derived DBH using the reconstructed stems, resulting in 74% average accuracy and an RMSE of 7.52 cm. This approach can be used for automatic structural evaluation, and could contribute to more accurate biomass assessment of complex mangrove forest environments as part of forest inventories or carbon stock assessments.