Generalized 3D fragmentation index derived from lidar point clouds

Abstract 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 ve...

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Main Authors: Vaclav Petras, Douglas J. Newcomb, Helena Mitasova
Format: Article
Language:English
Published: SpringerOpen 2017-04-01
Series:Open Geospatial Data, Software and Standards
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40965-017-0021-8
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spelling doaj-1625dce24ba44ac3b1c610b6fa7552622020-11-25T00:42:41ZengSpringerOpenOpen Geospatial Data, Software and Standards2363-75012017-04-012111410.1186/s40965-017-0021-8Generalized 3D fragmentation index derived from lidar point cloudsVaclav Petras0Douglas J. Newcomb1Helena Mitasova2North Carolina State University, Marine, Earth, and Atmospheric SciencesU.S. Fish and Wildlife ServiceNorth Carolina State University, Marine, Earth, and Atmospheric SciencesAbstract 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.http://link.springer.com/article/10.1186/s40965-017-0021-83D rasterVoxel modelSpatial patternLidarRaster algebraSpatial indices
collection DOAJ
language English
format Article
sources DOAJ
author Vaclav Petras
Douglas J. Newcomb
Helena Mitasova
spellingShingle Vaclav Petras
Douglas J. Newcomb
Helena Mitasova
Generalized 3D fragmentation index derived from lidar point clouds
Open Geospatial Data, Software and Standards
3D raster
Voxel model
Spatial pattern
Lidar
Raster algebra
Spatial indices
author_facet Vaclav Petras
Douglas J. Newcomb
Helena Mitasova
author_sort Vaclav Petras
title Generalized 3D fragmentation index derived from lidar point clouds
title_short Generalized 3D fragmentation index derived from lidar point clouds
title_full Generalized 3D fragmentation index derived from lidar point clouds
title_fullStr Generalized 3D fragmentation index derived from lidar point clouds
title_full_unstemmed Generalized 3D fragmentation index derived from lidar point clouds
title_sort generalized 3d fragmentation index derived from lidar point clouds
publisher SpringerOpen
series Open Geospatial Data, Software and Standards
issn 2363-7501
publishDate 2017-04-01
description Abstract 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.
topic 3D raster
Voxel model
Spatial pattern
Lidar
Raster algebra
Spatial indices
url http://link.springer.com/article/10.1186/s40965-017-0021-8
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