A novel computational paradigm for creating a Triangular Irregular Network (TIN) from LiDAR data

Ali, Tarig; Mehrabian, Ali

doi:10.1016/j.na.2008.11.081

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…Compared to trees and sloping roofs, the surfaces of grass and the road are relatively flat, and a large number of points were deleted as local depressions and few points were defined as control points to create their flat surface models. The result is consistent with common sense that more points are needed in rugged areas and fewer points in relatively flat areas [31].…”

Section: Discussionsupporting

confidence: 89%

“…Specifically, Yang et al [30] constructed a multiresolution TIN model for visualization. Ali and Mehrabian [31] presented a computational framework to create a TIN for a relatively flat area, based on the graph-theoretical duality between Delaunay triangulation (DT) and Voronoi diagrams. Uysal and Polat [32] filtered out non-ground points from raw point clouds using a TIN algorithm to create digital elevation models in both urban and rural areas.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

Liu

2019

Forests

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LiDAR (Light Detection and Ranging)-based individual tree crown reconstruction is a challenge task due to the variable canopy morphologies and the penetrating properties of LiDAR to tree crown surfaces. Traditional methods, including LiDAR-derived rasterization, low-pass filtering smooth algorithm, and original triangular irregular network (TIN) model, have difficulties in balancing morphological accuracy and model smoothness. To address this issue, a scene-based TIN was generated with three steps based on the local scene principle. First, local Delaunay triangles were formed through connecting neighboring point sets. Second, key control points within each local Delaunay triangle, including steeple, inverted tip, ridge, saddle, and horseshoe shape control points, were extracted by analyzing multiple local scenes. These key points were derived to determine the fluctuations of forest canopies. Third, the scene-based TIN model was generated using the control points as nodes. Visual analysis indicates the new model can accurately reconstruct different canopy shapes with a relatively smooth surface, and statistical analysis of individual trees confirms that the overall error of the new model is smaller than others. Especially, the scene-based TIN derived raster reduced the average error to 0.18 m, with a standard deviation of 0.41, while the average errors of LiDAR-derived raster, low-pass filtered smooth raster, and original TIN derived raster have average errors of 0.96, 2.05, and 1.00 m, respectively. The local scene-based control point extraction also reduces data storage due to the elimination of redundant points, and furthermore the different point densities on different objects are beneficial for canopy segmentation.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

Liu

2019

Forests

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“…TINs are digital data structures that are widely used to represent surfaces in geographic information system (GIS) and photogrammetry studies [18,19]. The first TIN model was proposed in the early 1970s as a simple method for building a surface using a set of irregularly spaced points.…”

Section: Generation Of the Triangular Irregular Network Modelmentioning

confidence: 99%

A Novel Absolute Orientation Method Using Local Similarities Representation

Yan

Wan

Sun

et al. 2016

IJGI

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Abstract:Absolute orientation is an important method in the field of photogrammetry. The technique is used to transform points between a local coordinate reference system and a global (geodetic) reference system. The classical transformation method uses a single set of similarity transformation parameters. However, the root mean square error (RMSE) of the classical method is large, especially for large-scale aerial photogrammetry analyses in which the points used are triangulated through free-net bundle adjustment. To improve the transformation accuracy, this study proposes a novel absolute orientation method in which the transformation uses various sets of local similarities. A Triangular Irregular Network (TIN) model is applied to divide the Ground Control Points (GCPs) into numerous triangles. Local similarities can then be computed using the three vertices of each triangle. These local similarities are combined to formulate the new transformation based on a weighting function. Both simulated and real data sets were used to assess the accuracy of the proposed method. The proposed method yields significantly improved plane and z-direction transformed point accuracies compared with the classical method. On a real data set with a mapping scale of 1:30,000 for a 53 km × 35 km study area, the plane and z RMSEs can be reduced from 1.2 m and 12.4 m to 0.4 m and 3.2 m, respectively.

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“…Capitalizing on new methods and techniques for computational analysis of lidar is imperative in producing the quick and reliable results for coastal evaluations (Ali and Mehrabian, 2009;Hart and Knight, 2009;Kempeneers et al, 2009;Leigh, Kidner, and Thomas, 2009;Palaseanu-Lovejoy et al, 2009;Yates et al, 2008). For episodic events, such as Hurricane Ike, quick and reliable analysis of the situation is critical in making decisions that can result in life-saving procedures.…”

Section: Lessons Learned: Ground-truthing Of Sedimentary and Vegetatimentioning

confidence: 99%

The Use of Terrestrial Laser Scanning (TLS) in Dune Ecosystems: The Lessons Learned

Feagin¹,

Williams²,

Popescu³

et al. 2014

Journal of Coastal Research

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I www.JCRonline.oi Feagin, R.A.; Williams, A.M.; Popescu, S.; Stukey, J., and Washington-Allen, R.A., 2014. The use of terrestrial laser scanning (TLS) in dune ecosystems: the lessons learned. Journal of Coastal Research, 30(1), 111-119. Coconut Creek (Florida), ISSN 0749-0208.This paper presents a methodology for using terrestrial laser scanning (TLS) to quantify sand dune geomorphology. As an example of the use of TLS, we present methods that were used to investigate changes in sediment and vegetation volumes after Hurricane Ike. We collected TLS data within a 100 m X 100 m plot on the East Matagorda Peninsula, Texas, from early September 2008 (before landfall) to early October 2009 (a year after landfall). Terrestrial laser scanning-collected laser point clouds were then interpolated into several grid sizes. From several interpolated grid sizes, 0.50 m X 0.50 m grids were determined best for analysis as they were able to compromise two competing resolutionrelated issues: gaps caused hy vegetation shadows and the natural contours of the dune. We outline several additional lessons to aid coastal researchers in strengthening their own future work: the use of reference survey stakes in an unstahle environment, the development of a novel method to test for errors in point cloud registration among multiple dates, how hest to interpret sediment and vegetation change analysis as derived from interpolated grids, and suggestions for incorporating mass-based sedimentary and biomass-based vegetation field studies within the volumetric context of TLS analysis.

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A novel computational paradigm for creating a Triangular Irregular Network (TIN) from LiDAR data

Cited by 17 publications

References 4 publications

Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

A Novel Absolute Orientation Method Using Local Similarities Representation

The Use of Terrestrial Laser Scanning (TLS) in Dune Ecosystems: The Lessons Learned

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