Spatio-Temporal Analysis of Land Subsidence in Beijing Plain Based on InSAR and PCA
HE Xu1, 2, 3, HE Yi1, 2, 3*, ZHANG Li-feng1, 2, 3, CHEN Yi1, 2, 3, PU Hong-yu1, 2, 3, CHEN Bao-shan1, 2, 3
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Abstract：Since the 1970s, uneven ground subsidence caused by groundwater overdraft and uneven thickness of compressible layers has gradually developed into one of the most serious geological hazards in Beijing Plain. There are few research reports on the temporal and spatial analysis of land subsidence in the latest period in Beijing Plain. Therefore, this paper used SBAS-InSAR technology to obtain the ground deformation data of the Beijing Plain from May 2017 to May 2020 based on 39 Sentinel-1A spectral images acquired by active microwaves. The principal component analysis method was used to analyse land subsidence’s temporal and spatial characteristics in the Beijing Plain. In the SBAS-InSAR technology processing, the time baseline was set to 120 days, and the threshold of the space baseline was set to 45% of the maximum normal baseline, and 154 interference pairs were generated, and then all interference pairs were registered and interfered. After the interference, the results were flattened, Goldstein flattening for filtering, generated coherence coefficient and used minimum cost flow algorithm for phase unwrapping. The 73 high-quality interference pairs were screened for orbit refinement and re-leveling to estimate and remove the residual phase. The atmospheric phase was removed through temporal high-pass filtering and spatial low-pass filtering. Finally, the least-squares method and singular value decomposition were used to obtain land subsidence data of the Beijing Plain from May 2017 to May 2020. During the monitoring period, the maximum average deformation rate was -114.9 mm·yr-1, and the maximum cumulative subsidence was 345.9 mm. which was located in Chaoyang Jinzhan. Compared with 2018, the ranges where the settlement rate of the central plain decreased by exceeding 50 mm·yr-1 in 2019 in the Haidian District, Changping district, but the settlement rate range of Daxing District was gradually increasing. It analysed the land subsidence of the Beijing Plain by using the principal component analysis method. It was concluded that the first three principal components explained 99.11% of the data characteristics. The first principal component explained 96.48% of the characteristics, reflecting the long-term and unrecoverable process of groundwater subsidence caused by long-term groundwater extraction. However, the replenishment of groundwater by the South Water entering Beijing slowed down the settlement rate in the central plain. The second principal component explained the 2.11% feature, highlighting the interannual settlement process, which was related to factors such as the thickness of the compressible layer and the type of land use; The third principal component explained 0.52% of the characteristics of the data set and emphasized the seasonal elastic deformation regulated by rainfall. The research results of this paper can provide a certain scientific basis for the comprehensive management of land subsidence in the Beijing Plain.
Key words：Beijing Plain;SBAS-InSAR;Land subsidence;Principal component analysis;South water into Beijing
何 旭，何 毅，张立峰，陈 毅，蒲虹宇，陈宝山. 联合InSAR与PCA的北京平原地面沉降时空分析[J]. 光谱学与光谱分析, 2022, 42(07): 2315-2324.
HE Xu, HE Yi, ZHANG Li-feng, CHEN Yi, PU Hong-yu, CHEN Bao-shan. Spatio-Temporal Analysis of Land Subsidence in Beijing Plain Based on InSAR and PCA. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2315-2324.
[1] Miller M M，Shirzaei M. Remote Sensing of Environment, 2019, 225: 368.
[2] He Yi, Chen Youdong, Wang Wenhui, et al. Advances in Space Research. 2020, 67(4): 1267.
[3] YANG Yan, LIU He, LUO Yong, et al(杨 艳, 刘 贺, 罗 勇, 等). Shanghai Land & Resources(上海国土资源), 2021, 42(1): 7.
[4] Chen Beibei, Gong Huili, Chen Yun, et al. Science of The Total Environment, 2020, 735: 139111.
[5] Chaussard E, Burgmann R, Shirzaei M, et al. Journal of Geophysical Research, 2014, 119(8): 6572.
[6] Rudolph M L, Shirzaei M, Manga M, et al. Geophysical Research Letters, 2013, 40(6): 1089.
[7] LÜ Jin-bo, WANG Chun-jun, LIU Hong, et al(吕金波, 王纯君, 刘 鸿, 等). Urban Geology(城市地质), 2017, 12(3): 19.
[8] Shi Liyuan, Gong Huili, Chen Beibei, et al. Remote Sensing, 2020, 12(24): 4044.
[9] QIN Huan-huan, ZHENG Chun-miao, SUN Zhan-xue, et al(秦欢欢, 郑春苗, 孙占学, 等). Journal of Irrigation and Drainage(灌溉排水学报), 2019, 38(3): 108.
[10] Berardino P, Fornaro G, Lanari R, et al. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375.
[11] YU Xiang-wei, XUE Dong-jian, CHEN Feng-jiao(余祥伟, 薛东剑, 陈凤娇). Mountain Research(山地学报), 2020, 38(6): 926.
[12] Dai Keren, Liu Guoxiang, Li Zhenhong, et al. Sensors, 2018, 18(6): 1876.
[13] ZHOU Chao-dong, GONG Hui-li, ZHANG You-quan, et al(周朝栋, 宫辉力, 张有全, 等). Remote Sensing Information(遥感信息), 2017, 32(1): 17.
[14] PANG Jiang-qian, LI Xiao-min, WU Yin-jie, et al(庞江倩，李晓敏，吴寅洁，等). Beijing Statistical Yearbook 2020(北京统计年鉴2020). Beijing: China Statistics Press(北京：中国统计出版社), 2020.
[15] Zhu Lin, Gong Huili, Chen Yun, et al. Engineering Geology, 2020, 276: 105763.
[16] LEI Kun-chao, LUO Yong, CHEN Bei-bei, et al(雷坤超, 罗 勇, 陈蓓蓓, 等). Geology in China(中国地质), 2016, 43(4): 1457.
