Fuzzy mapping of tropical land cover along an environmental gradient from remotely sensed data with an artificial neural network |
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| Authors: | Giles M Foody Doreen S Boyd |
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| Institution: | (1) Department of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, UK (e-mail: g.m.foody@soton.ac.uk), GB;(2) School of Geography, Kingston University, Penrhyn Road, Kingston-upon-Thames KT1 2EE, UK (e-mail: d.boyd@kingston.ac.uk), GB |
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| Abstract: | Remote sensing is the only feasible means of mapping and monitoring land cover at regional to global scales. Unfortunately
the maps are generally derived through the use of a conventional 'hard' classification algorithm and depict classes separated
by sharp boundaries. Such approaches and representations are often inappropriate particularly when the land cover being represented
may be considered to be fuzzy. The definition of boundaries between classes can therefore be difficult from remotely sensed
data, particularly for continuous land cover classes which are separated by a fuzzy boundary which may also vary spatially
in time. In this paper a neural network was used to derive fuzzy classifications of land cover along a transect crossing the
transition from moist semi-deciduous forest to savanna in West Africa in February and December 1990. The fuzzy classifications
revealed both sharp and gradual boundaries between classes located along the transect. In particular, the fuzzy classifications
enabled the definition of important boundary properties, such as width and temporal displacement. |
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| Keywords: | : Remote sensing fuzzi classification boundaries neural network |
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