Abstract
This paper introduces the semantic variogram, which is a measure of spatial variation based upon semantic similarity metrics calculated for nominal land cover class definitions. Traditional approaches for measuring spatial autocorrelation for nominal geographical data compare classes between pairs of observations to determine a simple binary measure of similarity (identical/different). These binary values are summarized over many sample pairs separated by various distances to characterize some spatial metric of correlation, or variation. The use of binary similarity measures ignores potentially substantial ranges in similarity between different classes. Through the development of category representations capable of producing quantifiable measures of pair wise class similarity, descriptive spatial statistics that operate upon ratio data may be employed. These measures, including the semantic variogram proposed in this work, may characterize spatial variability of categorical maps more sensitively than traditional measures. We apply the semantic variogram to National Land Cover Data (NLCD) for three different study sites, and compare results to those from a multiple class indicator semivariogram. We demonstrate that substantial differences exist in observed short-range variability for the two metrics in all sites. The semantic variograms detect much lower short-range variability due to the tendency of semantically similar classes to be closer together.
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References
Ahlqvist O (2004) A parameterized representation of uncertain conceptual spaces. Transactions in GIS 8(4):493–514
Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964
Bourgault G, Marcotte D (1991) Multivariable variogram and its application to the linear model of coregionalization. Mathematical Geology 23(7):899–928
Dale VH, King AW, Mann LK, Washington-Allen RA, McCord RA (1998) Assessing Land-Use Impacts on Natural Resources. Environmental Management 22(2):203–211
DeFrie RS, Field CB, Fung I, Justice CO, Los S, Matson PA, Matthews E, Mooney HA, Potter CS, Prentice K, Sellers PJ, Townshend JRG, Tucker CJ, Ustin SL, Vitousek PM (1995) Mapping the land surface for global atmosphere-biosphere models: toward continuous distributions of vegetation’s functional properties. J of Geophysical Research: Atmosphere 100:20867–20882
Di Gregorio A, Jansen LJM (1998) Land Cover Classification System: Classification Concepts And User Manual. FAO, Rome, 179 p
Goovaerts P (1997) Geostatistics for Natural Resources Evaluation. Oxford University Press, New York
Kaufman A, Gupta MM (1985) Introduction to fuzzy arithmetic. Van Nostrand Reinhold Company, New York, 351 p
Lambin E, Turner B, Geist H, Agbola S, Angelsen A, Bruce J, Coomes O, Dirzo R, Fischer G, Folke C, George P, Homewood K, Imbernon J, Leemans R, Lin X, Moran E, Mortimorep M, Ramakrishnan P, Richards J, Skanes H, Steffen W, Stone G, Svedin U, Veldkamp T, Vogel C, Xu J (2001) The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change 11:261–269
Nunes C, Augé JI (eds) (1999) Land-Use and Land-Cover Change (LUCC): implementation strategy. IGBP report(48), IHDP report (10). International Geosphere-Biosphere Programme, Stockholm, 125 p
O’Sullivan DO, Unwin DJ (2004) Geographic Information Analysis. Wiley, Hoboken, NJ
R Development Core Team (2005) R: A Language and Environment for Statistical Computing. URL: http://www.R-project.org/. R Foundation, Vienna, Austria. Accessed 12/1/2005
USGSa (2005) National Land Cover Dataset 1992. http://landcover.usgs.gov/natllandcover.asp. United States Geological Survey, Reston, Virginia. Accessed 12/1/2005
USGSb (2005) Seamless Data Distribution System. http://seamless.usgs.gov/ United States Geological Survey, Reston, Virginia. Accessed 12/1/2005
Wang H, Hall CAS, Cornell JD (2002) Spatial dependence and the relationship of soil organic carbon and soil moisture in the Luquillo Experimental Forest, Puerto Rico. Landscape Ecology 17(8):671–684
Wu F, Webster CJ (1998) Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B 25:103–126
Wu J, Norvell WA, Hopkins DG, Welch RM (2002) Spatial variability of grain cadmium and soil characteristics in a durum wheat field. Soil Science Society of America J 66:268–275
Yang L, Stehman SV, Smith JH, Wickham JD (2001) Thematic sccuracy of MRLC land cover for the eastern United States. Remote Sensing of Environment 76:418–422
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© 2006 Springer-Verlag Berlin Heidelberg
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Ahlqvist, O., Shortridge, A. (2006). Characterizing Land Cover Structure with Semantic Variograms. In: Riedl, A., Kainz, W., Elmes, G.A. (eds) Progress in Spatial Data Handling. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35589-8_26
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DOI: https://doi.org/10.1007/3-540-35589-8_26
Publisher Name: Springer, Berlin, Heidelberg
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