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<p>Photovoltaic power plants are increasingly being established to reduce carbon emissions, but their environmental impacts remain under debate. Here, we assess vegetation conditions within these facilities by integrating nationwide field surveys in China with satellite observations, using high-resolution unmanned aerial vehicle imagery to bridge spatial scale differences between field and satellite observations. Our results show that vegetation beneath and between the photovoltaic panels is strongly correlated and the inclusion of under-panel vegetation raises the average normalized difference vegetation index from 0.248 ± 0.158 to 0.298 ± 0.193. Compared to satellite estimates alone, our method reduces bias by 16.98%. At the regional scale, approximately 61.59% of the power plants did not suppress vegetation growth. This approach enables more accurate environmental assessments of the development of photovoltaic power plants and supports better-informed planning and management of solar energy infrastructure.</p>

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Leveraging unmanned aerial vehicle images improves vegetation mapping in photovoltaic power plants

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月26日

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  • Zhigang Zou,
  • Qian Ding,
  • Xinhui Zhou,
  • Pukaiyuan Yang,
  • Jingyi Liu,
  • Chongbin Xu,
  • Wu Yang

摘要

Photovoltaic power plants are increasingly being established to reduce carbon emissions, but their environmental impacts remain under debate. Here, we assess vegetation conditions within these facilities by integrating nationwide field surveys in China with satellite observations, using high-resolution unmanned aerial vehicle imagery to bridge spatial scale differences between field and satellite observations. Our results show that vegetation beneath and between the photovoltaic panels is strongly correlated and the inclusion of under-panel vegetation raises the average normalized difference vegetation index from 0.248 ± 0.158 to 0.298 ± 0.193. Compared to satellite estimates alone, our method reduces bias by 16.98%. At the regional scale, approximately 61.59% of the power plants did not suppress vegetation growth. This approach enables more accurate environmental assessments of the development of photovoltaic power plants and supports better-informed planning and management of solar energy infrastructure.