吉林大学学报(地球科学版) ›› 2018, Vol. 48 ›› Issue (4): 1182-1191.doi: 10.13278/j.cnki.jjuese.20160356

• 地质工程与环境工程 • 上一篇    下一篇

雷达数据辅助下的滑坡易发性评价

赵金童, 牛瑞卿, 姚琦, 武雪玲   

  1. 中国地质大学地球物理与空间信息学院, 武汉 430074
  • 收稿日期:2017-11-07 出版日期:2018-07-26 发布日期:2018-07-26
  • 通讯作者: 牛瑞卿(1969-),男,教授,博士生导师,主要从事3S与地质灾害研究,E-mail:rqniu@163.com E-mail:rqniu@163.com
  • 作者简介:赵金童(1992-),男,硕士,主要从事遥感、地质灾害方面的研究,E-mail:rszhaojt@163.com
  • 基金资助:
    国家高技术研究发展计划(“863”计划)项目(2012AA121303);国家重点基础研究发展计划(“973”计划)项目(2011CB710601)

Landslide Susceptibility Assessment Aided by SAR Data

Zhao Jintong, Niu Ruiqing, Yao Qi, Wu Xueling   

  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
  • Received:2017-11-07 Online:2018-07-26 Published:2018-07-26
  • Supported by:
    Supported by National High-Tech R&D Program ("973" Program) of China (2012AA121303) and National Basic Research Program ("973" Program) of China (2011CB710601)

摘要: 岩土体含水量对滑坡,尤其是土质滑坡的稳定性具有极大的影响。本文以三峡库区秭归段内土质滑坡作为研究对象,利用Sentinel-1雷达数据反演地表岩土体含水量来替代传统的湿度指数因子,在保持其他因子不变的情况下,构建二元逻辑回归模型进行滑坡易发性评价。结果表明,利用成功率曲线对结果进行分析,采用岩土体含水量因子时预测精度达到80.2%,高于采用地形湿度指数的77.2%。利用雷达数据反演得到的岩土体含水量代替地形湿度指数进行滑坡易发性评价精度较高、预测能力较强。

关键词: 滑坡易发性, Sentinel-1, 水-云模型, 岩土体含水量, 逻辑回归, 雷达

Abstract: Soil moisture content has a great influence on stability of landslides, especially on a soil landslide. The aim of this study is to analyze landslide susceptibility by using soil moisture content instead of moisture index in Zigui County of the Three Gorges Reservoir area. The soil moisture content was invertedby using the Sentinel-1 data. The success rate curve showed that the prediction accuracy reached 80.2%, higher than that with the terrain wetness index of 77.2%. The results suggests that using soil moisture content can predict landslides better than using the moisture index, and it is more effective.

Key words: landslide susceptibility, Sentinel-1, water-cloud model, soil moisture content, logistic regression, SAR

中图分类号: 

  • P642.22
[1] Carrara A. Multivariate Models for Landslide Hazard Evaluation[J]. Journal of the International Association for Mathematical Geology, 1983,15(3):403-426.
[2] Lee S. Application of Logistic Regression Model and Its Validation for Landslide Susceptibility Mapping Using GIS and Remote Sensing Data Journals[J]. International Journal of Remote Sensing, 2005,26(7):1477-1491.
[3] Ballabio C, Sterlacchini S. Support Vector Machines for Landslide Susceptibility Mapping:The Staffora River Basin Case Study, Italy[J]. Mathematical Geosciences, 2012,44(1):47-70.
[4] 晏同珍,杨安顺,方云. 滑坡学[M]. 武汉:中国地质大学出版社, 1998. Yan Tongzhen, Yang Anshun, Fang Yun. Landsile Science[M]. Wuhan:China University of Geosciences Press, 1998.
[5] 殷坤龙. 滑坡灾害预测预报[M]. 武汉:中国地质大学出版社, 2004. Yin Kunlong. Prediction and Forecast of Landslide Disaster[M]. Wuhan:China University of Geosciences Press, 2004.
[6] 牛瑞卿,彭令,叶润青,等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版), 2012,42(2):430-439. Niu Ruiqing, Peng Ling, Ye Runqing, et al. Landslide Susceptibility Assess Based on Rough Sets and Support Vector Machine[J]. Journal of Jilin University:Earth Science Edition, 2012,42(2):430-439.
[7] 彭令,徐素宁,彭军还. 多源遥感数据支持下区域滑坡灾害风险评价[J].吉林大学学报(地球科学版),2016,46(1):175-186. Peng Ling, Xu Suning, Peng Junhuan. Regional Landslide Risk Assessment Using Multi-Source Remote Sensing Data[J]. Journal of Jilin University(Earth Science Edition), 2016,46(1):175-186.
[8] 武雪玲,沈少青,牛瑞卿. GIS支持下应用PSO_SVM模型预测滑坡易发性[J]. 武汉大学学报(信息科学版), 2016,41(5):665-671. Wu Xueling, Shen Shaoqing, Niu Ruiqing. Landslide Susceptibility Prediction Using GIS and PSO_SVM[J]. Geomatics and Information Science of Wuhan University, 2016,41(5):665-671.
[9] Paloscia S, Pettinato S, Santi E, et al. Soil Moisture Mapping Using Sentinel-1 Images:Algorithm and Preliminary Validation[J]. Remote Sensing of Environment, 2013,134:234-248.
[10] 何连,秦其明,任华忠,等. 利用多时相Sentinel-1 SAR数据反演农田地表土壤水分[J]. 农业工程学报, 2016,32(3):142-148. He Lian, Qin Qiming,Ren Huazhong, et al. Soil Moisture Retrieval Using Multi-Temporal Sentinel-1 SAR Data in Agricultural Areas[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(3):142-148.
[11] Oh Y, Sarabandi K, Ulaby F T. An Empirical-Model and an Inversion Technique for Radar Scattering from Bare Soil Surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30(2):370-381.
[12] Fung A K, Li Z Q, Chen K S. Backscattering from a Randomly Rough Dielectric Surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30(2):356-369.
[13] Fung A K, Chen K S. An Update on the IEM Surface Backscattering Model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,1(2):75-77.
[14] Ulaby F T, Sarabandi K, Mcdonald K, et al. Michi-gan Microwave Canopy Scattering Model[J]. International Journal of Remote Sensing, 1990, 11(7):1223-1253.
[15] Attema E P W, Ulaby F T. Vegetation Modeled as a Water Cloud[J]. Radio Science, 1978,13(2):357-364.
[16] Hassaballa A A, Althuwaynee O F, Pradhan B. Extraction of Soil Moisture from RADARSAT-1 and Its Role in the Formation of the 6 December 2008 Landslide at Bukit Antarabangsa, Kuala Lumpur[J]. Arabian Journal of Geosciences, 2014,7(7):2831-2840.
[17] Brocca L, Ponziani F, Moramarco T, et al. Impro-ving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data:A Case Study of the Torgiovannetto Landslide in Central Italy[J]. Remote Sensing, 2012,4(5):1232-1244.
[18] Zribi M, Dechambre M. A New Empirical Model to Retrieve Soil Moisture and Roughness from C-band Radar Data[J]. Remote Sensing of Environment, 2003, 84(1):42-52.
[19] 李震,陈权,任鑫. Envisat-1双极化雷达数据建模及应用[J]. 遥感学报, 2006,10(5):777-782. Li Zhen, Chen Quan, Ren Xin. Modeling Envisat Dual Polarized Data and Its Applications[J]. Journal of Remote Sensing, 2006,10(5):777-782.
[20] 李震,廖静娟. 合成孔径雷达地表参数反演模型与方法[M]. 北京:科学出版社, 2011. Li Zhen, Liao Jingjuan. Surface Parameter Inversion Model and Method Based on SAR[M]. Beijing:Science Press, 2011.
[21] 文海家,胡东萍,王桂林. 汶川县地震滑坡易发性LR与NN评价比较研究[J]. 土木工程学报, 2014(增刊1):17-23. Wen Haijia, Hu Dongping, Wang Guilin. A Comparative Study on the Susceptibility Mapping of Earthquake Triggered Landslide by Neural Network and Logistic Regression Model, Wenchuan County[J]. China Civil Engineering Journal, 2014(Sup.1):17-23.
[22] Jackson T. Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans[J]. Remote Sensing of Environment, 2004,92(4):475-482.
[23] Gao B. NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space[J]. Remote Sensing of Environment, 1996,58(3):257-266.
[24] Bindlish R, Barros A P. Parameterization of Vege-tation Backscatter in Radar-Based, Soil Moisture Estimation[J]. Remote Sensing of Environment, 2001,76(1):130-137.
[25] Uhlemann S, Chambers J, Wilkinson P, et al. Four-Dimensional Imaging of Moisture Dynamics During Landslide Reactivation[J]. Journal of Geophysical Research Earth Surface, 2017, 122(1):.
[26] 邓孺孺,陈晓翔,何执兼. GIS支持下的深层土壤含水量遥感调查方法[J]. 中山大学学报(自然科学版), 1997(3):103-106. Deng Ruru, Chen Xiaoxiang, He Zhiqian. A GIS Based Remote Sensing Method for Underground Soil Moisture Test[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 1997(3):103-106.
[27] 彭令. 三峡库区滑坡灾害风险评估研究[D]. 武汉:中国地质大学, 2013. Peng Ling. Landslide Risk Assessment in the Three Gorges Reservoir[D]. Wuhan:China University of Geosciences, 2013.
[28] Chung C, Fabbri A G. Probabilistic Prediction Models for Landslide Hazard Mapping[J]. Photogrammetric Engineering and Remote Sensing, 1999,65(12):1389-1399.
[1] 胥为, 周云轩, 沈芳, 田波, 于鹏. 基于Sentinel-1A雷达影像的崇明东滩芦苇盐沼植被识别提取[J]. 吉林大学学报(地球科学版), 2018, 48(4): 1192-1200.
[2] 冯晅, 梁帅帅, 恩和得力海, 张明贺, 董泽君, 周皓秋, 齐嘉慧, 赵玮昌. 全极化探地雷达地下管道分类识别技术[J]. 吉林大学学报(地球科学版), 2018, 48(2): 364-372.
[3] 王文天, 刘四新, 鹿琪, 李宏卿, 傅磊. 基于改进残差法的定向钻孔雷达三维成像算法[J]. 吉林大学学报(地球科学版), 2018, 48(2): 402-410.
[4] 刘新彤, 刘四新, 孟旭, 傅磊. 低频缺失下跨孔雷达包络波形反演[J]. 吉林大学学报(地球科学版), 2018, 48(2): 474-482.
[5] 梁文婧, 冯晅, 刘财, 恩和得力海, 张明贺, 梁帅帅. 多输入多输出极化步进频率探地雷达硬件系统开发[J]. 吉林大学学报(地球科学版), 2018, 48(2): 483-490.
[6] 王宪楠, 刘四新, 程浩. Shearlet变换在GPR数据随机噪声压制中的应用[J]. 吉林大学学报(地球科学版), 2017, 47(6): 1855-1864.
[7] 曾昭发, 李文奔, 习建军, 黄玲, 王者江. 基于DOA估计的阵列式探地雷达逆向投影目标成像方法[J]. 吉林大学学报(地球科学版), 2017, 47(4): 1308-1318.
[8] 习建军, 曾昭发, 黄玲, 崔丹丹, 王者江. 阵列式探地雷达信号极化场特征[J]. 吉林大学学报(地球科学版), 2017, 47(2): 633-644.
[9] 孟庆生, 韩凯, 刘涛, 高镇. 软土基坑隔水帷幕渗漏检测技术[J]. 吉林大学学报(地球科学版), 2016, 46(1): 295-302.
[10] 曲昕馨,李桐林,王飞. 基于数字图像分割法的跨孔雷达走时层析成像[J]. 吉林大学学报(地球科学版), 2014, 44(4): 1340-1347.
[11] 朱自强,彭凌星,鲁光银,密士文. 基于互相关函数对钻孔雷达层析成像的改进[J]. 吉林大学学报(地球科学版), 2014, 44(2): 668-674.
[12] 冉利民, 刘四新, 李玉喜, 李健伟. 影响跨孔雷达层析成像效果的几个因素[J]. 吉林大学学报(地球科学版), 2013, 43(5): 1672-1680.
[13] 刘财, 杨宝俊, 鹿琪, 冯晅, 刘洋, 王典. 黑龙江板块构造地球物理研究基本进展[J]. J4, 2012, 42(5): 1497-1505.
[14] 张丽丽, 刘四新, 吴俊军, 贾亮, 康晓涛. 基于分数阶傅里叶变换的探地雷达子波提取算法[J]. J4, 2012, 42(2): 569-574.
[15] 侯卫生, 陈国能, 庄文明, 彭卓伦, 孟凡强, 张澄博, 张柯. 西淋岗第四系断层探测及活动性评价[J]. J4, 2011, 41(3): 925-931.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!