Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction [An article from: Remote Sensing of Environment] Buy on Amazon

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Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction [An article from: Remote Sensing of Environment]

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PublisherElsevier
ISBN / ASINB000RR8GZ2
ISBN-13978B000RR8GZ8
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States  🇺🇸

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This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.
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