SEMI-AUTOMATED TREE SPECIES CLASSIFICATION BASED ON ROUGHNESS PARAMETERS USING AIRBORNE LIDAR DATA

Abstract

Automated tree species classification using high density airborne LiDAR data supports precise forest inventory. This work shows a method based on evaluating roughness descriptors from aerial LiDAR data to automatically classify tree species. The proposed method includes treetops detection, neighbouring distance analysis for selecting the interest points, 3D fit surface creation, evaluation of roughness parameters, and K-means clustering. Among the evaluated roughness parameters, Skewness (Rsk) and Kurtosis (Rku) show robust classification. A synthetic point cloud was generated to test the methodology in a mixed forest formed by three tree species, Pinus sp., Quercus sp., and Eucalyptus sp. The Overall Accuracy (OA) of the classification method was 80 % for Quercus sp., 100 % for Pinus sp. and 80.6 % for Eucalyptus sp. In addition, the methodology was tested in three study areas and the results demonstrate that roughness parameters can be used to individual tree species classification in a mixed temperate forest with an OA of 82% in study area 1, 93 % in study area 2 and 92 % in study area 3. Keywords: aerial LiDAR, point cloud processing, tree species classification, spatial analysis, roughness parameters