Articles | Volume 13, issue 5
https://doi.org/10.5194/esurf-13-1039-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-13-1039-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2025
Research article |
| 21 Oct 2025
| 21 Oct 2025
Detection of landslide timing, reactivation and precursory motion during the 2018, Lombok, Indonesia earthquake sequence with Sentinel-1
Università degli Studi di Milano-Bicocca, Milan, Italy
European Space Agency (ESA) ESRIN, Frascati, Italy
David G. Milledge
Newcastle University, Newcastle upon Tyne, United Kingdom
Maria Francesca Ferrario
Università degli Studi dell’Insubria, Como, Italy
Related authors
Katy Burrows, Odin Marc, and Dominique Remy
Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, https://doi.org/10.5194/nhess-22-2637-2022, 2022
Short summary
Short summary
The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi
Nat. Hazards Earth Syst. Sci., 21, 2993–3014, https://doi.org/10.5194/nhess-21-2993-2021, https://doi.org/10.5194/nhess-21-2993-2021, 2021
Short summary
Short summary
When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: (1) models which predict landslide locations based on, e.g., slope and ground shaking data and (2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
Katy Burrows, Richard J. Walters, David Milledge, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020, https://doi.org/10.5194/nhess-20-3197-2020, 2020
Short summary
Short summary
Satellite radar could provide information on landslide locations within days of an earthquake or rainfall event anywhere on Earth, but until now there has been a lack of systematic testing of possible radar methods, and most methods have been demonstrated using a single case study event and data from a single satellite sensor. Here we test five methods on four events, demonstrating their wide applicability and making recommendations on when different methods should be applied in the future.
Franz A. Livio, Anna M. Blumetti, Valerio Comerci, Francesca Ferrario, Gilberto Binda, Marco Caciagli, Michela Colombo, Pio Di Manna, Fernando Ferri, Fiorenzo Fumanti, Roberto Gambillara, Maurizio Guerra, Luca Guerrieri, Paolo Lorenzoni, Valerio Materni, Francesco Miscione, Rosa Nappi, Rosella Nave, Kathleen Nicoll, Alba Peiro, Marco Pizza, Roberto Pompili, Luca M. Puzzilli, Mauro Roma, Aurora Rossi, Valerio Ruscito, Vincenzo Sapia, Argelia Silva Fragoso, Emanuele Scaramuzzo, Frank Thomas, Giorgio Tringali, Stefano Urbini, Andrea Zerboni, and Alessandro M. Michetti
EGUsphere, https://doi.org/10.5194/egusphere-2025-2531, https://doi.org/10.5194/egusphere-2025-2531, 2025
Short summary
Short summary
The Rieti Basin in Central Italy, though surrounded by active faults, has been largely overlooked in earthquake studies. To better understand its seismic past, we dug 17 trenches and discovered evidence of 15 ancient earthquakes over the past ca. 20,000 years. The findings show that earthquakes in this area tend to cluster in time, likely due to stress shifting between nearby faults, and can reach a magnitude of 6.5.
Franz Livio, Maria Francesca Ferrario, Elisa Martinelli, Sahra Talamo, Silvia Cercatillo, and Alessandro Maria Michetti
Nat. Hazards Earth Syst. Sci., 23, 3407–3424, https://doi.org/10.5194/nhess-23-3407-2023, https://doi.org/10.5194/nhess-23-3407-2023, 2023
Short summary
Short summary
Here we document the occurrence of an historical earthquake that occurred in the European western Southern Alps in the sixth century CE. Analysis of the effects due to earthquake shaking in the city of Como (N Italy) and a comparison with dated offshore landslides in the Alpine lakes allowed us to make an inference about the possible magnitude and the location of the seismic source for this event.
Maria Francesca Ferrario
Nat. Hazards Earth Syst. Sci., 22, 3527–3542, https://doi.org/10.5194/nhess-22-3527-2022, https://doi.org/10.5194/nhess-22-3527-2022, 2022
Short summary
Short summary
I mapped over 5000 landslides triggered by a moment magnitude 6.0 earthquake that occurred in 2015 in the Sabah region (Malaysia). I analyzed their number, dimension and spatial distribution by dividing the territory into 1 km2 cells. I applied the Environmental Seismic Intensity (ESI-07) scale, which allows the categorization of earthquake damage due to environmental effects. The presented approach promotes the collaboration among the experts in landslide mapping and in ESI-07 assignment.
Katy Burrows, Odin Marc, and Dominique Remy
Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, https://doi.org/10.5194/nhess-22-2637-2022, 2022
Short summary
Short summary
The locations of triggered landslides following a rainfall event can be identified in optical satellite images. However cloud cover associated with the rainfall means that these images cannot be used to identify landslide timing. Timings of landslides triggered during long rainfall events are often unknown. Here we present methods of using Sentinel-1 satellite radar data, acquired every 12 d globally in all weather conditions, to better constrain the timings of rainfall-triggered landslides.
David G. Milledge, Dino G. Bellugi, Jack Watt, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 22, 481–508, https://doi.org/10.5194/nhess-22-481-2022, https://doi.org/10.5194/nhess-22-481-2022, 2022
Short summary
Short summary
Earthquakes can trigger thousands of landslides, causing severe and widespread damage. Efforts to understand what controls these landslides rely heavily on costly and time-consuming manual mapping from satellite imagery. We developed a new method that automatically detects landslides triggered by earthquakes using thousands of free satellite images. We found that in the majority of cases, it was as skilful at identifying the locations of landslides as the manual maps that we tested it against.
Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi
Nat. Hazards Earth Syst. Sci., 21, 2993–3014, https://doi.org/10.5194/nhess-21-2993-2021, https://doi.org/10.5194/nhess-21-2993-2021, 2021
Short summary
Short summary
When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: (1) models which predict landslide locations based on, e.g., slope and ground shaking data and (2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
Maria Francesca Ferrario and Franz Livio
Solid Earth, 12, 1197–1209, https://doi.org/10.5194/se-12-1197-2021, https://doi.org/10.5194/se-12-1197-2021, 2021
Short summary
Short summary
Moderate to strong earthquakes commonly produce surface faulting, either along the primary fault or as distributed rupture on nearby faults. Hazard assessment for distributed normal faulting is based on empirical relations derived almost 15 years ago. In this study, we derive updated empirical regressions of the probability of distributed faulting as a function of distance from the primary fault, and we propose a conservative scenario to consider the full spectrum of potential rupture.
Katy Burrows, Richard J. Walters, David Milledge, and Alexander L. Densmore
Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020, https://doi.org/10.5194/nhess-20-3197-2020, 2020
Short summary
Short summary
Satellite radar could provide information on landslide locations within days of an earthquake or rainfall event anywhere on Earth, but until now there has been a lack of systematic testing of possible radar methods, and most methods have been demonstrated using a single case study event and data from a single satellite sensor. Here we test five methods on four events, demonstrating their wide applicability and making recommendations on when different methods should be applied in the future.
Cited articles
Alfaro, P., Delgado, J., García-Tortosa, F., Lenti, L., López, J., López-Casado, C., and Martino, S.: Widespread landslides induced by the Mw 5.1 earthquake of 11 May 2011 in Lorca, SE Spain, Engineering Geology, 137, 40–52, https://doi.org/10.1016/j.enggeo.2012.04.002, 2012. a, b
Bertone, A., Zucca, F., Marin, C., Notarnicola, C., Cuozzo, G., Krainer, K., Mair, V., Riccardi, P., Callegari, M., and Seppi, R.: An unsupervised method to detect rock glacier activity by using Sentinel-1 SAR interferometric coherence: A regional-scale study in the Eastern European Alps, Remote Sensing, 11, 1711, https://doi.org/10.3390/rs11141711, 2019. a, b
Burrows, K.: KABurrows/Supplement-to-nhess-2022-21: v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.6984291, 2022. a
Burrows, K.: Geometric SAR properties for landslides, version 1, Zenodo [code], https://doi.org/10.5281/zenodo.12579939, 2024. a
Burrows, K., Walters, R. J., Milledge, D., Spaans, K., and Densmore, A. L.: A New Method for Large-Scale Landslide Classification from Satellite Radar, Remote Sensing, 11, 237, https://doi.org/10.3390/rs11030237, 2019. a, b, c
Burrows, K., Marc, O., and Remy, D.: Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding, Nat. Hazards Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Burrows, K., Marc, O., and Andermann, C.: Retrieval of Monsoon Landslide Timings With Sentinel-1 Reveals the Effects of Earthquakes and Extreme Rainfall, Geophysical Research Letters, 50, e2023GL104720, https://doi.org/10.1029/2023GL104720, 2023. a, b
Burrows, K., Ferrario, M. F., and Milledge, D.: Landslides triggered during the 2018 Lombok, Indonesia earthquake sequence timed with Sentinel-1, Zenodo [data set], https://doi.org/10.5281/zenodo.15516233, 2025. a
Cabré, A., Remy, D., Aguilar, G., Carretier, S., and Riquelme, R.: Mapping rainstorm erosion associated with an individual storm from InSAR coherence loss validated by field evidence for the Atacama Desert, Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.4868, 2020. a, b, c, d
Cabré, A., Remy, D., Marc, O., Burrows, K., and Carretier, S.: Flash floods triggered by the 15–17th March 2022 rainstorm event in the Atacama Desert mapped from InSAR coherence time series, Natural Hazards, 116, 1345–1353, https://doi.org/10.1007/s11069-022-05707-y, 2023. a
Croissant, T., Steer, P., Lague, D., Davy, P., Jeandet, L., and Hilton, R. G.: Seismic cycles, earthquakes, landslides and sediment fluxes: Linking tectonics to surface processes using a reduced-complexity model, Geomorphology, 339, 87–103, https://doi.org/10.1016/j.geomorph.2019.04.017, 2019. a
Dahlquist, M. P. and West, A. J.: Initiation and runout of post-seismic debris flows: Insights from the 2015 Gorkha Earthquake, Geophysical Research Letters, https://doi.org/10.1029/2019GL083548, 2019. a
Davidson, M. W. and Furnell, R.: ROSE-L: Copernicus l-band SAR mission, in: 2021 IEEE international geoscience and remote sensing symposium IGARSS, 872–873, IEEE, https://doi.org/10.1109/IGARSS47720.2021.9554018, 2021. a
llite: a regionally applicable methodology illustrated in African cloud-covered tropical environments, Nat. Hazards Earth Syst. Sci., 22, 3679–3700, https://doi.org/10.5194/nhess-22-3679-2022, 2022. a, b, c, d
Ding, X.-L., Li, Z.-W., Zhu, J.-J., Feng, G.-C., and Long, J.-P.: Atmospheric effects on InSAR measurements and their mitigation, Sensors, 8, 5426–5448, https://doi.org/10.3390/s8095426, 2008. a
Dini, B., Doin, P., Lacroix, P., and Gay, M.: Satellite-based InSAR: application and signal extraction for the detection of landslide precursors, in: GRETSI'22: XXVIIIth Francophone Symposium on Signal and Image Processing, GRETSI, https://gretsi.fr/data/colloque/pdf/2022_dini987.pdf (last access: April 2024), 2022. a, b, c
Dong, J., Zhang, L., Li, M., Yu, Y., Liao, M., Gong, J., and Luo, H.: Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets, Landslides, 15, 135–144, https://doi.org/10.1007/s10346-017-0914-8, 2018. a
Dossa, G. G., Paudel, E., Fujinuma, J., Yu, H., Chutipong, W., Zhang, Y., Paz, S., and Harrison, R. D.: Factors determining forest diversity and biomass on a tropical volcano, Mt. Rinjani, Lombok, Indonesia, PLoS One, 8, e67720, https://doi.org/10.1371/journal.pone.0067720, 2013. a, b
Duda, R. O. and Hart, P. E.: Pattern Classification and Scene Analysis, Wiley, Hoboken, NJ, ISBN 0471223611, 1973. a
ESA: Sentinel-1A change in the orbital control strategy, https://sentinels.copernicus.eu/web/sentinel/-/sentinel-1a-change-in-the-orbit-control-strategy (last access: September 2024), 2024. a
Fan, X., Yunus, A. P., Scaringi, G., Catani, F., Siva Subramanian, S., Xu, Q., and Huang, R.: Rapidly evolving controls of landslides after a strong earthquake and implications for hazard assessments, Geophysical Research Letters, 48, e2020GL090509, https://doi.org/10.1029/2020GL090509, 2021. a, b, c
Ferrario, M. F., Perez, J., Dizon, M., Livio, F., Rimando, J., and Michetti, A.: Environmental effects following a seismic sequence: the 2019 Cotabato—Davao del Sur (Philippines) earthquakes, Natural Hazards, 1–23, https://doi.org/10.1007/s11069-024-06467-7, 2024. a, b, c
Fu, S., de Jong, S. M., Hou, X., de Vries, J., Deijns, A., and de Haas, T.: A landslide dating framework using a combination of Sentinel-1 SAR and-2 optical imagery, Engineering Geology, 329, 107388, https://doi.org/10.1016/j.enggeo.2023.107388, 2024. a, b
Ge, P., Gokon, H., Meguro, K., and Koshimura, S.: Study on the intensity and coherence information of high-resolution ALOS-2 SAR images for rapid massive landslide mapping at a pixel level, Remote Sensing, 11, 2808, https://doi.org/10.3390/rs11232808, 2019. a
Giffard-Roisin, S., Boudaour, S., Doin, M.-P., Yan, Y., and Atto, A.: Land cover classification of the Alps from InSAR temporal coherence matrices, Frontiers in Remote Sensing, 3, 932491, https://doi.org/10.3389/frsen.2022.932491, 2022. a, b
Godt, J., Sener, B., Verdin, K., Wald, D., Earle, P., Harp, E., and Jibson, R.: Rapid assessment of earthquake-induced landsliding, in: Proceedings of the first world landslide forum, Tokyo, United Nations University, vol. 4, 219–222, https://earthquake.usgs.gov/static/lfs/data/pager/Godt_et_al_(2009)_PAGER_Landslides.pdf (last access: October 2025), 2008. a, b
Goorabi, A.: Detection of landslide induced by large earthquake using InSAR coherence techniques–Northwest Zagros, Iran, The Egyptian Journal of Remote Sensing and Space Science, 23, 195–205, https://doi.org/10.1016/j.ejrs.2019.04.002, 2020. a, b, c
Görüm, T., Tanyas, H., Karabacak, F., Yılmaz, A., Girgin, S., Allstadt, K. E., Süzen, M. L., and Burgi, P.: Preliminary documentation of coseismic ground failure triggered by the February 6, 2023 Türkiye earthquake sequence, Engineering Geology, 327, 107315, https://doi.org/10.1016/j.enggeo.2023.107315, 2023. a
Hallal, N., Hamidatou, M., Hamai, L., Aguemoune, S., and Lamali, A.: Landslides triggered by the August 2020 Mw 5.0 Mila, Algeria, earthquake: spatial distribution and susceptibility mapping, Euro-Mediterranean Journal for Environmental Integration, 1–23, https://doi.org/10.1007/s41207-024-00471-w, 2024. a, b
Handwerger, A. L., Huang, M.-H., Jones, S. Y., Amatya, P., Kerner, H. R., and Kirschbaum, D. B.: Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine, Nat. Hazards Earth Syst. Sci., 22, 753–773, https://doi.org/10.5194/nhess-22-753-2022, 2022. a
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochemistry, Geophysics, Geosystems, 13, https://doi.org/10.1029/2012GC004370, 2012. a
Hoen, E. W. and Zebker, H. A.: Penetration depths inferred from interferometric volume decorrelation observed over the Greenland ice sheet, IEEE Transactions on Geoscience and Remote Sensing, 38, 2571–2583, https://doi.org/10.1109/36.885204, 2000. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., and Yoo, S.-H.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD) Version, 4, https://gpm.nasa.gov/sites/default/files/2020-05/IMERG_ATBD_V06.3.pdf (last access: October 2025), 2015. a
Jacob, A. W., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J. M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziółkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Franquet, J. J. M., Duro, J., and Engdalh, M.: Sentinel-1 InSAR coherence for land cover mapping: A comparison of multiple feature-based classifiers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 535–552, https://doi.org/10.1109/JSTARS.2019.2958847, 2020. a, b, c, d
Jacquemart, M. and Tiampo, K.: Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the Mud Creek landslide, California, Nat. Hazards Earth Syst. Sci., 21, 629–642, https://doi.org/10.5194/nhess-21-629-2021, 2021. a, b, c, d
Jones, C. E., Manjusree, P., and Rao, S.: The NISAR Mission's capabilities for natural hazards monitoring, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 1711–1714, https://doi.org/10.1109/IGARSS47720.2021.9553295, 2021a. a
Jones, J. N., Boulton, S. J., Stokes, M., Bennett, G. L., and Whitworth, M. R.: 30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events, Nature Communications, 12, 6701, https://doi.org/10.1038/s41467-021-26964-8, 2021b. a
Jung, J. and Yun, S.-H.: Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 Hokkaido landslides, Remote Sensing, 12, 265, https://doi.org/10.3390/rs12020265, 2020. a, b, c
Kellndorfer, J., Cartus, O., Lavalle, M., Magnard, C., Milillo, P., Oveisgharan, S., Osmanoglu, B., Rosen, P. A., and Wegmüller, U.: Global seasonal Sentinel-1 interferometric coherence and backscatter data set, Scientific Data, 9, 73, https://doi.org/10.1038/s41597-022-01189-6, 2022. a, b
Lacroix, P., Huanca, J., Albinez, L., and Taipe, E.: Precursory Motion and Time-Of-Failure Prediction of the Achoma Landslide, Peru, From High Frequency PlanetScope Satellites, Geophysical Research Letters, 50, e2023GL105413, https://doi.org/10.1029/2023GL105413, 2023. a
Lee, H. and Liu, J. G.: Spatial decorrelation due to topography in the interferometric SAR coherence imagery, in: IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293), IEEE, 1, 485–487, https://doi.org/10.1109/IGARSS.1999.773541, 1999. a
Li, Z., Wright, T., Hooper, A., Crippa, P., Gonzalez, P., Walters, R., Elliott, J., Ebmeier, S., Hatton, E., and Parsons, B.: Towards InSAR everywhere, all the time with Sentinel-1, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 763–766, https://doi.org/10.5194/isprs-archives-XLI-B4-763-2016, 2016. a
Liu, J. G., Lee, H., and Pearson, T.: Detection of rapid erosion in SE Spain using ERS SAR interferometric coherence imagery, in: Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, SPIE, 3868, 525–535, https://doi.org/10.1117/12.373131, 1999. a, b
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide inventories and their statistical properties, Earth Surface Processes and Landforms, 29, 687–711, https://doi.org/10.1002/esp.1064, 2004. a
Marc, O., Hovius, N., Meunier, P., Uchida, T., and Hayashi, S.-i.: Transient changes of landslide rates after earthquakes, Geology, 43, 883–886, https://doi.org/10.1130/G36961.1, 2015. a, b
Marc, O., Meunier, P., and Hovius, N.: Prediction of the area affected by earthquake-induced landsliding based on seismological parameters, Nat. Hazards Earth Syst. Sci., 17, 1159–1175, https://doi.org/10.5194/nhess-17-1159-2017, 2017. a
Martino, S., Bozzano, F., Caporossi, P., D’Angiò, D., Della Seta, M., Esposito, C., Fantini, A., Fiorucci, M., Giannini, L. M. Iannucci, R., Marmoni, G. M., Mazzanti, P., Missori, C., Moretto, S., Piacentini, D., Rivellino, S., Romeo, R. W., Sarandrea, P., Schilirò, L., Troiani, F., and Varone, C.: Impact of landslides on transportation routes during the 2016–2017 Central Italy seismic sequence, Landslides, 16, 1221–1241, https://doi.org/10.1007/s10346-019-01162-2, 2019. a, b
Martino, S., Fiorucci, M., Marmoni, G., Casaburi, L., Antonielli, B., and Mazzanti, P.: Increase in landslide activity after a low-magnitude earthquake as inferred from DInSAR interferometry, Scientific Reports, 12, 2686, https://doi.org/10.1038/s41598-022-06508-w, 2022. a
Milledge, D. G., Bellugi, D. G., Watt, J., and Densmore, A. L.: Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping, Nat. Hazards Earth Syst. Sci., 22, 481–508, https://doi.org/10.5194/nhess-22-481-2022, 2022. a
Mondini, A. C.: Measures of spatial autocorrelation changes in multitemporal SAR images for event landslides detection, Remote Sensing, 9, 554, https://doi.org/10.3390/rs9060554, 2017. a, b
Mondini, A. C., Guzzetti, F., Chang, K.-T., Monserrat, O., Martha, T. R., and Manconi, A.: Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future, Earth-Science Reviews, 103574, https://doi.org/10.1016/j.earscirev.2021.103574, 2021. a
Novellino, A., Pennington, C., Leeming, K., Taylor, S., Alvarez, I. G., McAllister, E., Arnhardt, C., and Winson, A.: Mapping landslides from space: A review, Landslides, 1–12, https://doi.org/10.1007/s10346-024-02215-x, 2024. a
Nowicki Jessee, M., Hamburger, M. W., Allstadt, K., Wald, D. J., Robeson, S. M., Tanyas, H., Hearne, M., and Thompson, E. M.: A global empirical model for near-real-time assessment of seismically induced landslides, Journal of Geophysical Research: Earth Surface, 123, 1835–1859, https://doi.org/10.1029/2017JF004494, 2018. a, b, c, d, e
Parker, R. N., Densmore, A. L., Rosser, N. J., De Michele, M., Li, Y., Huang, R., Whadcoat, S., and Petley, D. N.: Mass wasting triggered by the 2008 Wenchuan earthquake is greater than orogenic growth, Nature Geoscience, 4, 449–452, https://doi.org/10.1038/ngeo1154, 2011. a
Petley, D.: Landslides on the Shakadang Trail, Taroko, Taiwan, https://eos.org/thelandslideblog/shakadang-trail (last access: December 2024), 2024. a
Robinson, T. R., Rosser, N., and Walters, R. J.: The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters, Scientific Reports, 9, 1–9, https://doi.org/10.1038/s41598-019-49008-0, 2019. a
Salman, R., Lindsey, E. O., Lythgoe, K. H., Bradley, K., Muzli, M., Yun, S.-H., Chin, S. T., J. Tay, C. W., Costa, F., Wei, S., and Hill, E. M.: Cascading partial rupture of the Flores thrust during the 2018 Lombok earthquake sequence, Indonesia, Seismological Research Letters, 91, 2141–2151, https://doi.org/10.1785/0220190378, 2020. a
Scheip, C. M. and Wegmann, K. W.: HazMapper: a global open-source natural hazard mapping application in Google Earth Engine, Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, 2021. a
Scott, C., Lohman, R., and Jordan, T.: InSAR constraints on soil moisture evolution after the March 2015 extreme precipitation event in Chile, Scientific Reports, 7, 4903, https://doi.org/10.1038/s41598-017-05123-4, 2017. a, b
Sepúlveda, S. A., Serey, A., Lara, M., Pavez, A., and Rebolledo, S.: Landslides induced by the April 2007 Aysén fjord earthquake, Chilean Patagonia, Landslides, 7, 483–492, https://doi.org/10.1007/s10346-010-0203-2, 2010. a
Tanyaş, H. and Lombardo, L.: Variation in landslide-affected area under the control of ground motion and topography, Engineering Geology, 260, 105 229, https://doi.org/10.1016/j.enggeo.2019.105229, 2019. a
Tanyaş, H., Kirschbaum, D., Görüm, T., van Westen, C. J., Tang, C., and Lombardo, L.: A closer look at factors governing landslide recovery time in post-seismic periods, Geomorphology, 391, 107912, https://doi.org/10.1016/j.geomorph.2021.107912, 2021. a
Tanyaş, H., Hill, K., Mahoney, L., Fadel, I., and Lombardo, L.: The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake, Engineering Geology, 297, 106504, https://doi.org/10.1016/j.enggeo.2021.106504, 2022. a, b, c, d
Tiwari, B., Ajmera, B., and Dhital, S.: Characteristics of moderate-to large-scale landslides triggered by the Mw 7.8 2015 Gorkha earthquake and its aftershocks, Landslides, 14, 1279–1318, https://doi.org/10.1007/s10346-016-0789-0, 2017. a, b, c, d
USGS: ShakeMap Event Page “M 6.4 – 33 km NNW of Labuan Lombok, Indonesia”, https://earthquake.usgs.gov/earthquakes/eventpage/us2000ggbs/executive (last access: September 2023), 2018a. a
USGS: ShakeMap Event Page ”M 6.9 - 20 km NNW of Labuan Lombok, Indonesia”, https://earthquake.usgs.gov/earthquakes/eventpage/us1000gda5/executive#executive (last access: September 2023), 2018d. a
Wang, W., Motagh, M., Xia, Z., Plank, S., Li, Z., Orynbaikyzy, A., Zhou, C., and Roessner, S.: A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding, International Journal of Applied Earth Observation and Geoinformation, 129, 103795, https://doi.org/10.1016/j.jag.2024.103795, 2024. a, b, c
Webb, T., Wadge, G., and Pascal, K.: Mapping water vapour variability over a mountainous tropical island using InSAR and an atmospheric model for geodetic observations, Remote Sensing of Environment, 237, 111560, https://doi.org/10.1016/j.rse.2019.111560, 2020. a
Xu, C., Ma, S., Tan, Z., Xie, C., Toda, S., and Huang, X.: Landslides triggered by the 2016 Mj 7.3 Kumamoto, Japan, earthquake, Landslides, 15, 551–564, https://doi.org/10.1007/s10346-017-0929-1, 2018. a
Yun, S.-H., Hudnut, K., Owen, S., Webb, F., Simons, M., Sacco, P., Gurrola, E., Manipon, G., Liang, C., Fielding, E., Milillo, P., Hua, H., and Coletta, A.: Rapid Damage Mapping for the 2015 Mw 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from COSMO–SkyMed and ALOS-2 Satellites, Seismological Research Letters, 86, 1549–1556, https://doi.org/10.1785/0220150152, 2015. a, b, c
Yunus, A., Fan, X., Tang, X., Jie, D., Xu, Q., and Huang, R.: Decadal vegetation succession from MODIS reveals the spatio-temporal evolution of post-seismic landsliding after the 2008 Wenchuan earthquake, Remote Sensing of Environment, 236, 111476, https://doi.org/10.1016/j.rse.2019.111476, 2020. a
Zebker, H. A. and Villasenor, J.: Decorrelation in interferometric radar echoes, IEEE Transactions on geoscience and remote sensing, 30, 950–959, 1992. a
Zhao, B., Liao, H., and Su, L.: Landslides triggered by the 2018 Lombok earthquake sequence, Indonesia, Catena, 207, 105676, https://doi.org/10.1016/j.catena.2021.105676, 2021. a, b, c, d
Short summary
In 2018, 6 moderate-large earthquakes occurred in Lombok, Indonesia over a 3-week period, triggering landslides across the island. Their locations were previously mapped with optical satellite images, but information on which earthquake triggered which landslide was limited. Here we use Sentinel-1 satellite images to determine when during the earthquake sequence many of the landslides failed and so build a more complete picture of how landslide activity evolved through time.
In 2018, 6 moderate-large earthquakes occurred in Lombok, Indonesia over a 3-week period,...
