Optimized extraction of photovoltaic power stations based on object image analysis and Sentinel images

doi:10.13474/j.cnki.11-2246.2025.0621

Abstract

Abstract: To achieve the energy goal of carbon peak and carbon neutrality, the construction of photovoltaic (PV) power stations has been growing rapidly, and the statistics of distribution and scale of PV power stations are beneficial to energy management and planning. Existing methods of applying remote sensing image data for PV power stations extraction suffer from the lack of extraction fineness and the loss of small-area PV power stations. In this study, an object-based image analysis method is adopted to extract spectral, index and geometric features using Sentinel-2 and Sentinel-1 image data, establish a random forest extraction model, and analyze the optimal segmentation parameters for multi-scale segmentation in different terrains. The optimal global segmentation parameters are segmentation scale, shape factor and compactness of 50, 0.7 and 0.5 respectively. The model established in this paper obtaines an accuracy of 98.21% for PV plant users and 95.85% for producers on the validation set. Finally, the distribution map of PV power stations in Hubei province in 2024 is drawn, and the installed area of PV power stations in the province is 230.8 km².

Key words: photovoltaic power plants, object-based image analysis, Sentinel images, Hubei province

CLC Number:

P237

ZHOU Chaohui, LI Linze, ZHANG Jicheng, MAO Hongzhi, HAN Tao. Optimized extraction of photovoltaic power stations based on object image analysis and Sentinel images[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 123-129.

References

[1] 国家能源局.2023年光伏发电建设情况[R/OL].(2024-02-28) [2024-10-22].https://www.nea.gov.cn/2024-02/28/c_1310765696.htm.
[2] ASSOCIATION S E I.U.S.Solar Market Insight 2023 Year in Review [R/OL].(2024-03-06) [2024-10-15].https://www.seia.org/us-solar-market-insight.
[3] 王胜利,张连蓬,朱寿红,等.多共性特征联合的Landsat 8 OLI遥感影像光伏电站提取[J].测绘通报,2018(11): 46-52.
[4] 李彦甫,刘勇.基于对象影像分析方法支持下的多源遥感影像光伏电场提取[J].测绘与空间地理信息,2016,39(3): 68-72.
[5] XIA Zilong,LI Yingjie,CHEN Ruishan,et al.Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing[J].Energy Reports,2022,8: 4117-4127.
[6] GE Fan,WANG Guizhou,HE Guojin,et al.A hierarchical information extraction method for large-scale centralized photovoltaic power plants based on multi-source remote sensing images[J].Remote Sensing,2022,14(17): 4211.
[7] KRUITWAGEN L,STORY K T,FRIEDRICH J,et al.A global inventory of photovoltaic solar energy generating units[J].Nature,2021,598(7882): 604-610.
[8] MAO Hongzhi,CHEN Xie,LUO Yongqiang,et al.Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images[J].Renewable and Sustainable Energy Reviews,2023,179: 113276.
[9] 陈笛,彭秋志,黄培依,等.采用注意力机制与改进YOLOv5的光伏用地检测[J].自然资源遥感,2023,35(4): 90-95.
[10] FENG Q,NIU B,REN Y,et al.A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020[J].Scientific Data,2024,11(1): 198.
[11] ZHANG Xunhe,ZERAATPISHEH M,RAHMAN M M,et al.Texture is important in improving the accuracy of mapping photovoltaic power plants: a case study of Ningxia autonomous region,China[J].Remote Sensing,2021,13(19): 3909.
[12] JIANG Wenhao,TIAN Bo,DUAN Yuanqiang,et al.Rapid mapping and spatial analysis on the distribution of photovoltaic power stations with Sentinel-1&2 images in Chinese coastal provinces[J].International Journal of Applied Earth Observation and Geoinformation,2023,118: 103280.
[13] 梁斯铭.基于卫星和航拍正射图像的光伏目标识别与提取方法研究[D].杭州: 浙江大学,2021.
[14] PLAKMAN V,ROSIER J,VAN VLIET J.Solar park detection from publicly available satellite imagery[J].GIScience & Remote Sensing,2022,59(1): 462-481.
[15] LADISA C,CAPOLUPO A,RIPA M N,et al.Combining OBIA approach and machine learning algorithm to extract photovoltaic panels from Sentinel-2 images automatically[C]//Proceedings of 2022 Remote Sensing for Agriculture,Ecosystems,and Hydrology XXIV.Berlin:SPIE,2022.
[16] WANG Zirui,HAO Zhen,JIA Xiaofeng,et al.Unravelling spatiotemporal patterns of solar photovoltaic plants development in China in the 21st century[J].Environmental Research Letters,2024,19(3): 034005.