Articles containing the keyword 'over-segmentation'

Forest stands, crucial for inventory, planning, and management, traditionally rely on time-consuming visual analysis by forest managers. To enhance efficiency, there is a growing need for automated methods that take into account essential forest attributes. In response, we propose a novel approach utilizing airborne Light Detection and Ranging (LiDAR) and hyperspectral data for automated forest stand delineation. Our approach initiates with over-segmentation of the Canopy Height Model (CHM), followed by attribute calculation for each segment using both CHM and hyperspectral data. Two rules are applied to merge homogeneous segments and eliminate others based on calculated attributes. The effectiveness of our method was validated using three types of reference forest stands with two indices: the explained variance (R²) and Intersection over Union (IoU). Results from our study demonstrated notable accuracy, with a R² of 97.35% and 97.86% for mean tree height and mean diameter at breast height (DBH), respectively. The R² for mean canopy height is 81.80%, outperforming manual delineation by 7.31% and multi-scale segmentation results by 2.13%. Furthermore, our approach achieved high IoU values, which indicates a strong spatial agreement with manually delineated forest stands and leading to fewer manual adjustments when applied directly to forest management. In conclusion, our forest stand delineation method enhances both internal consistency and spatial accuracy. This method contributes to improving practical performance and forest management efficiency.

• Xiong, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China; School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China https://orcid.org/0000-0003-4432-2485 E-mail: xiongh29@mail2.sysu.edu.cn
• Pang, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China https://orcid.org/0000-0002-9760-6580 E-mail: pangy@ifrit.ac.cn
• Jia, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China E-mail: jiawen@ifrit.ac.cn
• Bai, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China E-mail: baiyu9224@163.com