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

文献类型：期刊论文

作者Wang, Xinxin2,3; Xiao, Xiangming4; Zhang, Xi2,3; Ye, Hui5; Dong, Jinwei6; He, Qiang2,3; Wang, Xubang7,8; Liu, Jianquan7,8; Li, Bo1,9,10; Wu, Jihua7,8
刊名JOURNAL OF CLEANER PRODUCTION
出版日期2023-09-10
卷号417页码:11
关键词Photovoltaic solar power plants Landsat imagery Random forest Morphological characteristics Land use changes Gansu province
ISSN号0959-6526
DOI10.1016/j.jclepro.2023.138015
通讯作者Wu, Jihua(wjh@lzu.edu.cn)
英文摘要Numbers and sizes of photovoltaic solar power plants have grown unprecedentedly over the last few years in China, which aims to achieve a carbon emission peak by 2030 and carbon neutrality by 2060. Thus, timely and accurate monitoring of photovoltaic solar power plants is crucial to the design and management of renewable electricity systems in China. Random forest algorithm has been used to map photovoltaic solar power plants at multiple scales, however, it always causes several salt-and-pepper noises, limiting its application at larger spatial scales. Here we first develop a photovoltaic solar power plant mapping method through integrating time series Landsat imagery, random forest, and morphological characteristics. Then we apply this method in Gansu Province, which has abundant solar and wind energy resources and provide large amounts of potential lands for photovoltaic development, and generate the annual photovoltaic maps from 2015 to 2020. We further analyze the spatial-temporal dynamics of sizes and areas of photovoltaic solar power plants and major land cover conversion of expansive photovoltaic regions. Finally, we discuss the reliability, uncertainties, implications, and future development of our improved methods. We find our photovoltaic mapping method can remove most of salt-and-pepper noises effectively, and the resultant maps in Gansu for 2020 have very high accuracies with user's and producer's accuracies of 97.57% and 99.22%, respectively. There are 165.29 km2 photovoltaic solar power plants in Gansu for 2020, and most of which are located in the northwestern Gansu. In addition, the photovoltaic with patch size > 1 km2 and & LE; 2 km2 (53.4 km2, 32.3%) has largest patch number (39, 15.7%). The improved photovoltaic mapping methods and further analysis in this study provide critical information for accurate and automatic classification of photovoltaic solar power plants in the future, as well as the environmental and sustainable development of solar energy in China.
WOS关键词ENVIRONMENTAL IMPACTS ; SURFACE-WATER ; SATELLITE ; EUROPE
资助项目Gansu Provincial Key Program of Science Fund[22JR5RA396] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-sp51]
WOS研究方向Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001037533400001
出版者ELSEVIER SCI LTD
资助机构Gansu Provincial Key Program of Science Fund ; Fundamental Research Funds for the Central Universities
源URL[http://ir.igsnrr.ac.cn/handle/311030/196021]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Jihua
作者单位1.Yunnan Univ, Yunnan Key Lab Plant Reprod Adaptat & Evolutionary, Kunming 650504, Yunnan, Peoples R China
2.Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn, Minist Educ,Natl Observat & Res Stn Wetland Ecosys, Shanghai 200438, Peoples R China
3.Fudan Univ, Inst Ecochongming, Sch Life Sci, Shanghai 200438, Peoples R China
4.Univ Oklahoma, Ctr Earth Observat & Modeling, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
5.Jiujiang Univ, Sch Tourism & Geog, Jiujiang 332005, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
7.Lanzhou Univ, State Key Lab Herbage Improvement & Grassland Agro, Lanzhou 730000, Peoples R China
8.Lanzhou Univ, Coll Ecol, Lanzhou 730000, Peoples R China
9.Yunnan Univ, Key Lab Transboundary Ecosecur Southwest China, Key Minist Educ, Kunming 650504, Yunnan, Peoples R China
10.Yunnan Univ, Inst Biodivers, Ctr Invas Biol, Sch Ecol & Environm Sci, Kunming 650504, Yunnan, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xinxin,Xiao, Xiangming,Zhang, Xi,et al. Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China[J]. JOURNAL OF CLEANER PRODUCTION,2023,417:11.
APA Wang, Xinxin.,Xiao, Xiangming.,Zhang, Xi.,Ye, Hui.,Dong, Jinwei.,...&Wu, Jihua.(2023).Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China.JOURNAL OF CLEANER PRODUCTION,417,11.
MLA Wang, Xinxin,et al."Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China".JOURNAL OF CLEANER PRODUCTION 417(2023):11.

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来源：地理科学与资源研究所

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