Skip to main content
Log in

Spatial and semantic dimensions of landscape heterogeneity

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

Abstract

This paper addresses the challenge of measuring spatial heterogeneity in categorical map data. Spatial heterogeneity is a complex notion that involves both spatial variability and attribute variability, and metrics to capture this are a product of their developers’ simplifying assumptions about both spatial and attribute dimensions. We argue that the predominantly binary treatment of categorical data is frequently an unnecessary oversimplification that can be replaced by ordered measures based on semantic similarity evaluations. We develop a typology of autocorrelation metrics for categorical data that identifies a critical gap: existing measures are limited in their ability to capture variability of both spatial and attribute dimensions simultaneously. We demonstrate an approach to formally characterize the semantic similarity between pairs of categorical data classes as a continuous numeric variable. A series of experiments on synthetic and actual land cover data contrasts the information content provided by metrics representative of all portions of the typology: the recently proposed semantic variogram, the indicator variogram, the contagion index, and the edge contrast index. Experimental results suggest that the typology captures essential qualities of metric information richness. Among our findings is that the commonly used contagion index is directly correlated with Moran’s I for 2-class maps but it fails to distinguish between negatively and positively autocorrelated patterns. We identify the semantic variogram as the only metric that can simultaneously detect both spatial and semantic attribute aspects of categorical autocorrelation. The semantic variogram is also relatively robust to attribute scale changes and therefore less sensitive to class aggregation than the other metrics.

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

Similar content being viewed by others

References

  • Ahlqvist O (2004) A parameterized representation of uncertain conceptual spaces. Trans GIS 8(4):493–514

    Article  Google Scholar 

  • Ahlqvist O (2008) Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: a study of 1992 and 2001 US National land cover database changes. Remote Sens Environ 112(3):1226–1241

    Article  Google Scholar 

  • Ahlqvist O, Shortridge A (2006) Characterizing land cover structure with semantic variograms. In progress in spatial data handling—12th international symposium on spatial data handling. Springer, Verlag, pp 401–415

    Google Scholar 

  • Altobelli A, Bressan E, Feoli E, Ganis P, Martini F (2006) Digital representation of spatial variation of multivariate landscape data. Community Ecol 7(2):181–188

    Article  Google Scholar 

  • Anderson JR (1976) A land use and land cover classification system for use with remote sensor data. US Government Print. Office

  • Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Pearson Education Limited, Essex

    Google Scholar 

  • Bailey D, Herzog F, Augenstein I, Aviron S, Billeter R, Szerencsits E, Baudry J (2007) Thematic resolution matters: indicators of landscape pattern for European agro-ecosystems. Ecol Indic 7(3):692–709. doi:10.1016/j.ecolind.2006.08.001

    Article  Google Scholar 

  • Baldwin JB, Weaver K, Schnekenburger F, Perera AH (2004) Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation. Landscape Ecol 19:255–271

    Article  Google Scholar 

  • Ban H, Ahlqvist O (2007) Visualizing the uncertainty of urban ontology terms. In ontologies for urban development, studies in computational intelligence, vol 61. Springer, Berlin Heidelberg, pp 85–94

    Google Scholar 

  • Bouchon-Meunier B, Rifqi M, Bothorel S (1996) Towards general measures of comparison of objects. Fuzzy Sets Syst 84(2):143–153

    Article  Google Scholar 

  • Bourgault G, Marcotte D (1991) Multivariable variogram and its application to the linear model of coregionalization. Math Geol 23(7):899–928

    Article  Google Scholar 

  • Bregt AK, Stoorvogel JJ, Bouma J, Stein A (1992) Mapping ordinal data in soil survey: a Costa Rica example. Soil Sci Soc Am J 56(2):525–531

    Article  Google Scholar 

  • Buyantuyev A, Wu J (2007) Effects of thematic resolution on landscape pattern analysis. Landscape Ecol 22(1):7–13

    Article  Google Scholar 

  • Cliff AD, Ord JK (1981) Spatial processes: models & applications. Pion, London

    Google Scholar 

  • DeFries RS, Field CB, Fung I, Justice CO, Los S, Matson PA et al (1995) Mapping the land surface for global atmosphere-biosphere models: toward continuous distributions of vegetation’s functional properties. J Geophys Res 100(D10):20867–20882

    Article  Google Scholar 

  • DeGraaf RM, Yamasaki M (2002) Effects of edge contrast on redback salamander distribution in even-aged northern hardwoods. Forest Sci 48(2):351–363

    Google Scholar 

  • Desrochers A, Hanski IK, Selonen V (2003) Siberian flying squirrel responses to high-and low-contrast forest edges. Landscape Ecol 18(5):543–552

    Article  Google Scholar 

  • Di Gregorio A, Jansen LJM (2000) Land cover classification system: LCCS: classification concepts and user manual. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • Feng CC, Flewelling DM (2004) Assessment of semantic similarity between land use/land cover classification systems. Comput Environ Urban Syst 28(3):229–246

    Article  Google Scholar 

  • Ferreras P (2001) Landscape structure and asymmetrical inter-patch connectivity in a metapopulation of the endangered Iberian lynx. Biol Conserv 100(1):125–136

    Article  Google Scholar 

  • Fritz S, See L (2005) Comparison of land cover maps using fuzzy agreement. Int J Geogr Inf Sci 19(7):787–807

    Article  Google Scholar 

  • Fritz S, See L (2008) Identifying and quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications. Glob Change Biology 14(5):1057–1075

    Article  Google Scholar 

  • Gärdenfors P (2000) Conceptual spaces: the geometry of thought. MIT Press, Cambrige

    Google Scholar 

  • Gatrell AC (1979) Autocorrelation in spaces. Environ Plan A 11:507–516

    Article  Google Scholar 

  • Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modelling and spatial prediction of soil attributes. Int J Geogr Inf Sci 9(4):421–432

    Article  Google Scholar 

  • Getis A (1991) Spatial interaction and spatial autocorrelation: a cross-product approach. Environ Plan A 23(9):1269–1277

    Article  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York

    Google Scholar 

  • Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1(2):143–156

    Article  Google Scholar 

  • Haining RP (1981) Analysing univariate maps. Progr Hum Geogr 5:58–78

    Google Scholar 

  • Hubert LJ, Golledge RG, Costanzo CM (1981) Generalized procedures for evaluating spatial autocorrelation. Geogr Anal 13(3):224–233

    Google Scholar 

  • Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of international conference on research in computational linguistics, (ROCLING X), pp 19–33. Retrieved from http://wortschatz.uni-leipzig.de/~sbordag/aalw05/Referate/03_Assoziationen_BudanitskyResnik/Jiang_Conrath_97.pdf

  • Kaufmann A, Gupta MM (1985) Introduction to fuzzy arithmetic. Van Nostrand Reinhold Co, New York, p 351

    Google Scholar 

  • Kotliar NB, Wiens JA (1990) Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos 59(2):253–260

    Article  Google Scholar 

  • Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW et al (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Chang Part A Hum Policy Dimens 11(4):261–269

    Google Scholar 

  • Levin SA (1992) The problem of pattern and scale in ecology: The Robert H. MacArthur Award Lecture. Ecology 73(6):1943–1967

    Article  Google Scholar 

  • Li H, Reynolds JF (1993) A new contagion index to quantify spatial patterns of landscapes. Landscape Ecol 8(3):155–162

    Article  Google Scholar 

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landscape Ecol 19(4):389–399

    Article  Google Scholar 

  • Makido Y, Shortridge A (2007) Weighting function alternatives for a sub-pixel allocation model. Photogrammetric engineering and remote sensing (in press)

  • Marceau DJ, Hay GJ (1999) Remote sensing contributions to the scale issue. Can Remote Sens 25(4):357–366

    Google Scholar 

  • McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland

    Google Scholar 

  • Meisel JE, Turner MG (1998) Scale detection in real and artificial landscapes using semivariance analysis. Landscape Ecol 13(6):347–362

    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(4):435–455

    Article  Google Scholar 

  • O’Neill RV, Krummel JR, Gardner RH, Sugihara G, Jackson B, DeAngelis DL et al (1988) Indices of landscape pattern. Landscape Ecol 1(3):153–162

    Article  Google Scholar 

  • O’Sullivan D, Unwin DJ (2003) Geographic information analysis. Wiley Hoboken, NJ

    Google Scholar 

  • Patton DR (1975) A diversity index for quantifying habitat” edge”. Wildl Soc Bull 3(4):171–173

    Google Scholar 

  • Phillips JD (2002) Spatial structures and scale in categorical maps. Geogr Environ Model 6(1):41–57

    Article  Google Scholar 

  • Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19(1):17–30

    Article  Google Scholar 

  • Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. JAIR 11:95–130

    Google Scholar 

  • Romme WH (1982) Fire and landscape diversity in subalpine forests of yellowstone national park. Ecol Monogr 52(2):199–221

    Article  Google Scholar 

  • Schwering A (2008) Approaches to semantic similarity measurement for geo-spatial data: a survey. Trans in GIS 12(1):5–29

    Article  Google Scholar 

  • Shortridge A (2007) Practical limits of Moran’s Autocorrelation index for raster class maps. Comput Environ Urban Syst 31(3):362–371

    Article  Google Scholar 

  • Sokal R (1978) Spatial autocorrelation in biology. 1. Methodology. Biol J Linn Soc 10(2):199

    Article  Google Scholar 

  • Stevens SS (1946) On the theory of scales of measurement. Science 103(2684):677–680

    Article  PubMed  CAS  Google Scholar 

  • St-Onge BA, Cavayas F (1995) Estimating forest stand structure from high resolution imagery using the directional variogram. Int J Remote Sens 16(11):1999–2021

    Article  Google Scholar 

  • R Development Core Team (2009) R: a language and environment for statistical computing. Retrieved from http://www.R-project.org/

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

    Article  Google Scholar 

  • Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352

    Article  Google Scholar 

  • USGS (2006) Seamless Data Distribution System. http://seamless.usgs.gov/. Accessed Mar 2006

  • Uuemaa E, Roosaare J, Kanal A, Mander Ü (2008) Spatial correlograms of soil cover as an indicator of landscape heterogeneity. Ecol Indic 8(6):783–794

    Article  Google Scholar 

  • Velleman PF, Wilkinson L (1993) Nominal, ordinal, interval, and ratio typologies are misleading. Am Stat 47(1):65–72

    Article  Google Scholar 

  • Wadsworth RA, Comber AJ, Fisher PF (2006) Expert knowledge and embedded knowledge: or why long rambling class descriptions are useful. In: Progress in Spatial data handling, proceedings of the 12th international symposium on spatial data handling, SDH 2006. Berlin: Springer, pp 197–213

  • Willson MF, Traveset A (1992) The ecology of seed dispersal. In: Seeds: the ecology of regeneration in plant communities. CAB International, pp 85–110

  • Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol 17(4):355–365

    Article  Google Scholar 

  • Wu F, Webster CJ (1998) Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environ Plan B 25:103–126

    Article  Google Scholar 

  • Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecol 17(8):761–782

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ola Ahlqvist.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ahlqvist, O., Shortridge, A. Spatial and semantic dimensions of landscape heterogeneity. Landscape Ecol 25, 573–590 (2010). https://doi.org/10.1007/s10980-009-9435-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-009-9435-8

Keywords

Navigation