The impact of the wind farm on the vegetation and the microclimate via remote sensing: a case study in Hebei Province, China
Tingting Fei , Dangui Lu , Yan Li , Danlin Li , Shuo Tian , Akbar Amar , Xiuchun Yang , Zhouyuan Li
Energy, Ecology and Environment ›› : 1 -18.
The impact of the wind farm on the vegetation and the microclimate via remote sensing: a case study in Hebei Province, China
Wind energy, a green and sustainable clean energy source, has been rapidly developing worldwide. However, its complex impacts on ecosystems remain unclear. The timing of wind farm construction and the spatial scope of their effects on vegetation and microclimates are poorly understood. To address this gap, we conducted our study from a vegetation-climate coupling perspective. First, we introduced a change detection method based on the normalized difference vegetation index (NDVI) to identify the construction years of wind farms; second, we explored the ecological impacts by integrating vegetation index, albedo, land surface temperature (TS), and evapotranspiration (ET) within multi-layer buffer zones. Representative wind farms in Hebei Province, China, were selected as a case study. Using Landsat imagery and a global wind power dataset, we quantified changes before and after construction. The results indicate that the number of wind farms increased continuously from 2001 to 2020, with peak construction years in 2006, 2008, and 2013 respectively. Wind farm construction led to a decline in vegetation greenness, reduced surface roughness, increased temperature, and decreased humidity, with these effects varying across buffer zones. The maximum changing ratio for the indices were − 21.73% of ΔNDVI, 6.59% of Δalbedo, 6.29% of ΔTS and − 19.22% of ΔET, with the most significant impacts observed on vegetation and evapotranspiration. The thresholds of these impacts were concentrated within 300–400 m of the buffer zones, providing a basis for categorizing the impact levels of wind farm construction. These innovations address critical gaps in assessing wind energy’s ecological footprint. To summarize, this study makes three key advances: (1) proposing a sliding-window NDVI differencing method to detect construction timing without relying on official records; (2) integrating multi-dimensional indicators (NDVI, albedo, TS, ET) to reveal coupled vegetation-microclimate impacts; and (3) quantifying nonlinear impact thresholds through spatial buffer analysis, offering actionable guidance for wind farm zoning. The findings offer valuable insights for balancing wind energy development with grassland ecosystem sustainability.
Wind farms / Normalized difference vegetation index / Albedo / Surface temperature / Evapotranspiration / Microclimate
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
Dabrowska-Zielinska K, Budzynska M, Tomaszewska M, Bartold M, Gatkowska M (2015) The study of multifrequency microwave satellite images for vegetation biomass and humidity of the area under Ramsar convention. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 5198–5200. https://doi.org/10.1109/IGARSS.2015.7327005 |
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
Zhu R, Shi W, Wang Y, Wang W, Huang Q (2018) Research suggestions on the ecological and climate environmental effects of wind power development and utilization in China. China Eng Sci 20(3). https://doi.org/10.15302/J-SSCAE-2018.03.006 |
The Author(s), under exclusive licence to the International Society of Energy and Environmental Science
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