3D Modeling of Individual Trees from LiDAR and Photogrammetric Point Clouds by Explicit Parametric Representations for Green Open Space (GOS) Management

Abstract

The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting methods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. However, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…length x height) of a static photo (Linhares et al, 2020). RGB cameras have been used for the study of vegetation indices based on RGB information (Ilniyaz et al, 2022;Talavera et al, 2022;, forest canopy mapping and modeling (Nasiri et al, 2022;Suwardhi et al, 2022;Trencanovaé t al., 2022), tree identification and characterization(Onishi and Ise, 2021), and among others.…”

“…Due to the resolution that the LiDAR point cloud is capable of generating, individual trees can be identified, and thus tree metrics can be directly computed. In (Vizireanu et al, 2020;Neuville et al, 2021), DBH is estimated based only on LiDAR retrieved data, other forest attributes estimated by LiDAR cloud points are canopy cover (Cai et al, 2021), which can be derived through the density of vegetation points, this metric is also used to predict biomass near rivers (Resop et al, 2021), and with the purpose of determining crown fuels (Suwardhi et al, 2022). Morphological features derived from LiDAR point cloud can be key factors to determine and differentiate between alive trees and snags or deciduous and evergreen trees, this study is done by Stitt et al (2022).…”

“…Morphological features derived from LiDAR point cloud can be key factors to determine and differentiate between alive trees and snags or deciduous and evergreen trees, this study is done by Stitt et al (2022). The use of LiDAR has helped researchers investigate the following: tree modeling (Suwardhi et al, 2022), biomass estimation (Torre-Tojal et al, 2022), and tree classification (Hell et al, 2022) among others.…”

UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management

“…Despite approximately 56.2% of the world's population residing in urban areas [2], the supply of UGS is diminishing to a level that poses a significant threat to urban ecosystems [3]. Urban atmospheres, with people, cars, and industries, are contributing to 70% of the total CO 2 emissions [4,5]. As a result, trees in urban areas are increasingly recognized as a key tool for absorbing CO 2 and mitigating the significant impacts of such emissions [6,7].…”

A point cloud optimization algorithm based on nonadjacent low-altitude remote sensing images

“…With the accelerated development of urbanization, the study of spatial geographic information achievement has elicited general interest in the area of remote sensing, photogrammetry, computer vision and machine intelligence. The reconstruction of fine-detailed digital surface model (DSM) from multi-view stereo (MVS) matching is one of the focal topics which has gained considerable attention in the past decades [1][2][3] .…”