Journal of
Geography and Regional Planning

  • Abbreviation: J. Geogr. Reg. Plann.
  • Language: English
  • ISSN: 2070-1845
  • DOI: 10.5897/JGRP
  • Start Year: 2008
  • Published Articles: 385

Full Length Research Paper

Spectral vegetation indices performance evaluated for Cholistan Desert

  Farooq Ahmad  
Department of Geography, University of the Punjab, Lahore, Pakistan.
Email: [email protected]

  •  Accepted: 29 November 2011
  •  Published: 18 March 2012

Abstract

 

To enhance the vegetation signal in remotely sensed data and provide an approximate measure of live green vegetation, a number of spectral vegetation indices have been developed to estimate biophysical parameters of vegetation. The sensitivity of the normalized difference vegetation index (NDVI) to the soil background and atmospheric effects has generated an increasing interest in the development of new indices. The modified soil-adjusted vegetation index (MSAVI) and its later revision, MSAVI2, are soil-adjusted vegetation indices that seek to address some of the limitations of NDVI when applied to areas with a high degree of exposed soil surface because the reflectance of light in the red and near-infrared (NIR) spectra can influence vegetation index values. The soil-adjusted vegetation index (SAVI) was developed as a modification ofthe NDVI to correct for the influence of soil brightness when vegetative cover is low. The problem with the SAVI is that it required specifying the soil-brightness correction factorL through trial-and-error based on the amount of vegetation. This article focuses on testing and comparing the sensitivity of vegetation indices to soil background effects. Five vegetation indices; NDVI, transformed normalized difference vegetation index (TNDVI), enhanced vegetation index (EVI), SAVI and MSAVI2 were quantitatively evaluated using Landsat ETM+ dataset over the Cholistan Desert to find the best vegetation index for use in sparsely vegetated semi-arid and arid tracts of Pakistan.

 

Key words: Atmospheric effects, Cholistan, Landsat ETM+, Remote sensing, Spectral vegetation indices.