Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

[1]  I. Papoutsis,et al.  A reasoned bibliography on SAR interferometry applications and outlook on big interferometric data processing , 2020 .

[2]  Nicola Casagli,et al.  Review of Satellite Interferometry for Landslide Detection in Italy , 2020, Remote. Sens..

[3]  Avadh Bihari Narayan,et al.  MULTI-SENSOR GEODETIC APPROACH FOR LANDSLIDE DETECTION AND MONITORING , 2018, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[4]  Chris Rizos,et al.  Uncovering common misconceptions in GNSS Precise Point Positioning and its future prospect , 2016, GPS Solutions.

[5]  D. Lichti,et al.  Angular resolution of terrestrial laser scanners , 2006 .

[6]  Alessandro Corsini,et al.  Deformation responses of slow moving landslides to seasonal rainfall in the Northern Apennines, measured by InSAR , 2018 .

[7]  Veronica Tofani,et al.  Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy) , 2019, Landslides.

[8]  C. Werner,et al.  Radar interferogram filtering for geophysical applications , 1998 .

[9]  M. Crosetto,et al.  Analysis of Damage to Buildings in Urban Centers on Unstable Slopes via TerraSAR-X PSI Data: The Case Study of El Papiol Town (Spain) , 2019, IEEE Geoscience and Remote Sensing Letters.

[10]  Filippo Carraro,et al.  THE 3D SURVEY OF THE ROMAN BRIDGE OF SAN LORENZO IN PADOVA (ITALY): A COMPARISON BETWEEN SFM AND TLS METHODOLOGIES APPLIED TO THE ARCH STRUCTURE , 2019, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[11]  D. Kirschbaum,et al.  InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal , 2020 .

[12]  Cemal Ozer Yigit,et al.  Experimental assessment of post-processed kinematic Precise Point Positioning method for structural health monitoring , 2016 .

[13]  Nicola Casagli,et al.  The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service , 2020, Remote. Sens..

[14]  Davide Notti,et al.  Mapping Vulnerable Urban Areas Affected by Slow-Moving Landslides Using Sentinel-1 InSAR Data , 2017, Remote. Sens..

[15]  Simona Verde,et al.  A multi-disciplinary approach for the damage analysis of cultural heritage: The case study of the St. Gerlando Cathedral in Agrigento , 2019 .

[16]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  Weihua Zhao,et al.  Landslide evolution assessment based on InSAR and real-time monitoring of a large reactivated landslide, Wenchuan, China , 2020 .

[18]  P. Arias,et al.  Terrestrial laser scanning used to determine the geometry of a granite boulder for stability analysis purposes , 2009 .

[19]  Gabriele Guidi,et al.  High-accuracy 3D modeling of cultural heritage: the digitizing of Donatello's "Maddalena" , 2004, IEEE Transactions on Image Processing.

[20]  Fabiana Calò,et al.  Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy , 2014 .

[21]  D. Jean Hutchinson,et al.  The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds , 2020, Remote. Sens..

[22]  M. Fabris,et al.  An archival geomatics approach in the study of a landslide , 2015 .

[23]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[24]  Gianfranco Fornaro,et al.  A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms , 2002, IEEE Trans. Geosci. Remote. Sens..

[25]  Jingnan Liu,et al.  Error analysis of high-rate GNSS precise point positioning for seismic wave measurement , 2017 .

[26]  Comparison Between PS and SBAS InSAR Techniques in Monitoring Shallow Landslides , 2020, Understanding and Reducing Landslide Disaster Risk.

[27]  M. Floris,et al.  Estimation of land subsidence in deltaic areas through differential SAR interferometry: the Po River Delta case study (Northeast Italy) , 2018, International Journal of Remote Sensing.

[28]  Jordi J. Mallorquí,et al.  Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[29]  Fabio Bovenga,et al.  Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives , 2014 .

[30]  Richard Szeliski,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[31]  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.

[32]  Fabio Rocca,et al.  Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis , 2004, Science.

[33]  Michele Monego,et al.  3D survey of Sarno Baths (Pompeii) by integrated geomatic methodologies , 2019 .

[34]  Daniele Perissin,et al.  Pre-Collapse Space Geodetic Observations of Critical Infrastructure: The Morandi Bridge, Genoa, Italy , 2019, Remote. Sens..

[35]  M. Floris,et al.  Testing Sentinel-1A Data in Landslide Monitoring: A Case Study from North-Eastern Italian Pre-Alps , 2017 .

[36]  Núria Devanthéry,et al.  Persistent Scatterer Interferometry: A review , 2016 .

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

[38]  Alessandro Fontana,et al.  Subsidence Zonation Through Satellite Interferometry in Coastal Plain Environments of NE Italy: A Possible Tool for Geological and Geomorphological Mapping in Urban Areas , 2019, Remote. Sens..

[39]  Diego González-Aguilera,et al.  A New Approach for Structural Monitoring of Large Dams with a Three-Dimensional Laser Scanner , 2008, Sensors.

[40]  S. Leroueil,et al.  The Varnes classification of landslide types, an update , 2014, Landslides.

[41]  Ho Tong Minh Dinh,et al.  Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives , 2020, Remote. Sens..

[42]  J. Corominas,et al.  Using Global Positioning System techniques in landslide monitoring , 2000 .

[43]  Davide Notti,et al.  Sentinel-1 DInSAR for Monitoring Active Landslides in Critical Infrastructures: The Case of the Rules Reservoir (Southern Spain) , 2020, Remote. Sens..

[44]  S. Ullman The interpretation of structure from motion , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[45]  Nicola Casagli,et al.  Analysis of building deformation in landslide area using multisensor PSInSAR™ technique , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Bin Li,et al.  Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method , 2018, Remote. Sens..

[47]  S. Buckley,et al.  Terrestrial laser scanning in geology: data acquisition, processing and accuracy considerations , 2008, Journal of the Geological Society.

[48]  S. Robson,et al.  Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application , 2012 .

[49]  Nicola Casagli,et al.  Multi-Temporal Evaluation of Landslide Movements and Impacts on Buildings in San Fratello (Italy) By Means of C-Band and X-Band PSI Data , 2015, Pure and Applied Geophysics.

[50]  Michele Manunta,et al.  A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Michele Monego,et al.  3-D survey applied to industrial archaeology by TLS methodology , 2017 .

[52]  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.

[53]  Fabio Rocca,et al.  Permanent scatterers in SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..

[54]  D. Agnew,et al.  The complete (3‐D) surface displacement field in the epicentral area of the 1999 MW7.1 Hector Mine Earthquake, California, from space geodetic observations , 2001 .