Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 3, pp. 26-48

The state of the art and prospects of remote sensing data application in the study of exogenous geological processes by the example of landslides

I.O. Smirnova 1 , A.A. Kirsanov 1 
1 A.P. Karpinsky Russian Geological Research Institute, Saint Petersburg, Russia
Accepted: 31.03.2021
DOI: 10.21046/2070-7401-2021-18-3-26-48
Due to the active improvement of the technology for receiving and processing remote sensing data, the areas of practical application of remote methods have significantly expanded for studying and monitoring natural disasters caused by exogenous geological processes. Among these processes landslides are widespread and lead to loss of life and significant damage. The paper provides an overview and comparative assessment of the latest foreign and Russian landslide studies conducted using various remote sensing data (multispectral, thermal, radar, lidar, obtained from satellite, manned and unmanned aerial vehicle) and their advanced processing techniques for the detection, inventory, mapping of landslides, developing of landslide susceptibility maps, landslide hazard analysis, as well as landslide monitoring at a range of scales. The factors that cause landslides are analyzed. It is noted that the analysis of remote sensing data should be carried out on the basis of GIS in combination with landscape, topographic, geological, geophysical and field data, as well as on the basis of mathematical models created using statistical methods, including machine learning methods. The state of the art and prospects of remote sensing methods in landslide studies are characterized.
Keywords: remote sensing data, landslides, exogenous geological processes, monitoring, processing techniques
Full text

References:

  1. Bezgodova O. V., Rasputina E. A., GIS-based automated mapping hazardous exogenous processes in the Tunka depression, Geodeziya i kartografiya, 2020, No. 3, pp. 8–20 (in Russian), DOI: 10.22389/0016-7126-2020-957-3-8-20.
  2. Bezgodova O. V., Istomina E. A., Ovchinnikova E. V., Assessment of hazardous exogenous processes of the Mondy depression based on morphometric and landscape analysis, Geodezija i kartografiya, 2018, Vol. 79, No. 8, pp. 28–37 (in Russian), DOI: 10.22389/0016-7126-2018-938-8-28-37.
  3. Belyaev M. Yu., Sarmin E. E., Burtsev M. A., Balashov I. V., Esakov A. M., Tolpin V. A., Using data from the “Scenario” space experiment to assess the state of the Bureya Riverbed after the collapse of rocks, Materialy Semnadtsatoi Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 17th Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), 11–15 Nov. 2019, Moscow: IKI RAN, 2019, p. 76 (in Russian), DOI: 10.21046/17DZZconf-2019a.
  4. Bondur V. G., Zakharova L. N., Zakharov A. I., Chimitdorzhiev T. N., Dmitriev A. V., Dagurov P. N. (2019a), Long-term monitoring of landslide process on the Bureya riverbank according to interferometric L-band radar data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 5, pp. 113–119 (in Russian), DOI: 10.21046/2070-7401-2019-16-5-113-119.
  5. Bondur V. G., Zakharova L. N., Zakharov A. I., Chimitdorzhiev T. N., Dmitriev A. V., Dagurov P. N. (2019b), Monitoring of Landslide Processes by Means of L-Band Radar Interferometric Observations: Bureya River Bank Caving Case, Issledovanie Zemli iz kosmosa, 2019, No. 5, pp. 3–14 (in Russian), DOI: 10.31857/S0205 9614201953 14.
  6. Bondur V. G., Zakharova L. N., Zakharov A. I. (2019c), Monitoring of the Landslide Area State on Bureya River in 2018–2019 According to Radar and Optical Satellite Images, Issledovanie Zemli iz kosmosa, 2019, No. 6, pp. 26–35 (in Russian), DOI: 10.31857/S0205-96142019626-35.
  7. Viktorov A. S., Georgievskii B. V., Kapralova V. N., Orlov T. V., Trapeznikova O. N., Zverev A. V., The case study of remote sensing monitoring for geological hazards along pipeline systems (Eastern Siberia), Geoekologiya. Inzhenernaya geologiya. Gidrogeologiya. Geokriologiya, 2018, No. 6, pp. 50–58 (in Russian), DOI: 10.1134/S0869780318050095.
  8. Temporary requirements for the use of remote sensing data for monitoring exogenous geological processes as part of the state monitoring of the subsurface, Zerkal O. V., Mirnova A. V., Azarkina N. N., Voevoda V. M., Evdokimov S. V. (eds.), Moscow: Geoinformmark, 2000, 52 p. (in Russian).
  9. Zakharov A. I., Zakharova L. N., Krasnogorsky M. G., Monitoring of the landslide activity using radar interferometric observations of trihedral corner reflectors, Issledovanie Zemli iz kosmosa, 2018, No. 3, pp. 80–92 (in Russian), DOI: 10.7868/S0205961418030065.
  10. Zakharova L. N., Zakharov A. I., Interferometric observation of landslide area dynamics on the Bureya River by means of Sentinel-1 radar data in 2017–2018, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 273–277 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-273-277.
  11. Zerkal O. V., Makhinov A. N., Kudymov A. V., Kharitonov M. E., Fomenko I. K., Barykina O. S., Bureya landslide on 11 December 2018. Bureya landslide on 11 December 2018. Conditions of the formation and features of the development mechanism, GeoRisk World, 2019, Vol. 13, No. 4, pp. 18–30 (in Russian), DOI: 10.25296/1997-8669-2019-13-4-18-30.
  12. Engineering and geological surveys for construction. SP 11-105-97. Part II. Rules for the production of works in areas of development of dangerous geological and engineering-geological processes, Moscow: Gosstroi Rossii, 2003, 93 p. (in Russian).
  13. Karaev Yu. I., Application of remote sensing in the study of natural hazards and risks, Groznenskii estestvennonauchnyi byulleten’, 2017, No. 3(7), pp. 42–47 (in Russian).
  14. Kramareva L. S., Loupian E. A., Amelchenko Yu. A., Burtsev M. A., Krasheninnikova Yu. S., Sukhanova V. V., Shamilova Yu. A., Observation of the hill collapse zone near the Bureya River on December 11, 2018, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 7, pp. 266–271 (in Russian), DOI: 10.21046/2070-7401-2018-15-7-266-271.
  15. Kramareva L. S., Loupian E. A., Amelchenko Yu. A., Burtsev M. A., Krasheninnikova Yu. S., Sukhanova V. V., Shamilova Yu. A., Boroditskaya A. V. (2019a), Observing the progress of blasting operations and channeling in the area of the rock slide on the Bureya River, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 1, pp. 259–265 (in Russian), DOI: 10.21046/2070-7401-2019-16-1-259-265.
  16. Kramareva L. S., Loupian E. A., Amelchenko Yu. A., Belyaev M. Yu., Burtsev M. A., Sukhanova V. V., Shamilova Yu. A., Esakov A. M. (2019b), Dynamics of the channel on the Bureya River in the hill slope collapse area, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 278–283 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-278-283.
  17. Loupian E. A., Proshin A. A., Bourtsev M. A., Kashnitskii A. V., Balashov I. V., Bartalev S. A., Konstantinova A. M., Kobets D. A., Mazurov A. A., Marchenkov V. V., Matveev A. M., Radchenko M. V., Sychugov I. G., Tolpin V. A., Uvarov I. A., Experience of development and operation of the IKI-Monitoring center for collective use of systems for archiving, processing and analyzing satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 3, pp. 151–170 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-151-170.
  18. Menshikov S. N., Melnikov I. V., Osokin A. B., Smolov G. K., Belenov A. V., Abrosimov A. V., Sizov O. S., Satellite observations help monitor hazardous exogenic processes across Yamal Peninsula fields, Gazovaya promyshlennost’, 2016, No. 7–8, pp. 126–132 (in Russian).
  19. Methodological recommendations for the organization and management of state monitoring of exogenous geological processes, Sheko A. I., Krupoderov V. S., Dyakonova V. I., Kruglova L. V., Malneva I. V., Parfenov S. I., Postoev G. P. (eds.), Moscow: VSEGINGEO, 1997, 39 p. (in Russian).
  20. Ostroukhov A. V., Kim V. I., Makhinov A. N., Estimation of the morphometric parameters of the landslide on the Bureinskoe Reservoir and its consequences on the basis of remote sensing data and field measurements, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 1, pp. 254–258 (in Russian), DOI: 10.21046/2070-7401-2019-16-1-254-258.
  21. Smolianinova E. I., Kiseleva E. A., Dmitriev P. N., Mikhailov V. O., On the possibility of using Sentinel-1 SAR interferometry to study landslide activity in the mountain cluster of the Big Sochi area, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 103–111 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-103-111.
  22. Smolianinova E. I., Kiseleva E. A., Mikhailov V. O., Sentinel-1 InSAR for investigation of active deformation areas: case study of the coastal region of the Big Sochi, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 5, pp. 147–155 (in Russian), DOI: 10.21046/2070-7401-2019-16-5-147-155.
  23. Smolianinova E. I., Mikhailov V. O., Dmitriev P. N., Possibilities of using SAR interferometry of a series of different-frequency satellite radar images with different survey geometries for the study and monitoring of landslide activity in the Big Sochi area (by example of Adler neighborhoods in 2007-2019), Materialy 18-i Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 18th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAN, 2020, p. 98 (in Russian), DOI: 10.21046/18DZZconf-2020a.
  24. Solonko E. V., Khlebnikova E. P., Using multi-temporal satellite images to assess the development of landslide processes in the city of Barnaul, Distantsionnye metody zondirovaniya Zemli i fotogrammetriya, monitoring okruzhayushchei sredy, geoekologiya (Remote Sensing Methods of the Earth and Photogrammetry, Environmental Monitoring, Geoecology: Proc. Intern. Scientific Conf. Interexpo GEO-Siberia-2016), Novosibirsk: SGGA, 2016, Vol. 1, pp. 59–63 (in Russian).
  25. Abancó C., Bennett G., Briant J., Battiston S., Towards an automatic landslide mapping tool based on satellite imagery and geomorphological parameters. A study of the Itogon area (Philippines) after Typhoon Mangkhut, 22 nd EGU General Assembly, 4–8 May, 2020, id. 17940, DOI: 10.5194/egusphere-egu2020-17940.
  26. Ahmed B., Dewan A., Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh, Remote Sensing, 2017, Vol. 9, Art. No. 304, DOI: 10.3390/rs9090304.
  27. Barra A., Reyes-Carmona C., Monserrat O., Glave J. P., Herrera G., Mateos R. M., Sarro R., Bejar M., Azañón J. M., Crosetto M., Sentinel-1 for Granada coast landslides monitoring and potential damage assessment, 22 nd EGU General Assembly, 4–8 May 2020, id. 19236, DOI: 10.5194/egusphere-egu2020-19236.
  28. Behling R., Roessner S., Spatiotemporal Landslide Mapper for Large Areas Using Optical Satellite Time Series Data, Proc. 4 th World•Landslide Forum (WLF), Ljubljana, Slovenia, 2017, Springer Intern. Publishing AG, 2017, pp. 143–152, DOI: 10.1007/978-3-319-53498-5_17.
  29. Bickel V. T., Manconi A., Amann F., Quantitative Assessment of Digital Image Correlation Methods to Detect and Monitor Surface Displacements of Large Slope Instabilities, Remote Sensing, 2018, Vol. 10, Art. No. 865, DOI: 10.3390/rs10060865.
  30. Bivic R. L., Allemand P., Quiquerez A., Delacourt C., Potential and limitation of SPOT-5 Ortho-image correlation to investigate the Cinematics of landslides: The example of “Mare à Poule d’Eau” (Réunion, France), Remote Sensing, 2017, Vol. 9, Art. No. 106, DOI: 10.3390/rs9090106.
  31. Bliss C., Wainwright J., Donoghue D., Jordan C., Estimating soil moisture from COSMO-SkyMed data at an active landslide site in North Yorkshire, UK, 22 nd EGU General Assembly, 4–8 May, 2020, id. 22063, DOI: 10.5194/egusphere-egu2020-22063.
  32. Bozzano F., Mazzanti P., Perissin D., Rocca A., Pari P., Discenza M., Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis, Remote Sensing, 2017, Vol. 9, Art. No. 267, DOI: 10.3390/rs9090267.
  33. Catani F., Landslide recognition by deep learning of non-standard multi-source images, 22 nd EGU General Assembly, 4–8 May, 2020, id. 19477, DOI: 10.5194/egusphere-egu2020-19477.
  34. Comert R., Avdan U., Gorum T., Nefeslioglu H. A., Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data, Engineering Geology, 2019, Vol. 260, Art. No. 105264, DOI: 10.1016/j.enggeo.2019.105264.
  35. Cuervas-Mons J., Monserrat O., Domínguez-Cuesta M. J., Mateos-Redondo F., González-Pumariega P., López-Fernández C., Valenzuela P., Barra A., Pascual-Lombardía P., Jiménez-Sánchez M., DInSAR and topographic techniques applied to study the Tazones Lighthouse landslide (N Spain), 22 nd EGU General Assembly, 4–8 May, 2020, id. 10455, DOI: 10.5194/egusphere-egu2020-10455.
  36. Deijns A. J., Bevington A. R., Van Zadelhoff F., De Jong S. M., Geertsema M., McDougall S., Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada, Intern. J. Applied Earth Observation and Geoinformation, 2020, Vol. 84, Art. No. 101943, DOI: 10.1016/j.jag.2019 101943.
  37. Devoto S., Macovaz V., Mantovani M., Soldati M., Furlani S., Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides, Remote Sensing, 2020, Vol. 12, Art. No. 3566, DOI: 10.3390/rs12213566.
  38. Di Napoli M., Marsiglia P., Di Martire D., Ramondini M., Ullo S. L., Calcaterra D., Landslide susceptibility assessment of wildfire burnt areas through earth-observation techniques and a machine learning-based approach, Remote Sensing, 2020, Vol. 12, Art. No. 2505, DOI: 10.3390/rs12212505.
  39. Fang Z., Wang Y., Duan G., Peng L., Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China, Remote Sensing, 2021. Vol. 13(2), Art. No. 238, DOI: 10.3390/rs13020238.
  40. Frodella W., Gigli G., Morelli S., Lombardi L., Casagli N., Landslide Mapping and Characterization through Infrared Thermography (IRT): Suggestions for a Methodological Approach from Some Case Studies, Remote Sensing, 2017, Vol. 9, Art. No. 1281, DOI: 10.3390/rs9091281.
  41. Golovko D., Roessner S., Behling R., Wetzel H-U., Kleinschmit B., Evaluation of remote-sensing-based landslide inventories for hazard assessment in southern Kyrgyzstan, Remote Sensing, 2017, Vol. 9, Art. No. 943, DOI: 10.3390/rs9090943.
  42. Hastaoglu K. O., Gül Y. H., Poyraz F., Kara B. C., Monitoring 3D areal displacements by a new methodology and software using UAV photogrammetry, Intern. J. Applied Earth Observation and Geoinformation, 2019, Vol. 83, Art. No. 101916, DOI: 10.1016/j.jag.2019.101916.
  43. Hermle D., Keuschnig M., Krautblatter M., Potential of multisensor assessment using digital image correlation for landslide detection and monitoring, 22 nd EGU General Assembly, 4–8 May, 2020, id. 16982, DOI: 10.5194/egusphere-egu2020-16982, 2020.
  44. Intrieri E., Frodella W., Raspini F., Bardi F., Tofani F., Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry, Remote Sensing, 2020, Vol. 12, Art. No. 1462, DOI: 10.3390/rs12091462.
  45. Jongmans D., Fiolleau S., Bièvre G., Chambon G., Lacroix P., Combination of high frequency (Sentinel-2) and high resolution (Pléiades) satellite images for the monitoring of clayey landslide reactivations, application to the Harmalière landslide (French Alps), 22 nd EGU General Assembly, 4–8 May, 2020, id. 9368, DOI: 10.5194/egusphere-egu2020-9368.
  46. Khan H., Shafique M., Khan M., Bacha M., Shah S. U., Calligaris C., Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan, Egyptian J. Remote Sensing and Space Sciences, 2019, Vol. 22, pp. 11–24, DOI: 10.1016/j.ejrs.2018.03.004.
  47. Kirschbaum D., Stanley T., Satellite based assessment of rainfall-triggered landslide hazard for situational awareness, Earth’s Future, 2018, Vol. 6, pp. 505–523, DOI: 10.1002/2017EF00071.
  48. Konishi T., Suga Y., Landslide detection using COSMO-SkyMed images: A case study of a landslide event on Kii peninsula, Japan, European J. Remote Sensing, 2018, Vol. 51, No. 1, pp. 205–221, DOI: 10.1080/22797254.2017.1418185.
  49. Lacroix P., Bièvre G., Pathier E., Kniess U., Jongmans D., Use of Sentinel-2 images for the detection of precursory motions before landslide failures, Remote Sensing of Environment, 2018, Vol. 215, pp. 507–516, DOI: 10.1016/j.rse.2018.03.042.
  50. Lee D. H., Kim Y. T., Lee S. R., Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions, Remote Sensing, 2020, Vol. 12, Art. No. 1194, DOI: 10.3390/rs12071194.
  51. Li W., Fan X., Huang F., Chen W., Hong H., Huang J., Guo Z., Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Evironmental Factors, Remote Sensing, 2020, Vol. 12, Art. No. 4134, DOI: 10.3390/rs12244134.
  52. Melis M. T., Da Pelo S., Erbì I., Loche M., Deiana G., Demurtas V., Meloni M. A., Dessì F., Funedda A., Scaioni M., Scaringi G., Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides, Remote Sensing, 2020, Vol. 12, Art. No. 1971, DOI: 10.3390/rs12121971.
  53. Motagh M., Roessner S., Akbari B., Behling R., Stefanova-Vassileva M., Haghshenas-Haghighi M., Ulrich-Wetzel H., Landslides triggered by 2019 extreme rainfall and flood events in Iran: Results from satellite remote sensing and field survey, 22 nd EGU General Assembly, 4–8 May, 2020, id. 10715, DOI: 10.5194/egusphere-egu2020-10715.
  54. Pappalardo G., Mineo S., Angrisani A. C., Di Martire D., Calcaterra D., Combining field data with infrared thermography and DInSAR surveys to evaluate the activity of landslides: The case study of Randazzo Landslide (NE Sicily), Landslides, 2018, Vol. 15, pp. 2173–2193, DOI: 10.1007/s10346-018-1026-9.
  55. Park H. J., Jang J. Y., Lee J. H., Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method, Remote Sensing, 2017, Vol. 9, Art. No. 487, DOI: 10.3390/rs9090487.
  56. Piroton V., Schlögel R., Havenith H. B., Monitoring recent activity of the Koytash Landslide (Kyrgyzstan) using radar and optical remote sensing techniques, 22 nd EGU General Assembly, 4–8 May, 2020, id. 20180, DOI: 10.5194/egusphere-egu2020-20180.
  57. Prakash N., Manconi A., Loew S., Mapping landslides from EO data using deep-learning methods, 22 nd EGU General Assembly, 4–8 May, 2020, id. 11876, DOI: 10.5194/egusphere-egu2020-11876.
  58. Rabby Y. W., Ishtiaque A., Rahman S., Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh, Remote Sensing, 2020, Vol. 12, Art. No. 2718, DOI: 10.3390/rs12172718.
  59. Ray R. L., Lazzari M., Olutimehin T., Remote Sensing Approaches and Related Techniques to Map and Study Landslides, In: Landslides — Investigation and Monitoring, IntechOpen, 2020, DOI: 10.5772/intechopen.93681.
  60. Reyes-Carmona C., Galve J. P., Barra A., Monserrat O., Mateos R. M., Azañón J. M., Pérez-Peña J. V., Ruano P., The Sentinel-1 CNR-IREA SBAS service of the European Space Agency’s Geohazard Exploitation Platform (GEP) as a powerful tool for landslide activity detection and monitoring, 22 ndEGU General Assembly, 4–8 May, 2020, id. 19410, DOI: 10.5194/egusphere-egu2020-19410.
  61. Roessner S., Behling R., Motagh M., Ulrich-Wetzel H., Multi-scale analysis of landslide occurrence and evolution using optical and radar time series, 22 nd EGU General Assembly, 4–8 May, 2020, id. 20702, DOI: 10.5194/egusphere-egu2020-20702.
  62. Rosi A., Tofani V., Tanteri L., Tacconi Stefanelli C., Agostini A., Catani F., Casagli N., The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution, Landslides, 2018, Vol. 15, pp. 5–19, DOI: 10.1007/s10346-017-0861-4.
  63. Roy J., Saha S., Arabameri A., Blaschke T., Bui D., Tien A., Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India, Remote Sensing, 2019, Vol. 11, Art. No. 2866, DOI: 10.3390/rs11232866.
  64. Schiller A., Vecchiotti F., Amabile A. S., Guardiani C., Dhital M. R., Dhakal A., Pant B. R., Ostermann M., Supper R., Ground motion and PSI density analysis from Envisat and Sentinel 1A InSAR data in the context of a complex landslide monitoring strategy in Karnali river basin, Far-Western Nepal, 22 nd EGU General Assembly, 4–8 May, 2020, id. 21875, DOI: 10.5194/egusphere-egu2020-21875.
  65. Shahzad N., Ding X., Abbas S., Prediction ability of machine learning algorithms in Himalaya region of Pakistan for landslide susceptibility mapping, 22 nd EGU General Assembly, 4–8 May, 2020, id. 6757, DOI: 10.5194/egusphere-egu2020-6757.
  66. Solari L., Bianchini S., Franceschini R., Barra A., Monserrat O., Thuegaz P., Bertolo D., Crosetto M., Catani F., Satellite interferometric data for landslide intensity evaluation in mountainous regions, Intern. J. Applied Earth Observation and Geoinformation, 2020, Vol. 87, Art. No. 102028, DOI: 10.1016/j.jag.2019.102028.
  67. Sun W., Tian Y., Mu X., Zhai J., Gao P., Zhao G., Loess landslide inventory map based on GF-1 satellite imagery, Remote Sensing, 2017, Vol. 9, Art. No. 314, DOI: 10.3390/rs9040314.
  68. Wang Q., Wang Y., Niu R., Peng L., Integration of information theory, K-means cluster analysis and the logistic regression model for landslide susceptibility mapping in the three gorges area, China, Remote Sensing, 2017, Vol. 9, Art. No. 938, DOI: 10.3390/rs9090938.
  69. Ye C., Li Y., Cui P., Li L., Pirasteh S., Marcato J., Goncalves W. N., Li J., Landslide detection of hyperspectral remote sensing data based on deep learning with constrains, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2019, Vol. 12, No. 12, pp. 5047–5060, DOI: 10.1109/JSTARS.2019.2951725.
  70. Zhang P., Xu C., Ma S., Shao X., Tian Y., Wen B., Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan, Remote Sensing, 2020, Vol. 12(23), Art. No. 3992, DOI: 10.3390/rs12233992.
  71. Zhao F., Mallorqui J. J., Iglesias R., Gili J. A., Corominas J., Landslide monitoring using multi-temporal SAR interferometry with advanced persistent scatters identification methods and super high-spatial resolution TerraSAR-X images, Remote Sensing, 2018, Vol. 10, Art. No. 921, DOI: 10.3390/rs10060921.