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Remote Sens. 2016, 8(10), 872; doi:10.3390/rs8100872

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

1
Department of Earth & Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
2
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
3
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada
4
Alberta Agriculture and Forestry, Edmonton, AB T6H 5T6, Canada (Formerly with Agricultural Financial Services Corporation)
Current address: School of Natural Resources, University of Nebraska, Lincoln, NE 68583-0989, USA.
*
Authors to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Lenio Soares Galvao, Clement Atzberger and Prasad S. Thenkabail
Received: 28 April 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 22 October 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [3971 KB, uploaded 22 October 2016]   |  

Abstract

Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI–biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program. View Full-Text
Keywords: NDVI; prairie yield; biomass; productivity; drought; MODIS; SPOT NDVI; prairie yield; biomass; productivity; drought; MODIS; SPOT
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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. (CC BY 4.0).

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MDPI and ACS Style

Wehlage, D.C.; Gamon, J.A.; Thayer, D.; Hildebrand, D.V. Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements. Remote Sens. 2016, 8, 872.

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