3D segmentation of single trees exploiting full waveform LIDAR data

Reitberger, J.; Schnörr, Cl.; Krzystek, Peter; Stilla, Uwe

doi:10.1016/j.isprsjprs.2009.04.002

Cited by 411 publications

(420 citation statements)

References 14 publications

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“…In the ITD approach, either the canopy height model (CHM) [14,15] or the ALS raw data point clouds [3,16,17] are used to be segmented into single tree objects. Properties at single tree level can subsequently be estimated using the tree segments, such as tree height, crown diameter, tree species, crown base height and stem volume.…”

Section: Introductionmentioning

confidence: 99%

“…Novel methods for ITD approach tackle conceptually the segmentation problem with a 3D approach instead of using only the CHM. In combination with full waveform data Reitberger et al [16] successfully demonstrated that the overall detection rate of single trees could be significantly improved, especially in heterogeneous forest types. The improvement happened mostly to the lower forest layers with 20%.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a detailed understanding of the impact of the varying key control parameters on the performance of tree detection is required. In the former work of Reitberger et al [16], the smoothing factor and segmentation threshold were already investigated in a preliminary configuration with a limited point density and value domain. In the past several years, a few authors have been dealing with similar topics.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Yao

Krull

Krzystek

et al. 2014

Forests

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Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m 2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m 2 , 10 pts/m 2 , 15 pts/m 2 and 20 pts/m 2 , respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m 2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures. OPEN ACCESSForests 2014, 5 1123

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Yao

Krull

Krzystek

et al. 2014

Forests

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“…ALS has been used extensively to delineate single trees and analyse their vertical trees (e.g. Reitberger et al 2009). However, the extraction of tree irregularities due to landslides from ALS data has not been studied so far, mainly because of the high density data required for a detailed analysis (Bucksch et al 2010).…”

Section: Landslide Activity Analysismentioning

confidence: 99%

“…Skeltree did not have a single tree delineation algorithm included, and required a point cloud of a single tree as input. Different automatic single tree delineation methods were tested to generate input for SkelTre: the normalized cut segmentation (Reitberger et al, 2009), and TreeVaW (Popescu et al, 2002). However, these algorithms did not provide 3D point clouds that represented only a single tree, as these methods assumed the trees to be near vertical.…”

Section: Tree Inclination and Orientation From Skeletonizationmentioning

confidence: 99%

Airborne laser scanning : for forested landslides investigation in temperate and tropical invironments

Razak¹

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Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

Xie

Wang

et al. 2023

Ecology and Evolution

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Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m 2 . Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match ) of tree tops is up to 0.92, the coefficient of determination (R 2 ) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

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3D segmentation of single trees exploiting full waveform LIDAR data

Cited by 411 publications

References 14 publications

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Airborne laser scanning : for forested landslides investigation in temperate and tropical invironments

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

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