scholarly journals Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data

2017 ◽  
Vol 9 (2) ◽  
pp. 148 ◽  
Author(s):  
Xingbo Hu ◽  
Wei Chen ◽  
Weiyang Xu
Keyword(s):  
Mean Shift ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Trees ◽  
Airborne Lidar Data

scholarly journals Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 571 ◽  
Author(s):  
Chen ◽  
Xiang ◽  
Moriya
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Lidar Data ◽  
Individual Tree ◽  
Matching Algorithm ◽  
Crown Width ◽  
Individual Trees ◽  
Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

A hybrid method for segmenting individual trees from airborne lidar data

2019 ◽  
Vol 163 ◽  
pp. 104871 ◽  
Author(s):  
Li Liu ◽  
Samsung Lim ◽  
Xuesong Shen ◽  
Marta Yebra
Keyword(s):  
Hybrid Method ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Trees ◽  
Airborne Lidar Data

scholarly journals Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1263 ◽  
Author(s):  
Wen Xiao ◽  
Aleksandra Zaforemska ◽  
Magdalena Smigaj ◽  
Yunsheng Wang ◽  
Rachel Gaulton
Keyword(s):  
Mean Shift ◽  
Point Clouds ◽  
Forest Tree ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Mean Shift Algorithm ◽  
Detailed Assessment ◽  
The Mean ◽  
Airborne Lidar Data

Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data
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