FOREST SPECIES CLASSIFICATION BASED ON THREE-DIMENSIONAL COORDINATE AND INTENSITY INFORMATION OF AIRBORNE LIDAR DATA WITH RANDOM FOREST METHOD

Abstract. Forest species is a basic parameter of forest ecosystem. The accurate identification of forest species can not only improve the estimation accuracy of other forest structural parameters, but also have important significance for forest resource monitoring and management. As an active remote sensing technology, the LiDAR could not only acquire the three-dimensional coordinate information of the object, but also acquire the intensity information. The airborne LiDAR data have been successfully used in forest species classification research. However, most of the research is based on the three-dimensional coordinate information of LiDAR data. It's fact that the parameters derived from the intensity data are closely related to the spectral reflection of forest species and could be beneficial for forest species classification, but the research with LiDAR intensity data is fewer. Therefore, it is necessary to explore the potential of LiDAR intensity data on forest species classification and test if the combined application of the three-dimensional coordinate and intensity information can improve the forest species classification accuracy. In this paper, the Moon Lake National Forest Park located in Changchun is selected as the study area, which planted with Scotch pine, Larch pine, Mongolian oak, aspen and other tree species. Two kinds of parameters are separately derived from the three-dimensional coordinate and intensity information of airborne LiDAR data. Then Random Forest is used to classify the forest species based on the above parameters. The main purposes of this study are: (1) to test if the parameters derived from the three-dimensional coordinate information of LiDAR data can be used to identify the forest species; (2) to test if the parameters derived from the intensity information of LiDAR data can be used to identify the forest species; (3) to test if the combined application of the three-dimensional coordinate and the intensity information can improve the accuracy of forest tree species identification. It was found that the classification accuracy of forest species based on structural parameters derived from the three-dimensional coordinate information was 87.54% and Kappa coefficient was 0.81. The classification accuracy based on the parameters derived from LiDAR intensity information was 89.23% and Kappa coefficient was 0.83. And the classification accuracy based on three-dimensional coordinate and intensity information was 92.35% and Kappa coefficient was 0.88. The results demonstrated that both the parameters derived from LiDAR three-dimensional coordinate and intensity information can identify forest species. The results based on LiDAR intensity information are better than that of three-dimensional coordinate information. And the combined application of the two information can improve the classification accuracy of forest species. Therefore, further research should make use of the three-dimensional coordinates and intensity information of LiDAR data to improve the accuracy of results.