Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

Wang, Xinxin; Xiao, Xiangming; Zhang, Xi; Ye, Hui; Jie, Dong; He, Qiang; Wang, X. F.; Liu, Jianquan; Li, Bo; Wu, Jihua

doi:10.1016/j.jclepro.2023.138015

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…High-resolution remote sensing imagery achieves satisfactory accuracy in segmentation (Yu et al 2018, Kruitwagen et al 2021, yet their application on a large scale, such as at the national or global level, poses challenges for timely monitoring. Medium-resolution remote sensing imagery such as Landsat (Zhang et al 2022, Xia et al 2022a, Wang et al 2023a and Sentinel-1/2 (Xia et al 2022b, Jiang et al 2023, Tao et al 2023 is more commonly utilized because of its rapid data updates and cost-free accessibility. In addition, PV plant extraction methods have been widely discussed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

“…Methods for investigating the installation date were also explored, such as date detection based on spatial classification results (Xia et al 2022a(Xia et al , 2022b) and spectrum-reflectance-based methods, such as Continuous monitoring of Land Disturbance (COLD) algorithm (Tao et al 2023). Other attributes, such as regional land cover (Kim et al 2021, Kruitwagen et al 2021, Xia et al 2022a, Tao et al 2023, Wang et al 2023a, 2023b and slope (Charabi and Gastli 2011) of the construction site, were investigated as well.…”

Section: Introductionmentioning

confidence: 99%

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Unravelling spatiotemporal patterns of solar photovoltaic plants development in China in the 21st century

Wang,

Hao,

Jia

et al. 2024

Environ. Res. Lett.

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Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km2, which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China's PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China's PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China's optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

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