AUTOMATIC DETECTION OF DEAD TREE FROM UAV IMAGERY THROUGH COMBINATION OF RANDOM FOREST AND VEGETATION INDEX

摘要

Recently, a number of conifers dying due to climate change are being found in high altitude areas of Korean national parks. The existing tree investigation method has been judged by the human visual interpretation, however, with UAV, it is advantageous to measure large areas at once, and to construct and analyze this as spatial information. In this study, we propose a method to detect the location of wilted or dead trees in UAV orthophoto. A natural monument called Yew Trees at Sobaeksan. located in the highlands of Sobaeksan national park, was selected as an experimental area and an orthophoto image of a 2cm resolution was produced by UAV digital photogrammetry. Image segmentation and image classification were performed sequentially through object-based image analysis (OBIA): The orthophoto image was split by a large-scale mean-shift (LSMS) segmentation method, and a random forest algorithm was applied for classification. Each segment was identified as a dead tree, a living tree, a shadow, and a bare area. Because the UAV used in this study only contained an RGB sensor, the vegetation index was used as an additional feature to improve the accuracy of the dead tree detection. The normalized green-red difference index (NGRDI) applicable to the RGB sensor was chosen. All data processing was done using Free and Open Source Software for Geospatial (FOSS4G) including OpenDroneMap, OrfeoToolBox, and QGIS. Experimental results showed that the dead tree can be automatically detected from UAV imagery with a confidence level of more than 80%. We also confirmed that the combination of random forest and vegetation index can complement the limit of RGB sensor data.