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

作者：Zhigang Zou, Qian Ding, Xinhui Zhou, Pinpin Yang, Jingyi Liu, Chongbin Xu, Wu Yang · 发表于：Communications Earth & Environment · 年份：2025 · DOI：10.1038/s43247-025-02710-6 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Remote Sensing and Land Use

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. Combining unmanned aerial vehicle data with satellite ones can provide higher accuracy in the assessment of vegetation conditions in large-scale photovoltaic power plants, according to a new study based on a nationwide field survey across China.