Developing a more accurate method for individual plant segmentation of urban tree and shrub communities using LiDAR technology

Liu, Yang; Zhang, Xuguang; Ma, Zitong; Dong, Nalin; Xie, Dongbo; Li, Rui; Johnston, Douglas M.; Gao, Yu; Li, Yonghua; Lei, Yakai

doi:10.1080/01426397.2022.2144813

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Qin et al [46] proposed a watershed-spectral-texturecontrolled normalized cut (WST-Ncut) algorithm However, vegetation spectral and textural information often vary over time. Liu et al [65] developed a multiround comparative shortest-path algorithm (M-CSP) to segment trees and shrub plants in urban environments. Lu et al [33] proposed a bottom-up approach to segment trees from LiDAR data and achieved F-scores exceeding 0.9 in leaf-off forests.…”

Section: Tree Segmentationmentioning

confidence: 99%

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Wang

Yao

et al. 2023

Remote Sensing

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The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide the precise height and detailed structure of trees, and can estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. First, the tree crowns are segmented using the profile-rotating algorithm, and tree positions are registered based on dominant tree heights. Second, considering the correlation between crown shape and tree species, we use DBN classifier to identify species using features of crowns, which are extracted to 1D array. Third, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass, and stock volume can be extracted from trees, enabling accurate large-scale forest surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east–west crown width, north–south crown width, diameter at breast height, aboveground biomass, and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based survey is practical and can be widely applied in forest resource monitoring.

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Section: Tree Segmentationmentioning

confidence: 99%

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Wang

Yao

et al. 2023

Remote Sensing

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Quantification of the spatial distribution of individual mangrove tree species derived from LiDAR point clouds

Kasai,

Yanagisawa,

Goto

2024

Prog Earth Planet Sci

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Mangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compounded mobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicated that ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution microtopography and tree information inside the forest, although the measurement area was limited to comparison with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From these point clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individually from composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordance with changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography.

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Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Wang¹,

Yao²,

Ma³

et al. 2023

Preprint

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The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide precise tree height and detailed vertical structure of the tree stands. Coordinating some representative ground sample plots, LiDAR can be used to estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. Firstly, the tree crowns are segmented using the profile-rotating algorithm, and dominant trees height are used to check and rectify the tree locations. Secondly, considering the correlation between forestry parameters and tree species, we establish models to distinguish between species using geometric characteristics of tree crowns. Thirdly, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass and stock volume can be extracted from trees. The prediction models of forestry parameters can also be verified, which can be extended to accurate large-scale forestry surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east-west canopy width, north-south canopy width, diameter at breast height, above-ground biomass and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based estimation of forestry parameters is practical and that these forestry parameter prediction models can be widely applied in forest resource monitoring.

show abstract

Developing a more accurate method for individual plant segmentation of urban tree and shrub communities using LiDAR technology

Cited by 3 publications

References 41 publications

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Quantification of the spatial distribution of individual mangrove tree species derived from LiDAR point clouds

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

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