scholarly journals CANOPY LIDAR POINT CLOUD DATA K-MEANS CLUSTERING WATERSHED SEGMENTATION METHOD

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%.

scholarly journals A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

2021 ◽  
Vol 13 (8) ◽  
pp. 1442
Author(s):  
Kaisen Ma ◽  
Yujiu Xiong ◽  
Fugen Jiang ◽  
Song Chen ◽  
Hua Sun
Keyword(s):  
Point Cloud ◽  
Local Maximum ◽  
Window Size ◽  
Point Clouds ◽  
High Density ◽  
Watershed Algorithm ◽  
Canopy Area ◽  
Individual Trees ◽  
Optimal Window ◽  
Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

scholarly journals Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

2021 ◽  
Vol 13 (1) ◽  
pp. 705-716
Author(s):  
Qiuji Chen ◽  
Xin Wang ◽  
Mengru Hang ◽  
Jiye Li
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Tree Height ◽  
Mining Area ◽  
Research Area ◽  
Airborne Lidar ◽  
Shaanxi Province ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

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

2018 ◽  
Vol 10 (5) ◽  
pp. 686 ◽  
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

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

Sensors ◽  
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.

scholarly journals Graphic: Graph-Based Hierarchical Clustering for Single-Molecule Localization Microscopy

2020 ◽  
Author(s):  
Mehrsa Pourya ◽  
Shayan Aziznejad ◽  
Michael Unser ◽  
Daniel Sage
Keyword(s):  
Single Molecule ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Weighted Graph ◽  
Point Cloud Data ◽  
Localization Microscopy ◽  
Cloud Data ◽  
Multiscale Clustering ◽  
Novel Method ◽  
Single Molecule Localization Microscopy

ABSTRACTWe propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different scales. It assumes a prior neither on the shape of particles nor on the background noise. Our multiscale clustering pipeline is built upon graph theory. At each scale, we first construct a weighted graph that represents the SMLM data. Next, we find clusters using spectral clustering. We then use the output of this clustering algorithm to build the graph in the next scale; in this way, we ensure consistency over different scales. We illustrate our method with examples that highlight some of its important properties.

scholarly journals Leaf-Off and Leaf-On UAV LiDAR Surveys for Single-Tree Inventory in Forest Plantations

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 115
Author(s):  
Yi-Chun Lin ◽  
Jidong Liu ◽  
Songlin Fei ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
Quality Data ◽  
Individual Tree ◽  
Single Tree ◽  
Density Based Clustering ◽  
Tree Inventory

LiDAR technology has been proven to be an effective remote sensing technique for forest inventory and management. Among existing remote sensing platforms, unmanned aerial vehicles (UAV) are rapidly gaining popularity for their capability to provide high-resolution and accurate point clouds. However, the ability of a UAV LiDAR survey to map under canopy features is determined by the degree of penetration, which in turn depends on the percentage of canopy cover. In this study, a custom-built UAV-based mobile mapping system is used for simultaneously collecting LiDAR and imagery data under different leaf cover scenarios in a forest plantation. Bare earth point cloud, digital terrain model (DTM), normalized height point cloud, and quantitative measures for single-tree inventory are derived from UAV LiDAR data. The impact of different leaf cover scenarios (leaf-off, partial leaf cover, and full leaf cover) on the quality of the products from UAV surveys is investigated. Moreover, a bottom-up individual tree localization and segmentation approach based on 2D peak detection and Voronoi diagram is proposed and compared against an existing density-based clustering algorithm. Experimental results show that point clouds from different leaf cover scenarios are in good agreement within a 1-to-10 cm range. Despite the point density of bare earth point cloud under leaf-on conditions being substantially lower than that under leaf-off conditions, the terrain models derived from the three scenarios are comparable. Once the quality of the DTMs is verified, normalized height point clouds that characterize the vertical forest structure can be generated by removing the terrain effect. Individual tree detection with an overall accuracy of 0.98 and 0.88 is achieved under leaf-off and partial leaf cover conditions, respectively. Both the proposed tree localization approach and the density-based clustering algorithm cannot detect tree trunks under full leaf cover conditions. Overall, the proposed approach outperforms the existing clustering algorithm owing to its low false positive rate, especially under leaf-on conditions. These findings suggest that the high-quality data from UAV LiDAR can effectively map the terrain and derive forest structural measures for single-tree inventories even under a partial leaf cover scenario.

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