Abstract
Data on the distribution of vegetation in space and time are required in a range of studies. Such data are, however, typically unavailable or are of poor quality (Williams 1994; DeFries and Townshend 1994). Often the only practicable means of acquiring data on vegetation distribution at appropriate spatial and temporal resolutions is through remote sensing (Townshend et al. 1991; Skole 1994). The considerable potential of remote sensing for mapping and monitoring vegetation has, however, frequently not been fully realized. Of the many reasons for this, one major limitation has been the reliance on conventional supervised image classification approaches as the tool for mapping.
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References
Bauer ME, Burk TE, Ek AR, Coppin PR, Lime SD, Walsh TA, Walters DK (1994) Satellite inventory of Minnesota forest resources. Photogrammetric Engineering and Remote Sensing 60:287-298
Bowers TL, Rowan LC (1996) Remote mineralogic and lithologic mapping of the Ice River Alkaline Complex, British Columbia, Canada, using AVIRIS data. Photogrammetric Engineering and Remote Sensing 62:1379-1385
Campbell JB (1996) Introduction to remote sensing, second edition. Taylor and Francis, London
Campbell WG, Mortenson DC (1989) Ensuring the quality of geographic information system data: A practical application of quality control. Photogrammetric Engineering and Remote Sensing 55:1613-1618
DeFries RS, Townshend JRG (1994) Global land cover: Comparison of ground based data sets to classifications with AVHRR data. In: Foody GM, Curran PJ (eds) Environmental remote sensing from regional to global scales. Wiley, Chichester, pp 84-110
DeFries 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 vegetations functional-properties. Journal of Geophysical Research-Atmospheres 100:20867-20882
Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. Int J Rem Sen 17:1317-1340
Foody GM (1999) The continuum of classification fuzziness in thematic mapping. Photogrammetric engineering and remote sensing 65:443-451
Foody GM, Campbell NA, Trodd NM, Wood TF (1992) Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification. Photogrammetric Engineering and Remote Sensing 58:1335-1341
Foody GM, Lucas RM, Curran PJ, Honzak M (1997) Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery. Plant Ecology 131:143-154
Kent M, Gill WJ, Weaver RE, Armitage RP (1997) Landscape and plant community boundaries in biogeography. Progress in Physical Geography 23:315-353
Klir GJ, Folger TA (1988) Fuzzy sets, uncertainty and information. Prentice-Hall International, London
Maselli F, Conese C, Petkov L (1994) Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. ISPRS Journal of Photogrammetry and Remote Sensing 49:13-20
Schalkoff R (1992) Pattern recognition: Statistical, structural and neuronal approaches. Wiley, New York
Sheppard CRC, Matheson K, Bythell JC, Murphy P, Myers CB, Blake B (1995) Habitat mapping in the Caribbean for management and conservation: Use and assessment of aerial photography. Aquatic Conservation: Marine and Freshwater Ecosystems 5:277-298
Skole DL (1994) Data on globalland-cover changes: Acquisition, assessment and analysis. In: Meyer WB, Turner II BL (eds) Changes in land use and land cover: A global perspective. Cambridge University Press, Cambridge, pp 437-471
Townshend J, Justice C, Li W, Gurney C, McManus J (1991) Global land cover classification by remote sensing: Present capabilities and future possibilities. Remote Sensing of Environment 35:243-255
Trodd NM, Foody GM, Wood TF (1989) Maximum likelihood and maximum information: Mapping heathland with the aid of probabilities derived from remotely sensed data. In: Remote Sensing Society (ed) Remote sensing for operational applications. Nottingham, pp 421-426
Wang F (1990) Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 28:194-201
Williams M (1994) Forest and tree cover. In: Meyer WB, Turner II BL (eds) Changes in land use and land cover: A global perspective. Cambridge University Press, Cambridge, pp 97-124
Wilson AK (1997) An integrated data system for airborne remote sensing. Int J Rem Sen 18:1889-1901
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Foody, G.M. (2000). Soft Mapping of Coastal Vegetation from Remotely Sensed Imagery with a Feed-Forward Neuronal Network. In: Lek, S., Guégan, JF. (eds) Artificial Neuronal Networks. Environmental Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57030-8_3
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DOI: https://doi.org/10.1007/978-3-642-57030-8_3
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