Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China
文献类型：期刊论文
| 作者 | Chen, Zhenghang1; Kang, Yawen1; Sun, Zhongxiao1; Wu, Feng2; Zhang, Qian1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 14期号:11页码:19 |
| 关键词 | PV plant machine learning algorithm Landsat 8 OLI images XGBoost |
| DOI | 10.3390/rs14112697 |
| 通讯作者 | Zhang, Qian(qian.zhang@cau.edu.cn) |
| 英文摘要 | Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km(2) and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants. |
| WOS关键词 | DIFFERENCE WATER INDEX ; SOLAR ; PERFORMANCE ; IMAGERY ; NDWI |
| 资助项目 | National Science Foundation of China[51861125101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20100104] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000808846400001 |
| 出版者 | MDPI |
| 资助机构 | National Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/179049]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Qian |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Zhenghang,Kang, Yawen,Sun, Zhongxiao,et al. Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China[J]. REMOTE SENSING,2022,14(11):19. |
| APA | Chen, Zhenghang,Kang, Yawen,Sun, Zhongxiao,Wu, Feng,&Zhang, Qian.(2022).Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China.REMOTE SENSING,14(11),19. |
| MLA | Chen, Zhenghang,et al."Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China".REMOTE SENSING 14.11(2022):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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