Exploratory quantification of 3D spatial competition in ecotone of trees and bamboos using terrestrial laser scanner

Lin, Jiayuan; Chen, Yangyu; Jiang, Rui; Li, T.-T.

doi:10.1016/j.foreco.2023.121085

Cited by 3 publications

(1 citation statement)

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“…Manned aircraft-borne LiDAR can scan the point clouds of a forest and extract individual tree heights with sound quality [13], but the prohibitive cost for small areas and relatively higher personnel risk make it hard to be widely deployed and used periodically. Terrestrial LiDAR can acquire the dense and high-precision point clouds of trees at ground level, but its spatial coverage is extremely limited [14][15][16]. For these three types of LiDAR, their ranging accuracy increases sequentially, but the spatial coverage decreases accordingly.…”

Section: Introductionmentioning

confidence: 99%

Effects of Illumination Conditions on Individual Tree Height Extraction Using UAV LiDAR: Pilot Study of a Planted Coniferous Stand

Li,

Lin,

et al. 2024

Forests

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Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R2 and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R2 and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.

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