Abstract
In the identification of landscape features vagueness arises from the fact that the attributes and parameters that make up a landscape vary over space and scale. In most existing studies, these two kinds of vagueness are studied separately. This paper investigates their combination (double vagueness) in the identification of coastal landscape units. Fuzzy set theory is used to describe the vagueness of geomorphic features based on continuity in space. The vagueness resulting from the scale of measurement is evaluated by statistical indicators. The differences of fuzzy objects derived from data at differing resolutions are studied in order to examine these higher-order uncertainties. Multi-scale analysis of the landscape is carried out using a moving window, ranging in size from 60x60 meters to 1500x1500 meters. The statistics of the fuzziness of the fuzzy landscape units are calculated, and the variability of them with scale is assessed. It shows that a major affect of scale on the mapping of geomorphic landscape units, is determination of the area of those units. This result implies that caution must be exercised in comparing landscapes at different scales and in choosing the resolution of the data that best describes the process under study.
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References
Brassel, K. E. and Weibel, R., 1988, A Review and Conceptual Framework for Automated Map Generalisation, International Journal of Geographical Information Systems, 2, 229–244.
Burrough, P. A., van Gaans, P. F. M., and MacMillan, R. A., 2000, High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113, 37–52.
Cheng, T., Molenaar, M. and Bouloucos, T., 1997, Identification of fuzzy objects from field observation data. In Spatial Information Theory: A Theoretical Basis for GIS, Lecture Notes in Computer Sciences, edited by S. C. Hirtle and A. U. Frank (Berlin: Spring-Verlag), Vol. 1329, pp. 241–259.
Cheng, T. and Molenaar, M, 1999, Objects with fuzzy spatial extent. Photogrammetric Engineering and Remote Sensing, 65, 797–801.
Cheng, T., 2002, Fuzzy spatial objects, their change and uncertainties, Photogrammetric Engineering and Remote Sensing, 68, 41–49.
Fisher, P., Wood, J., Cheng, T., and Rogers, P., 2004, Where is Helvellyn? Fuzziness of multiscale landscape morphometry. Transactions of the Institute of British Geographers, 29, 106–128.
Foody, G. M., 2002, Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.
Fotheringham, A. S., and Wong, D. W. S., 1991, The modifiable areal unit problem in statistical analysis, Environment and Planning, 23, 1025–1044.
Francis, J. M. and Kopatek, J. M., 2000, Multiscale effects of grain size on land-scape pattern analysis. Geographic Information Sciences, 6, 27–37.
Hay, G. J., Blaschke., T., Marceau, D. J., and Bouchard, A., 2003, A comparision of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry & Remote Sensing, 57, 327–345.
He, H.S., Venturta, S. J., and Mladenoff, D.J., 2002, Effects of spatial aggregation approaches on classified satellite imagery. International Journal of Geographical Information Science, 16, 93–109.
Heuvelink, G. B. M. and Burrough, P., 2002, Developments in statistical approaches to spatial uncertainty and its Propagation, International Journal of Geographical Information Science, 16, 111–113.
Jelinski, D. E., and Wu, J., 1996, The modifiable area unit problem and implications for landscape. Landscape Ecology, 11, 129–140.
Li, Z. L. and Openshaw, S., 1993, A natural principle for objective generalisation of digital map data. Cartography and Geographic Information System, 20, 19–29.
Mackay, D. S., Samanta, S., Ahl, D. E., Ewers, B. E., Gower, S., and Burrows, S. N., 2003, Automated parameterization of land surface process models using fuzzy logic. Transactions in GIS, 7, 139–153.
Marceau, D. J., 1999, The scale issue in the social and natural sciences. Canadian Journal of Remote Sensing, 25, 347–356.
McMaster, R. B. and Shea, K. S., 1992, Generalization in Digital Cartography (Association of American Geographers), 134p.
Openshaw, S., 1983, The Modifiable Areal Unit Problem, CATMOG 38 (Norwich, UK: Geo Books).
Robinson, V. B., 2003, A perspective on the fundamentals of fuzzy sets and their use in geographic information systems. Transactions in GIS, 7, 3–30.
Tate, N. and Atkinson, P. (eds.)., 2001. Modelling Scale in Geographical Information Science (Chichester: Jone Wiley & Sons), 277pp.
Usery, E. L., 1996, A conceptual framework and fuzzy set implementation for geographic feature. In Geographic Objects with Indeterminate Boundaries, edited by P. A. Burrough and A. U. Frank (London: Taylor & Francis), pp. 71–85.
Wang, F. and Hall, G. B., 1996, Fuzzy representation of geographical boundaries in GIS, International Journal of Geographical Information Systems, 10, 573–590
Wood, J., 1996, The Geomorphological Characterisation of Digital Elevation Models Unpublished PhD Thesis, Department of Geography, University of Leicester. (http://www.soi.city.ac.uk/~jwo/phd), Accessed February 2003.
Wu, J., and Qi, Y., 2000, Dealing with scale in landscape analysis: an overview. Geographic Information Sciences, 6, 1–5.
Wu, J., Jelinski, D. E., Luck, M. and Tueller, P.T., 2000, Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. Geographic Information Sciences, 6, 6–19.
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Cheng, T., Fisher, P., Li, Z. (2005). Double Vagueness: Effect of Scale on the Modelling of Fuzzy Spatial Objects. In: Developments in Spatial Data Handling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26772-7_23
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DOI: https://doi.org/10.1007/3-540-26772-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22610-9
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