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Remote Sens. 2013, 5(9), 4347-4369;

Assessing Regional Climate and Local Landcover Impacts on Vegetation with Remote Sensing

Department of Geography, University of Kansas, 1475 Jayhawk Blvd. 214B Lindley Hall, Lawrence, KS 66045, USA
Author to whom correspondence should be addressed.
Received: 1 July 2013 / Revised: 26 August 2013 / Accepted: 3 September 2013 / Published: 9 September 2013
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Landcover change alters not only the surface landscape but also regional carbon and water cycling. The objective of this study was to assess the potential impacts of landcover change across the Kansas River Basin (KRB) by comparing local microclimatic impacts and regional scale climate influences. This was done using a 25-year time series of Normalized Difference Vegetation Index (NDVI) and precipitation (PPT) data analyzed using multi-resolution information theory metrics. Results showed both entropy of PPT and NDVI varied along a pronounced PPT gradient. The scalewise relative entropy of NDVI was the most informative at the annual scale, while for PPT the scalewise relative entropy varied temporally and by landcover type. The relative entropy of NDVI and PPT as a function of landcover showed the most information at the 512-day scale for all landcover types, implying different landcover types had the same response across the entire KRB. This implies that land use decisions may dramatically alter the local time scales of responses to global climate change. Additionally, altering land cover (e.g., for biofuel production) may impact ecosystem functioning at local to regional scales and these impacts must be considered for accurately assessing future implications of climate change. View Full-Text
Keywords: landcover; information theory; wavelet analysis; precipitation; NDVI; vegetation landcover; information theory; wavelet analysis; precipitation; NDVI; vegetation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Lin, P.-L.; Brunsell, N. Assessing Regional Climate and Local Landcover Impacts on Vegetation with Remote Sensing. Remote Sens. 2013, 5, 4347-4369.

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