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Stock Volume Dependency of Forest Drought Responses in Yunnan, China

1,2, 1,2,*, 3,4, 1,2,3, 5, 1,2 and 1,2
Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, 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
Earth and Environmental Sciences Department, the Graduate Center of the City University of New York, New York, NY 10016, 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
Author to whom correspondence should be addressed.
Forests 2018, 9(4), 209;
Received: 20 February 2018 / Revised: 5 April 2018 / Accepted: 13 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Remotely Sensing of Drought-Induced Forest Change and Recovery)
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Revealing forest drought response characteristics and the potential impact factors is quite an important scientific issue against the background of global climate change, which is the foundation to reliably evaluate and predict the effects of future drought. Due to the high spatial heterogeneity of forest properties such as biomass, forest age, and height, and the distinct differences in drought stress in terms of frequency, intensity, and duration, current studies still contain many uncertainties. In this research, we used the forests in Yunnan Province in Southwest China as an example and aimed to reveal the potential impacts of forest properties (i.e., stock volume) on drought response characteristics. Specifically, we divided the forest into five groups of stock volume density values and then analyzed their drought response differences. To depict forest response to drought intensity, the standardized precipitation evapotranspiration index (SPEI) was chosen as the explanatory variable, and the change in remote sensing-based enhanced vegetation index (deficit of MODIS-EVI, dEVI) was chosen as the response variable of drought stress. Given that the SPEI has different time scales, we first analyzed the statistical dependency of SPEIs with different time scales (1 to 36 months) to the response variable (i.e., dEVI). The optimal time scale of SPEI (SPEIopt) to interpret the maximum variation of dEVI (R-square) was then chosen to build the ultimate statistical models for the five groups of stock volume density. The main findings were as follows: (1) the impacts of drought showed hysteresis and cumulative effects, and the length of the hysteresis increased with stock volume densities; (2) forests with high stock volume densities required more soil water and were therefore more sensitive to the changes in water deficit; (3) compared with the optimal time scale of SPEI (SPEIopt), the SPEI with the commonly used time scale (e.g., 1, 6, and 12 months) could not well reflect the impacts of drought on forests and the simulation error of dEVI increased with stock volume densities; and (4) forests with higher stock volume densities were likely to experience a greater risk of degradation following higher atmospheric concentrations of greenhouse gases (Representative Concentration Pathway (RCP) 8.5). As a result, both the time scale of the meteorological drought index and the spatial difference in forest stock volumes should be considered when evaluating forest drought responses at regional and global scales. View Full-Text
Keywords: drought; SPEI; EVI; stock volume; forest drought; SPEI; EVI; stock volume; forest

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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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Luo, H.; Zhou, T.; Yi, C.; Xu, P.; Zhao, X.; Gao, S.; Liu, X. Stock Volume Dependency of Forest Drought Responses in Yunnan, China. Forests 2018, 9, 209.

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