Remote Sens. 2009, 1(4), 1257-1272; doi:10.3390/rs1041257
Article

Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal

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Received: 21 October 2009; in revised form: 23 November 2009 / Accepted: 2 December 2009 / Published: 8 December 2009
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective of this study was to explore and evaluate the use of modified band and ancillary data in Landsat and IRS image classification, and to produce a land use land cover map of the Galaudu watershed of Nepal. Classification of land uses were explored using supervised and unsupervised classification for 12 feature sets containing the LandsatMSS, TM and IRS original bands, ratios, normalized difference vegetation index, principal components and a digital elevation model. Overall, the supervised classification method produced higher accuracy than the unsupervised approach. The result from the combination of bands ration 4/3, 5/4 and 5/7 ranked the highest in terms of accuracy (82.86%), while the combination of bands 2, 3 and 4 ranked the lowest (45.29%). Inclusion of DEM as a component band shows promising results.
Keywords: Landsat; IRS; mountain shadow; land use land cover classification; band ratios; ancillary data; Nepal
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MDPI and ACS Style

Bahadur K.C., K. Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal. Remote Sens. 2009, 1, 1257-1272.

AMA Style

Bahadur K.C. K. Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal. Remote Sensing. 2009; 1(4):1257-1272.

Chicago/Turabian Style

Bahadur K.C., Krishna. 2009. "Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal." Remote Sens. 1, no. 4: 1257-1272.

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