Please wait a minute...
 
自然资源遥感  2022, Vol. 34 Issue (3): 138-145    DOI: 10.6046/zrzyyg.2021245
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于DS-InSAR的乌达煤田火区长时序地表形变监测与分析
李柱1(), 范洪冬1(), 高彦涛2, 许耀宗1
1.中国矿业大学矿山生态修复教育部工程研究中心,徐州 221116
2.河南省地质矿产勘查开发局测绘地理信息院,郑州 450006
DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field
LI Zhu1(), FAN Hongdong1(), GAO Yantao2, XU Yaozong1
1. Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, China
2. Institute of Surveying Mapping and Geoinformation, Henan Bureau of GEO-Exploration and Mineral Development, Zhengzhou 450006, China
全文: PDF(4317 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

煤火燃烧不仅浪费了大量煤炭资源,而且严重破坏了火区生态环境,而传统监测方法存在范围小、频率低、成本高、危险大等问题。为此,研究了一种基于分布式目标合成孔径雷达干涉测量(distributed scatterer interferometric synthetic aperture Radar,DS-InSAR)技术的煤田火区监测方法。该方法通过快速同质点识别算法(fast statistically homogeneous pixels selection,FaSHPS)选取同质点,然后利用特征值分解方法对这些同质点进行相位优化,并根据时间相干性获取最终的分布式目标,最后结合短基线集(small baseline subsets,SBAS)InSAR处理步骤解算时序地表形变。以2017年3月—2019年4月63景Sentinel-1A影像为数据源,利用本文方法获取了乌达煤田时序地表沉降,并与临时相干点合成孔径雷达干涉测量(temporarily coherent point interferometric synthetic aperture Radar,TCP-InSAR)技术监测结果进行了可靠性验证,结果表明: 两者间的相关系数为0.84,监测点位密度比TCP-InSAR提高1.24倍; 乌达煤田存在严重的地表形变现象,研究区域内最大形变速率为-215 mm/a; 煤火区在秋冬季节地表形变变化相对较快,且具有多个形变延伸方向及发育程度不同的沉降中心。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
李柱
范洪冬
高彦涛
许耀宗
关键词 DS-InSAR形变监测煤火监测长时序乌达煤田火区    
Abstract

Coal fire not only wastes a lot of coal resources but also severely damages the ecological environment of the fire area. However, conventional monitoring methods suffer disadvantages such as a small scope, low frequency, high cost, and great danger. Therefore, this study developed a monitoring method of coal field fire based on the distributed scatterer interferometric synthetic aperture Radar (DS-InSAR) technology. This method successively selects homogeneous pixels using the fast statistically homogeneous pixels selection (FaSHPS) algorithm, optimizes the phases of these pixels using the eigenvalue decomposition method, obtains the final distributed targets based on the temporal coherence, and calculates the time-series surface deformation by combining the small baseline subsets (SBAS) InSAR technique. Taking 63 scenes of Sentinel-1A images from March 2017 to April 2019 as the data source, this study obtained the time series surface subsidence in the Wuda coal field using this method and then verified the reliability of the results by comparison with the monitoring results obtained using the temporarily coherent point interferometric synthetic aperture Radar (TCP-InSAR) technology. As a result, the correlation coefficient between the two methods was 0.84, but the density of monitoring sites obtained using the method proposed in this study was 1.24 times higher than that of TCP-InSAR. The monitoring results show that the surface of the Wuda coal field deforms severely, with a maximum deformation rate of -215 mm/a, and that the deformation occurs more rapidly during autumn and winter and has multiple extensional directions and multiple subsidence centers at varying degrees.

Key wordsDS-InSAR    deformation monitoring    coal fire monitoring    long time series    fire area of Wuda coal field
收稿日期: 2021-08-12      出版日期: 2022-09-21
ZTFLH:  TP79  
基金资助:国家重点研发计划项目“矿区地表形变InSAR监测及地球物理模拟分析”(2017YFE0107100);国家自然科学基金项目“关闭矿井地表沉陷机理规律及预测方法研究”(51774270);“大形变梯度条件下时序TCP-InSAR监测矿区动态沉陷关键问题研究”(41604005);江苏省自然科学基金项目“顾及空间领域异质性的SAR影像自适应变化检测方法研究”(BK20190645)
通讯作者: 范洪冬
作者简介: 李 柱(1998-),男,硕士研究生,主要从事InSAR技术应用研究。Email: lizhu@cumt.edu.cn
引用本文:   
李柱, 范洪冬, 高彦涛, 许耀宗. 基于DS-InSAR的乌达煤田火区长时序地表形变监测与分析[J]. 自然资源遥感, 2022, 34(3): 138-145.
LI Zhu, FAN Hongdong, GAO Yantao, XU Yaozong. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field. Remote Sensing for Natural Resources, 2022, 34(3): 138-145.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021245      或      https://www.gtzyyg.com/CN/Y2022/V34/I3/138
Fig.1  研究区域
Fig.2  数据处理流程
Fig.3  乌达煤田地表形变速率
Fig.4  TCP-InSAR和DS-InSAR同名点对形变速率相关性和差异分布直方图
Fig.5  乌达煤田地表形变时序累积图
Fig.6  特征点时序形变图
Fig.7  典型煤火区三维形变图
Fig.8  典型煤火区剖面累积形变图
[1] Song Z, Kuenzer C, Zhu H, et al. Analysis of coal fire dynamics in the Wuda syncline impacted by fire-fighting activities based on in-situ observations and Landsat8 remote sensing data[J]. International Journal of Coal Geology, 2015,141-142:91-102.
[2] Kuenzer C, Zhang J, Sun Y, et al. Coal fires revisited:The Wuda coal field in the aftermath of extensive coal fire research and accelerating extinguishing activities[J]. International Journal of Coal Geology, 2012, 102:75-86
doi: 10.1016/j.coal.2012.07.006
[3] Liang Y, Liang H, Zhu S. Mercury emission from coal seam fire at Wuda,Inner Mongolia,China[J]. Atmospheric Environment, 2014, 83:176-184.
doi: 10.1016/j.atmosenv.2013.09.001
[4] 张志敏, 江利明, 柳林, 等. 利用Landsat热红外影像探测地下煤火区范围——以乌达煤田为例[J]. 测绘通报, 2018(3):93-97.
Zhang Z M, Jiang L M, Liu L, et al. Detecting the underground coal fire by using Landsat thermal infrared imagery:Taking Wuda coalfield as an example[J]. Bulletin of Surveying and Mapping, 2018(3):93-97.
[5] Du X, Cao D, Mishra D, et al. Self-adaptive gradient-based thresholding method for coal fire detection using ASTER thermal infrared data,part I:Methodology and decadal change detection[J]. Remote Sensing, 2015, 7(6):6576-6610.
doi: 10.3390/rs70606576
[6] 李峰, 梁汉东, 赵小平, 等. 内蒙古乌达煤田煤火治理效果的遥感监测与评估[J]. 国土资源遥感, 2017, 29(3):217-223.doi: 10.6046/gtzyyg.2017.03.32.
doi: 10.6046/gtzyyg.2017.03.32
Li F, Liang H D, Zhao X P, et al. Remote sensing monitoring and assessment of fire-fighting effects in Wuda coal field,Inner Mongolia[J]. Remote Sensing for Land and Resources, 2017, 29(3):217-223.doi: 10.6046/gtzyyg.2017.03.32.
doi: 10.6046/gtzyyg.2017.03.32
[7] Li F, Li J, Liu X, et al. Coal fire detection and evolution of trend analysis based on CBERS-04 thermal infrared imagery[J]. Environmental Earth Sciences, 2020, 79(16):1-15.
doi: 10.1007/s12665-019-8746-6
[8] 蒋卫国, 武建军, 顾磊, 等. 基于夜间热红外光谱的地下煤火监测方法研究[J]. 光谱学与光谱分析, 2011, 31(2):357-361.
Jiang W G, Wu J J, Gu L, et al. Monitoring method of undergound coal fire based on nigth thermal infrared remote sensing technology[J]. Spectroscopy and Spectral Analysis, 2011, 31(2):357-361.
[9] 郑美楠, 邓喀中, 陈华, 等. 时序累积DInSAR与GIS结合的矿区沉降监测与分析[J]. 煤矿安全, 2017, 48(1):160-163.
Zheng M N, Deng K Z, Chen H, et al. Monitoring and analysis of mining subsidence base on timing accumulation DInSAR and GIS[J]. Safety in Coal Mines, 2017, 48(1):160-163.
[10] 赵立峰, 范洪冬, 渠俊峰, 等. 基于DS-InSAR的张双楼煤矿长时序地表形变监测方法[J]. 金属矿山, 2021(8):142-149.
Zhao L F, Fan H D, Qu J F, et al. Long time-series surface deformation method of Zhangshuanglou coal mine based on DS-InSAR[J]. Metal Mine, 2021(8):142-149.
[11] Voigt S, Tetzlaff A, Zhang J, et al. Integrating satellite remote sensing techniques for detection and analysis of uncontrolled coal seam fires in North China[J]. Elsevier, 2004, 59(1-2):121-136.
[12] Xu Y, Fan H, Dang L. Monitoring coal seam fires in Xinjiang using comprehensive thermal infrared and time series InSAR detection[J]. International Journal of Remote Sensing, 2021, 42(6):2220-2245.
doi: 10.1080/01431161.2020.1823045
[13] Liu J, Wang Y, Yan S, et al. Underground coal fire detection and monitoring based on Landsat8 and Sentinel-1 data sets in Miquan fire area,Xinjiang[J]. Remote Sensing, 2021, 13(6):1141.
doi: 10.3390/rs13061141
[14] 许怡, 范洪冬, 党立波. 基于TIRS和TCP-InSAR的新疆广域煤田火区探测方法[J]. 金属矿山, 2019(10):164-171.
Xu Y, Fan H D, Dang L B. Detection method of fire area in Xinjiang wide area coalfield based on TIRS and TCP-InSAR[J]. Metal Mine, 2019(10):164-171.
[15] Liu J, Wang Y, Li Y, et al. Underground coal fires identification and monitoring using time-series InSAR with persistent and distributed scatterers:A case study of Miquan coal fire zone in Xinjiang,China[J]. IEEE Access, 2019, 7:164492-164506.
doi: 10.1109/ACCESS.2019.2952363
[16] Riyas M, Syed T, Kumar H, et al. Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield,India using Sentinel-1 SAR data[J]. Remote Sensing, 2021, 13(8):1521.
doi: 10.3390/rs13081521
[17] Zhou L, Zhang D, Wang J, et al. Mapping land subsidence related to underground coal fires in the Wuda coalfield (Northern China) using a small stack of ALOS PALSAR differential interferograms[J]. Remote Sensing, 2013, 5(3):1152-1176.
doi: 10.3390/rs5031152
[18] 黄昭权, 张登荣, 王帆, 等. 基于差分干涉SAR的煤田火区地表形变监测[J]. 国土资源遥感, 2010, 22(4):85-90.doi: 10.6046/gtzyyg.2010.04.18.
doi: 10.6046/gtzyyg.2010.04.18
Huang Z Q, Zhang D R, Wang F, et al. Differential SAR interfero-metry for the monitoring of underground coal spontaneous combustion zone surface deformation[J]. Remote Sensing for Land and Resources, 2010, 22(4):85-90.doi: 10.6046/gtzyyg.2010.04.18.
doi: 10.6046/gtzyyg.2010.04.18
[19] Jiang L, Lin H, Ma J, et al. Potential of small-baseline SAR interferometry for monitoring land subsidence related to underground coal fires:Wuda (Northern China) case study[J]. Remote Sensing of Environment, 2011, 115(2):257-268.
doi: 10.1016/j.rse.2010.08.008
[20] Ferretti A, Fumagalli A, Novali F, et al. A new algorithm for processing interferometric data-stacks:SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9):3460-3470.
doi: 10.1109/TGRS.2011.2124465
[21] Jiang M, Ding X, Hanssen R F, et al. Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(3):1213-1224.
doi: 10.1109/TGRS.2014.2336237
[22] Cao N, Lee H, Jung H C. Mathematical framework for phase-triangulation algorithms in distributed-scatterer interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9):1838-1842.
doi: 10.1109/LGRS.2015.2430752
[23] 张建明, 管海晏, 曹代勇, 等. 中国地下煤火研究与治理[M]. 北京: 煤炭工业出版社, 2008:12-21.
Zhang J M, Guan H Y, Cao D Y, et al. Underground coal fires in China:Origin,detection,fire-fighting,and prevention[M]. Beijing: China Coal Industry Publishing House, 2018:12-21.
[1] 麻学飞, 张双成, 惠文华, 许强. 山西省临汾市矿区地表形变InSAR大范围探测与监测[J]. 自然资源遥感, 2022, 34(3): 146-153.
[2] 杨旺, 何毅, 张立峰, 王文辉, 陈有东, 陈毅. 甘肃金川矿区地表三维形变InSAR监测[J]. 自然资源遥感, 2022, 34(1): 177-188.
[3] 刘志敏, 李永生, 张景发, 罗毅, 刘斌. 基于SBAS-InSAR的长治矿区地表形变监测[J]. 国土资源遥感, 2014, 26(3): 37-42.
[4] 黄昭权, 张登荣, 王帆, 党福星, 李志忠. 基于差分干涉SAR的煤田火区地表形变监测[J]. 国土资源遥感, 2010, 22(4): 85-90.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发