Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Wang, Di; Hollaus, Markus; Puttonen, Eetu; Pfeifer, Norbert

doi:10.3390/rs8120974

Cited by 49 publications

(38 citation statements)

References 54 publications

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“…The acquired high‐density point clouds allow very precise measurements of tree structures in a non‐destructive way. These measurements include forest inventory (Liang et al, ) leaf angle distribution (Vicari, Pisek, & Disney, ), structural parameters (Trochta, Krůček, Vrška, & Král, ; Wang, Hollaus, Puttonen, & Pfeifer, ), above‐ground volume and biomass (AGB) (Calders et al, ; Gonzalez de Tanago et al, ; Momo Takoudjou et al, ). Moreover, allometric equations can be non‐destructively performed with data derived from TLS (Lau et al, ; Momo Takoudjou et al, ).…”

Section: Introductionmentioning

confidence: 99%

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

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Section: Introductionmentioning

confidence: 99%

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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“…If the curvature is determined in one point, the direction for the principal curvature with a value of 0 is the direction of generators, while the other principal curvature is the inverse of the radius. Tree reconstruction involves elongated, but not straight, objects (Thies et al, 2004, Raumonen et al, 2013, Wang et al, 2016a. These approaches often use the idea of following the form by advancing and adapting a base model to the given point cloud.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

Automated Reconstruction of Historic Roof Structures From Point Clouds – Development and Examples

Pöchtrager

Styhler-Aydın

Döring-Williams

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The analysis of historic roof constructions is an important task for planning the adaptive reuse of buildings or for maintenance and restoration issues. Current approaches to modeling roof constructions consist of several consecutive operations that need to be done manually or using semi-automatic routines. To increase efficiency and allow the focus to be on analysis rather than on data processing, a set of methods was developed for the fully automated analysis of the roof constructions, including integration of architectural and structural modeling. Terrestrial laser scanning permits high-detail surveying of large-scale structures within a short time. Whereas 3-D laser scan data consist of millions of single points on the object surface, we need a geometric description of structural elements in order to obtain a structural model consisting of beam axis and connections. Preliminary results showed that the developed methods work well for beams in flawless condition with a quadratic cross section and no bending. Deformations or damages such as cracks and cuts on the wooden beams can lead to incomplete representations in the model. Overall, a high degree of automation was achieved.

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“…Sun et al [27] obtained estimates of tree DBH with error less than 5% using the convex hull algorithm in a pure Chinese fir plantation. Some studies also used a combination of the above algorithms to estimate DBH from trees of irregular cross section shape [25,29,30].…”

Section: Introductionmentioning

confidence: 99%

“…There were many DBH estimation algorithms used in these studies, including the Hough-transform [15][16][17], linear least square (algebraic) circle fitting [2,18,19], nonlinear least square (geometric) circle fitting [15,[19][20][21], cylinder fitting [10,[22][23][24][25], Random Sample Consensus (RANSAC) algorithm [26], and the convex hull algorithm [27]. Heinzel et al [17] extracted the single stem cross section at breast height, then used the Hough-transform to fit a circle from the extracted section.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

et al. 2018

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Abstract:The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation.

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Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Cited by 49 publications

References 54 publications

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

Automated Reconstruction of Historic Roof Structures From Point Clouds – Development and Examples

Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

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