References

Fuzzy Geography/GIS

Agrawal, A., Kumar, N., and Radhakrishna, M., 2007. Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation. International Journal of Remote Sensing 28 (20), 4597-4608.

Ahlqvist, O., 2005. Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science 18 (7): 831-857

Altman, D., 1994. Fuzzy set theoretic approaches for handling imprecision in spatial analysis. International Journal of Geographical Information Systems 8: 271-289

Anile, M., and Spinella, S., 2008. Interpolation with data containing interval, fuzzy, and possibilistic uncertainty. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 47-53.

Arnot, C., Fisher, P.F., Wadsworth, R., and Wellens, J., 2004. Landscape Metrics with Ecotones: pattern under uncertainty. Landscape Ecology 19: 181-195

Arnot, C., and Fisher, P., 2007. Mapping the ecotone with fuzzy sets. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 19-32.

Baja, S., Chapman, D.M. and Dragovich, D., 2002. A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environmental Management 29 (5): 647-661.

Banai, R., 1993. Fuzziness in Geographical Information Systems: Contributions from the analytic hierarchy process. International Journal of Geographical Information Systems 7: 315-329.

Banai-Kashani, R., 1989. A new method for site suitability analysis: The analytic hierarchy process. Environmental Management 13 (6): 685-693.

Bardossy, A., Bogardi, I., and Kelly, W.E., 1990. Kriging with imprecise (fuzzy) variograms I: Theory. Mathematical Geology 22 (1): 63-79

Bardossy, A., Bogardi, I., and Kelly, W.E., 1990. Kriging with imprecise (fuzzy) variograms II: Application. Mathematical Geology 22 (1): 81-94.

Bastin, L., 1997. Comparison of fuzzy c-mean classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing 18 (17): 3629-3648.

Bastin, L., Fisher, P.F., Bacon, M.C., Arnot C.N.W, and Hughes, M.J., 2007. Reliability of vegetation community information derived using Decorana ordination and fuzzy c-means clustering. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp53-74.

Benedikt, J., Reinberg, S., and Riedl, L., 2002. A GIS application to enhance cell-based information modeling. Information Sciences 142, 151-160.

Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58, 239-258.

Bezdek, J.C., Ehrlich, R., and Full, W., 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10: 191-203

Bittner, T., and Smith, B., 2003. Vague reference and approximating judgments. Spatial Cognition and Computation, 3: 137-156

Bordogna, G., Pagani, M., and Pasi, G., 2007. A flexible decision support approach to model ill-defined knowledge in GIS. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 133-152.

Burrough, P.A., 1989. Fuzzy mathematical methods for soil survey and land evaluation: Journal of Soil Science 40 (3): 477-492.

Burrough, P.A., 1996. Natural objects with indeterminate boundareies. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 3-28

Burrough, P.A., MacMillan, R.A., and van Deursen, W., 1992. Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science 43, 193-210.

Burrough, P.A., Gaans, P.F.M., van, and Hootsmans, R., 1997. Continuous classification in soil survey: spatial correlation, confusion and boundaries. Geoderma 77 (2-4): 115-138

Caluwe, R.de, de Tre, G., and Bordogna G., 2004. Basic notions and rationale of the handling of imperfect information in spatio-temporal databases. In R.de Caluwe, G. de Tre, and G.Bordogna (editors), Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer, Berlin, pp 1-8

Cannon, R.L., Dave, J.V., and Bezdek, J.C., 1986. Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 8 (2): 248-255.

Cheng, T., 2002. Fuzzy objects: their changes and uncertainties. Photogrammetric Engineering and Remote Sensing 68 (1): 41-49

Cheng, T., and Molenaar, M., 1999. Diachronic analysis of fuzzy objects. GeoInformatica 3 (4): 337-355

Cheng, T., and Molenaar, M., 1999. Objects with fuzzy spatial extent. Photogrammetric Engineering and Remote Sensing 65 (7): 797-801

Cheng, T., Molenaar, M., and Lin, H. 2001. Formalizing fuzzy objects from uncertain classification results. International Journal of Geographical Information Science 15 (1): 27-42.

Cheng, T., Fisher P., and Li, Z., 2004. Double vagueness: uncertainty in multi-scale fuzzy assignment of duneness. Geo-Spatial Information Science 7: 58-66.

Clementini, E., 2004. Modeling spatial objects affected by uncertainty. In R.de Caluwe, G. de Tre, and G.Bordogna (editors), Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer, Berlin, pp 211-236

Clementini, E., and Felice, P. di, 1996. An algebraic model for spatial objects with indeterminate boundaries. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 155-169

Cohn, A.G., and Gotts, N.M., 1996. The ‘egg-yolk’ representation of regions with indeterminate boundaries. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 171-167

Couclelis, H., 1996. Towards an operational typology of geographic entities with ill defined boundaries. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 45-55

Dale, M.B.,1988. Some fuzzy approaches to phytosociology: Ideals and instances Folia Geobotanica et Phytotaxonomica 23: 239-274

Davidson, D.A., Theocharopoulos, S.P., and Bloksma, R.J., 1994. A land evaluation project in Greece using GIS and based on Boolean fuzzy set methodologies. International Journal of Geographical Information Systems, 8: 369-384

Dilo, A., By, R.A. de, and Stein, A., 2007. A system of types and operators for handling vague spatial objects. International Journal of Geographical Information Systems, 21: 397-426

Dixon, B., 2005. Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool. Applied Geography 25: 327-347.

Dixon, B., 2005. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrology 309, 17-38.

Dobermann, A., and Oberthür, T., 1997. Fuzzy mapping of soil fertility: a case study on irrigated Riceland in the Philippines. Geoderma 77 (2-4): 317-339

Dragicevic, S., Marceau, D.J., and Marois, C., 2001. Space, time, and dynamics modeling in historical GIS databases: a fuzzy logic approach. Environment and Planning B: Planning and Design 28: 545-562.

Dragicevic, S., 2004. Fuzzy sets for representing the spatial and temporal dimensions in GIS databases. In R.de Caluwe, G. de Tre, and G.Bordogna (editors), Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer, Berlin, pp 11-27.

Dragicevic, S., 2005. Multi-dimensional interpolations with fuzzy sets. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 143-158

Dragicevic, S. and Marceau, D.J., 2000. A fuzzy set approach for modeling time in GIS. International Journal of Geographical Information Systems, 14: 225-245

Edwards, G., and Lowell, K.E., 1996. Modeling uncertainty in photointerpreted boundaries. Photogrammetric Engineering and Remote Sensing 62: 337-391

Erwig, M., and Schneider, M., 1997. Vague Regions. In M.Scholl and A.Voisard (editors) Proceedings of the 5th International Symposium on Advances in Spatial Databases (SSD), LNCS 1262, Springer, Berlin, 298-320

Fisher, P.F., 1994. Probable and Fuzzy concepts of the uncertain viewshed. In Innovations in GIS 1, Edited by M.Worboys, Taylor & Francis, London, 161-175.

Fisher, P., 1996. Boolean and fuzzy regions. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 87-94

Fisher, P.F., 1997. The pixel: a snare and a delusion. International Journal of Remote Sensing 18 (3): 679-685.

Fisher, P.F., 2000. Sorites Paradox and Vague Geographies. Fuzzy Sets and Systems 113 (1): 7-18.

Fisher, P.F., .Arnot, C., Wadsworth, R., and Wellens, J., 2006. Detecting Change in Vague Interpretations of Landscapes. Ecological Informatics 1 (2): 163-178.

Fisher, P., and Arnot, C., 2007. Mapping type 2 change in fuzzy land cover. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 167-186

Fisher, P.F., Cheng, T., and Wood, J., 2007. Higher order vagueness in geographical information: Empirical geographical population of Type n fuzzy sets. GeoInformatica 11 (3): 311-330.

Fisher, P.F., and Pathirana, S., 1990. The evaluation of fuzzy membership of land cover classes in the suburban zone Remote Sensing of Environment 34: 121-132

Fisher, P.F., and Pathirana, S., 1993. The ordering of multitemporal fuzzy land-cover information derived from Landsat MSS data GeoCarto International 8: 5-14

Fisher, P.F., and Wood, J., 1998. What is a Mountain? or The Englishman who went up a Boolean Geographical concept but realised it was Fuzzy. Geography 83 (3): 247-256

Fisher, P, Wood, J., and Cheng, T., 2005. Fuzziness and ambiguity in multi-scale analysis of landscape morphometry. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy

Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 209-232.

Fisher, P.F., Wood, J., and Cheng, T., 2007. Higher order vagueness in a dynamic landscape: Multi-resolution morphometric analysis of a coastal dunefield. Journal of Environmental Informatics 9 (1): 56-70.

Fonte, C., 2008. Geographical entities as surfaces. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 63-83.

Fonte, C.C., and Lodwick, W.A., 2004. Areas of fuzzy geographical entities. International Journal of Geographical Information Science 18 (2): 127-150.

Fonte, C.C., and Lodwick, W.A., 2005. Modelling the fuzzy spatial extent of geographical entities. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 11-142.

Fonte, C., Santos, J., Goncalves, G., Spinella, S., and Anile, M. 2008. Applications. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 111-128.

Foody, G.M., 1992. A fuzzy sets approach to the representation of vegetation continua from remotely sensed data: An example from lowland heath Photogrammetric Engineering and Remote Sensing 58: 221-225

Foody, G. M., 1995. Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS Journal of Photogrammetry and Remote Sensing 50, 2–12.

Foody, G.M., 1996. Approaches to the production and evaluation of fuzzy land cover classification from remotely-sensed data International Journal of Remote Sensing 17: 1317-1340

Foody, G.M., 1996. Relating the land-cover composition of mixed pixels to artificial neural network classification output. Photogrammetric Engineering and Remote Sensing 62: 491-499

Foody, G. M., 1997, Fully fuzzy supervised classification of land cover from remotely sensed imagery with an arti.cial neural network. Neural Computing and Applications, 5, 238–247.

Foody, G. M., 1999, The continuum of classification fuzziness in thematic mapping. Photogrammetric Engineering and Remote Sensing, 65, 443–451.

Foody, G. M., and Arora, M. K., 1996, Incorporation of mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recognition Letters, 17, 1389–1398.

Foody, G.M., and Cox, D.P., 1994. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions International Journal of Remote Sensing 15: 619-631

Foody, G.M.., Campbell, N.A., Trodd, N.M., and Wood, T.F., 1992. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification Photogrammetric Engineering and Remote Sensing 58: 1335-1341

Frank, A., 1996. The prevalence of objects with sharp boundaries in GIS. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 29-40

Frank, A., 2007. Incompleteness, error, approximation and uncertainty: An ontological approach to data quality. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 107-131.

Gale, S., 1972. Inexactness, fuzzy sets, and the foundation of Behavioral Geography. Geographical Analysis, 4: 337-349.

Gallo, G., 2008. Visualization and analysis of surfaces. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 105-110.

Galton, A., 2003. Granularity-sensitive spatial attributes. Spatial Cognition and Computation, 3: 97-118.

Gedeon, T.D., Wong, K.W., Wong, P.M., and Huang, Y., 2003. Spatial interpolation using fuzzy reasoning. Transactions in GIS 7 (1): 55-66

Gopal, S. and Woodcock, C.E., 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 60: 181-188.

Graniero, P.A., and Robinson, V.B., 2003. A real-time adaptive sampling method for field mapping in patchy, heterogeneous environments. Transactions in GIS 7 (1): 31-53.

Groenemans, R., Ranst, E. van, and Kerre, E., 1997. Ruzzy relational calculus in land evaluation. Geoderma 77 (2-4):283-298

Graniero, P.A., and Robinson, V.B., 2006. A probe mechanism to couple spatially explicit agents and landscape models in an integrated modeling framework. International Journal of Geographical Information Science 20 (9): 965-990

Grubesic, T.H., 2006. On the application of fuzzy clustering for crime hot spot detection. Journal of Quantitative Criminology 22 (1), 77-105.

Gruijter, J.J. de, Walvoort, D.J.J., and Gaans, P.F.M. van, 1997. Continuous soil maps: a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 77 (2-4): 169-195

Guesgen, H.W., 2005. Fuzzy reasoning about geographic regions. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 1-14

Guesgen, H.W., Hertzberg, J., Lobb, R., and Mantler, A., 2003. Buffering fuzzy maps in GIS. Spatial Cognition and Computation, 3: 205-220

Hadzilacos, T., 1996. On layer-based systems for undetermined boundaries. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 237-255

Hagen-Zanker, A., Staatman, B., and Uljee, I., 2005. Further developments of a fuzzy set map comparison approach. International Journal of Geographical Information Science 19 (7): 769-785.

Hagen, A., 2003. Fuzzy set approach to assessing similarity to categorical maps. International Journal of Geographical Information Science 17 (3): 235-249.

Hall, G.B., Wang, F., and Subaryono, 1992. Comparison of Boolean and fuzzy classification methods in land suitability analysis by using geographical information systems. Environment and Planning A, 24: 497-516.

Hall, O., and Arnberg, W., 2002. A method for landscape regionalization based on fuzzy membership signatures. Landscape and Urban Planning 59, 227-240.

Harris, T.R., Stoddard, S.W., and Bezdek, J.C., 1993. Application of fuzzy-set clustering for regional typologies. Growth and Change, 24 (Spring), 155-165

Hendricks Franssen, H.J.W.M., Eijnsbergen, A.C. van, and Stein, A., 1997. Use of spatial prediction techniques and fuzzy classification for mapping soil pollutants. Geoderma 77 (2-4): 243-262

Hwang, S., and Thill, J.-C.., 2005. Modeling localities with fuzzy sets and GIS. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 71-104.

Irvin, B.J., Ventura, S.J., and Slater, B.K., 1997. Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma 77 (2-4): 137-154

Isik, N., and Yazici, A., 2007. Association rule mining using fuzzy spatial data cubes. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 201-224.

Jankowski, P., 2007. A rough set-based approach to handling uncertainty in geographic data classification. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 75-87.

Jiang, H., and Eastman, J.R, 2000. Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Systems, 14: 173-184

Kent, J.T., and Mardia, K.V., 1988. Spatial classification using fuzzy membership models. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 10 (5): 659-671.

Kollias, V.J., and Voliotis, A., 1991. Fuzzy reasoning in the development of geographical information systems FRSIS: a prototype soil information system with fuzzy retrieval capabilities. International Journal of Geographical Information Systems 5 (2): 209-223

Kollias, V.J., Kalivas, D.P., and Yassoglou, N.J., 1999. Mapping the soil resources of a recent alluvial plain in Greece using fuzzy sets in a GIS environment. European Journal of Soil Science 50, 261-273.

Kratochwil, S. and Benedikt, J., 2005. Talking space – A social and fuzzy logical GIS perspective on modeling spatial dynamics. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 159-184.

Kronenfeld, B.J., 2003. Implications of a data reduction framework to assignment of fuzzy membership values in continuous class maps. . Spatial Cognition and Computation, 3: 221-237.

Kulik, L., 2003. Spatial vagueness and second-order vagueness. Spatial Cognition and Computation, 3: 157-183

Ladner, R., Petry, F., and Cobb, M.A., 2003. Fuzzy set approaches to spatial data mining of association rules. Transactions in GIS 7 (1): 123-138

Lagacherie, P., Andrieux, P., and Bouzigues, R., 1996. Fuzziness and uncertainty of soil boundaries: From reality to coding in GIS. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 275-286

Lagacherie, P., Cazemier, D.R., Gaans, P.F.M. van, Burrough, P.A., 1997. Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area. Geoderma 77 (2-4): 197-216

Lark, R.M., and Bolam, H.C., 1997. Uncertainty in prediction and interpretation of spatially variable data on soils. Geoderma 77 (2-4): 263-282

Legleiter, C.J., and Goodchild, M.F., 2005. Alternative representation of in-stream habitat: classification using remote sensing, hydraulic modeling and fuzzy logic. International Journal of Geographical Information Science 19 (1): 29-50.

Leung, Y.C., 1979. Locational choice: A fuzzy set approach Geographical Bulletin , 15: 28-34Leung, Y.C., 1982. Approximate characterization of some fundamental concepts of spatial

analysis. Geographical Analysis 14 (1): 29-40. Leung, Y.C., 1983. Fuzzy sets approach top spatial analysis and planning – a nontechnical

evaluation. Geografiska annaler 65B: 65-75Leung, Y.C., 1984. Towards a flexible framework for regionalization. Environment and

Planning A 16: 1613-1632.Leung, Y.C., 1985. Basic issues of fuzzy set theoretic spatial analysis. Papers of the Regional

Science Association 58: 35-46.Leung, Y.C., 1987. On the imprecision of boundaries Geographical Analysis 19: 125-151

Leung, Y.C., 1988. Spatial Analysis and Planning under Imprecision New York: Elsevier. Liu, Z., and George, R., 2005. Mining weather data using fuzzy cluster analysis. In F.E.Petry,

V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp. 103-119.

Lodwick, W. (editor), 2008. Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton.

Lodwick, W. , Anile, M., and Spinella, S., 2008. Introduction. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 1-46.

Mannan, B., Roy, J., and Ray A.K., 1998. Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images. International Journal of Remote Sensing 19 (4): 767-774.

McBratney, A.B., and De Gruijter, J.J., 1992. A continuum approach to soil classification by modified fuzzy k-means with extragrades. Journal of Soil Science 43: 159-175

McBratney, A.B., and.Moore, A.W., 1985.Application of fuzzy sets to climatic classification Agricultural and Forest Meteorology 35: 165-185

McBratney, A.B., and Odeh, I.O.A., 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77 (2-4): 85-113

Mackay, D.S., Samanta, S., Ahl, D.E., Ewers, B.E., Gower, S.T., and Burrows, S.N., 2003. Automated parameterization of land surface process models using fuzzy logic. Transactions in GIS 7 (1): 139-153.

Martino, F. di, Loia, V., Sessa, S., and Giordano, M., 2005. A valuation of the reliability of a GIS based on the fuzzy logic in a concrete case study. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 185-208.

Matsakis, P., and Nikitenko, D., 2005. Combined extraction of directional and topological relationship information from 2D concave objects. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp15-40.

Maxaheri, S.A., McBratney, A.B., and Koppi, A.J., 1997. Sensitivity of memberships to attribute variation around selected centroids and intergrades in the continuous Australian Great Soil Groups classification system. Geoderma 77 (2-4): 155-168

Mays, M.D., Bogardi, I., and Bardossy, A., 1997. Fuzzy logic and risk-based soil interpretations. Geoderma 77 (2-4): 299-315

Molenaar, M., 1996. A syntactic approach for handling the semantics of fuzzy spatial objects. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 207-224

Molenaar, M., and Cheng, T., Fuzzy spatial objects and their dynamics. ISPRS Journal of Photogrammetry and Remote Sensing 55 (3) 164-175

Montello, D.R., Goodchild, M.F., Gottsegen, J., and Fohl, P., 2003. Where’s downtown?: Behavioral methods for determining referents of vague spatial queries. Spatial Cognition and Computation, 3: 185-204

Moraczewski, I.R., 1993a.Fuzzy logic for phytosociology 1: Syntaxa as vague concepts Vegetatio, 106: 1-11

Moraczewski, I.R., 1993b.Fuzzy logic for phytosociology 2: Generalization and prediction Vegetatio 106: 13-20

Morris, A., 2003. A framework for modeling uncertainty in spatial databases. Transactions in GIS 7 (1): 83-101

Morris, A., and Jankowski, P., 2005. Spatial decision making using fuzzy GIS. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp275-298.

Munda, G., 1995. Multicriteria Evaluation in a Fuzzy Environment: Theory and applications in ecological economics Physica-Verlag, Heidelberg

Oberthür, T., Dobermann, A., and Aylward, M., 2000. Using auxiliary information to adjust fuzzy membership function s for improved mapping of soil qualities. International Journal of Geographical Information Systems, 14: 431-454

Oh, K., and Jeong, Y., 2002. The usefulness of the GIS – fuzzy set approach in evaluating the urban residential environment. Environment and Planning B: Planning and Design 29, 589-606.

Papadias, D., 1999. Processing fuzzy spatial queries: a configuration similarity approach International Journal of Geographical Information Science, 13, 93 - 118

Papadias, D., Karacapilidis, N., and Arkoumanis, D., 1999. Processing fuzzy spatial queries: a configuration similarity approach. International Journal of Geographical Information Science 13 (2): 93-118.

Pauly, A., and Schneider, M., 2005. Topological Predicates between Vague Spatial Objects. In C.B.Medeiros, M.J.Egenhofer, E.Bertino (Editors) Advances in Spatial and Temporal Databases SSTD 2005, Proceedings of the 9th International Symposium on Spatial and Temporal Databases LNCS 3633, Springer, Berlin, pp 418-432,

Pauly, A., and Schneider, M., 2005. Identifying Topological Predicates for Vague Spatial Objects.In Proceedings of the 20th ACM Symposium on Applied Computing (ACM SAC), ACM Press, New York, pp 587-591

Pauly, A., and Schneider, M.,2007 Querying Vague Spatial Objects in Databases with VASA. 5th Int. Symp. on Spatial Data Quality (ISSDQ),

Pauly, A., and Schneider, M., 2008. Vague Spatial Data Types. In Encyclopedia of GIS, Springer, Berlin, pp 1213-1217

Pauly, A., and Schneider, M., 2008. Spatial Vagueness and Imprecision in Databases. In Proceedings of the 23rd Annual ACM Symposium on Applied Computing (ACM SAC), ACM Press, New York, pp 875-879

Petry, F.E., Ladner, R., and Somodevilla, M., 2007. Indexing implementation for vague spatial regions with R-trees and grid files. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 187-199.

Pipkin, J.S., 1978. Fuzzy sets and spatial choice Annals of the Association of American Geographers 68: 196-204

Ponsard, C., 1977. Hierarchie des places centrales et graphes psi-flous Environment and Planning A 9: 1233-1252

Pontius, R.G. Jr, and Cheuk, M.L, 2006. A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science 20 (1): 1-30.

Ragin, C.C., 2000. Fuzzy-Set Social Science. University Press, ChicagoRicotta, C., 2004. Evaluating the classification accuracy of fuzzy thematic maps with a simple

parametric measure. International Journal of Remote Sensing 25 (11), 2169-2176.Ricotta, C., 2005. On possible measures for evaluating the degree of uncertainty of fuzzy

thematic maps. International Journal of Remote Sensing 26 (24), 5573-5583.Roberts, D.W., 1986. Ordination on the basis of fuzzy set theory. Vegetatio 66 (3): 123-131Roberts, D.W., 1989. Fuzzy systems vegetation theory. Vegetatio 83 (1-2): 71-80.Roberts, D.W., 1989. Analysis of forest succession with fuzzy graph theory. Ecological

Modelling 45 (4): 261-274.

Robinson, V.B., 2003. A perspective on the fundamentals of fuzzy sets and their use in geographic information systems. Transactions in GIS 7 (1): 3-30

Robinson, V.B., 2007. Issues and challenges of incorporating fuzzy sets in ecological modeling. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 33-51.

Robinson, V.B., and Graniero, P.A., 2005. Spatially explicit individual-based ecological modeling with mobile fuzzy agents. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 299-334.

Robinson, V.B., and Strahler, A.H., 1984. Issues in designing geographic information systems under conditions of inexactness in Proceedings of the 10th International Symposium on Machine Processing of Remotely Sensed Data Lafayette: Purdue University, 198-204

Robinson, V.B., and Thongs, D., 1986. Fuzzy set theory applied to the mixed pixel problem of multispectral landcover databases in B.Opitz editor. Geographic Information Systems in Government Hampton: A Deerpak Publishing, 871-885

Santos, J., 2008. Surface Modeling. In Lodwick, W. (editor), Fuzzy Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms, and Applications. CRC Press, Boca Raton, pp 85-104

Schiewe, J., and Ehlers, M., 2007. Fuzzy models for handling uncertainty in the integration of high resolution remotely sensed data and GIS. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 89-106.Stell, J.G., 2003. Qualitative extents for spatio-temporal granularity. Spatial Cognition and Computation, 3: 119-136.

Schneider,  M.,  1996. Modelling spatial objects with undetermined boundaries using Realm/ROSE approach. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp 141-154

Schneider,  M., 1999 Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types In R.H.Guting, D.Papadias and F.Lochovsky (editors) Proceedings of Advances in Spatial Databases, SSD99, Lecture Notes in Computer Science 1651, Springer, Berlin. pp 330-351

Schneider,  M., 2000. Finite Resolution Crisp and Fuzzy Spatial Objects. In Proceeedings of the 9th International Symposium on Spatial Data Handling (SDH), Beijing 5a.3-17,

Schneider,  M., 2000. Metric operations on fuzzy spatial objects in databases. In Proceedings of the 8th ACM Iinternational Symposium on Advances in Geographic Information Systems, ACM Press, New York pp21-26.

Schneider,  M., 2001. A Design of Topological Predicates for Complex Crisp and Fuzzy Regions. In H.S.Kunii, S.Jajodia, and A.Sølvberg (editors), Proceedings of the 20th International Conference on Conceptual Modeling, ER2001, Lecture Notes in Computer Science 2224, Springer, Berlin. pp 103-116

Schneider,  M., 2002. Design and implementation of finite resolution crisp and fuzzy spatial objects. Data & Knowledge Engineering, 44, 81-108

Schneider, M., 2004. Vague Spatial Data Types. In Proceedings of the ISSDQ’04, GeoInfo-Series No 28a, Department of Geoinformation and Cartography, Viennna University of Technology. pp 83-97

Schneider, M. , 2004. Fuzzy Spatial Data Types and Predicates: Their Definition and Integration into Query Languages. In R.de Caluwe, G. de Tre, and G.Bordogna (editors), Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer, Berlin, pp 265-293

Shi, X., Zhu, A-X., and Wang, R., 2005. Fuzzy representation of spatial terrain features using a similarity-based approach. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy

Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 233-251.

Smithson, M., 1987. Fuzzy Set Analysis for Behavioral and Social Sciences. Springer, Berlin.Smithson, M., and Verkuilen, J., 2006. Fuzzy Set Theory: Applications in the Social Sciences.

Sage, Thousand Oaks, Ca.Somodevilla, M.J., and Petry, F.E., 2004. Fuzzy minimum bounding rectangles. In R.de

Caluwe, G. de Tre, and G.Bordogna (editors), Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer, Berlin, pp 237-263

Stoms, D.M., McDonald, J.M., and Davis, F.W., 2002. Fuzzy assessment of land suitability for scientific research reserves. Environmental Management 29 (4): 545-558.

Sui, D.Z., 1992. A fuzzy GIS modeling approach for urban land evaluation. Computers, Environment and Urban Systems 16: 101-115.

Tang, H.J., Debavye, J., Ruan, D., and van Ranst, E., 1991. Land suitability classification based on fuzzy set theory. Pedologie 41: 277-290.

Tang, H.J., and van Ranst, E.,1992. Testing of fuzzy set theory in land suitability assessment for rainfed grain and maize production. Pedologie 42: 129-147.

Tang, J., Wang, L., and Myint, S.W., 2007. Improving urban classification through fuzzy supervised classification and spectral mixture analysis. International Journal of Remote Sensing 28 (18): 4047-4063.

Tang, X.M., Kainz, W., and Fang, Y., 2005. Reasoning about changes of land covers with fuzzy settings. International Journal of Remote Sensing 26 (14): 3025-3046.

Urbanski, J.A., 1999. The use of fuzzy sets in the evaluation of the environment of coastal waters. International Journal of Geographical Information Science 13 (7): 723-730

Usery, E.L., 1996. A conceptual framework and fuzzy set implementation for geographic features. In P.A.Burrough and A. Frank (editors), Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, UK. pp71-85

Van der Meer, F., 2003. Bayesian inversion of imaging spectrometer data using a fuzzy geological outcrop model. International Journal of Remote Sensing 24 (22): 4301-4310.

Verstraete, J., Hallez, A., and Tre, G. de, 2007. Fuzzy regions: Theory and applications. In A. Morris and S.Kokhan (editors), Geographic Uncertainty in Environmental Security, Springer, Dordrecht, pp 1-17.

Verstraete, J., Tre, G. de, Caluwe, R. de, and Hallex, A., 2005. Field based methods for the modeling of fuzzy spatial data. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp 41-69.

Wang, F., 1990. Improving remote sensing image analysis through fuzzy information representation Photogrammetric Engineering and Remote Sensing 56: 1163-1169

Wang, F., 1990. Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 28:194-201

Wang, F., 1994. Towards a natural language user interface: an approach of fuzzy query International Journal of Geographical Information Systems 8: 143-162

Wang, F., 2003. Handling grammatical errors, ambiguity and impreciseness in GIS natural language queries. Transactions in GIS 7 (1): 103-121

Wang, F., and Hall, G.B., 1996. Fuzzy representation of geographical boundaries. International Journal of Geographical Information Systems 10 (5): 573-590

Wang, F., Hall, G.B., and Subaryono, 1990. Fuzzy information represetnation and processing in conventional GIS software: database design and application. International Journal of Geographical Information Systems 4 (3): 261-283

Winter, S., 2000. Uncertain topological relations between imprecise regions. International Journal of Geographical Information Science, 14 (5), 411-430

Witlox, F., and Derudder, B., 2005. Spatial decision-making using fuzzy decision tables: Theory, application and limitations. In F.E.Petry, V.B.Robinson, and M.A.Cobb (editors), Fuzzy Modeling with Spatial Information for Geographic Problems, Springer, Berlin, pp253-274.

Wood, L.J., and Dragicevic, S., 2007. GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection. Biodiversity and Conservation 16: 2539-2558.

Wood, T.F., and Foody, G.M., 1989. Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths. International Journal of Remote Sensing 10: 181-191

Woodcock, C.E. and Gopal, S. 2000.Fuzzy set theory and thematic maps: accuracy assessment and area estimation. International Journal of Geographical Information Science 14 (2): 153-172.

Woodcock, C.E., Gopal, S., and Albert, W., 1996. Evaluation of the potential for providing secondary labels in vegetation maps. Photogrammetric Engineering and Remote Sensing

Zhan, F.B., 2003. Cognitive evidence of vagueness associated with some linguidtic terms in descriptions of spatial relations. . Spatial Cognition and Computation, 3: 239-255

Zhan, F.B., and Lin, H., 2003. Overlay of two simple polygons with indeterminate boundaries. Transactions in GIS 7 (1): 67-81.

Zhang, J., and Foody, G.M., 1998. A fuzzy classification of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing 19 (14): 2721-2738.

Zhang, J., and Foody, G.M., 2001. Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches. International Journal of Remote Sensing 22 (4): 615-628.

Zhang, J., and Kirby, R.P., 1997. An evaluation of fuzzy approaches to mapping land cover from aerial photographs. ISPRS Journal of Photogrammetry and Remote Sensing 52 (2): 193-201.

Zhang, J., and Stuart, N., 2001. Fuzzy methods for categorical mapping with image based land cover data. International Journal of Geographical Information Science, 15 (2): 175-195.

Zhu, A-X., 1997. A similarity model for representing soil spatial information. Geoderma 77 (2-4): 217-242

Zhu, A-X., 1997. Measuring uncertainty in class assignment for natural resource maps under fuzzy logic. Photogrammetric Engineering and Remote Sensing 63: 1195-1202

Zhu, A-X., 2001. Modeling spatial variation of classification accuracy under fuzzy logic. In Hunsaker, C.T., Goodchild, M.F., Friedl, M.A., and Case, T.J. (editors), Spatial Uncertainty in Ecology: Implications fro Remote Sensing and GIS Applications, Springer, Berlin, pp 330-350

Zhu, A-X., Band, L.E., Dutton, B., and Nimlos, T.J., 1996. Automated soil inference under fuzzy logic. Ecological Modeling 90: 123-145

Zhu, A-X., Band, L.E., Vertessy, R., and Dutton, B., 1997. Derivation of soil property using a soil-land inference model (SoLIM). Soil Science Society of American Journal, 61 (2): 523-533

Zhu, A.X., Hudson, B., Burt, J., Lubich, K., and Simonson, D., 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of American Journal 65: 1463-1472.