Spatial indices for measuring three-dimensional patterns in a voxel-based space

Jjumba, Anthony; Dragićević, Suzana

doi:10.1007/s10109-016-0231-0

Cited by 10 publications

(8 citation statements)

References 61 publications

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“…The 3D version of the 2D forest fragmentation index, similarly to the 3D Moran's I presented by Jjumba and Dragicevic [38], shows that 2D indices can be transfered to 3D while keeping similar semantics as the 2D version. Additionally, we can observe that 3D raster algebra and 3D raster tools in general can be used for analysis in the same manner as the 2D equivalents.…”

Section: Processing Of 3d Rastersmentioning

confidence: 61%

“…Metrics for data represented as 3D rasters were presented in the past for plot and artificial data [5,38] and specific applications [12,20,43]. We present a general fragmentation index based on its 2D version [26] and apply it to a sample study area.…”

Section: Discussionmentioning

confidence: 99%

“…A value of a 3D cell (voxel) is determined by presence, count, or properties of the points which fall into a 3D space occupied by a given cell [12,21,38,42]. We use point heights relative to the ground surface.…”

Section: Vegetation Structure Reconstructionmentioning

confidence: 99%

“…These indices were implemented in various software packages including SPAN software [32], the r.le software package coupled with GRASS GIS [33] and later replaced by different set of modules for GRASS GIS called r.li [34], the well-known FRAGSTATS software package for computing spatial indices [35], the GuidosToolbox software package for the assessment of pattern, connectivity, and fragmentation [36], and the SDMTools R package for species distribution modeling [37]. Jjumba and Dragicevic [38] presented a set of indices for the basic analysis of data represented as 3D rasters. Parrot et al [39] defined 3D metrics for the analysis of spatio-temporal data in ecology.…”

Section: Introductionmentioning

confidence: 99%

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Generalized 3D fragmentation index derived from lidar point clouds

Petras

Newcomb

Mitášová

2017

Open geospatial data, softw. stand.

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Background: Point clouds with increased point densities create new opportunities for analyzing landscape structure in 3D space. Taking advantage of these dense point clouds we have extended a 2D forest fragmentation index developed for regional scale analyses into a 3D index for analyzing vegetation structure at a much finer scale. Methods: Based on the presence or absence of points in a 3D raster (voxel model) the 3D fragmentation index is used to evaluate the configuration of a cell's 3D neighborhood resulting in fragmentation classes such as interior, edge, or patch. In order to incorporate 3D fragmentation into subsequent conventional 2D analyses, we developed a transformation of this 3D fragmentation index into a series of 2D rasters based on index classes. Results: We applied this method to a point cloud obtained by airborne lidar capturing a suburban area with mixed forest cover. All processing and visualization was done in GRASS GIS, an open source, geospatial processing and remote sensing tool. The newly developed code is also publicly available and open source. The entire processing chain is available and executable through Docker for maximum reproducibility. Conclusions: We demonstrated that this proposed index can be used to describe different types of vegetation structure making it a promising tool for remote sensing and landscape ecology. Finally, we suggest that processing point clouds using 3D raster methods including 3D raster algebra is as straightforward as using well-established 2D raster and image processing methods.

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Section: Processing Of 3d Rastersmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Vegetation Structure Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalized 3D fragmentation index derived from lidar point clouds

Petras

Newcomb

Mitášová

2017

Open geospatial data, softw. stand.

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“…Despite the advantages of GVS, GVS-based algorithms have been rarely used for 3D building detection. The existing voxel-based algorithms are designed based on binary (a voxel with value 0 or 1) or density (a voxel has a value corresponding to the number of LIDAR points within the voxel) voxel representation and have been used in applications such as spatial indexes [34–35], forest structure [36–38], biomass [39], and topographic and geographic representations [40–41]. The advantages of the proposed VSBD algorithm are that it is designed based on a GVS and, as a 3D building detection algorithm, it makes better use of 3D connectivity among voxels and intensity information.…”

Section: Introductionmentioning

confidence: 99%

Voxel segmentation-based 3D building detection algorithm for airborne LIDAR data

Wang

et al. 2018

PLoS ONE

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Among traditional Light Detection And Ranging (LIDAR) data representations such as raster grid, triangulated irregular network, point clouds and octree, the explicit 3D nature of voxel-based representation makes it a promising alternative. Despite the benefit of voxel-based representation, voxel-based algorithms have rarely been used for building detection. In this paper, a voxel segmentation-based 3D building detection algorithm is developed for separating building and nonbuilding voxels. The proposed algorithm first voxelizes the LIDAR point cloud into a grayscale voxel structure in which the grayscale of the voxel corresponds to the quantized mean intensity of the LIDAR points within the voxel. The voxelized dataset is segmented into multiple 3D-connected regions depending on the connectivity and grayscale similarity among voxels. The 3D-connected regions corresponding to the building roof and facade are detected sequentially according to characteristics such as their area, density, elevation difference and location. The obtained results for the detected buildings are evaluated by the LIDAR data provided by working group III/4 of ISPRS, which demonstrate a high rate of success. Average completeness, correctness, quality, and kappa coefficient indexes values of 90.0%, 96.0%, 88.1% and 88.7%, respectively, are obtained for buildings.

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Urban morphological regionalization based on 3D building blocks—A case in the central area of Chengdu, China

Yang

Zhang

et al. 2022

Computers, Environment and Urban Systems

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Spatial indices for measuring three-dimensional patterns in a voxel-based space

Cited by 10 publications

References 61 publications

Generalized 3D fragmentation index derived from lidar point clouds

Generalized 3D fragmentation index derived from lidar point clouds

Voxel segmentation-based 3D building detection algorithm for airborne LIDAR data

Urban morphological regionalization based on 3D building blocks—A case in the central area of Chengdu, China

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