雷达数据辅助下的滑坡易发性评价

doi:10.13278/j.cnki.jjuese.20160356

Abstract

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

CLC Number:

P642.22

Zhao Jintong, Niu Ruiqing, Yao Qi, Wu Xueling. Landslide Susceptibility Assessment Aided by SAR Data[J].Journal of Jilin University(Earth Science Edition), 2018, 48(4): 1182-1191.

References

[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.

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