Satellite observations of offshore wind turbines in the North Sea (2008–2018)

An accurate and detailed determination of the status of offshore wind farms (OWFs) is crucial for offshore wind energy development, assessment, and management. However, existing OWF maps have several knowledge gaps, and it is difficult to keep these maps up-to-date over large sea areas. To address these issues, the North Sea and surrounding waters were selected as a case study, and a visual saliency detection (VSD) algorithm was developed, based on time-series of multi-source optical satellite images, to determine the status of offshore wind turbines (OWTs) (i.e. their locations and installation dates). A total of 4277 OWTs were detected in 71 OWFs in the North Sea and surrounding waters, as of July 2018, with an overall accuracy of 97.98%, a commission error rate of 1.69%, and an omission error rate of 0.33%. Besides, a proliferation of OWFs was observed in the North Sea and surrounding waters using time-series satellite monitoring, with an average annual growth rate of 22.99% during the past decade (2008 2018). Furthermore, the proposed VSD algorithm was applied to assess offshore wind energy utilisation and to map the global distribution of 6166 OWTs in 131 OWFs. This study contributes in providing a robust and cost-efficient framework for investigating OWFs over a large sea area. To the best of our knowledge, this is the first spatiotemporally-detailed inventory of OWFs, which will complement official databases. Moreover, it provides a reference for assessing the potential impact of active and decommissioned OWFs in marine ecosystems

精准且详尽地判定海上风电场（offshore wind farms, OWFs）的状态，对于海上风电的开发、评估与管理而言至关重要。然而，现有海上风电场地图仍存在诸多知识盲区，且在广袤海域中难以保持地图的时效性。为破解上述难题，本研究以北海及周边海域为研究案例，基于多源光学卫星影像时序数据，开发了一种视觉显著性检测（visual saliency detection, VSD）算法，用以研判海上风电机组（offshore wind turbines, OWTs）的状态——包括其部署位置与安装日期。截至2018年7月，研究团队在北海及周边海域的71座海上风电场中共检测到4277台海上风电机组，整体识别准确率达97.98%，误报率为1.69%，漏报率为0.33%。此外，通过时序卫星监测可知，北海及周边海域的海上风电场数量呈快速激增态势，2008至2018年的十年间年均增长率达22.99%。进一步地，本研究所提出的视觉显著性检测算法还被应用于海上风电利用情况评估，并绘制了全球范围内131座海上风电场中6166台海上风电机组的分布图谱。本研究为大范围海域内的海上风电场调查提供了一套稳健且经济高效的技术框架。据我们所知，本研究首次构建了具备时空精细粒度的海上风电场清单，可作为官方数据库的有益补充。此外，该研究成果还可为评估现役与退役海上风电场对海洋生态系统的潜在影响提供参考依据。