Estimation of Individual Tree Parameters Using Small-Footprint LiDAR with Different Density in a Coniferous Forest

Qi Sheng He; Na Li

doi:10.4028/www.scientific.net/AMR.518-523.5320

Paper Titles

Soil C:N:P Stoichiometry across Forest Communities in Eastern Guangdong, China
p.5297

Species Diversity of Pseudolarix amabilis Population in Yaoluoping Natural Reserve of Anhui Province
p.5302

Study on Effect of Drought Based on Time Series on Grassland Vegetation in Eastern Inner Mongolia
p.5306

Deviation of Mass-Density Allometry in Ramet Populations of Switchgrass
p.5316

Estimation of Individual Tree Parameters Using Small-Footprint LiDAR with Different Density in a Coniferous Forest
p.5320

Patterns of Indigenous Tree Species Distribution in Guangdong Province and the Implications for Tree Introduction
p.5324

Simulation of Jatropha curcas L. Root in Response to Salt Stress Based on 3D Visualization
p.5330

Stand Structure and Carbon Accounting for Subtropical Forests along the Dongjiang River Basin, South China
p.5335

Structure and Dynamics of Pseudolarix amabilis Population in Yaoluoping Natural Reserve of Anhui Province
p.5341

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 518-523Estimation of Individual Tree Parameters Using...

Estimation of Individual Tree Parameters Using Small-Footprint LiDAR with Different Density in a Coniferous Forest

Abstract:

In this paper, the effects of different LiDAR point density on individual tree parameters including tree height and crown diameter were investigated for the coniferous tree species in the Qilian Mountain area within Gansu province, western China. 10 different density data were acquired in field survey area, with the minimum density of 0.234 points/m2 and the maximum density of 0.6941 points/m2 for per flight. By summing up the different flight data, the different density LIDAR data from 0.234 points/m2 to 5.226 points/m2 for extracting tree height and crown diameter can be analyzed. The result showed that the number of extraction points and the extraction accuracy of tree height and crown width arrived at relative high level in point density of about 2.5 points per m2. When the point density increased, the increased extraction points and the extraction accuracy of tree height and crown width became slow. It means that about 2.5 points per m2 of LiDAR data density may provide relative high accurate individual tree parameters estimation.