Wind farms (WFs) can affect the local climate, and local climate change may influence underlying vegetation. Some studies have shown that WFs affect certain aspects of the regional climate, such as temperature and rainfall. However, there is still no evidence to demonstrate whether WFs can affect local vegetation growth, a significant part of the overall assessment of WF effects. In this research, based on the moderate-resolution imaging spectroradiometer vegetation index, productivity and other remote-sensing data from 2003 to 2014, the effects of WFs in the Bashang area of Northern China on vegetation growth and productivity in the summer (June–August) were analyzed. The results showed that: (1) WFs had a significant inhibiting effect on vegetation growth, as demonstrated by decreases in the leaf area index, the enhanced vegetation index, and the normalized difference vegetation index of approximately 14.5%, 14.8%, and 8.9%, respectively, in the 2003–2014 summers. There was also an inhibiting effect of 8.9% on summer gross primary production and 4.0% on annual net primary production coupled with WFs; and (2) the major impact factors might be the changes in temperature and soil moisture: WFs suppressed soil moisture and enhanced water stress in the study area. This research provides significant observational evidence that WFs can inhibit the growth and productivity of the underlying vegetation.
Bijian Tang, Donghai Wu, Xiang Zhao, Tao Zhou, Wenqian Zhao, and Hong Wei
State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing, College of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University; Joint Center for Global Change Studies (JCGCS), Beijing; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University;
Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University; and Shaanxi Jinkong Compass Information Service Co., Xi’an, China
Remote Sensing 2017, 9(4), 332; doi: 10.3390/rs9040332 
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