Centro de Investigación y Estudios Avanzados del Maule, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile.
Centro de Desarrollo del Secano Interior, Facultad de Ciencias Agrarias y Forestales, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile.
Sensors (Basel). 2022 Feb 9;22(4):1331. doi: 10.3390/s22041331.
The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of . UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation.
树木的位置和树冠的个体化是估计直径、高度和生物量等变量的重要参数。无人机影像的极高空间分辨率支持这些过程。从 RGB 无人机图像生成密集的 3D 点云，用于获取数字高程模型 (DEM)。从这个 DEM 中，衍生出一个用于个体树木识别的树冠高度模型 (CHM)。尽管结果令人满意，但如果工作区域的植被密度很高，那么这种检测的质量就会降低。本研究的目的是评估彩色植被指数 (CVI) 在. 的树冠个体化过程中的应用。进行了无人机飞行，并获得了 3D 密集点云和正射镶嵌图。然后，将 CVI 应用于 3D 点云，以区分植被和非植被类，从而获得 DEM 和 CHM。随后，将自动冠层识别程序应用于 CHM。通过将其与无人机正射镶嵌图上的手动个体树木识别结果以及通过应用渐进三角不规则网络到 3D 点云获得的结果进行对比，对结果进行了评估。结果表明，在高密度植被条件下，3D 点云的颜色信息是支持树木个体化的一种替代方法。
