Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests

Wu, Bin; Yu, Bo; Wu, Qiusheng; Huang, Yan; Chen, Zuoqi; Wu, Jianping

doi:10.1016/j.jag.2016.06.003

Cited by 77 publications

(61 citation statements)

References 52 publications

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“…It is computed as the difference between the digital surface model (DSM) and the digital elevation model (DEM). In our study, the DSM was generated from the airborne LiDAR point clouds by using the linear triangulated irregular network (TIN) interpolation method [23][24][25][26]. The DEM was then interpolated from the ground points which was classified using a progressive morphological filter [55].…”

Section: Building Contours Generationmentioning

confidence: 99%

“…The selection of cell size is also a key issue in the generation of DSM and DEM. According to [23,56], the cell size (c) can be determined by the following formula:…”

Section: Building Contours Generationmentioning

confidence: 99%

“…The second step is contour generation. Before creating vector contours, a Gaussian smoothing filter was applied to the nDSM to reduce data noise [23]:…”

Section: Building Contours Generationmentioning

confidence: 99%

“…Previous studies [23,53,58] showed that such contour clusters could be further represented by graph-based localized contour trees. Detailed information about the conceptual framework of the localized contour tree method can be found in [23,53].…”

Section: Graph-based Localized Contour Tree Constructionmentioning

confidence: 99%

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A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

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Section: Building Contours Generationmentioning

confidence: 99%

“…The selection of cell size is also a key issue in the generation of DSM and DEM. According to [23,56], the cell size (c) can be determined by the following formula:…”

Section: Building Contours Generationmentioning

confidence: 99%

“…The second step is contour generation. Before creating vector contours, a Gaussian smoothing filter was applied to the nDSM to reduce data noise [23]:…”

Section: Building Contours Generationmentioning

confidence: 99%

Section: Graph-based Localized Contour Tree Constructionmentioning

confidence: 99%

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A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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“…Moreover, ALS point clouds have other benefits such as no effects of relief displacement, penetration of vegetation, and insensitivity to lighting conditions [1]. Therefore, ALS technique has been effectively used for ground point detection [3][4][5][6][7], topographic mapping [8], 3D city modelling [9][10][11][12][13], object recognition [14][15][16], solar energy estimation [17], etc.…”

Section: Introductionmentioning

confidence: 99%

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

Lin

Zhang

2017

Remote Sensing

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This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.

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Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches

et al. 2019

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Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests

Cited by 77 publications

References 52 publications

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches

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