Single Tree Canopy Extraction from LiDAR Point Cloud Data

2020 ◽

Vol VI-3/W1-2020 ◽

pp. 67-73

Author(s):

Y. Mu ◽

G. Zhou ◽

H. Wang

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Local Maximum ◽

Tree Canopy ◽

Second Step ◽

Watershed Algorithm ◽

Target Area ◽

Cloud Data ◽

Single Tree ◽

Variable Window

Abstract. Airborne laser LiDAR has widely applied in the accurate extraction of single tree canopy for inventory of precision forestry. Due to the over-segmentation phenomenon occurring in the traditional watershed single-wood segmentation, this paper presents a method, called K – means clustering watershed for single tree segmentation. This method consists of four aspects: The first step is to filter the point cloud to eliminate the interference factors such as ground elevation and other factors that interfere with the LiDAR point cloud segmentation; The second step is to optimize the generation of CHM, generate a CMM based on CHM variable window detection, and obtain the treetop position to provide the pixel center position for subsequent K – means cluster segmentation; The third step is to use the K – means clustering algorithm to perform initial cluster segmentation to extract the target pixels of interest. At this time, the local maximum value detected by the variable window in the second step is used as the center pixel of the cluster; In the fourth step, an improved watershed algorithm based on the similarity of 4 neighborhoods is proposed. The improved watershed algorithm is applied to the K – means initial clustering image to segment the target area, and the over-segmentation results are subsequently processed, and the over-segmentation blocks are combined according to certain criteria. Identify the contour of single canopy from the CHM images of the experimental forest data. The experimental results show that the proposed algorithm can effectively solve the over-segmentation problem happening the traditional watershed algorithm. The accuracy of F, R and P parameters can be improved by 7.1%, 11% and 9.8%.

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Apple tree canopy leaf spatial location automated extraction based on point cloud data

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2019.104975 ◽

2019 ◽

Vol 166 ◽

pp. 104975 ◽

Cited By ~ 3

Author(s):

Cailing Guo ◽

Gang Liu ◽

Weijie Zhang ◽

Juan Feng

Keyword(s):

Point Cloud ◽

Apple Tree ◽

Spatial Location ◽

Tree Canopy ◽

Point Cloud Data ◽

Automated Extraction ◽

Cloud Data

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Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Remote Sensing ◽

10.3390/rs10050686 ◽

2018 ◽

Vol 10 (5) ◽

pp. 686 ◽

Cited By ~ 4

Author(s):

Donghui Xie ◽

Xiangyu Wang ◽

Jianbo Qi ◽

Yiming Chen ◽

Xihan Mu ◽

...

Keyword(s):

Point Cloud ◽

Point Cloud Data ◽

Terrestrial Lidar ◽

Cloud Data ◽

Single Tree

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CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray

Sensors ◽

10.3390/s21124252 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4252

Author(s):

Chenchen Gu ◽

Changyuan Zhai ◽

Xiu Wang ◽

Songlin Wang

Keyword(s):

Point Cloud ◽

Tree Canopy ◽

Point Cloud Data ◽

Cross Sectional ◽

Cloud Data ◽

Spray System ◽

Canopy Volume ◽

High Degree ◽

Degree Of Similarity ◽

Moving Speed

Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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