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Remote Sens. 2017, 9(6), 526; doi:10.3390/rs9060526

Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions

1
Ecoclimatology, Department of Ecology and Ecosystem Management, Technische Universität München, Freising 85354, Germany
2
Land Surface-Atmosphere Interactions, Department of Ecology and Ecosystem Management, Technische Universität München, Freising 85354, Germany
3
Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Zaragoza 50059, Spain
4
Institute for Advanced Study, Technische Universität München, Garching 85748, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: John S. Kimball, Kaiyu Guan, Lars T. Waser and Prasad S. Thenkabail
Received: 22 February 2017 / Revised: 17 May 2017 / Accepted: 22 May 2017 / Published: 25 May 2017
(This article belongs to the Special Issue Ecophysiological Remote Sensing)
View Full-Text   |   Download PDF [2412 KB, uploaded 25 May 2017]   |  

Abstract

In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69 forest sites scattered in the Northern Hemisphere. We noted that the relationship between RWI and NDVI varies over space and between tree types (deciduous versus coniferous), bioclimatic zones, cumulative NDVI periods, and spatial resolutions. The high-spatial-resolution NDVI (MODIS) reflected stronger growth patterns than those with coarse-spatial-resolution NDVI (GIMMS3g). In the second section, we explore the link between RWI, climate and NDVI phenological metrics (in place of NDVI) for the same forest sites using random forest models to assess the complicated and nonlinear relationships among them. The results are as following (a) The model using high-spatial-resolution NDVI time series explained a higher proportion of the variance in RWI than that of the model using coarse-spatial-resolution NDVI time series. (b) Amongst all NDVI phenological metrics, summer NDVI sum could best explain RWI followed by the previous year’s summer NDVI sum and the previous year’s spring NDVI sum. (c) We demonstrated the potential of NDVI metrics derived from phenology to improve the existing RWI-climate relationships. However, further research is required to investigate the robustness of the relationship between NDVI and RWI, particularly when more tree-ring data and longer records of the high-spatial-resolution NDVI become available. View Full-Text
Keywords: radial growth; NDVI; MODIS; GIMMS3g; phenology; dendroecology; scPDSI radial growth; NDVI; MODIS; GIMMS3g; phenology; dendroecology; scPDSI
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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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Bhuyan, U.; Zang, C.; Vicente-Serrano, S.M.; Menzel, A. Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions. Remote Sens. 2017, 9, 526.

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