Quantitative analysis of uneven subsidence by Moran’s I and cross wavelet

doi:10.6046/gtzyyg.2020.02.24

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(7753 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

To address the problem that quantitative analysis of uneven subsidence is rare, the authors used the Permanent Scatterer Interferometry (PSI) to monitor land subsidence in the Beijing plain. According to the different shallow surface spatial utilizations, the authors selected 5 typical areas in the subsidence funnel region. Based on spatial autocorrelation analysis and wavelet analysis, the authors quantified the degree of spatial and annual time series uneven subsidence in each area, and studied the influence of different shallow surface spatial utilization and groundwater level variation on spatial and annual time series uneven subsidence. The results are as follows: ①Annual time series subsidence’s Moran index degrees of 5 areas are the same as those of the accumulated subsidence: I₅>I₃>I₁>I₂>I₄. According to the utilization of shallow surface space, the degree of uneven subsidence of 1, 2, 5 areas are positively correlated with the complexity of space utilization, and the factors affecting the uneven subsidence degree of area 3, 4 are complicated. ②It is found that the variation and duration of groundwater level fluctuation are the main factors affecting the uneven degree of time series subsidence.

Keywords uneven subsidence Moran’s I shallow surface space utilization groundwater cross wavelet

TP79

Corresponding Authors: Huili GONG E-mail: gonghl_1956@126.com

Issue Date: 18 June 2020

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yike SUN
	Huili GONG
	Beibei CHEN
	Chaofan ZHOU
	Wenfeng CHEN
	Xiaojing ZHANG

Cite this article:

Yike SUN,Huili GONG,Beibei CHEN, et al. Quantitative analysis of uneven subsidence by Moran’s I and cross wavelet[J]. Remote Sensing for Land & Resources, 2020, 32(2): 186-195.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.24 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/186

Fig.1 Overview of the Beijing Plain

Fig.2 Flow chart of PSI

Tab.1 Relationship between uncorrected P value, Z score and confidence

Fig.3 Annual settlement rate of the Beijing plain from 2011 to 2015

Fig.4 Spatial distribution of cumulative settlement by InSAR in the Beijing plain from 2011 to 2015

Fig.5 Verification diagram of Radar interferometry level

Fig.6 Location of five typical areas

Fig.7 Moran’s I based on cumulative settlement and annual time series subsidence in 5 regions from 2011 to 2015

Fig.8 Evolution of Moran’s I of land subsidence in 5 typical areas from 2011 to 2015

Fig.9 Temporal subsidence changes in 5 typical areas from 2011 to 2015

Fig.10 Space utilization situation of tyical areas

Tab.2 Specific information on shallow surface space utilization of typical areas

Tab.3 Geological background in typical area(m)

Fig.11 land subsidence variation and groundwater level variation by cross wavelet method in area 1

Fig.12 Land subsidence variation and groundwater level variation by cross wavelet method in area 2

Fig.13 Land subsidence variation and groundwater level variation by cross wavelet method in area 3

Fig.14 Land subsidence variation and groundwater level variation by cross wavelet method in area 4

Fig.15 Land subsidence variation and groundwater level variation by cross wavelet method in area 5

[1]	宫辉力, 张有全, 李小娟, 等. 基于永久散射体雷达干涉测量技术的北京市地面沉降研究[J]. 自然科学进展, 2009,19(11):1261-1266.
[1]	Gong H L, Zhang Y Q, Li X J, et al. Land subsidence research in Beijing based on permanent scatterer radar interferometry[J]. Progress in natural science, 2009,19(11):1261-1266. doi: 10.1016/j.pnsc.2009.02.005 url: https://linkinghub.elsevier.com/retrieve/pii/S1002007109001920
[2]	杨艳, 贾三满, 王海刚. 北京平原区地面沉降现状及发展趋势分析[J]. 上海国土资源, 2010,31(4):23-28.
[2]	Yang Y, Jia S M, Wang H G. Analysis of land subsidence status and development trend in Beijing plain area[J]. Land and Resources of Shanghai, 2010,31(4):23-28.
[3]	Li Y S, Jingfa Z, Zhang J, et al. Land subsidence in Beijing City from InSAR time series analysis with small baseline subset[J]. Geomatics & Information Science of Wuhan University, 2013,38(11):1374-1377.
[4]	雷坤超, 陈蓓蓓, 贾三满, 等. 基于PS-InSAR技术的北京地面沉降特征及成因初探[J]. 光谱学与光谱分析, 2014,34(8):2185-2189.
[4]	Lei K C, Chen B B, Jia S M, et al. Preliminary study on characteristics and causes of land subsidence in Beijing based on PS-InSAR technology[J]. Spectroscopy and spectral analysis, 2014,34(8):2185-2189.
[5]	周超凡. 基于PS-InSAR技术与信息熵的北京典型区域不均匀地面沉降研究[D]. 北京:首都师范大学, 2014.
[5]	Zhou C F. Study on uneven land subsidence in typical areas of Beijing based on PS-InSAR technology and information entropy[D]. Beijing:Capital Normal University, 2014.
[6]	杜钊锋. 北京典型区域不均匀地面沉降成因机制分析[D]. 北京:首都师范大学, 2013.
[6]	Du Z F. Cause analysis of uneven land subsidence in typical areas of Beijing[D]. Beijing:Capital Normal University, 2013.
[7]	陈蓓蓓, 宫辉力, 李小娟, 等. 综合时序InSAR和GIS技术地面沉降时序演化规律研究[J]. 光谱学与光谱分析, 2014,34(4):1017-1025.
[7]	Chen B B, Gong H L, Li X J, et al. Study on temporal evolution of land subsidence by integrated time series InSAR and GIS technology[J]. Spectroscopy and spectral analysis, 2014,34(4):1017-1025.
[8]	Hu B, Wang H S, Sun Y L, et al. Long-term land subsidence monitoring of Beijing(China) using the small baseline subset(SBAS) technique[J]. Remote Sensing, 2014,6(5):3648-3661.
[9]	周丙锋, 李小娟, 李燕萍. 北京平原地区地面沉降特征和研究途径的进展与思考[J].中国矿业, 2014(12):51-56.
[9]	Zhou B F, Li X J, Li Y P. Progress and reflection on land subsidence characteristics and research approaches in Beijing Plain[J].China Mining Industry, 2014(12):51-56.
[10]	Chen B, Gong H, Li X, et al. Spatial-temporal evolution patterns of land subsidence with different situation of space utilization[J]. Natural Hazards, 2015,77(3):1765-1783.
[11]	周超凡, 宫辉力, 陈蓓蓓, 等. 北京市典型地区地面沉降空间格局分析[J].遥感信息, 2017(4):24-29.
[11]	Zhou C F, Gong H L, Chen B B, et al. Spatial pattern analysis of land subsidence in typical areas of Beijing[J].Remote sensing information, 2017(4):24-29.
[12]	袁立明. 全球地面沉降扫描[J].地球, 2012(s1):156-159.
[12]	Yuan L M. Scanning of Global Land Subsidence[J].Earth, 2012(s1):156-159.
[13]	Ferretti A, Prati C, Rocca F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry[J]. IEEE Transactions on Geoscience & Remote Sensing, 2000,38(5):2202-2212.
[14]	Ferretti A, Colesanti C, Prati C, et al. Radar permanent scatterers identification in urban areas:Target characterization and sub-pixel analysis [C]//Remote Sensing and Data Fusion Over Urban Areas,Ieee/isprs Joint Workshop.Rome:IEEE, 2001: 52.
[15]	Hooper A, Zebker H, Segall P, et al. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers[J]. Geophysical Research Letters, 2004,31(23):1-5.
[16]	Tobler W R. A Computer Movie Simulating Urban Growth in the Detroit Region[C]//International Geographical Union,Commission on Quantitative Methods.Economic Geography.America:Clark University, 1970(46):234-240.doi: 10.2307/143141

[1]	Xiaojing ZHANG, Beibei CHEN, Kunchao LEI, Wenfeng CHEN, Mingliang GAO, Chaofan ZHOU, Guangyao DUAN. Characteristics of land subsidence along Beijing-Tianjin inter-city railway (Beijing section)[J]. Remote Sensing for Land & Resources, 2019, 31(1): 171-179.
[2]	Gang LIU, Yunpeng YAN, Jianyu LIU. Research on relationship between lakes and tectonic background in western Tibetan Plateau using remote sensing[J]. Remote Sensing for Land & Resources, 2018, 30(2): 154-161.
[3]	WANG Xuqing, WANG Yu, GUO Yingping. Application of ZY1-02C satellite data to the survey of groundwater overflow zone in the front of alluvial-pluvial fan zone of Golmud River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 155-159.
[4]	LI Man, GE Daqing, ZHANG Ling, LIU Bin, GUO Xiaofang, WANG Yan. Characteristics and influencing factors of land subsidence in Caofeidian Newly-developed Area based on PSInSAR technique[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 119-126.
[5]	YANG Chengsheng, LIU Yuanyuan, AO Meng. Study of land subsidence and groundwater activity using SBAS time-series analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 127-132.
[6]	LUO Hao, WANG Hong, SHI Changhui. Retrieving groundwater in Yellow River Delta area using remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 145-152.
[7]	YU De-Hao, LONG Fan, FANG Hong-Bin, HAN Tian-Cheng. Research on Shallow Groundwater Information Extraction Based on Data Fusion[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 114-119.
[8]	WU Quan-yuan, HOU Wei, AN Guo-qiang . EXPLOITATION AND UTILIZATION PLANNING OF LONG KOU GROUNDWATER WITH SUPPORT OF RS AND GIS TECHNIQUE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2001, 13(3): 41-46.

Viewed

Full text

Abstract

Cited

Shared

Discussed