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Extending morphological pattern segmentation to 3D voxels

Abstract

Context

This 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.

Objectives

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.

Methods

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.

Conclusions

The work helps to bring pattern metrics into the 3-dimensional world, particularly given the reliance on adjacency and connectivity assessments.

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Data availability

Not applicable.

Code availability

The code for achieving 3D morphological segmentation is being prepared for free and open distribution on the Comprehensive R Archive Network. The code is also available from the author of this short communication.

Notes

  1. morph3d is a complete software tool and will be available via CRAN (Comprehensive R Archive Network) for free and open distribution in the package morph. A copy of the code can also be obtained from the author.

References

  • Adler D, Murdoch D (2021) rgl: 3D visualization using OpenGL. Version 0.105.22 https://CRAN.R-project.org/package=rgl. Accessed 2 Nov 2021

  • Csárdi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Syst 1695(5):1–9

    Google Scholar 

  • Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Ind 8:691–703

    Article  Google Scholar 

  • Fahrig L (2017) Ecological responses to habitat fragmentation per se. Ann Rev Ecol Evol Syst 48:1–23. https://doi.org/10.1146/annurev-ecolsys-110316-022612

  • Frazier AE, Kedron P (2017) Landscape metrics: past progress and future directions. Curr Landsc Ecol Rep 2:63–72.

    Article  Google Scholar 

  • Guerra-Hernández J, Cosenza DN, Rodriguez LCE, Silva M, Tomé M, Díaz-Varela RA, González-Ferreiro E (2018) Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. Int J Remote Sens 39:5211–5235.

    Article  Google Scholar 

  • Kedron P, Zhao Y, Frazier AE (2019) Three dimensional (3D) spatial metrics for objects. Landscape Ecol 34:2123–2132.

    Article  Google Scholar 

  • Li HB, Wu JG (2004) Use and misuse of landscape indices. Landscape Ecol 19:389–399

    Article  Google Scholar 

  • Liu M, Hu Y-M, Li C-L (2017) Landscape metrics for three-dimensional urban building pattern recognition. Appl Geogr 87:66–72.

    CAS  Article  Google Scholar 

  • Neel MC, McGarigal K, Cushman SA (2004) Behavior of class-level landscape metrics across gradients of class aggregation and area. Landscape Ecol 19:435–455

    Article  Google Scholar 

  • Nowosad J, Stepinski TF (2021) Pattern-based identification and mapping of landscape types using multi-thematic data. Int J Geogr Inf Sci. https://doi.org/10.1080/13658816.2021.1893324

    Article  Google Scholar 

  • Petras V, Newcomb DJ, Mitasova H (2017) Generalized 3D fragmentation index derived from lisar point clouds. Open Geospat Data, Softw Stand 2:9. https://doi.org/10.1186/s40965-017-0021

  • Pflugmacher D, Cohen WB, Kennedy RE, Yang Z (2014) Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics. Remote Sens Environ 151:124–137.

    Article  Google Scholar 

  • Popescu SC, Zhao K (2008) A voxel-based lidar method for estimating crown base height for deciduous and pine trees. Remote Sens Environ 112:767–781

    Article  Google Scholar 

  • R Core Team (2021) R: a language and environment for statistical computing. Version 4.0.4. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 2 Nov 2021

  • Rahman MdS (2017) Basic graph theory. Springer International Publishing, Cham

    Book  Google Scholar 

  • Remmel TK (2018) An incremental and philosophically different approach to measuring raster patch porosity. Sustainability 10(10):3413. https://doi.org/10.3390/su10103413

  • Remmel TK (2020) Distributions of hyper-local configuration elements to characterize, compare, and assess landscape-level spatial patterns. Entropy 22:420.

    Article  Google Scholar 

  • Remmel TK, Csillag F (2003) When are two landscape pattern indices significantly different? J Geogr Syst 5:331–351

    Article  Google Scholar 

  • Remmel TK, Fortin M-J (2013) Categorical, class-focused map patterns: characterization and comparison. Landscape Ecol 28:1587–1599

    Article  Google Scholar 

  • Riitters KH, O’Neill RV, Hunsaker CT, Wickham JD, Yankee DH, Timmins SP, Jones KB, Jackson BL (1995) A factor analysis of landscape pattern and structure metrics. Landscape Ecol 10:23–39

    Article  Google Scholar 

  • Soille P, Vogt P (2009) Morphological segmentation of binary patterns. Pattern Recogn Lett 30:456–459.

    Article  Google Scholar 

  • Szpakowski D, Jensen J (2019) A review of the applications of remote sensing in fire ecology. Remote Sens 11:2638.

    Article  Google Scholar 

  • Uuemaa E, Antrop M, Roosaare J, Marja R (2009) Landscape metrics and indices: an overview of their use in landscape research. Living Rev Landsc Res 3:1–28

    Article  Google Scholar 

  • Vogt P, Riitters K (2017) GuidosToolbox: universal digital image object analysis. Eur J Remote Sens 50:352–361.

    Article  Google Scholar 

  • Vogt P, Riitters KH, Estreguil C, Kozak J, Wade TG, Wickham JD (2007) Mapping spatial patterns with morphological image processing. Landscape Ecol 22:171–177.

    Article  Google Scholar 

  • White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW (2017) A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens Environ 194:303–321.

    Article  Google Scholar 

  • Wickham H (2007) Reshaping data with the reshape package. J Stat Soft 21:1–20.

    Article  Google Scholar 

  • Wickham H (2019) Stringr: simple, consistent wrappers for common string operations. Version R package version 1.4.0URL https://CRAN.R-project.org/package=stringr. Accessed 2 Nov 2021

  • Wu B, Yu B, Yue W, Shu S, Tan W, Hu C, Huang Y, Wu J, Liu H (2013) A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data. Remote Sens 5:584–611

  • Ye H, Yang Z, Xu X (2020) Ecological corridors analysis based on MSPA and MCR model—a case study of the Tomur world natural heritage region. Sustainability 12:959.

    Article  Google Scholar 

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Acknowledgements

I acknowledge the valuable and constructive feedback from two anonymous reviewers whose advice and suggestions helped improve the clarity of this manuscript. I value discussions with Dr. Connie Ko who was an excellent sounding board for ideas. This work was funded through a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.

Funding

This work was funded by a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant (RGPIN-2021-03645) to the author.

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I conceived the idea of 3D morphological segmentation, developed the logic, wrote the code, and developed this short communication without external assistance.

Corresponding author

Correspondence to Tarmo K. Remmel.

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There are no conflicts of interest or competing interests.

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As the only author of this short communication, I provide consent for publication.

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Remmel, T.K. Extending morphological pattern segmentation to 3D voxels. Landsc Ecol 37, 373–380 (2022). https://doi.org/10.1007/s10980-021-01384-7

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  • DOI: https://doi.org/10.1007/s10980-021-01384-7

Keywords

  • Morphology
  • Landscape pattern
  • 3D segmentation
  • Volumetric data
  • Landscape structure