基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法

doi:10.12082/dqxxkx.2019.180630

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (7): 1109-1120.doi: 10.12082/dqxxkx.2019.180630

基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法

郭瑞¹(), 李素敏^1,^2,^3,^*(), 陈娅男¹, 袁利伟^2,^3,⁴

1. 昆明理工大学国土资源工程学院, 昆明 650093
2. 云南省高校高原山区空间信息测绘技术应用工程研究中心, 昆明 650093
3. 中国有色金属工业协会智慧矿山地理空间信息集成创新重点实验室,昆明650093
4. 昆明理工大学公共安全与应急管理学院,昆明 650093

收稿日期:2018-12-04 修回日期:2019-03-08 出版日期:2019-07-25 发布日期:2019-07-31
通讯作者: 李素敏 E-mail:grzte@qq.com;153064487@qq.com
作者简介:
作者简介：郭瑞（1993-）,男,河南洛阳人,硕士生,研究方向为InSAR在变形监测及防灾减灾中的应用。E-mail：grzte@qq.com
基金资助:
国家自然科学基金项目(41161062、41861054)

A Method based on SBAS-InSAR for Comprehensive Identification of Potential Goaf Landslide

Rui GUO¹(), Sumin LI^1,^2,^3,^*(), Ya'nan CHEN¹, Liwei YUAN^2,^3,⁴

1. Kunming University of Science and technology, School of land and resources engineering, Kunming 650093, China
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China
3. Key Laboratory of Intelligent Mine Geospatial Information Integration and Innovation, Key Laboratory of China Nonferrous Metals Industry Association, Kunming 650093, China
4. Kunming University of Science and technology, School of Public Safety and emergency management, Kunming 650093, China

Received:2018-12-04 Revised:2019-03-08 Online:2019-07-25 Published:2019-07-31
Contact: Sumin LI E-mail:grzte@qq.com;153064487@qq.com
Supported by:
National Natural Science Foundation of China, No.41161062, 41861051

摘要/Abstract

摘要：

针对位于山区且受大量采空区影响的边坡,利用传统测量方法监测耗费人力、物力且光学遥感难以定量识别其是否为潜在滑坡的问题,本文提出一种融合研究区小基线集（SBAS-InSAR）地表监测数据、坡度及坡向的识别方法。通过SBAS-InSAR技术获得研究区地表雷达视线（LOS）方向形变速率,将其转化为垂直方向形变速率,并根据研究区DEM建立坡度及坡向分析图,根据不同山体的坡度、坡向找到易发生滑坡的区域,融入该区域垂直方向的时序形变速率,对其进行滑坡识别。实验表明：卡房镇周边受采空区的影响较大,多数区域垂直方向年形变速率大于10 mm/a;通过本文方法对研究区潜在滑坡进行识别,发现在研究区的21处历史滑坡点中,有16处被识别为潜在滑坡,5处未被识别但也位于发生形变的区域内,表明本文方法对潜在滑坡的识别精度高,具有可行性。该研究为识别采空附近的潜在滑坡提供了一种新的思路,可以有效识别采空区附近山体边坡是否处于潜在的、不明显的滑动状态,对滑坡灾害具有预警作用。

关键词: SBAS-InSAR, 采空区, 潜在滑坡, 坡度, 坡向, 识别方法, 云南省个旧市卡房镇

Abstract:

Traditional methods for monitoring goaf landslides cost manpower and material resources, while optical remote sensing is difficult for quantitatively identifying potential landslides. This paper used InSAR to monitor the slopes of a mountane area in Kafang Town of Yunnan Province that is affected by goafs. To date, there have been some methods for identifying the occurrence of (potential) landslides, but most of them are based on the line-of-sight (LOS) direction deformation or slope direction that is converted from the LOS direction. Yet, when landslide is monitored based on the LOS direction, the actual deformation trend of the landslide cannot be captured. The deformation based on the slope direction is limited by the different slopes of mountains during the conversion process, and cannot reflect the specific landslide deformation. In this context, this paper proposed a new method that integrated the ground monitoring data of SBAS-InSAR, slope, and aspect. The deformation rate of LOS was obtained by SBAS-InSAR, and then it was converted into the vertical deformation rate. Based on the SRTM DEM data of 30 m resolution in the study area, the GIS analysis tool was used to generate the slope and aspect maps. Combined with the satellite parameters of Sentinel-1A, the radar visibility of the study area was partitioned to obtain effective observation values. The slope and aspect were re-extracted to detect areas where landslide is likely to occur, and then they were integrated into the vertical deformation rate to identify potential landslides. The identified potential landslide areas were compared with historical records to evaluate the accuracy of our method. The result showed that the surrounding areas of Kafang town were notably affected by goafs, and that the vertical deformation rate of most areas was more than 10 mm/a. With the proposed method, we found that 16 of the 21 historical landslide points in the study area were identified as potential landslides while 5 were not identified (but also located in the deformation region). We conclude that our proposed method for identifying potential landslides was highly accurate and feasible. This study provides a way to detect potential landslides near the goafs, by determining whether mountain slopes are in a potential and inconspicuous sliding state or not, and accordingly, helps provide early warnings of landslide disasters.

Key words: SBAS-InSAR, goaf, potential landslide, slop, slop direction, identification method, Kafang Town, Gejiu City, Yunnan Province

郭瑞, 李素敏, 陈娅男, 袁利伟. 基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法[J]. 地球信息科学学报, 2019, 21(7): 1109-1120.

Rui GUO, Sumin LI, Ya'nan CHEN, Liwei YUAN. A Method based on SBAS-InSAR for Comprehensive Identification of Potential Goaf Landslide[J]. Journal of Geo-information Science, 2019, 21(7): 1109-1120.

图/表 18

图1

图2

表1

图3

图4

图5

图6

表2

图7

图8

图9

图10

图11

图12

图13

图14

图15

表3

参考文献 32

[1]	陈玺. SBAS-InSAR技术在秦州区地表形变监测与滑坡敏感性评价中的应用研究[D].兰州:兰州大学,2018.
	[ Chen X, Detecting ground deformation and assessing landslide susceptibility in Qinzhou district based on SBAS-InSAR technique[D]. Lanzhou: Lanzhou University, 2018. ]
[2]	康亚,赵超英,张勤,等.InSAR滑坡探测技术研究——以金沙江乌东德水电站段为例[J].大地测量与地球动力学,2018,38(10):1053-1057.
	[ Kang Y, Zhao C Y, Zhang Q, et al.Research on the InSAR technique of landslide detection:A case study of wudongde hydropower station section,Jinshajiang[J]. Journal of Geodesy and Geodynamics, 2018,38(10):1053-1057. ]
[3]	康亚. InSAR技术在西南山区滑坡探测与监测的应用[D].西安:长安大学,2016.
	[ Kang Y.Landslide detection and monitoring over southwestern mountainous area with InSAR[D]. Xi'an: Chang'an University, 2016. ]
[4]	王桂杰,谢谟文,邱骋. D-INSAR技术在大范围滑坡监测中的应用[J].岩土力学,2010,31(4):1337-1344.
	[ Wang G J, Xie M W, Qiu P.Application of D-INSAR technique to landslide monitoring[J]. Rock and Soil Mechanics, 2010,31(4):1337-1344. ]
[5]	Colesanti C, Ferretti A, Prati C, et al. Monitoring landslides and tectonic motions with the Permanent Scatterers technique[J]. Engineering Geology, 2003,68(s1-2):3-14. doi: 10.1016/S0013-7952(02)00195-3
[6]	涂鹏飞,岑仲阳,谌华.应用重轨星载InSAR技术监测三峡库区滑坡形变探讨[J].遥感技术与应用,2010,25(6):886-890.
	[ Tu P F, Qin Z Y, Chen H.Monitoring landslides deformation in three gorges reservoir area by using the repeat-pass spaceborne InSAR[J]. Remote Sensing Technology and Application, 2010,25(6):886-890. ]
[7]	Berger M, Moreno J, Johannessen J A, et al.ESA's sentinel missions in support of Earth system science[J]. Remote Sensing Environment, 2012,120:84-90 doi: 10.1016/j.rse.2011.07.023
[8]	Berardino P, Fornaro G, Lanari R, et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,40(11):2375-2383 doi: 10.1109/TGRS.2002.803792
[9]	Wasowski, Bovenga F.Investigating landslides and unstable slopes with satellite multi temporal interferometry: Current issues and future perspectives[J]. Engineering Geology, 2014,174:103-138 doi: 10.1016/j.enggeo.2014.03.003
[10]	廖明生,唐婧,王腾,等.高分辨率SAR数据在三峡库区滑坡监测中的应用[J].中国科学:地球科学,2012,42(2):217-229.
	[ Liao M S, Tang Q, Wang T, et al.Landslide monitoring with high-resolution SAR data in the Three Gorges region[J]. Scientia Sinica (Terrae), 2012,42(2):217-229. ]
[11]	Cascini L, Fornaro G, Peduto D.Advanced low-and full-resolution D-InSAR map generate-on for slow-moving landslide analysis at different scales[J]. Engineering Geology, 2010,112(1-4):29-42. doi: 10.1016/j.enggeo.2010.01.003
[12]	Dong J, Zhang L, Tang M, et al.Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China[J]. Remote Sensing of Environment, 2018,205:180-198. ] doi: 10.1016/j.rse.2017.11.022
[13]	刘筱怡,杨志华,郭长宝.基于SBAS-InSAR的鲜水河断裂带蠕滑型滑坡特征研究[J].现代地质,2017,31(5):445-977.
	[ Liu X Y, Yang Z H, Guo C B.Study of slow-moving landslide characteristics based on the SBAS-InSAR in the Xianshuihe fault zone[J]. Geoscience, 2017,31(5):445-977. ]
[14]	徐建军. 煤矿老采空区滑坡勘查与机理分析——以山西清徐李家楼滑坡为例[J].中国地质灾害与防治学报,2015,26(4):25-29.
	[ Xu J J.Survey and mechanism analysis of landslide in old coal mine goaf: An example of Lijialou landslide in Qingxu, Shanxi[J]. The Chinese Journal of Geological Hazard and Control, 2015,26(4):25-29. ]
[15]	樊晓一,张友谊,杨建荣.汶川地震滑坡发育特征及其影响因素[J].自然灾害学报,2012,21(1):128-134.
	[ Fan X Y, Zhang Y Y, Yang J R.Developmental characteristics and influence factors of landslides in Wenchuan earthquake[J]. Journal of Natural Disasters, 2012,21(1):128-134. ]
[16]	曹洪洋,郝东恒,白聚波.区域滑坡灾害地形地貌因子敏感性分析研究[J].中国安全科学学报,2011,21(11):3-7.
	[ Cao H Y, Hao D H, Bai J B.Sensitivity analysis of topographic and geomorphologic factors to regional landslides[J]. China Safety Science Journal, 2011,21(11):3-7. ]
[17]	苏巧梅,赵尚民,郭建立.霍西煤矿区地表滑坡灾害敏感性数值建模与等级划分[J].地球信息科学学报,2017,19(12):1613-1622.
	[ Su Q M, Zhao S M, Guo J L.Numerical modeling and degree division to landslide susceptibility in the ground surface of Huoxi coal mine area[J]. Journal of Geo-information Science, 2017,19(12):1613-1622. ]
[18]	聂兵其. 基于InSAR的滑坡形变探测及隐患识别研究—以丹巴县城区为例[D].成都:成都理工大学,2018.
	[ Nie B Q.Landslide deformation detection and identification based on InSAR technology: A case of Danba county[D]. Chengdu: Chengdu University of Technology, 2018. ]
[19]	熊海仙,黄光庆,宫清华.数字地形分析在滑坡研究中的应用综述[J].热带地理,2015,35(1):139-146.
	[ Xiong H L, Huang G Q, Gong Q H.A review on application of digital terrain analysis in landslide researches[J]. Tropical Geography, 2015,35(1):139-146. ]
[20]	杨城,林广发,张明锋,等.基于DEM的福建省土质滑坡敏感性评价[J].地球信息科学学报,2016,18(12):1624-1633.
	[ Yang C, Lin G F, Zhang M F, et al.Soil landslide susceptibility assessment based on DEM[J]. Journal of Geo-Information Science, 2016,18(12):1624-1633. ]
[21]	范景辉,邱阔天,夏耶.三峡库区范家坪滑坡地表形变InSAR监测与综合分析[J].地质通报,2017,36(9):1665-1673.
	[ Fan J H, Qiu K T, Xia Y.InSAR monitoring and synthetic and of the surface deformation of Fanjiaping landslide in the Three Gorges Reservoir area[J]. Geological Bulletin of China, 2017,36(9):1665-1673. ]
[22]	Copernicus open access hub[EB/OL]. .
[23]	Global data explorer[EB/OL]. .
[24]	Sentinel-1 quality control[EB/OL]. .
[25]	温浩. 基于MTI技术的岷江流域滑坡识别研究[D].南京:南京师范大学,2015.
	[ Wen H.Study on landslide recognition in Minjiang Basin based on MTI technology[D]. Nanjing: Nanjing Normal University, 2015 ]
[26]	张诗茄,蒋建军,缪亚敏.基于SBAS技术的岷江流域潜在滑坡识别[J].山地学报,2018,36(1):91-97.
	[ Zhang S J, Jiang J J, Liao Y M.Application of the SBAS technique in potential landslide identification in the Minjiang Wastershed[J]. Mountain Research, 2018,36(1):91-97. ]
[27]	张路,廖明生,董杰,等.基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别——以四川丹巴为例[J].武汉大学学报·信息科学版,2018,43(12):2039-2049.
	[ Zhang L, Liao M S, Dong J, et al.Early detection of landslide hazards in mountainous areas of west China using Time Series SAR interferometry: A case study of Danba, Sichuan[J]. Geomatics and Information Science of Wuhan University, 2018,43(12):2039-2049. ]
[28]	Meisina C, Zucca F, Notti D, et al.Geological interpretation of PSInSAR data at regional scale[J]. Sensors, 2008,8(11):7469-7492. doi: 10.3390/s8117469
[29]	郭果,陈筠,李明惠.土质滑坡发育概率与坡度间关系研究[J].工程地质学,2013,21(4):607-612.
	[ Chen G, Chen J, Li M H.Statistic relationship between slope gradient and landslide probability in soil slopes around reservoir[J]. Journal of Engineering Geology, 2013,21(4):607-612. ]
[30]	熊俊楠,赵云亮,程维明.四川省山洪灾害时空分布规律及其影响因素研究[J].地球信息科学学报,2018,20(10):1443-1456.
	[ Xiong J N, Zhao Y L, Cheng W M.Temporal-spatial distribution and the influencing factors of Mountain-Flood disasters in Sichuan province[J]. Journal of Geo-Information Science. 2018, 20(10):1443-1456. ]
[31]	牛全福,冯尊斌,党星海,等.黄土区滑坡研究中地形因子的选取与适宜性分析[J].地球信息科学学报,2017,19(12):1584-1592.
	[ Niu Q F, Feng Z B, Dang X H, et al.Suitability analysis of topographic factors in loess landslide research[J]. Journal of Geo-information Science. 2017,19(12):1584-1592. ]
[32]	中国地质调查局地质环境监测院.中国地质灾害分布图[EB/OL]. , 2015-07-01.
	[ China Geological Survey Bureau Geological Environment Monitoring Institute. China's geological disaster map[EB/OL]. ,2015-07- 01. ]

可见性分区	坡向	说明
不可见	东、东南、东北	坡度<39.5°时发生透视收缩,坡度>39.5°时发生叠掩
可见	西、西南、西北坡度<50.5°	坡度>50.5°时发生阴影
低敏感性	南、北	指对南、北向形变监测能力较低

序号	年平均垂直形变速率/（mm/a）	坡度/°	坡向	滑坡识别
1	<10	35	阳坡	易发区
2	30	25	阳坡	高发区
3	50	<20	阴坡	低发区
4	45	20	阳坡	易发区
5	25	20	阳坡	易发区
6	<10	30	阴坡	低发区
7	<10	<20	阳坡	稳定区
8	<10	25	阳坡	易发区
9	30	<20	阳坡	易发区
10	0~30	20~50	阴坡和阳坡	易发区和高发区
11	<10	0~20	阴坡和阳坡	未识别

基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法

A Method based on SBAS-InSAR for Comprehensive Identification of Potential Goaf Landslide

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 18

参考文献 32

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	刘凯斯, 王彦兵, 宫辉力, 李小娟, 余洁. 机载LiDAR点云数据的二面角滤波算法[J]. 地球信息科学学报, 2018, 20(4): 414-421.
[2]	宋金超, 李新虎, 吝涛, 张国钦, 叶红, 何晓燕, 葛汝冰. 基于夜晚灯光数据和Google Earth的城市建成区提取分析[J]. 地球信息科学学报, 2015, 17(6): 750-756.
[3]	陈楠. 基于信息损失量的坡度精度分析[J]. 地球信息科学学报, 2014, 16(6): 852-858.
[4]	孙庆龄, 冯险峰, 肖潇. 武陵山区植被净第一性生产力的时空格局及其与地形因子的关系[J]. 地球信息科学学报, 2014, 16(6): 915-924.
[5]	陈楠. DEM分辨率与平均坡度的关系分析[J]. 地球信息科学学报, 2014, 16(4): 524-630.
[6]	杨存建, 赵曦琳, 周其林, 倪静, 王琴. 丘陵地区DEM与坡度的尺度效应分析[J]. 地球信息科学学报, 2013, 15(6): 814-818.
[7]	刘伟科, 刘国林, 付政庆. 顾及地形坡度的非线性最小二乘相位解缠[J]. 地球信息科学学报, 2013, 15(1): 137-143.
[8]	赵明伟, 汤国安, 张磊, 田剑, 宋效东. 基于DEM的5节点二阶差分坡度算法模型与实验分析[J]. 地球信息科学学报, 2012, 14(3): 286-291.
[9]	陈传法, 岳天祥. 基于条件随机模拟的DEM误差分析——以董志塬水土流失等级划分为例[J]. 地球信息科学学报, 2009, 11(5): 572-576.
[10]	贾旖旎, 汤国安, 刘学军. 高程内插方法对DEM所提取坡度、坡向精度的影响[J]. 地球信息科学学报, 2009, 11(1): 36-42.
[11]	贾敦新, 汤国安, 王春, 贾旖旎. DEM数据误差与地形描述误差对坡度精度的影响[J]. 地球信息科学学报, 2009, 11(1): 43-49.
[12]	王春, 汤国安, 李发源, 朱雪坚, 贾旖旎. 基于DEM提取坡谱信息的不确定性[J]. 地球信息科学学报, 2008, 10(4): 539-544.
[13]	郎玲玲, 程维明, 朱启疆, 龙恩. 多尺度DEM提取地势起伏度的对比分析——以福建低山丘陵区为例[J]. 地球信息科学学报, 2007, 9(6): 1-6,135-136.
[14]	刘敏, 汤国安, 王春, 党维勤. DEM提取坡度信息的不确定性分析[J]. 地球信息科学学报, 2007, 9(2): 65-69.
[15]	陈楠, 王钦敏, 汤国安. 黄土高原丘坡信息DEM提取算法的应用[J]. 地球信息科学学报, 2006, 8(3): 69-75.