Satellite-based analysis uncovers uneven solar PV distribution across Japan and its consumption of forest and...

The recent development of solar photovoltaics (PV) has generated considerable interest in energy management, appropriate environmental impact assessment, and the seamless integration of PV technology into society. A critical first step in exploring these research opportunities is the creation of a comprehensive PV database that describes the locations and extents of existing PV installations. Automated solar PV detection in satellite remote sensing, based on a machine learning approach, is particularly suitable for studying the characteristics of national-scale solar PV distribution and its impact on the environment. In our study, we first proposed an XGBoost-based solar PV detection with post-processing procedures supported by a dedicated solar PV spectral index. This approach was applied to Sentinel-2 images acquired in 2022 to create a national solar PV database in Japan. The resulting solar PV map showed a high degree of accuracy, with an overall accuracy of 0.984. Our dataset revealed the presence of solar PVs covering a total area of 571 km² in Japan. The comparison of PV extents with the land cover map showed that the megawatt-scale solar PV facilities were predominantly located in forested areas, suggesting potential changes to existing forest ecosystems and the local environment at these facility locations. Conversely, smaller megawatt-scale PV systems showed a similar preference for both farmland and forest. PV expansion also contributed to forest fragmentation at forest edge areas. To further investigate these findings, we did the clustering analyses to identify high-concentration PV areas and analyzed the distribution of solar PVs alongside socio-economic and environmental factors using an explainable AI approach based on Shapley values. Through the study, we showed how the established PV dataset can be used to uncover spatial patterns and driving factors of PV deployment. Our results indicate that site selection is influenced by a multitude of variables-such as local environmental conditions, power demand, and installation costs-highlighting the need for well-informed strategies when deploying solar PV. Overall, this study demonstrates the efficacy of integrating machine learning models, spectral indices, and post-processing techniques with satellite remote sensing data to accurately map and analyze solar PV installations. Regular updates of these maps from freely available satellite datasets provide valuable insights for policymakers and stakeholders, enabling data-driven decisions regarding the placement, monitoring, and management of PV systems, and supporting a timely transition to a renewable-powered society.