Estimation of Lai Using Lidar Remote Sensing in Forest

Light Detection and Ranging (LiDAR) has been used to extract surface information as it can acquire highly accurate object shape characteristics using geo-registered 3D-points. Therefore, LiDAR can be used to effectively measure tree parameters in forested areas. In this research, we estimated the LAI (Leaf Area Index) for Pinus koraiensis, Larix leptolepis and Quercus spp. using LiDAR data. For calculating the LAI (Leaf Area Index), the LPI (Laser Penetration Index) and LII (Laser Interception Index) were generated by LiDAR data having High Vegetation Returns (HVR), Medium Vegetation Returns (MVR), Low Vegetation Returns (LVR) and Ground Returns (GR). The LPI was calculated with point density using first returns (h ≥ 1m) and ground returns (h < 1m), and the LII was computed with the ratio of all returns to HVR and MVR. The LAI is calculated through the regression analysis by tree species with the LPI and LII. Afterward, we assessed the accuracy of LiDAR-derived and field-measured LAI with the coefficient of determination and root mean square error. As a result, the slope of Pinus koraiensis was the steepest, and the slope of Quercus spp. was the gentlest of three tree species. This can be explained by the fact that the amount of transmitted sunlight through the canopy in Quercus spp. can be different by seasons. Moreover, in the LAI generated by the LII, the coefficients of determination were estimated higher than those by the LPI. This can be attributed to the fact that the original information of the number of laser points was lost when the point data was transformed to raster data for generating the LPI. And the LII allows normalizing biased local variation of the number of laser points while the raster data has some noise due to unbalanced distribution of laser points.