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Biomass and leaf-area index maps derived from SPOT images for Toolik Lake and Imnavait Creek areas, Alaska

Published online by Cambridge University Press:  27 October 2009

Margaret M. Shippert ,
Donald A. Walker ,
Nancy A. Auerbach  and
Brad E. Lewis
Margaret M. Shippert
Affiliation:
Institute for Arctic and Alpine Research, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA
Donald A. Walker
Affiliation:
Institute for Arctic and Alpine Research, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA
Nancy A. Auerbach
Affiliation:
Institute for Arctic and Alpine Research, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA
Brad E. Lewis
Affiliation:
Institute for Arctic and Alpine Research, Campus Box 450, University of Colorado, Boulder, Colorado 80309-0450, USA
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Abstract

A new emphasis on understanding natural systems at large spatial scales has led to an interest in deriving ecological variables from satellite reflectance images. The normalized difference vegetation index (NDVI) is a measure of canopy greenness that can be derived from reflectances at near-infrared and red wavelengths. For this study we investigated the relationships between NDVI and leaf-area index (LAI), intercepted photosynthetically active radiation (IPAR), and biomass in an Arctic tundra ecosystem. Reflectance spectra from a portable field spectrometer, LAI, IPAR, and biomass data were collected for 180 vegetation samples near Toolik Lake and Imnavait Creek, Alaska, during July and August 1993. NDVI values were calculated from red and near-infrared reflectances of the field spectrometer spectra. Strong linear relationships are seen between mean NDVI for major vegetation categories and mean LAI and biomass. The relationship between mean NDVI and mean IPAR for these categories is not significant. Average NDVI values for major vegetation categories calculated from a SPOT image of the study area were found to be highly linearly correlated to average field NDVI measurements for the same categories. This indicates that in this case it is appropriate to apply equations derived for field-based NDVI measurements to NDVI images. Using the regression equations for those relationships, biomass and LAI images were calculated from the SPOT NDVI image. The resulting images show expected trends in LAI and biomass across the landscape.

Type
Articles
Information
Polar Record , Volume 31 , Issue 177 , April 1995 , pp. 147 - 154
Copyright
Copyright © Cambridge University Press 1995

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References

Asrar, G., Kanemasu, E.T., Jackson, R.D., and Pinter, P.J.. 1985. Estimation of total above-ground phytomass production using remotely sensed data. Remote Sensing of Environment 17: 211–220.CrossRefGoogle Scholar
Auerbach, N.A. 1992. Effects of road and dust disturbance in minerotrophic and acidic tundra ecosystems, northern Alaska. Unpublished MS thesis, University of Colorado.Google Scholar
Baret, G. and Guyot, G.. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35: 161–173.CrossRefGoogle Scholar
Biscoe, P.V., Gallagher, J.N., Littleton, E.J., Monteith, J.L., and Scott, R.K.. 1975. Barley and its environment, IV. Sources of assimilate for the grain. Journal of Applied Ecology 12 295–318.Google Scholar
Box, E.O., Holben, B.N., and Kalb, V.. 1989. Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetation 80: 71–89.CrossRefGoogle Scholar
Colwell, J.E. 1973. Bidirectional spectral reflectance of grass canopies for determination of above ground standing biomass. Unpublished PhD thesis, University of Michigan.Google Scholar
Colwell, J.E. 1974. Vegetation canopy reflectance. Remote Sensing of Environment 3: 175–183.CrossRefGoogle Scholar
Costanza, R., and Maxwell, T.. 1994. Resolution and predictability: an approach to the scaling problem. Landscape Ecology 9 (1): 47–57.CrossRefGoogle Scholar
Gallo, K.P., and Daughtry, C.S.T.. 1987. Differences in vegetation indices for simulated Landsat-5 MSS and TM, NOAA-9 AVHRR, and SPOT-1 sensor systems. Remote Sensing of Environment 23: 439–452.CrossRefGoogle Scholar
Hansen, B.U. 1991. Monitoring natural vegetation in southern Greenland using NOAA AVHRR and field measurements. Arctic 44 (1): 94–101.CrossRefGoogle Scholar
Hatfield, J.L., Asrar, G., and Kanemasu, E.T.. 1984. Intercepted photosynthetically active radiation estimated by spectral reflectance. Remote Sensing of Environment 14: 65–75.CrossRefGoogle Scholar
Hodges, T., and Kanemasu, E.T.. 1977. Modeling daily dry matter production of winter wheat. Agronomy Journal 69: 974–978.CrossRefGoogle Scholar
Hope, A.S., Kimball, J.S., and Stow, D.A.. 1993. The relationship between tussock tundra spectral reflectance properties and biomass and vegetation composition. International Journal of Remote Sensing 14 (10): 1861–1874.CrossRefGoogle Scholar
Jordan, C.F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50: 663–666.CrossRefGoogle Scholar
Perry, C.R., and Lautenschlager, L.F.. 1984. Functional equivalence of spectral vegetation indices. Remote Sensing of Environment 14: 169–182.CrossRefGoogle Scholar
Prince, S.D. 1991. Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981–1988. International Journal of Remote Sensing 12 (6): 1301–1311.CrossRefGoogle Scholar
Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W.. 1973. Monitoring vegetation systems in the great plains with ERTS. In: Third ERTS Symposium. Greenbelt, MD: NASA (SP-351) 1: 309–317.Google Scholar
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C.. 1974. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. Greenbelt, MD: NASA/GSFC (Type Ill Final Report).Google Scholar
Sellers, P.J. 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6 (8): 1335–1372.CrossRefGoogle Scholar
Sellers, P.J. 1987. Canopy reflectance, photosynthesis and transpiration. II: The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment 21: 143–183.CrossRefGoogle Scholar
Sellers, P.J. 1992. Canopy reflectance, photosynthesis, and transpiration. Ill: A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment 42: 187–216.CrossRefGoogle Scholar
Shaver, G., and Chapin, F.S. III. 1991. Production: biomass relationships and element cycling in contrasting Arctic vegetation types. Ecological Monographs 61 (1): 1–31.CrossRefGoogle Scholar
Steven, M.D., Biscoe, P.V., Jaggard, K.W.. 1983. Estimation of sugarbeet productivity from reflection in the red and infrared spectral bands. International Journal of Remote Sensing 4: 325–334.CrossRefGoogle Scholar
Stow, D.A., Hope, A.S., and George, T.H.. 1993. Reflectance characteristics of Arctic tundra vegetation from airborne radiometry. International Journal of Remote Sensing 14 (6): 1239–1244.CrossRefGoogle Scholar
Tucker, C.J. 1976. Asymptotic nature of grass canopy spectral reflectance. Applied Optics 16 (5): 1151–1156.CrossRefGoogle Scholar
Tucker, C.J., and Sellers, P.J.. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing 7 (11): 1395–1416.CrossRefGoogle Scholar
Walker, D.A., Auerbach, N.A., and Shippert, M.M.. 1995. NDVI, biomass, and landscape evolution of glaciated terrain in northern Alaska. Polar Record 31 (177): 169–178.CrossRefGoogle Scholar
Walker, D.A., Binnian, E.F., Evans, B.M., Lederer, N.D., Nordstrand, E., and Webber, P.J.. 1989. Terrain, vegetation and landscape evolution of the DOE R4D research site, Brooks Range foothills, Alaska. Holarctic Ecology 12: 238–261.Google Scholar
Walker, D.A., and Walker, M.D.. 1991. History and pattern of disturbance in Alaskan Arctic terrestrial ecosystems: a hierarchical approach to analyzing landscape change. Journal of Applied Ecology 28: 244–276.CrossRefGoogle Scholar
Wiegand, C.L., Richardson, A.J., and Kanemasu, E.T.. 1979. Leaf area index estimates for wheat from Landsat and their implications for evapotranspiration and crop modeling. Agronomy Journal 71: 336–342.CrossRefGoogle Scholar