Estimation of forest biophysical parameters using small-footprint LiDAR with different density in a coniferous forest

Abstract

In this paper, the effects of different LiDAR point density on estimating forest stand variables such as mean height, mean crown diameter, mean diameter breast height DBH, tree density and aboveground biomass were investigated for the coniferous tree species in the Qilian Mountain area within Gansu province, western China. For low-density LiDAR point data, the statistic including height quantiles, mean height, and fractional cover were used to establish stepwise multiple regression model, while for high-density LiDAR point data, we can firstly extract the each tree's parameters including tree height and crown diameter, and then get the forest stand variables through multiple regression analysis. The results showed that the two methods had the similar results, which showed that the low density LiDAR data was enough for forest stand variables mapping in region scale.