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

Authors: Abou EL-Magd, Islam; Tanton, T. W.

Source: International Journal of Remote Sensing, Volume 24, Number 21, 2003 , pp. 4197-4206(10)

Publisher: Taylor and Francis Ltd

Buy & download fulltext article:

OR

Price: $61.16 plus tax (Refund Policy)

Abstract:

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

More about this publication?
Related content

Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page