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Remote sensing unveils the explosive growth of global offshore wind turbines

Author

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  • Wang, Kechao
  • Xiao, Wu
  • He, Tingting
  • Zhang, Maoxin

Abstract

Clean energy has become an important component of the transition to a sustainable future, of which offshore wind power is a promising clean energy source. However, there are still gaps in the spatiotemporal data of OWTs with global coverage, precise location, freely available, and with long time series of installation time information. This study proposes a cost-effective method to investigate the global distribution and installation time of offshore wind turbines (OWTs) using remote sensing data and the GEE cloud computing platform. The process includes two parts, the first is OWTs extraction through synthetic SAR images using an adaptive Z-score threshold and morphological operations. The second part is installation time detection through synthetic Landsat optical images using LandTrendr algorithm. A total of 12,412 OWTs have been identified within the offshore Exclusive Economic Zones (EEZ) of countries worldwide, with 5915 in Europe, 6490 in Asia, and only 7 in the United States. Most OWTs are situated in nearshore areas and were constructed in a regular pattern. Installation dates of existing OWTs worldwide reveal an exponential growth trend since the 21st century, with Europe leading the way and Asia catching up rapidly, building two-thirds of its turbines after 2019. This information can be used for subsequent studies on the effectiveness of wind energy in reducing greenhouse gas emissions, the potential for future development and expansion of wind energy infrastructure, as well as to inform policies aimed at promoting the sustainability of the wind energy industry.

Suggested Citation

  • Wang, Kechao & Xiao, Wu & He, Tingting & Zhang, Maoxin, 2024. "Remote sensing unveils the explosive growth of global offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123010444
    DOI: 10.1016/j.rser.2023.114186
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