Assessment of the Sentinel-1 based ground motion data feasibility for large scale landslide monitoring

In this paper, a systematic procedure to assess the feasibility of Advanced Differential Interferometric SAR (A-DInSAR) technique for landslide monitoring using SAR images acquired by Sentinel-1 sensors is presented. The methodology is named “Assessment of the advanced differentiaL interferometric synthetic aperture radar technique Feasibility for large scale lAndslide monitoring – ALFA” and it is structured in two main phases, which includes pre-processing and post-processing elaborations. The methodology was developed and tested in the Alpine sector of the Piedmont region in Italy, which represents a landslide prone area. In particular, ALFA represents a methodology based on previous algorithms available in the literature to assess the a-prior feasibility assessment and post-processing analysis of A-DInSAR data for landslide, which introduces three novel aspects such as (1) a systematic scheme suitable within regional practices; (2) the use of Sentinel-1 data and the development of (3) an index to take into account of the kind of distribution of the measuring points along the landslide. The approach was applied over an area extended about 5300 km 2 affected by 5703 landslides mapped in the database of the Piedmont Region (Landslides information system in Piedmont—SIFRAP). Sentinel-1 images covering the period 2014–2018 were analysed. The results show the potential of the Sentinel-1 data for large-scale landslide monitoring. The developed methodology presents reliable tools that could be useful as feasibility for the use of Sentinel-1 data for landslide monitoring at regional and national scale.

[1]  Mingsheng Liao,et al.  Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets , 2017, Landslides.

[2]  G. Jenks The Data Model Concept in Statistical Mapping , 1967 .

[3]  Domenico Calcaterra,et al.  A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Luca Lanteri,et al.  The Integration Between Satellite Data and Conventional Monitoring System in Order to Update the Arpa Piemonte Landslide Inventory , 2013 .

[5]  Nicola Casagli,et al.  Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites , 2018, Scientific Reports.

[6]  Simon Plank,et al.  Assessment of number and distribution of persistent scatterers prior to radar acquisition using open access land cover and topographical data , 2013 .

[7]  M. Rossini,et al.  Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements , 2018, Landslides.

[8]  Davide Notti,et al.  Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees) , 2013 .

[9]  Antonio Morales,et al.  Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides , 2018, Remote. Sens..

[10]  Simon Plank,et al.  Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring , 2012 .

[11]  Roberta Bonì,et al.  Landslide state of activity maps by combining multi-temporal A-DInSAR (LAMBDA) , 2018, Remote Sensing of Environment.

[12]  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.

[13]  Daniele Perissin,et al.  Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis , 2017, Remote. Sens..

[14]  Marco Bianchi,et al.  Geological Interpretation of PSInSAR Data at Regional Scale , 2008, Sensors.

[15]  Davide Notti,et al.  Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study , 2010 .

[16]  Janusz Wasowski,et al.  Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry , 2006 .

[17]  G. Luzi,et al.  Remote sensing based retrieval of snow cover properties , 2008 .

[18]  Tazio Strozzi,et al.  ERS InSAR for Detecting Slope Movement in a Periglacial Mountain Environment (Western Valais Alps, Switzerland) , 2007 .

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

[20]  Claudio Prati,et al.  A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Fausto Guzzetti,et al.  Landslide fatalities and the evaluation of landslide risk in Italy , 2000 .

[22]  M. G. Ciminelli,et al.  Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data , 2017 .

[23]  Roberta Bonì,et al.  A methodology for ground motion area detection (GMA-D) using A-DInSAR time series in landslide investigations , 2018 .

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

[25]  Veronica Tofani,et al.  Combination of GNSS, satellite InSAR, and GBInSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment , 2019, Geomorphology.

[26]  C. Werner,et al.  Survey and monitoring of landslide displacements by means of L-band satellite SAR interferometry , 2005 .

[27]  J. Wasowski,et al.  Using COSMO/SkyMed X-band and ENVISAT C-band SAR interferometry for landslides analysis , 2012 .

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

[29]  Simon Plank,et al.  Feasibility Assessment of Landslide Monitoring by Means of SAR Interferometry: A Case Study in the Ötztal Alps, Austria , 2015 .

[30]  G. B. Crosta,et al.  Chasing a complete understanding of the triggering mechanisms of a large rapidly evolving rockslide , 2014, Landslides.

[31]  Davide Notti,et al.  A methodology for improving landslide PSI data analysis , 2014 .

[32]  Stuart Marsh,et al.  National geohazards mapping in Europe: Interferometric analysis of the Netherlands , 2019, Engineering Geology.

[33]  Randy Showstack,et al.  Sentinel Satellites Initiate New Era in Earth Observation , 2014 .