Wang, J.; Yao, C.; Ma, H.; Xu, J.; Qian, C. Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data. Remote Sens.2023, 15, 3060.
Wang, J.; Yao, C.; Ma, H.; Xu, J.; Qian, C. Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data. Remote Sens. 2023, 15, 3060.
Wang, J.; Yao, C.; Ma, H.; Xu, J.; Qian, C. Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data. Remote Sens.2023, 15, 3060.
Wang, J.; Yao, C.; Ma, H.; Xu, J.; Qian, C. Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data. Remote Sens. 2023, 15, 3060.
Abstract
The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide precise tree height and detailed vertical structure of the tree stands. Coordinating some representative ground sample plots, LiDAR can be used to estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. Firstly, the tree crowns are segmented using the profile-rotating algorithm, and dominant trees height are used to check and rectify the tree locations. Secondly, considering the correlation between forestry parameters and tree species, we establish models to distinguish between species using geometric characteristics of tree crowns. Thirdly, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass and stock volume can be extracted from trees. The prediction models of forestry parameters can also be verified, which can be extended to accurate large-scale forestry surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east-west canopy width, north-south canopy width, diameter at breast height, above-ground biomass and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based estimation of forestry parameters is practical and that these forestry parameter prediction models can be widely applied in forest resource monitoring.
Keywords
LiDAR; Tree Segmentation; Tree Species Identification; Tree Species Identification; DBN; Forest Parameter
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
