Skip to main content
SpringerLink
Log in

Three dimensional (3D) spatial metrics for objects

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

The increasing availability of lidar data and structure from motion processing techniques is moving pattern metric research toward the development of three-dimensional (3D) analysis. There is a need to develop spatial pattern metrics that leverage 3D datasets, such as those derived from lidar or unmanned aircraft systems technology, that are meaningful and interpretable across landscape contexts.

Objectives

We introduce a suite of 3D spatial pattern metrics that can be computed on gradient surfaces such as digital surface models, but are rooted in traditional, patch-based landscape metrics that are familiar and interpretable across landscape contexts.

Methods

We compute a suite of 3D metrics and demonstrate their use by analyzing a landscape pattern in the built environment of New Orleans in 2002 and 2008—pre- and post- Hurricane Katrina. Lidar data are used to segment individual buildings and calculate 3D patterns at the equivalent of the patch-, class- and landscape-levels for traditional landscape metrics.

Results

3D spatial metrics can characterize landscape patterns at multiple spatial scales. These metrics capture aspect of pattern that traditional patch-mosaic and surface metrics cannot.

Conclusions

Future research can build from these measures to develop other measures of 3D spatial patterns that are applicable for different ecological contexts. Continuing advances in full waveform lidar may contribute to the development of more complex metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Cao L, Coops NC, Hermosilla T, Innes J, Dai J, She G (2014) Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests. Remote Sens 6(8):7110–7135

    Article  Google Scholar 

  • Chen A, Xu B, Devereux B (2013) Urban landscape pattern analysis based on 3D landscape models. Appl Geogr 55:82–91

    Article  Google Scholar 

  • Cheuk ML, Yuan M (2009) Assessing spatial uncertainty of lidar-derived building model. Photogramm Eng Remote Sens 75(3):257–269

    Article  Google Scholar 

  • Cushman SA, Gutzweiler K, Evans JS, McGarigal K (2010) The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 83–108

    Chapter  Google Scholar 

  • Davis AY, Jung J, Pijanowski BC, Minor ES (2016) Combined vegetation volume and “greenness” affect urban air temperature. Appl Geogr 71:106–114

    Article  Google Scholar 

  • Dong P (2009) Characterization of individual tree crowns using three-dimensional shape signatures derived from LiDAR data. Int J Remote Sens 30(24):6621–6628

    Article  Google Scholar 

  • Dong P, Guo H (2012) A framework for automated assessment of post-earthquake building damage using geospatial data. Int J Remote Sens 33(1):81–100

    Article  Google Scholar 

  • Drake JB, Knox RG, Dubayah RO, Clark DB, Condit R, Blair JB, Hofton M (2003) Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob Ecol Biogeogr 12(2):147–159

    Article  Google Scholar 

  • Forman RT (1995) Some general principles of landscape and regional ecology. Landscape Ecol 10(3):33–142

    Article  Google Scholar 

  • Frazier AE, Bagchi-Sen S, Knight J (2013) The spatio-temporal impacts of demolition land use policy and crime in a shrinking city. Appl Geogr 1(41):55–64

    Article  Google Scholar 

  • Frazier AE, Kedron P (2017) Comparing forest fragmentation in Eastern US forests using patch-mosaic and gradient surface models. Ecol Inf 41:108–115

    Article  Google Scholar 

  • Frazier AE, Wang L (2011) Characterizing spatial patterns of invasive species using sub-pixel classifications. Remote Sens Environ 115(8):1997–2007

    Article  Google Scholar 

  • Frazier AE, Wang L (2013) Modeling landscape structure response across a gradient of land cover intensity. Landscape Ecol 28(2):233–246

    Article  Google Scholar 

  • Gardner RH (1999) RULE: a program for the generation of random maps and the analysis of spatial patterns. In: Klopatek JM, Gardner RH (eds) Landscape ecological analysis: issues and applications. Springer, New York, pp 280–303

    Chapter  Google Scholar 

  • Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760

    Article  CAS  Google Scholar 

  • He M, Zhu Q, Du Z, Hu H, Ding Y, Chen M (2016) A 3D shape descriptor based on contour clusters for damaged roof detection using airborne LiDAR point clouds. Remote Sens 8(3):189

    Article  Google Scholar 

  • Kedron P, Frazier AE, Ovando-Montejo GA, Wang J (2018) Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales. Landscape Ecol 33(9):1489–1504

    Article  Google Scholar 

  • Koziatek O, Dragićević S, Li S (2016) Geospatial modelling approach for 3D urban densification developments. Int Arch Photogramm Remote Sens Spatial Inf Sci 41:349–352

    Article  Google Scholar 

  • Kupfer JA (2012) Landscape ecology and biogeography: rethinking landscape metrics in a post-FRAGSTATS landscape. Prog Phys Geogr 3:400–420

    Article  Google Scholar 

  • Lausch A, Blaschke T, Haase D, Herzog F, Syrbe RU, Tischendorf L, Walz U (2015) Understanding and quantifying landscape structure—a review on relevant process characteristics, data models and landscape metrics. Ecol Model 295:31–41

    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

    Article  CAS  Google Scholar 

  • Loss SR, Will T, Loss SS, Marra PP (2014) Bird–building collisions in the United States: estimates of annual mortality and species vulnerability. Condor 116(1):8–23

    Article  Google Scholar 

  • McGarigal K, Cushman S (2005) The gradient concept of landscape structure. In: Wiens JA, Moss MR (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge, pp 112–119

    Chapter  Google Scholar 

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: Spatial Pattern Analysis Program for categorical and continuous maps. http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 10 Oct 2018

  • McGarigal K, Tagil S, Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24(3):433–450

    Article  Google Scholar 

  • Petras V, Newcomb DJ, Mitasova H (2017) Generalized 3D fragmentation index derived from lidar point clouds. Open Geospatial Data Softw Stand 2(1):9

    Article  Google Scholar 

  • Ritters K. (2018, this issue) Pattern metrics for a transdisciplinary landscape ecology. Landscape Ecol. https://doi.org/10.1007/s10980-018-0755-4

    Article  Google Scholar 

  • Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93(12):1879–1900

    Article  Google Scholar 

  • Tang S, Dong P, Buckles BP (2013) Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method. Int J Remote Sens 34(4):1373–1385

    Article  CAS  Google Scholar 

  • Turner MG (2005) Landscape ecology: what is the state of the science? Annu Rev Ecol Evol Syst 36:319–344

    Article  Google Scholar 

  • United States Census Bureau (2011) American housing survey 2009 New Orleans metropolitan tables. https://www.census.gov/content/dam/Census/programs-surveys/ahs/data/2011/h150-11.pdf. Accessed 1 Oct 2018

  • Walker CM, Flynn KC, Ovando-Montejo GA, Ellis EA, Frazier AE (2017) Does demolition improve biodiversity? Linking urban green space and socioeconomic characteristics to avian richness in a shrinking city. Urban Ecosyst 20(6):1191–1202

    Article  Google Scholar 

  • Wu J (2008) Toward a landscape ecology of cities: beyond buildings, trees, and urban forests. In: Ecology, planning, and management of urban forests. Springer, New York, pp 10–28

  • Wu Q, Guo F, Li H, Kang J (2017) Measuring landscape pattern in three dimensional space. Landsc Urban Plan 167:49–59

    Article  Google Scholar 

  • Xian G, Homer C (2010) Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sens Environ 114(8):1676–1686

    Article  Google Scholar 

  • Xiao Y, Wang C, Li J, Zhang W, Xi X, Wang C, Dong P (2015) Building segmentation and modeling from airborne LiDAR data. Int J Digit Earth 8(9):694–709

    Article  Google Scholar 

  • Zhao Y, Ovando-Montejo GA, Frazier AE, Mathews AJ, Flynn KC, Ellis EA (2017) Estimating work and home population using lidar-derived building volumes. Int J Remote Sens 38(4):1180–1196

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded by a grant to A. Frazier and P. Kedron from the U.S. National Science Foundation (#1561021). Some data used in the analysis were provided by the Army Geospatial Data Center through a NASA (National Aeronautics and Space Administration) EPSCoR (Experimental Program for Stimulation of Competitive Research) Grant #NNX15AK42A.

Author information

Authors and Affiliations

  1. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

    Peter Kedron & Amy E. Frazier

  2. Department of Environmental Studies, University of Illinois-Springfield, Springfield, IL, USA

    Yun Zhao

Authors
  1. Peter Kedron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amy E. Frazier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Kedron.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 26 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kedron, P., Zhao, Y. & Frazier, A.E. Three dimensional (3D) spatial metrics for objects. Landscape Ecol 34, 2123–2132 (2019). https://doi.org/10.1007/s10980-019-00861-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-019-00861-4

Keywords

Search

Navigation