Remmel, Tarmo K2023-03-102023-03-102022-01Remmel, T.K. 2022. Extending morphological pattern analysis to 3D voxels. Landscape Ecology 37(2):373-380.10.1007/s10980-021-01384-7http://hdl.handle.net/10315/40904This short communication introduces the logic, demonstrates its use, and identifies the availability of a new tool that extends the traditional 2D morphological segmentation of binary raster data into the 3-dimensional realm of voxels. A combination of 3-dimensional array data and network graph theory are implemented to facilitate the logical parsing of identified 3-dimensional features into their mutually exclusive constituent morphological classes. All processing is performed in the R environment, providing the ability for anyone to perform the demonstrated analyses on their own data. The only input requirement is a binary (1 = feature of interest, 0 otherwise) 3-dimensional array, where each voxel of interest is then classified into classes called outside, mass, skin, crumb, antenna, circuit, bond, and void that correspond their 2-dimensional equivalents of background, core, edge, islet, branch, loop, bridge, and perforation. An additional class called the void-volume identifies voxels belonging to the empty space within the object of interest. The work helps to bring pattern metrics into the 3-dimensional world, particularly given the reliance on adjacency and connectivity assessmentsenhttps://www.springernature.com/gp/open-research/policies/journal-policies "Where articles are published via the subscription route, Springer Nature permits authors to self-archive the accepted manuscript (AM), on their own personal website and/or in their funder or institutional repositories, for public release after an embargo period (see the table below). The accepted manuscript is the version post-peer review, but prior to copy-editing and typesetting, and does not reflect post-acceptance improvements, or any corrections. "Attribution-NonCommercial-ShareAlike 4.0 InternationalMorphologyLandscape pattern3D segmentationVolumetric dataLandscape structureResearch Paperhttps://www.springer.com/journal/10980https://www.springer.com/ushttps://link.springer.com/epdf/10.1007/s10980-021-01384-7?sharing_token=OHF_mmfn6K0mN0f_WVxWNfe4RwlQNchNByi7wbcMAY7dZUb7m4c7XZKm28LlVpRRy95kGv2vCYiuKPRiw5jpfGfJkQ1hRZqiVF8vb1-FGBKvSLt6AohNh6f4HbRoQaG0TMuqgr6FMMeVI94U7RlZ53vn8N99HgWmSi2QMLUEVfA%3D