AUTOMATIC INDIVIDUAL TREE SEGMENTATION AND 3D MODELING FOR GREEN OPEN SPACE MANAGEMENT USING LIDAR POINT CLOUD DATA AND PHOTOGRAMMETRY - Nur Fauzan, Kamal
Global environmental change is an ongoing issue and has been widely discussed from various perspectives. The three main factors contributing to global environmental change are increases in atmospheric carbon dioxide concentrations, changes in the global adhesion cycle, and land cover/land use changes. The increase in the concentration of carbon dioxide mainly occurs in urban areas because urban areas account for more than 70% of carbon dioxide emissions. The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling is one of the efforts in managing green open spaces. In this study, 3D modeling was carried out on point data obtained by UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the best fitting method on point cloud data. This study uses three fitting methods, namely the spherical best fitting method, the best fitting ellipsoid method, and the spherical harmonics best fitting method. The best-fitting spherical harmonics method produces the best results and an average R2 value of 0.711. In this study, Above-Ground Biomass calculations were also carried out from the modeling results using three methods with LiDAR and Photogrammetry data. AGB calculation using LiDAR data gives better results than using photogrammetric data. The AGB calculation using LiDAR data gives an error of 2-7% from the field validation results. AGB calculation using the spherical harmonics method gives results closest to the AGB value of field validation. However, a 3D model from photogrammetric data using the spherical harmonics method can be used for visualization purposes with a not too large area.