Next Article in Journal
An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method
Next Article in Special Issue
Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling
Previous Article in Journal
Upliftment Estimation of the Zagros Transverse Fault in Iran Using Geoinformatics Technology
Previous Article in Special Issue
Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests
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

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)
Download PDF [593 KB, uploaded 19 June 2014]
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 Landsat; IRS; mountain shadow; land use land cover classification; band ratios; ancillary data; Nepal
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.

Export to BibTeX |
EndNote


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.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert