Assessment of the Spatiotemporal Patterns and Ecological Impacts of Photovoltaic Power Plants in Central Asia
Gao Y. Yang Q. Chang P. Wang C. Wang X. Yu M. Liu R. Dilixiati B. Shi Q. Zhakypbek Y.
March 2026John Wiley and Sons Inc
Earths Future
2026#14Issue 3
Photovoltaic (PV) power plants rapidly expand in drylands because of the high solar potential and efficient land-use capabilities. However, existing PV data sets are often incomplete and lack installation timestamps, which significantly hinder comprehensive assessments of PV power plants ecological impacts. By integrating a random forest (RF) algorithm with change detection techniques and using Sentinel-2 and Landsat imagery, we have created a high-resolution geospatial data set of PV installations (2010–2023). The data set achieved an overall accuracy of 99.07% and a recall of 84.11% in identifying installation dates. By 2023, PV power plants covered 444.26 km2, predominantly on barren land (74.28%) and grasslands (23.18%). Ecological impacts were largely positive, with improvements in vegetation indices, soil moisture, and reductions in diurnal temperature range (DTR) and soil salinization. In hyper-arid regions, PV power plants reduced evapotranspiration and increased precipitation, while in semi-arid areas, they lowered albedo and mitigated DTR. In arid regions, vegetation showed notable enhancement. These findings highlight the dual role of PV power plants in renewable energy generation and ecological restoration, offering insights into sustainable energy planning and ecosystem management. The results demonstrate how strategic deployment of PV power plants can align with global climate goals and contribute to achieving the Sustainable Development Goals.
change detection , dryland , ecological impacts , installation date , photovoltaic power plants , spatiotemporal pattern
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College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
Xinjiang Field Scientific Observation and Research Station for the Oasisization Process in the Hinterland of the Taklamakan Desert, Yutian, China
State Grid Xinjiang Electric Power Company, Institute of Economic Technology, Urumqi, China
Xinjiang Uygur Autonomous Region Natural Resources Archives (Xinjiang Uygur Autonomous Region Natural Resources Data Center), Urumqi, China
Xinjiang LiDAR Applied Engineering Technology Research Center, Urumqi, China
College of Ecology and Environment, Xinjiang University, Urumqi, China
Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
Department of Mine Surveying and Geodesy, Institute Mining and Metallurgical Institute Named After O.A. Baikonurov, Satbayev University, Almaty, Kazakhstan
College of Geography and Remote Sensing Science
Xinjiang Key Laboratory of Oasis Ecology
Xinjiang Field Scientific Observation and Research Station for the Oasisization Process in the Hinterland of the Taklamakan Desert
State Grid Xinjiang Electric Power Company
Xinjiang Uygur Autonomous Region Natural Resources Archives (Xinjiang Uygur Autonomous Region Natural Resources Data Center)
Xinjiang LiDAR Applied Engineering Technology Research Center
College of Ecology and Environment
Key Laboratory of Oasis Ecology
Department of Mine Surveying and Geodesy
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