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Are vegetation indices useful in the Arctic?

Published online by Cambridge University Press:  27 October 2009

W. G. Rees ,
E. I. Golubeva  and
M. Williams
W. G. Rees
Affiliation:
Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge CB2 1ER
E. I. Golubeva
Affiliation:
Geography Faculty, Moscow State University, 119899 Moscow, Russia
M. Williams
Affiliation:
Centre for Glaciology, Institute of Earth Studies, University of Wales, Aberystwyth, Dyfed SY23 3DB
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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.

Type
Articles
Information
Polar Record , Volume 34 , Issue 191 , October 1998 , pp. 333 - 336
Copyright
Copyright © Cambridge University Press 1998

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References

Gauslaa, Y. 1984. Infrared and visible reflectance in different lichen species and its ecological significance. Holarctic Ecology 7 (1): 13–22.Google Scholar
Häme, T., Salli, A., and Lahti, K..1992. Estimation of carbon storage and organic matter in boreal forests using optical remote sensing data. In: Proceedings of the Central Symposium of the International Space Year Conference, München, 30 March-4 April 1992. Noordwijk: European Space Agency (ESA SP-341): 75–78.Google Scholar
Høgda, K.A., Tømmervik, H., Solheim, I., and Lauknes, I.. 1995. Mapping of air pollution effects on the vegetation cover in the Kirkenes-Nikel area using remote sensing. In: Proceedings of the IGARSS '95 Symposium, Firenze, 10–14 July 1995. New York: IEEE: 1249–1251.Google Scholar
Holben, B., Tucker, C.J., and Fan, C.-J.. 1980. Spectral assessment of soybean leaf area and leaf biomass. Photogrammetric Engineering and Remote Sensing 46: 651–656.Google Scholar
Kapitsa, A.P., and Golubeva, E.I.. 1997. The ecological structure of tundra plant cover as an ecological indicator in the Kola Peninsula. In: Crawford, R.M.M. (editor). Disturbance and recovery in Arctic lands. Dordrecht: Kluwer: 283–291.CrossRefGoogle Scholar
Petzold, D.E., and Goward, S.N.. 1988. Reflectance spectra of subarctic lichens. Remote Sensing of Environment 24: 481–492.CrossRefGoogle Scholar
Petzold, D.E., and Rencz, A.N.. 1975. The albedo of selected subarctic surfaces. Alpine Research 7: 393–398.CrossRefGoogle Scholar
Rees, W.G., and Kapitsa, A.P.. 1994. Industrial pollution in the Kol'skiy Poluostrov, Russia. Polar Record 30 (174): 181–188.CrossRefGoogle Scholar
Rees, W. G., and Williams, M.. 1997. Monitoring changes in land cover induced by atmospheric pollution in the Kola Peninsula, Russia, using Landsat-MSS data. International Journal of Remote Sensing 18 (8): 1703–1723.CrossRefGoogle Scholar
Satterwhite, M.B., Henley, J.P., and Carney, J.M.. 1985. Effects of lichens on the reflectance spectra of granitic rock surfaces. Remote Sensing of Environment 18: 105–112.Google Scholar
Shibayama, M., Salli, A., Häme, T., Iso-livari, L., Morinaga, S., Inoue, Y., and Akiyama, T.. 1995. Spectral detection of subarctic vegetation phenophases. In: Proceedings of the IGARSS '95 Symposium, Firenze, 10–14 July 1995. New York: IEEE: 1488–1490.Google Scholar
Shippert, M.M., Walker, D.A., Auerbach, N.A., and Lewis, B.E.. 1995. Biomass and leaf-area index maps derived from SPOT images for Toolik Lake and Imnavait Creek areas, Alaska. Polar Records 31 (177): 147–154.CrossRefGoogle Scholar
Stow, D., Burns, B., and Hope, A.. 1989. Mapping Arctic tundra vegetation types using digital SPOT/HRV-XS data: a preliminary assessment. International Journal of Remote Sensing 10 (8): 1451–1457.CrossRefGoogle Scholar
Tucker, C.J., Miller, L.D., and Pearson, R.L.. 1975. Shortgrass prairie spectral measurements. Photogrammetric Engineering and Remote Sensing 41: 1175–1183.Google Scholar
Tucker, C.J., Townshend, J.R.G., and Goff, T.E.. 1985. African land-cover classification using satellite data. Science 227: 369–374.CrossRefGoogle ScholarPubMed