State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 200062, Shanghai, China.
Department of Traffic Information and Control Engineering, Tongji University, 201804, Shanghai, China.
Sci Data. 2021 Jul 27;8(1):191. doi: 10.1038/s41597-021-00982-z.
Offshore wind farms are widely adopted by coastal countries to obtain clean and green energy; their environmental impact has gained an increasing amount of attention. Although offshore wind farm datasets are commercially available via energy industries, records of the exact spatial distribution of individual wind turbines and their construction trajectories are rather incomplete, especially at the global level. Here, we construct a global remote sensing-based offshore wind turbine (OWT) database derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. We developed a percentile-based yearly SAR image collection reduction and autoadaptive threshold algorithm in the Google Earth Engine platform to identify the spatiotemporal distribution of global OWTs. By 2019, 6,924 wind turbines were constructed in 14 coastal nations. An algorithm performance analysis and validation were performed, and the extraction accuracies exceeded 99% using an independent validation dataset. This dataset could further our understanding of the environmental impact of OWTs and support effective marine spatial planning for sustainable development.
沿海国家广泛采用海上风力发电场来获取清洁和绿色能源；其环境影响越来越受到关注。尽管能源行业可以提供海上风力发电场数据集，但个别风力涡轮机的确切空间分布及其建设轨迹的记录却相当不完整，特别是在全球范围内。在这里，我们构建了一个基于全球遥感的海上风力涡轮机（OWT）数据库，该数据库来自 2015 年至 2019 年的 Sentinel-1 合成孔径雷达（SAR）时间序列图像。我们在 Google Earth Engine 平台上开发了一种基于百分比的年度 SAR 图像采集减少和自适应阈值算法，以识别全球 OWT 的时空分布。到 2019 年，14 个沿海国家已经建造了 6924 台风力涡轮机。我们进行了算法性能分析和验证，使用独立验证数据集，提取精度超过 99%。该数据集可以帮助我们更好地了解 OWT 的环境影响，并支持可持续发展的有效海洋空间规划。
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