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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 12, No. 5, 2015, pp. 1515-1526
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Bioline Code: st15141
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 12, No. 5, 2015, pp. 1515-1526
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A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran
Mohammady, M.; Moradi, H.R.; Zeinivand, H. & Temme, A.J.A.M.
Abstract
Land use classification is often the first step in
land use studies and thus forms the basis for many earth
science studies. In this paper, we focus on low-cost techniques
for combining Landsat images with geographic
information system approaches to create a land use map. In
the Golestan region of Iran, we show that traditional
supervised and unsupervised methods do not result in
sufficiently accurate land use maps. Therefore, we evaluated
a synthetic approach combining supervised and
unsupervised methods with decision rules based on easily
accessible ancillary data. For accuracy assessment, confusion
matrices and kappa coefficients were calculated for the
maps created with the supervised, unsupervised and synthetic
approaches. Overall accuracy of the synthetic
approach was 98.2 %, which is over the 85 % level that is
considered satisfactory for planning and management
purposes. This shows that integration of remote sensing
data, ancillary data and decision rules provides better
classification accuracy than traditional methods, without
significant additional use of resources.
Keywords
Land use classification; ISODATA; Maximum likelihood; Synthetic method; Ancillary data; Iran
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