Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method

Tang, Shijun; Dong, Pinliang; Buckles, Bill P.

doi:10.1080/01431161.2012.720046

Cited by 40 publications

(23 citation statements)

References 30 publications

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“…This has been done with 2D k-means clustering in each slice and subsequent merging of clusters that overlap along the vertical direction [30] and by deriving the horizontal contours of the trees from a region-based level set method and constructing a 3D canopy surface by stacking the contours on top of each other [31]. Dividing the point cloud into slices is an intuitive approach and can be implemented in a computationally efficient way, but it does not make optimal use of the geometric information in the point cloud since it simplifies the data before delineating the tree crowns in the vertical direction.…”

Section: D Methodsmentioning

confidence: 99%

“…The delineated tree crowns in the top-most canopy layer may be used as initial values when delineating tree crowns and shrubs from the point cloud or the ALS returns assigned to the tree crowns may be excluded to enable analysis of the canopy structure below. Another common approach is to divide the point cloud into horizontal slices of a certain vertical thickness, analyze each slice separately to identify tree crowns in each slice, and aggregate the delineated tree crown slices to define three-dimensional tree crowns [30,31].…”

Section: D Methodsmentioning

confidence: 99%

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Individual Tree Crown Methods for 3D Data from Remote Sensing

Lindberg

Holmgren

2017

Curr Forestry Rep

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Purpose of Review The rapid development of remote sensing technology has made dense 3D data available from airborne laser scanning and recently also photogrammetric point clouds. This paper reviews methods for extraction of individual trees from 3D data and their applications in forestry and ecology. Recent Findings Methods for analysis of 3D data at tree level have been developed since the turn of the century. The first algorithms were based on 2D surface models of the upper contours of tree crowns. These methods are robust and provide information about the trees in the top-most canopy. There are also methods that use the complete 3D data. However, development of these 3D methods is still needed to include use of geometric properties. To detect a large fraction of the tallest trees, a surface model method generally gives the best results, but detection of smaller trees below the top-most canopy requires methods utilizing the whole point cloud. Several new sensors are now available with capability to describe the upper part of the canopy, which can be used to frequently update vegetation maps. Highly sensitive laser photo detectors have become available for civilian applications, which will enable acquisition of high-resolution 3D laser data for large areas to much lower costs. Summary Methods for ITC delineation from 3D data provide information about a large fraction of the trees, but there is still a challenge to make optimal use of the information from the whole point cloud. Newly developed sensors might make ITC methods cheaper and feasible for large areas.

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Section: D Methodsmentioning

confidence: 99%

Section: D Methodsmentioning

confidence: 99%

Individual Tree Crown Methods for 3D Data from Remote Sensing

Lindberg

Holmgren

2017

Curr Forestry Rep

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“…While there are many 3D delineation methods proposed in the literature, they are not all adequately validated as important accuracy measures such as omission and commission errors, or errors are not reported [e.g. Gupta et al, 2010;Tang et al, 2013]. When an ITC delineation method fail to detect individual trees present, it leads to omission errors.…”

Section: Introductionmentioning

confidence: 99%

“…Other 3D delineation methods first generated a preliminary watershed segmentation of the CHM to define tree segments and afterwards separated trees within each segment by normalized cut segmentation [Shi and Malik, 2000] on the ALS point cloud [Reitberger et al, 2009], or by a trough-finding algorithm on the ALS height histogram [Duncanson et al, 2014]. Some delineation methods first delineated tree crowns on the 2D horizontal projection images at different height levels, and then the 'tree' segments delineated from various layers were combined to form 3D tree crowns [Wang et al, 2008;Tang et al, 2013]. Some 3D methods are very particular in their approach [Li et al, 2012;Lähivaara et al, 2014;Lu et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns

Kandare

Ørka

Chan

et al. 2016

European Journal of Remote Sensing

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This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical merging based on overlapping among clusters. On an Alpine forest dataset, we analysed the effects of different forest structures and point cloud densities on tree crown delineation. Forest structure affects mainly the omission error, which eases with homogeneous tree spacing and sizes, while on the commission error forest structure has only slight effect. Delineation accuracy increases with higher point densities where MannWhitney-Wilcoxon test shows that accuracy differences between thinned data and original data are statistically significant.

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“…The dots and lines indicate the mean and standard deviation, respectively, within the area. Harvesting intensity 0 corresponds to the initial canopy 3D clustering followed by convex polytope reconstruction (Gupta et al 2010), stacking horizontal slices of the height values (Tang et al 2013), or voxel-based techniques (Schneider et al 2014; see also Koch et al 2014). All these methods involve certain parameters to be set by the operator, and the obtained results depend on these.…”

Section: Discussionmentioning

confidence: 99%

Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

Ruotsalainen

2017

Annals of Forest Science

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& Key messageWe present a data-driven technique to visualize forest landscapes and simulate their future development according to alternative management scenarios. Gentle harvesting intensities were preferred for maintaining scenic values in a test of eliciting public's preferences based on the simulated landscapes. & Context Visualizations of future forest landscapes according to alternative management scenarios are useful for eliciting stakeholders' preferences on the alternatives. However, conventional computer visualizations require laborious tree-wise measurements or simulators to generate these observations. & Aims We describe and evaluate an alternative approach, in which the visualization is based on reconstructing forest canopy from sparse density, leaf-off airborne laser scanning data. & Methods Computational geometry was employed to generate filtrations, i.e., ordered sets of simplices belonging to the three-dimensional triangulations of the point data. An appropriate degree of filtering was determined by analyzing the topological persistence of the filtrations. The topology was further utilized to simulate changes to canopy biomass, resembling harvests with varying retention levels. Relative priorities of recreational and scenic values of the harvests were estimated based on pairwise comparisons and analytic hierarchy process (AHP). & Results The canopy elements were co-located with the tree stems measured in the field, and the visualizations derived from the entire landscape showed reasonably realistic, despite a low numerical correspondence with plot-level forest attributes. The potential and limitations to improve the proposed parameterization are discussed. & Conclusion Although the criteria to evaluate the landscape visualization and simulation models were not conclusive, the results suggest that forest scenes may be feasibly reconstructed based on data already covering broad areas and readily available for practical applications.

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Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method

Cited by 40 publications

References 30 publications

Individual Tree Crown Methods for 3D Data from Remote Sensing

Individual Tree Crown Methods for 3D Data from Remote Sensing

Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns

Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

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