Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests

Lee, H.; Slatton, K. Clint; Roth, Brian E.; Cropper, Wendell P.

doi:10.1080/01431160902882561

Cited by 136 publications

(111 citation statements)

References 27 publications

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“…For the crown diameter estimation, the R 2 values for CDs (x direction, y direction, and average) are larger than 0.9 in the two case studies, which are much better than the results reported by using ALS data, i.e., 0.44 reported in [50] and 0.63 reported in [48]. It should be noted that previous studies on the crown diameter estimation by using ALS data mostly focus on natural forest regions.…”

Section: Accuracy Comparisoncontrasting

confidence: 42%

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

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Abstract:As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

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Section: Accuracy Comparisoncontrasting

confidence: 42%

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

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“…An adequate identification of suppressed trees relies on the analysis of full-waveform or laser-point data ( [11,12,17]). A 3D delineation is possible using voxel-based or vector-based approaches, for example multi-layer imaging techniques [18], variants of k-means clustering [19][20][21], adaptive 3D clustering [22], multi-scale clustering [23] or graph-based methods [2,12]. Generally the 3D approaches result in improved detection rates in vertically inhomogeneous forest stands and more complex and realistic crown shapes [9] compared to raster-based approaches.…”

Section: State Of the Artmentioning

confidence: 99%

aTrunk—An ALS-Based Trunk Detection Algorithm

et al. 2015

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This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m 2 , while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59 m average difference and 0.78 m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

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“…× TP recall = 100% TP + FN (11) × TP precision = 100% TP + FP (12) × 2TP F -Measure = 100% 2TP + FP + FN (13) …”

Section: Accuracy Assessment and Evaluation Of The Proposed Methodsmentioning

confidence: 99%

“…In recent years, light detection and ranging (LiDAR) has become an increasingly popular tool for forest monitoring and vegetation mapping tasks for its capacity of obtaining the spatial distribution of surface characteristics with dense point clouds [1][2][3]. Extensive studies have been investigated to estimate forest attributes from point clouds, including tree locations [4], heights [5][6][7], DBH [8], stem curves [9], crown widths [5,[10][11][12], crown base heights [13,14], wood volumes [15,16] and biomass [17][18][19][20]. Moreover, the improvement in LiDAR technology of higher pulse rates and the increasing LiDAR posting densities have provided better opportunities for obtaining forest data at a fine scale, and the forest inventory has transferred from an average forest stand scale to the individual tree level [21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method

et al. 2016

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Abstract:Laser scanning technology plays an important role in forest inventory, as it enables accurate 3D information capturing in a fast and environmentally-friendly manner. The goal of this study is to develop methods for detecting and discriminating individual trees from TLS point clouds of five plots in a boreal coniferous forest. The proposed hierarchical minimum cut method adopts the detected trunk points that are recognized according to pole like shape segmentation as foreground seed points and other points as background seed points, respectively. It constructs the undirected weighted graph of the foreground and background seed points to deduce a cost function for tree crown point segmentation with the decreasing ranking of tree trunk heights. The intermediate results lead to global optimization segmentation of individual trees in a hierarchical order. Finally, the structure metrics of the detected individual trees are calculated and checked with field observations. Plots with different attributes were selected to verify the proposed method, and the experimental studies show that the proposed method is efficient and robust for extracting individual trees from TLS point clouds in terms of the recall of 90.42%.

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Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests

Cited by 136 publications

References 27 publications

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

aTrunk—An ALS-Based Trunk Detection Algorithm

Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method

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