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Spatial Upscaling of Tree-Ring-Based Forest Response to Drought with Satellite Data

School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
School of Earth and Environmental Sciences, Queens College of the City University of New York, New York, NY 11367, USA
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
Earth and Environmental Sciences Department, the Graduate Center of the City University of New York, New York, NY 10016, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(20), 2344;
Received: 24 September 2019 / Revised: 7 October 2019 / Accepted: 8 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Remote Sensing of Forest Growth in a Changing Climate)
We have integrated the observational capability of satellite remote sensing with plot-scale tree-ring data to upscale the evaluation of forest responses to drought. Satellite data, such as the normalized difference vegetation index (NDVI), can provide a spatially continuous measure with limited temporal coverage, while tree-ring width index (RWI) provides an accurate assessment with a much longer time series at local scales. Here, we explored the relationship between RWI and NDVI of three dominant species in the Southwestern United States (SWUS) and predicted RWI spatial distribution from 2001 to 2017 based on Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km resolution NDVI data with stringent quality control. We detected the optimum time windows (around June–August) during which the RWI and NDVI were most closely correlated for each species, when the canopy growth had the greatest effect on growth of tree trunks. Then, using our upscaling algorithm of NDVI-based RWI, we were able to detect the significant impact of droughts in 2002 and in 2011–2014, which supported the validity of this algorithm in quantifying forest response to drought on a large scale. View Full-Text
Keywords: NDVI; tree ring; upscaling; drought NDVI; tree ring; upscaling; drought
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Xu, P.; Fang, W.; Zhou, T.; Zhao, X.; Luo, H.; Hendrey, G.; Yi, C. Spatial Upscaling of Tree-Ring-Based Forest Response to Drought with Satellite Data. Remote Sens. 2019, 11, 2344.

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