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Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification

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The accuracy of conventional land use classification of irrigated agriculture from optical satellite images using maximum likelihood supervised classification was compared with a classification based on multistage maximum likelihood supervised classification. In the multistage maximum likelihood classification series of sub-classifications were carried out which included masking and/or omitting certain crops from the classifications. These series of classifications improved the identification of individual crops/land use types. The output from the optimum sub-classifications were stacked to give an overall crop types/land use map. When the multistage classification was tested against a single stage classification on a large irrigation scheme in Central Asia the final accuracy of crop/land use classification increased from 85% to 94%. Field verification confirmed the accuracy at 93.5%. These results were achieved with a single Landsat 7 Enhanced Thematic Mapper (ETM+) sensor dataset as of 2 August 1999 over an area of 38.5 km2.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/0143116031000139791

Affiliations: Department of Civil and Environmental Engineering University of Southampton Highfield Southampton SO17 1BJ UK

Publication date: November 1, 2003

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