Skip to main content

Advertisement

Log in

Surface metrics: an alternative to patch metrics for the quantification of landscape structure

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

Abstract

Modern landscape ecology is based on the patch mosaic paradigm, in which landscapes are conceptualized and analyzed as mosaics of discrete patches. While this model has been widely successful, there are many situations where it is more meaningful to model landscape structure based on continuous rather than discrete spatial heterogeneity. The growing field of surface metrology offers a variety of surface metrics for quantifying landscape gradients, yet these metrics are largely unknown and/or unused by landscape ecologists. In this paper, we describe a suite of surface metrics with potential for landscape ecological application. We assessed the redundancy among metrics and sought to find groups of similarly behaved metrics by examining metric performance across 264 sample landscapes in western Turkey. For comparative purposes and to evaluate the robustness of the observed patterns, we examined 16 different patch mosaic models and 18 different landscape gradient models of landscape structure. Surface metrics were highly redundant, but less so than patch metrics, and consistently aggregated into four cohesive clusters of similarly behaved metrics representing surface roughness, shape of the surface height distribution, and angular and radial surface texture. While the surface roughness metrics have strong analogs among the patch metrics, the other surface components are largely unique to landscape gradients. We contend that the surface properties we identified are nearly universal and have potential to offer new insights into landscape pattern–process relationships.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Austin MP (1999a) The potential contribution of vegetation ecology to biodiversity research. Ecography 22:465–484. doi:10.1111/j.1600-0587.1999.tb01276.x

    Article  Google Scholar 

  • Austin MP (1999b) A silent clash of paradigms: some inconsistencies in community ecology. Oikos 86:170–178. doi:10.2307/3546582

    Article  Google Scholar 

  • Austin MP, Smith TM (1989) A new model for the continuum concept. Vegetatio 83:35–47. doi:10.1007/BF00031679

    Article  Google Scholar 

  • Barbato G, Carneiro K, Cuppini D, Garnaes J, Gori G, Hughes G, Jensen CP, Jorgensen JF, Jusko O, Livi S, McQuoid H, Nielsen L, Picotto GB, Wilening G (1995) Scanning tunnelling microscopy methods for the characterization of roughness and micro hardness measurements. Synthesis report for research contract with the European Union under its programme for applied metrology. European Commission Catalogue number: CD-NA-16145 EN-C Brussels Luxemburg

  • Beasom SL (1983) A technique for assessing land surface ruggedness. J Wildl Manage 47:1163–1166. doi:10.2307/3808184

    Article  Google Scholar 

  • Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Indic 8:691–703. doi:10.1016/j.ecolind.2007.12.002

    Article  Google Scholar 

  • Dorner B, Lertzman K, Fall J (2002) Landscape pattern in topographically complex landscapes: issues and techniques for analysis. Landscape Ecol 17:729–743. doi:10.1023/A:1022944019665

    Article  Google Scholar 

  • ERDAS (1999) ERDAS field guide, 5th edn. ERDAS Inc., Atlanta

    Google Scholar 

  • Fischer J, Lindenmayer DB (2006) Beyond fragmentation: the continuum model for fauna research and conservation in human-modified landscapes. Oikos 112:473–480. doi:10.1111/j.0030-1299.2006.14148.x

    Article  Google Scholar 

  • Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge

    Google Scholar 

  • Forman RTT, Godron M (1986) Landscape Ecol. Wiley, New York

    Google Scholar 

  • Gadelmawla ES (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT&E Int 37:577–588. doi:10.1016/j.ndteint.2004.03.004

    Article  Google Scholar 

  • Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2002) Roughness parameters. J Mater Process Technol 123:133–145. doi:10.1016/S0924-0136(02)00060-2

    Article  Google Scholar 

  • Hoechstetter S, Walz U, Dang LH, Thinh NX (2008) Effects of topography and surface roughness in analyses of landscape structure—a proposal to modify the existing set of landscape metrics. Landsc Online 1:1–14

    Google Scholar 

  • Jenness J (2004) Calculating landscape surface area from digital elevation models. Wildl Soc Bull 32:829–839. doi:10.2193/0091-7648(2004)032[0829:CLSAFD]2.0.CO;2

    Article  Google Scholar 

  • Jenness J (2005) Topographic position index (tip_jen.avx) extension for ArcView 3.x., Jenness Enterprises. Available from http://www.jennessent.com/arview/tpi.htm

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landscape Ecol 19:389–399. doi:10.1023/B:LAND.0000030441.15628.d6

    Article  Google Scholar 

  • Manning AD, Lindenmayer DB, Nix HA (2004) Continua and Umwelt: novel perspectives on viewing landscapes. Oikos 104:621–628. doi:10.1111/j.0030-1299.2004.12813.x

    Article  Google Scholar 

  • McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606. doi:10.1658/1100-9233(2002)013[0603:EFPADI]2.0.CO;2

    Article  Google Scholar 

  • McGarigal K (2002) Landscape pattern metrics. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmetrics, vol 2. Wiley, Chichester, pp 1135–1142

    Google Scholar 

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

    Google Scholar 

  • McGarigal K, Cushman SA, Stafford SG (2000) Multivariate statistics for wildlife and ecology research. Springer, New York

    Google Scholar 

  • McGarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available from http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • McIntyre S, Barrett GW (1992) Habitat variegation, an alternative to fragmentation. Conserv Biol 6:146–147. doi:10.1046/j.1523-1739.1992.610146.x

    Article  Google Scholar 

  • Melton MA (1957) An analysis of the relations among elements of climate, surface properties, and geomorphology. Columbia University, Department of Geology, Project NR 389–042, Tech. Rep. 11, New York, 102 pp

  • Moore ID, Gryson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30. doi:10.1002/hyp.3360050103

    Article  Google Scholar 

  • Moore ID, Gessler PE, Nielsen GA, Petersen GA (1993) Terrain attributes: estimation methods and scale effects. In: Jakeman AJ, Beck MB, McAleer M (eds) Modeling change in environmental systems. Wiley, London, pp 189–214

    Google Scholar 

  • MountainsMap™ Surface analysis software based upon mountains technology, Digital Surf. Available from http://www.digitalsurf.fr/en/index.html

  • NanoRule+™ AFM image analysis software, Pacific Nanotechnology, Inc.. Available from http://www.pacificnano.com/analysis-software.html

  • 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. doi:10.1023/B:LAND.0000030521.19856.cb

    Article  Google Scholar 

  • OmniSurf™ Image analysis software, Digital Metrology Solutions, Inc.. Available from http://www.digitalmetrology.com/

  • Parker KC, Bendix J (1996) Landscape-scale geomorphic influences on vegetation patterns in four environments. Phys Geogr 17:113–141

    Google Scholar 

  • Pike RJ (2000) Geomorphometry—diversity in quantitative surface analysis. Prog Phys Geogr 24:1–20

    Google Scholar 

  • R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-08-9. Available from http://www.R-project.org

  • Ramasawmy H, Stout KJ, Blunt L (2000) Effect of secondary processing on EDM surfaces. Surf Eng J 16:501–505

    Article  CAS  Google Scholar 

  • Sanson GD, Stolk R, Downes BJ (1995) A new method for characterizing surface roughness and available space in biological systems. Functional Ecol 9:127–135

    Article  Google Scholar 

  • Schumm SA (1956) Evolution of drainage basins and slopes in badlands at Perth Amboy, New Jersey. Bull Geol Soc Am 67:597–646

    Article  Google Scholar 

  • SPIP™ The scanning probe image processor. Image metrology APS, Lyngby. Available from http://www.imagemet.com/

  • Stout KJ, Sullivan PJ, Dong WP, Mainsah E, Lou N, Mathia T, Zahouani H (1994) The development of methods for the characterization of roughness on three dimensions. Publication no EUR 15178 EN of the Commission of the European Communities, Luxembourg

    Google Scholar 

  • Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Bull Geol Soc Am 63:1117–1142

    Article  Google Scholar 

  • Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York. Landscape Ecol 17:569–586

    Article  Google Scholar 

  • TrueMap™ Surface topography visualization and analysis software. TrueGage™ surface metrology. Available from http://www.truegage.com/

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment 8:127–150

    Article  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 

  • Turner MG, Gardner RH, O’Neill RV (2001) Landscape ecology in theory and practice. Springer, New York

    Google Scholar 

  • Villarrubia JS (1997) Algorithms for scanned probe microscope, image simulation, surface reconstruction and tip estimation. J Nat Inst Stand and Technol 102:435–454

    Google Scholar 

  • Wiens JA (1989) Spatial scaling in ecology. Functional Ecol 3:385–397

    Article  Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: principles and applications. Wiley, New York

    Google Scholar 

  • Wu JG, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecol 17:761–782

    Article  Google Scholar 

Download references

Acknowledgments

We thank Brad Compton and Brad Timm for comments on a draft of this manuscript. This material is based on work partially supported by the Cooperative State Research, Extension, Education Service, US Department of Agriculture, Massachusetts Agricultural Experiment Station and the Department of Natural Resources Conservation, under Project No. 3321, and The Scientific and Technological Research Council of Turkey, under project International Postdoctoral Research Scholarship Programme-2219.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kevin McGarigal.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(DOC 53 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

McGarigal, K., Tagil, S. & Cushman, S.A. Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24, 433–450 (2009). https://doi.org/10.1007/s10980-009-9327-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-009-9327-y

Keywords

Navigation