Global offshore wind turbine dataset

Ting Zhang; Bo Tian; Dhritiraj Sengupta; Lei Zhang; Yali Si

doi:10.1038/s41597-021-00982-z

Global offshore wind turbine dataset

Sci Data. 2021 Jul 27;8(1):191. doi: 10.1038/s41597-021-00982-z.

Authors

Ting Zhang¹, Bo Tian², Dhritiraj Sengupta¹, Lei Zhang³, Yali Si⁴

Affiliations

¹ State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 200062, Shanghai, China.
² State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 200062, Shanghai, China. btian@sklec.ecnu.edu.cn.
³ Department of Traffic Information and Control Engineering, Tongji University, 201804, Shanghai, China.
⁴ Institute of Environmental Sciences, Leiden University, 2333 CC, Leiden, Netherlands.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't