Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing studies and hampering civil protection operations. The unprecedented availability of SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to propose new solutions to detect landslides events. In this work, we perform a systematic assessment of Sentinel-1 SAR C-band images acquired before and after known events. We present the results of a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slope expositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results show that in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudes are unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On the other hand, the signal does not allow for a systematic use to produce inventories because only in 8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a few cases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggering factors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises to increase the availability of information on landslides at different spatial and temporal scales with benefits for event magnitude assessment during weather-related emergencies, model tuning, and landslide forecast model validation, in particular when accurate mapping is not required.

[1]  Riccardo Lanari,et al.  Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy , 2014 .

[2]  Guido Luzi,et al.  Sentinel-1 Data Analysis for Landslide Detection and Mapping: First Experiences in Italy and Spain , 2017 .

[3]  Yasushi Yamaguchi,et al.  Detection of Landslide Areas Using Satellite Radar Interferometry , 2000 .

[4]  Simon Plank,et al.  Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data , 2016, Remote. Sens..

[5]  André Stumpf,et al.  A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery , 2018, Remote. Sens..

[6]  Nicola Casagli,et al.  Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER , 2016 .

[7]  L. Starkel The role of catastrophic rainfall in the shaping of the relief of the Lower Himalaya (Darjeeling Hills) , 1972 .

[8]  Yunjin Kim,et al.  Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi‐Chi earthquake, Taiwan , 2003 .

[9]  Paolo Gamba,et al.  Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  S. Quegan,et al.  Understanding Synthetic Aperture Radar Images , 1998 .

[11]  Zhong Lu,et al.  Pre-, co-, and post- rockslide analysis with ALOS/PALSAR imagery: a case study of the Jiweishan rockslide, China , 2013 .

[12]  P. Reichenbach,et al.  Optimal landslide susceptibility zonation based on multiple forecasts , 2010 .

[13]  Zhihua Wang,et al.  GLACIER FRONTAL LINE EXTRACTION FROM SENTINEL-1 SAR IMAGERY IN PRYDZ AREA , 2018 .

[14]  K. W. Jaggard,et al.  Monitoring leaf area of sugar beet using ERS-1 SAR data , 1996 .

[15]  Mark van der Meijde,et al.  Spatiotemporal landslide detection for the 2005 Kashmir earthquake region. , 2010 .

[16]  A. Walther,et al.  InSAR processing for the recognition of landslides , 2008 .

[17]  Harald Johnsen,et al.  DEM corrected ERS-1 SAR data for snow monitoring , 1996 .

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

[19]  Stuart Marsh,et al.  Assessing the Feasibility of a National InSAR Ground Deformation Map of Great Britain with Sentinel-1 , 2017 .

[20]  P. Reichenbach,et al.  Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar , 2007 .

[21]  Veronica Tofani,et al.  Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning , 2017, Geoenvironmental Disasters.

[22]  Tapas Ranjan Martha,et al.  Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[23]  D. Petley Global patterns of loss of life from landslides , 2012 .

[24]  Francesca Bovolo,et al.  A detail-preserving scale-driven approach to change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Christophe Delacourt,et al.  Remote-sensing techniques for analysing landslide kinematics: a review , 2007 .

[26]  Colm Jordan,et al.  Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes , 2018 .

[27]  David M. Cruden,et al.  LANDSLIDES: INVESTIGATION AND MITIGATION. CHAPTER 3 - LANDSLIDE TYPES AND PROCESSES , 1996 .

[28]  Saibal Ghosh,et al.  Landslide Hazard Zonation in Darjeeling Himalayas: a Case Study on Integration of IRS and SRTM Data , 2008 .

[29]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Lorenzo Bruzzone,et al.  An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[31]  I. Marchesini,et al.  An approach to reduce mapping errors in the production of landslide inventory maps , 2015 .

[32]  Karsten Spaans,et al.  A New Method for Large-Scale Landslide Classification from Satellite Radar , 2019, Remote. Sens..

[33]  F. Guzzetti,et al.  Landslide inventory maps: New tools for an old problem , 2012 .

[34]  Laura L. Bourgeau-Chavez,et al.  Development of Methods for Detection and Monitoring of Fire Disturbance in the Alaskan Tundra Using a Two-Decade Long Record of Synthetic Aperture Radar Satellite Images , 2014, Remote. Sens..

[35]  Michele Crosetto,et al.  First insights on the potential of Sentinel-1 for landslides detection , 2016 .

[36]  Franz J. Meyer,et al.  Integrating SAR and derived products into operational volcano monitoring and decision support systems , 2015 .

[37]  Zhong Lu,et al.  Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA , 2012 .

[38]  David P. Roy,et al.  The Global Availability of Landsat 5 TM and Landsat 7 ETM+ Land Surface Observations and Implications for Global 30m Landsat Data Product Generation , 2013 .

[39]  Sandro Martinis,et al.  A fully automated TerraSAR-X based flood service , 2015 .

[40]  Jean-Pierre Fortin,et al.  The potential of times series of C-Band SAR data to monitor dry and shallow snow cover , 1998, IEEE Trans. Geosci. Remote. Sens..

[41]  Andre Cahyadi Kalia,et al.  A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany , 2017 .

[42]  Long Liu,et al.  Rice growth monitoring using simulated compact polarimetric C band SAR , 2014 .

[43]  W. Jetz,et al.  Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions , 2016, PLoS biology.

[44]  Olav Slaymaker,et al.  Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data , 2003 .

[45]  Ya-Qiu Jin,et al.  Automatic Detection of Terrain Surface Changes After Wenchuan Earthquake, May 2008, From ALOS SAR Images Using 2EM-MRF Method , 2009, IEEE Geoscience and Remote Sensing Letters.

[46]  Zhiming Lu,et al.  Study of high SAR backscattering caused by an increase of soil moisture over a sparsely vegetated area: Implications for characteristics of backscattering , 2002 .

[47]  R. Dekker Speckle filtering in satellite SAR change detection imagery , 1998 .

[48]  D. Varnes,et al.  Landslide types and processes , 2004 .

[49]  Alessandro C. Mondini,et al.  Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection , 2017, Remote. Sens..

[50]  J. Kong,et al.  Retrieval of forest biomass from SAR data , 1994 .

[51]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[52]  Malte Vöge,et al.  The use of SAR interferometry for landslide mapping in the Indian Himalayas , 2015 .

[53]  David P. Roy,et al.  A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring , 2017, Remote. Sens..

[54]  Matthieu Molinier,et al.  Polarimetric SAR Data in Land Cover Mapping in Boreal Zone , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Claudia Notarnicola,et al.  Retrieval of 3D-glacier movement by high resolution X-band SAR data , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[56]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[57]  Marcello Schiattarella,et al.  Interplay between mass movement and fluvial network organization: An example from southern Apennines, Italy , 2013 .

[58]  Fabiana Calò,et al.  A Review of Interferometric Synthetic Aperture RADAR (InSAR) Multi-Track Approaches for the Retrieval of Earth’s Surface Displacements , 2017 .

[59]  Simon Plank,et al.  Rapid Damage Assessment by Means of Multi-Temporal SAR - A Comprehensive Review and Outlook to Sentinel-1 , 2014, Remote. Sens..

[60]  Claudia Notarnicola,et al.  Wet Snow Cover Mapping Algorithm Based on Multitemporal COSMO-SkyMed X-Band SAR Images , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[61]  Ranjani Wasantha Kulawardhana Remote sensing and GIS technologies for monitoring and prediction of disasters , 2012, Int. J. Digit. Earth.

[62]  Greg D. Moore IT Disaster Response , 2016, Apress.

[63]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[64]  Daniele Giordan,et al.  Criteria for the optimal selection of remote sensing optical images to map event landslides , 2017 .

[65]  Jakob J. van Zyl,et al.  Change detection techniques for ERS-1 SAR data , 1993, IEEE Trans. Geosci. Remote. Sens..

[66]  Malcolm Davidson,et al.  Sentinel-1 System capabilities and applications , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[67]  Paolo Pasquali,et al.  Phase and amplitude analyses of SAR data for landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-Alps , 2017, Environmental Earth Sciences.

[68]  Yuzo Suga,et al.  Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan , 2018 .

[69]  Helmut Rott,et al.  Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR , 2016, Remote. Sens..

[70]  Zhong Lu,et al.  Remote Sensing of Landslides - A Review , 2018, Remote. Sens..

[71]  Mihai Niculiță,et al.  R script for automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs , 2016 .

[72]  Rafi Ahmad,et al.  Landslides Processes, Prediction, and Land Use: Water Resources Monograph 18 - by Roy C. Sidle and Hirotaka Ochiai , 2007 .

[73]  Alexei Pozdnoukhov,et al.  Machine Learning for Spatial Environmental Data: Theory, Applications, and Software , 2009 .

[74]  Roberto Tomás,et al.  Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry , 2016 .

[75]  R. Oberstadler,et al.  Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany , 1997 .

[76]  Frederic Masson,et al.  Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method , 2015 .

[77]  S. M. de Jong,et al.  Airborne laser scanning of forested landslides characterization: terrain model quality and visualization , 2011 .

[78]  Jing Liu,et al.  Potential of soil moisture estimation using C-band polarimetric SAR data in arid regions , 2018, International Journal of Remote Sensing.

[79]  Bruce D. Malamud,et al.  Power-law correlations of landslide areas in central Italy , 2001 .

[80]  Dwayne D. Tannant,et al.  UAV: Low-cost remote sensing for high-resolution investigation of landslides , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[81]  Franz J. Meyer,et al.  Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach , 2016, Remote. Sens..

[82]  V. Singhroy,et al.  Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images , 1998 .

[83]  Peter F. McGuire,et al.  Understanding the significance of radiometric calibration for synthetic aperture radar imagery , 2014, 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE).

[84]  Richard Dikau,et al.  The Recognition of Landslides , 1999 .

[85]  D. Turcotte,et al.  Landslide inventories and their statistical properties , 2004 .

[86]  Antonio Pepe,et al.  Automatic and Systematic Sentinel-1 SBAS-DInSAR Processing Chain for Deformation Time-series Generation , 2016 .

[87]  João Roberto dos Santos,et al.  Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[88]  Andrea Garzelli,et al.  Optimizing SAR change detection based on log-ratio features , 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).

[89]  Luca Lombardi,et al.  Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy) , 2015, Remote. Sens..

[90]  Paul Lundgren,et al.  Damage Proxy Map from InSAR Coherence Applied to February 2011 M6.3 Christchurch Earthquake, 2011 M9.0 Tohoku-oki Earthquake, and 2011 Kirishima Volcano Eruption , 2011 .

[91]  Qiang Xu,et al.  POTENTIAL LANDSLIDE EARLY DETECTION NEAR WENCHUAN BY A QUALITATIVELY MULTI-BASELINE DINSAR METHOD , 2018 .