Three-level frame and RD-schematic algorithm for automatic detection of individual trees from MLS point clouds

Lin, Yi; Hyyppä, Juha; Jaakkola, Anttoni; Yu, Xiaowei

doi:10.1080/01431161.2011.599349

Cited by 25 publications

(21 citation statements)

References 24 publications

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“…Actually, the associated researches on automatic extraction of single trees from VLS point clouds have not been reported extensively [45]. In most cases, the VLS-associated segmentation still employs the approaches developed in ALS and TLS domains.…”

Section: Discussionmentioning

confidence: 99%

From TLS to VLS: Biomass Estimation at Individual Tree Level

et al. 2010

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This study explores the applicability of vehicle-based laser scanning (VLS) for biomass estimation at individual tree level, since biomass serves as an essential biophysical parameter indicating tree health. Previous work suggests that terrestrial laser scanning (TLS) has been primarily validated for biomass prediction, however, in subject to laborious relocation in practice. VLS, as an advanced mode of TLS with more flexible mobility and also high sampling density, can work as a new efficient technique for surveying single trees. Combined with the positive binds between the biomass and TLS-samplings during manual defoliation, this work aims to seek the relations between biomass and VLS-samplings, by correlating the VLS-and TLS-samplings within the same crowns during natural foliation. The resulting R 2 values of the two correlations after normalization are larger than 0.88 and 0.61, respectively, and the associated root mean square errors (RMSEs) are less than 0.051 and 0.076. VLS, thus, can be validated for estimating biomass at the individual tree level, with the TLS-investigated data as a bridging reference.

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Section: Discussionmentioning

confidence: 99%

From TLS to VLS: Biomass Estimation at Individual Tree Level

et al. 2010

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“…Zou et al [31] did not extract the complete trees, but only point cloud segments within a defined radius around the stem position that were intended to be used for species classification. Some studies used mobile laser scanning data (MLS) from a moving vehicle to detect and isolate single trees [32][33][34]. However, these studies deal with trees in urban environments, which cannot be compared with complex natural forest structures.…”

Section: Introductionmentioning

confidence: 99%

Constrained Spectral Clustering of Individual Trees in Dense Forest Using Terrestrial Laser Scanning Data

Heinzel

Huber

2018

Remote Sensing

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Abstract:The present study introduces an advanced method for 3D segmentation of terrestrial laser scanning data into single tree clusters. It intentionally tackled difficult forest situations with dense and structured tree formations, which inventory practitioners are often faced with. The strongly interlocking tree crowns of different sizes and in different layers characterized the test conditions of close to nature forest plots. Volumetric 3D images of the plots were derived from the original point cloud data. A clustering method with automatically derived priors focused on the segmentation of these images by global optimization. Therefore, each image was segmented as a whole and partitioned into individual tree objects using a combination of state-of-the-art techniques. Multiple steps were combined in a workflow that included a morphological detection of the tree stems, the construction of a similarity graph from the image data, the computation of the eigenspectrum which was weighted with the tree stem priors and the final labelling of the transformed data points in a Markov Random Field framework. The detected trees were verified by number and position which allowed for comparison with other studies. Additionally, for a subset of the data, we provided a detailed verification of the three-dimensional extent of the complete trees. The detection rate by number and position was 97.40% for major trees with a stem diameter at breast height (DBH) ≥ 12 cm and 84.62% for regeneration trees with a DBH < 12 cm. The three-dimensional extent of the detected trees resulted in an average producer's accuracy of 93.66% and a user's accuracy of 94.06%. Overall, these numbers confirm the capacity of the method for accurate segmentation of strongly layered and understory trees. Future studies could test the method on wider areas with large scale data and different forest types in order to determine its general transferability.

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“…m e e argmin W C (4) It is proven that the minimum cut corresponds to the minimum cost function. Therefore, the segmentation result is controlled by the weights of edges.…”

Section: Isolating Tree Crown Points With the Hierarchical Minimum Cumentioning

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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Three-level frame and RD-schematic algorithm for automatic detection of individual trees from MLS point clouds

Cited by 25 publications

References 24 publications

From TLS to VLS: Biomass Estimation at Individual Tree Level

From TLS to VLS: Biomass Estimation at Individual Tree Level

Constrained Spectral Clustering of Individual Trees in Dense Forest Using Terrestrial Laser Scanning Data

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

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