Mapping photovoltaic power stations and assessing their environmental impacts from multi-sensor datasets in Massachusetts, United States

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Solar energy is often viewed as a sustainable alternative to non-renewable energy, yet the debate between solar energy promotion and environmental cost has received growing attention. Accurate geographic information of photovoltaic power stations is a prerequisite for quantifying cost and benefit of clean energy promotion. Therefore, this study aims to estimate the environmental impacts of photovoltaic power stations by geo-mapping solar panels over space and time. Based on the case of Massachusetts, United States, we classified the solar panel arrays using object-based image analysis on Sentinel-2 monthly composites, identified the per-array construction year based upon 20-year all available Landsat time-series dataset, and assessed the solar-induced environmental impacts with various environmental datasets. The accuracy assessment suggests that our classification performs well for detecting solar arrays (overall accuracy: 96%), depicting photovoltaic power stations geometry (average Jaccard Index value: 0.70), and capturing the construction years (percentage of temporal bias less than one year: 73%). Solar-induced land use and cover changes have largely occurred in forest and cropland, where 49% and 23% of the solar arrays have been installed, respectively. Geographic Information System (GIS) analysis uncovers that more than half of the mapped solar arrays were built in proximity (within 500 m) to rare wildlife habitats or adjacent to wetlands. This work exemplifies a new framework for identifying multifaceted land change information through a combination of finer-resolution Sentinel-2 images and long Landsat-based data archive. The findings can be useful for informing spatial planning and contributing to the growth, expansion, advancement, and location selection of solar installation arrays. The study also provides a new perspective for monitoring forest loss due to clean energy promotion and addressing critical issues of local conflicts between solar energy and environmental conservation. © 2023 Elsevier B.V.

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Remote Sensing Applications: Society and Environment

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deforestation, environmental change, image segmentation, photovoltaic power stations, solar panels, time series analysis