Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image TransformationsDocumenting Liquefaction

Documenting earthquake-induced liquefaction effects is important to validate empirical liquefaction susceptibility models and to enhance our understanding of the liquefaction process. Currently, after an earthquake, field-based mapping of liquefaction can be sporadic and limited due to inaccessibility and lack of resources. Alternatively, researchers have used change detection with remotely sensed pre- and post-earthquake satellite images to map earthquake-induced effects. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, liquefaction-induced surface changes should be associated with increased moisture, and spectral bands/transformations that are sensitive to soil moisture can be used to identify these areas. We verify our hypothesis using change detection with pre- and post-earthquake thermal and tasseled cap wetness images derived from available Landsat 7 Enhanced Thematic Mapper Plus (ETM + ) for the 2001 Bhuj earthquake in India. The tasseled cap wetness image is directly related to the soil moisture content, whereas the thermal image is inversely related to it. The change detection of the tasseled cap transform wetness image helped to delineate earthquake-induced liquefaction areas that corroborated well with previous studies. The extent of liquefaction varied within and between geomorphological units, which we believe can be attributed to differences in the soil moisture retention capacity within and between the geomorphological units.

[1]  Rajat Gupta,et al.  Remote Sensing Geology , 1991 .

[2]  Peter K. Robertson,et al.  Estimating liquefaction-induced lateral displacements using the standard penetration test or cone penetration test , 2004 .

[3]  Arun K. Saraf,et al.  Landsat-TM data for estimating ground temperature and depth of subsurface coal fire in the Jharia coalfield, India , 1995 .

[4]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[5]  S. Vicente‐Serrano,et al.  Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data , 2004 .

[6]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[7]  Peter Deer Digital Change Detection Techniques in Remote Sensing , 1995 .

[8]  D. Lu,et al.  Change detection techniques , 2004 .

[9]  Elfatih A. B. Eltahir,et al.  A Soil Moisture–Rainfall Feedback Mechanism: 2. Numerical experiments , 1998 .

[10]  S. Adler-Golden,et al.  Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN 4 , 2000 .

[11]  Masashi Matsuoka,et al.  Reconnaissance Technologies Used after the 2004 Niigata Ken Chuetsu, Japan, Earthquake , 2006 .

[12]  Anupma Prakash,et al.  Surface fires in Jharia coalfield, India-their distribution and estimation of area and temperature from TM data , 1999 .

[13]  M. R. Ghayamghamian,et al.  Building Damage and Seismic Intensity in Bam City from the2003 Bam, Iran, Earthquake , 2004 .

[14]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[15]  Gail P. Anderson,et al.  Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.

[16]  M. Bauer,et al.  Digital change detection in forest ecosystems with remote sensing imagery , 1996 .

[17]  Sanjeeb Bhoi,et al.  Changes observed in land and ocean after Gujarat earthquake of 26 January 2001 using IRS data , 2002 .

[18]  D. Mouat,et al.  Remote sensing techniques in the analysis of change detection , 1993 .

[19]  Timothy A. Warner,et al.  Normalization of Landsat thermal imagery for the effects of solar heating and topography , 2001 .

[20]  Masanobu Shinozuka,et al.  Earthquake-Induced Change Detection in the 2003 Bam, Iran, Earthquake by Complex Analysis Using Envisat ASAR Data , 2005 .

[21]  David P. Miller,et al.  Status of atmospheric correction using a MODTRAN4-based algorithm , 2000, SPIE Defense + Commercial Sensing.

[22]  Kusala Rajendran,et al.  Characteristics of Deformation and Past Seismicity Associated with the 1819 Kutch Earthquake, Northwestern India , 2001 .

[23]  Robert E. Kayen,et al.  REMOTE SENSING OBSERVATIONS OF LANDSLIDES AND GROUND DEFORMATION FROM THE 2004 NIIGATA KEN CHUETSU EARTHQUAKE , 2006 .

[24]  F. Yamazaki,et al.  Damage Detection for 2003 Bam, Iran, Earthquake Using Terra-ASTER Satellite Imagery , 2005 .

[25]  Gail P. Anderson,et al.  Analysis of Hyperion data with the FLAASH atmospheric correction algorithm , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[26]  Arun K. Saraf,et al.  Satellite data reveals 26 January 2001 Kutch earthquake-induced ground changes and appearance of water bodies , 2002 .

[27]  Robert E. Kayen,et al.  Terrestrial-LIDAR Visualization of Surface and Structural Deformations of the 2004 Niigata Ken Chuetsu, Japan, Earthquake , 2006 .

[28]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[29]  Rajat Gupta,et al.  Remote Sensing Delineation of Zones Susceptible to Seismically Induced Liquefaction in the Ganga Plains , 1995 .

[30]  Charles K. Huyck,et al.  Towards Rapid Citywide Damage Mapping Using Neighborhood Edge Dissimilarities in Very High-Resolution Optical Satellite Imagery—Application to the 2003 Bam, Iran, Earthquake , 2005 .

[31]  Thomas Oommen,et al.  Model Development and Validation for Intelligent Data Collection for Lateral Spread Displacements , 2010, J. Comput. Civ. Eng..

[32]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[33]  J. Srinivasulu,et al.  Estimation of spectral reflectance of snow from IRS-1D LISS-III sensor over the Himalayan terrain , 2004 .

[34]  Vinay K. Dadhwal,et al.  Bandpass solar exoatmospheric irradiance and Rayleigh optical thickness of sensors on board Indian Remote Sensing Satellites-1B, -1C, -1D, and P4 , 2002, IEEE Trans. Geosci. Remote. Sens..

[35]  Anupma Prakash,et al.  Land-use mapping and change detection in a coal mining area - a case study in the Jharia coalfield, India , 1998 .

[36]  Limin Yang,et al.  Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance , 2002 .

[37]  Jonathan D. Jordan,et al.  Use of Landsat Thermal-IR Data and GIS in Soil Moisture Assessment , 1993 .

[38]  B. Rastogi Damage due to the Mw 7.7 Kutch, India earthquake of 2001 , 2004 .

[39]  Ellen M. Rathje,et al.  The Role of Remote Sensing in Earthquake Science and Engineering: Opportunities and Challenges , 2008 .

[40]  Masashi Matsuoka,et al.  Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images , 2001 .

[41]  Thomas Oommen,et al.  Validation and Application of Empirical Liquefaction Models , 2010 .

[42]  Estimating the depth of buried hot features from thermal IR remote sensing data: a conceptual approach , 1995 .

[43]  M. Crawford,et al.  Damage Patterns from Satellite Images of the 2003 Bam, Iran, Earthquake , 2005 .

[44]  Thomas Oommen,et al.  Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression , 2011 .

[45]  Elfatih A. B. Eltahir,et al.  A Soil Moisture–Rainfall Feedback Mechanism: 1. Theory and observations , 1998 .

[46]  Shailesh Nayak,et al.  Mapping the liquefaction induced soil moisture changes using remote sensing technique: an attempt to map the earthquake induced liquefaction around Bhuj, Gujarat, India , 2006 .