Research Papers

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

[+] Author Affiliations
Sameer Saran

Indian Institute of Remote Sensing (NRSC), 4 Kalidas Road, Dehradun, Uttarankhand 248001 India

Geert Sterk

Utrecht University, Department of Physical Geography, P.O. Box 80115, Utrecht, TC 3508 Netherlands

Suresh Kumar

Indian Institute of Remote Sensing (NRSC), 4 Kalidas Road, Dehradun, Uttarakhand 248001 India

J. Appl. Remote Sens. 3(1), 033551 (October 2, 2009). doi:10.1117/1.3253618
History: Received November 9, 2007; Revised September 18, 2009; Accepted September 25, 2009; October 2, 2009; November 23, 2009; Online October 02, 2009
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Abstract

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Sameer Saran ; Geert Sterk and Suresh Kumar
"Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed", J. Appl. Remote Sens. 3(1), 033551 (October 2, 2009). ; http://dx.doi.org/10.1117/1.3253618


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