A Concave Hull Methodology for Calculating the Crown Volume of Individual Trees Based on Vehicle-Borne LiDAR Data

Zhuang, Y; Liu, Rufei; Cheng, Liang; Zhou, Xiao; Ruan, Xiaojun; Xiao, Yijia

doi:10.3390/rs11060623

Cited by 57 publications

(57 citation statements)

References 52 publications

(126 reference statements)

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“…The laser scanner used by Vsurs‐Q is a Faro X130. The scanner’s main technical indicators (Z. Yan et al, ) in Table are the key to the data quality. Two datasets were used in the evaluation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds

Liu

Wang

Zhuang

et al. 2020

The Photogrammetric Record

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For the classification of pole‐like objects (trees, lamp posts, traffic lights and traffic signs) in mobile laser scanning (MLS) point clouds, a hierarchical classification method is proposed. The method consists of three major steps. (1) The objects’ cylindrical column sections are detected based on the characteristics of arc‐like points using RANSAC after denoising. (2) These detected objects are roughly classified into trees and man‐made poles based on the azimuthal coverage of point clouds above the cylindrical column. (3) Eigenvalue analysis and the principal direction of the upper pole projections are used to differentiate lamp posts, traffic lights and traffic signs. Experimental analysis shows that the method can effectively identify different types of pole‐like objects.

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Section: Experiments and Resultsmentioning

confidence: 99%

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds

Liu

Wang

Zhuang

et al. 2020

The Photogrammetric Record

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“…Consequently, olive trees were characterized by the spectral reflectance and some morphological properties such as the height and volume. Yan et al [58] proposed the use of a concave hull operation for the extraction of structural plant parameters. In contrast, our solution is based on a voxel-based decomposition of the plant space using a 3D octree, which is widely used for the partition of three-dimensional space by recursive subdivision [56,59].…”

Section: Discussionmentioning

confidence: 99%

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

et al. 2020

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3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

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“…Currently, the studies of both VLS and BLS are in the stage of individual tree detection and segmentation because it is fundamental for the estimation of tree size parameters and AGB for individual trees. Yan et al [99] proposed a new method (i.e., a concave hull by slices method) for individual tree segmentation, which obtained better accuracy than five existing methods including manual measurement, 3D convex hull, 3D alpha shape, convex hull by slices and a voxelbased method. BLS arouses more and more interest, currently because it is superior to both TLS and VLS due to its flexibility to collect close-by measurements in all directions in a timely manner [100].…”

Section: Mobile Laser Scanning (Mls)mentioning

confidence: 99%

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Wang

et al. 2021

Forests

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Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

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A Concave Hull Methodology for Calculating the Crown Volume of Individual Trees Based on Vehicle-Borne LiDAR Data

Cited by 57 publications

References 52 publications

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

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