Are vegetation indices useful in the Arctic?

Abstract

This paper describes a preliminary investigation of the extent to which the normalised difference vegetation index (NDVI), derived from satellite optical imagery, can indicate the extent of damage to upland tundra (fruticose lichen and dwarf shrub) vegetation. We combine the results of a previously reported classification of Landsat multispectral scanner imagery from Kol'skiy Poluostrov, Russia, with field measurements of the biomass and spectral reflectance of tundra vegetation. The results show that the NDVI is not strongly influenced by biomass, but that differences in species composition and ground cover are significant. Other workers have concluded that vegetation indices are not useful for boreal forests. It is therefore suggested that the use of the NDVI by itself as an indicator of the state of disturbed vegetation in Arctic regions is not recommended.

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

“…The normalized difference vegetation index (NDVI; Rouse et al, 1974) is one of the most widely used. Within Arctic vegetation studies, it has been used at regional (Walker et al, 2002;Jia et al, 2003) and local scales Rees et al, 1998;McMichael et al, 1999).…”

Arctic shrub effects on NDVI, summer albedo and soil shading

“…However, our data and model results indicate a saturation of 400 NDVI for PAIs starting between 2 and 3 ( Figure 5, e). Thus NDVI might be less useful for relating further Arctic greening to biomass (Rees et al, 1998), especially as it also relates to other factors like moisture conditions (Gamon et al, 2013) and background type (Hope et al, 1993;Rocha & Shaver, 2009 canopy. This is consistent with the study by Boelman et al (2011) which also reports higher NDVI values for vegetation prior to leaf out when woody branch coverage is sparse as compared to more dense branch coverage, which masks the background vegetation.…”

Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO2Exchange across a Soil Moisture Gradient

“…These data provide a unique opportunity for investigating the biophysical properties of vegetation over space DAVID M. ATKINSON AND PAUL TREITZ / 161 ᭧ 2013 Regents of the University of Colorado 1523-0430/6 $7.00 and time (Tieszen et al, 1997;Stow et al, 1998Stow et al, , 2000Laidler et al, 2008). Remotely sensed data can: (i) provide baseline data to delineate vegetation community patterns (Spjelkavik, 1995;Hope et al, 1995;Rees et al, 1998;Walker et al, 1982Walker et al, , 2005Stow et al, 1989;Mosbech and Hansen, 1994;Muller et al, 1999); (ii) examine community structure (e.g., estimate aboveground biomass) (Hope et al, 1993;Spjelkavik, 1995;Shippert et al, 1995;Walker et al, 1995;Epstein et al, 2012); and (iii) predict CO 2 flux patterns at a variety of spatial scales (Stow et al, 1993b;Ostendorf and Reynolds, 1998;McMichael et al, 1999;Shaver et al, 2007). Stow et al (1998) stated that for carbon storage and flux patterns to be predicted from remote sensing data, measurements of these variables are required to calibrate and validate the models.…”

“…The normalized difference vegetation index (NDVI) (Rouse et al, 1974) has been the most commonly employed spectral vegetation index (VI) in biophysical analyses, including those conducted in Arctic regions (e.g., Shippert et al, 1995;Walker et al, 1995;Rees et al, 1998;Stow et al, 2004;La Puma et al, 2007;Laidler et al, 2008). The index is derived from the difference in reflectivity of the land cover in the near-infrared (NIR) band, where vegetation structure reflects strongly, and the red (R) band, where vegetation absorbs strongly as a function of chlorophyll concentration.…”