<![CDATA[Different techniques have been developed for segmenting individual trees using point clouds from UAVs and other remote sensing technologies. A more accurate and reasonably priced method is still required, nevertheless, especially for tropical natural forests. This study evaluates the accuracy of individual tree segmentation using point clouds derived from RGB images in Indonesian natural forests. Compared to other sensors like LiDAR, RGB-based point clouds are significantly more cost-effective. We employed a point cloud-based segmentation algorithm, which has demonstrated superior performance over raster-based or hybrid methods. The results show that this approach is feasible for segmenting individual trees, although it tends to produce over-segmentation. This was attributed to the constraints of incomplete ground measurements resulting from dense canopy cover. The method achieved an overall segmentation accuracy of r (0.68), p (0.76), and F (0.72). Tree position accuracy had an RMSE of 1.95 meters, while the RMSE for crown radius was 1.59 meters. Future work will focus on enhancing the quality of RGB point clouds and improving algorithms to increase segmentation accuracy in natural forests.]]>
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