Articles | Volume 21, issue 10
https://doi.org/10.5194/nhess-21-2993-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-2993-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integrating empirical models and satellite radar can improve landslide detection for emergency response
COMET, Department of Earth Sciences, Durham University, Durham, UK
now at: Géosciences Environnement Toulouse, Toulouse, France
David Milledge
School of Engineering, Newcastle University, Newcastle, UK
Richard J. Walters
COMET, Department of Earth Sciences, Durham University, Durham, UK
Dino Bellugi
Department of Geography, University of California, Berkeley, Berkeley, USA
Related authors
Katy Burrows, David G. Milledge, and Maria Francesca Ferrario
EGUsphere, https://doi.org/10.5194/egusphere-2024-3264, https://doi.org/10.5194/egusphere-2024-3264, 2024
Short summary
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.
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, 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.
Katy Burrows, David G. Milledge, and Maria Francesca Ferrario
EGUsphere, https://doi.org/10.5194/egusphere-2024-3264, https://doi.org/10.5194/egusphere-2024-3264, 2024
Short summary
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.
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, 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
Allstadt, K. E., Jibson, R. W., Thompson, E. M., Massey, C. I., Wald, D. J.,
Godt, J. W., and Rengers, F. K.: Improving Near-Real-Time Coseismic Landslide Models: Lessons Learned from the 2016 Kaikōura, New Zealand,
Earthquake Improving Near-Real-Time Coseismic Landslide Models, B. Seismol. Soc. Am., 108, 1649–1664, 2018. a
Burrows, K., Walters, R. J., Milledge, D., and Densmore, A. L.: A systematic exploration of satellite radar coherence methods for rapid landslide detection, Nat. Hazards Earth Syst. Sci., 20, 3197–3214, https://doi.org/10.5194/nhess-20-3197-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v
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 Surf. Proc. Land., 45, 2091–2106, 2020. a
Copernicus: Copernicus Open Access Hub, available at: https://scihub.copernicus.eu/, last access: November 2020. a
Czuchlewski, K. R., Weissel, J. K., and Kim, Y.: Polarimetric synthetic
aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi
earthquake, Taiwan, J. Geophys. Res.-Earth, 108, 6006, https://doi.org/10.1029/2003JF000037, 2003. a, b
Díaz-Uriarte, R. and De Andres, S. A.: Gene selection and classification
of microarray data using random forest, BMC Bioinform., 7, 3, 2006. a
Efron, B. and Tibshirani, R.: Improvements on cross-validation: the 632+ bootstrap method, J. Am. Stat. Assoc., 92, 548–560, 1997. a
ESA: Land Cover CCI Product User Guide Version 2, available at:
http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: June 2020), 2017. a
ESA: Climate Change Initiative, available at: https://www.esa-landcover-cci.org/, last access: June 2020. a
Fan, X., Yunus, A. P., Scaringi, G., Catani, F., Subramanian, S. S., Xu, Q.,
and Huang, R.: Rapidly evolving controls of landslides after a strong
earthquake an implications for hazard assessments, Geophys. Res. Lett., 48, e90509, https://doi.org/10.1029/2020GL090509, 2020. a, b, c
Farr, T.G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007. a
Fielding, E. J., Talebian, M., Rosen, P. A., Nazari, H., Jackson, J. A.,
Ghorashi, M., and Walker, R.: Surface ruptures and building damage of the
2003 Bam, Iran, earthquake mapped by satellite synthetic aperture radar
interferometric correlation, J. Geophys. Res.-Solid, 110, B03332,
https://doi.org/10.1029/2004JB003299, 2005. a, b
Fransson, J. E., Pantze, A., Eriksson, L. E., Soja, M. J., and Santoro, M.: Mapping of wind-thrown forests using satellite SAR images, in: 2010 IEEE International Geoscience and Remote Sensing Symposium, 25–30 July 2010, Honolulu, Hawaii, 1242–1245, 2010. a
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018. a
GDAL: GDAL documentation, available at: https://gdal.org/, last access: October 2021. a
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 Sens., 11, 2808, https://doi.org/10.3390/rs11232808, 2019. a, b, c
GSI Japan: Landslide map for the epicentral area of the 2018 Hokkaido
Eastern Iburi Earthquake, available at:
https://www.gsi.go.jp/BOUSAI/H30-hokkaidoiburi-east-earthquake-index.html
(last access: 13 December 2019), 2018. a
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a
receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, 1982. a
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM:
A representation of rock properties at the Earth surface, Geochem. Geophy. Geosy., 13, Q12004, https://doi.org/10.1029/2012GC004370, 2012. a, b
Inter-Agency Standing Committee: Multi-Sector Initial Rapid Asessment Guidance, available at:
https://www.humanitarianresponse.info/en/programme-cycle/space/document/multi-sector-initial-rapid-assessment-guidance-revision-july-2015
(last access: 16 October 2018), 2015. a
Just, D. and Bamler, R.: Phase statistics of interferograms with applications
to synthetic aperture radar, Appl. Optics, 33, 4361–4368, 1994. a
Kargel, J. S., Leonard, G. J., Shugar, D. H., Haritashya, U. K., Bevington, A., Fielding, E., Fujita, K., Geertsema, M., Miles, E., Steiner, J., Anderson, E., Bajracharya, S., Bawden, G. W., Breashears, D. F., Byers, A., Collins, B., Dhital, M. R., Donnellan, A., Evans, T. L., Geai, M. L., Glasscoe, M. T., Green, D., Gurung, D. R., Heijenk, R., Hilborn, A., Hudnut, K., Huyck, C., Immerzeel, W. W., Jiang, L., Jibson, R., Kääb, A., Khanal, N. R., Kirschbaum, D., Kraaijenbrink, P. D. A., Lamsal, D., Shiyin, L., Mingyang, L., McKinney, D., Nahirnick, N. K., Zhuotong, N., Ojha, S., Olsenholler, J., Painter, T. H., Pleasants, M., Pratima, K. C., Yuan, Q. I., Raup, B. H., Regmi, D., Rounce, D. R., Sakai, A., Donghui, S., Shea, J. M., Shrestha, A. B., Shukla, A., Stumm, D., van der Kooij, M., Voss, K., Xin, W.,
Weihs, B., Wolfe, D., Lizong, W., Xiaojun, Y., Yoder, M. R., and Young, N.: Geomorphic and geologic controls of geohazards induced by Nepal's 2015 Gorkha earthquake, Science, 351, aac8353, https://doi.org/10.1126/science.aac8353, 2016. a
Konishi, T. and Suga, Y.: Landslide detection with ALOS-2/PALSAR-2 data using convolutional neural networks: a case study of 2018 Hokkaido Eastern Iburi earthquake, Proc. SPIE, 11154, 111540H, https://doi.org/10.1117/12.2531695, 2019. a, b
Lazeckỳ, M., Spaans, K., González, P. J., Maghsoudi, Y., Morishita, 75 Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., Hooper, A., Juncu, D., McDougall, A., Walters, R. J., Watson, S. C., Weiss, J. R., and Wright, T. J.: LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity, Remote Sens., 12, 2430, https://doi.org/10.3390/rs12152430, 2020. a, b
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R News, 2, 18–22, 2002. a
Mondini, A. C., Santangelo, M., Rocchetti, M., Rossetto, E., Manconi, A., and
Monserrat, O.: Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection, Remote Sens., 11, 760, https://doi.org/10.3390/rs11070760, 2019. a, b, c
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-Sci. Rev., 216, 103574, https://doi.org/10.1016/j.earscirev.2021.103574, 2021. a, b
Moore, I. D., Grayson, R., and Ladson, A.: Digital terrain modelling: a review of hydrological, geomorphological, and biological applications, Hydrol. Process., 5, 3–30, 1991. a
Nowicki Jessee, M., Hamburger, M., Allstadt, K., Wald, D. J., Robeson, S.,
Tanyas, H., Hearne, M., and Thompson, E.: A Global Empirical Model for
Near-Real-Time Assessment of Seismically Induced Landslides, J. Geophys. Res.-Earth, 123, 1835–1859, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah, ai, aj, ak, al, am, an, ao, ap, aq, ar, as, at, au, av, aw
Olen, S. and Bookhagen, B.: Mapping damage-affected areas after natural hazard events using Sentinel-1 coherence time series, Remote Sens., 10, 1272, https://doi.org/10.3390/rs10081272, 2018. a
Park, S.-E. and Lee, S.-G.: On the use of single-, dual-, and quad-polarimetric SAR observation for landslide detection, ISPRS Int. J.
Geo-Inform., 8, 384, https://doi.org/10.3390/ijgi8090384, 2019. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b, c, d, e
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F., Champlain, D., and Godt, J. W.: Map data of landslides triggered by the 25 April 2015 Mw 7.8 Gorkha, Nepal earthquake, US Geological Survey data release [data set], https://doi.org/10.5066/F7DZ06F9, 2017. a, b, c, d, e, f, g, h, i, j
Robinson, T. R., Rosser, N. J., Densmore, A. L., Williams, J. G., Kincey, M. E., Benjamin, J., and Bell, H. J. A.: Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support, Nat. Hazards Earth Syst. Sci., 17, 1521–1540, https://doi.org/10.5194/nhess-17-1521-2017, 2017. a, b, c, d, e, f, g, h, i, j, k, l, m
Robinson, T. R., Rosser, N. J., Davies, T. R., Wilson, T. M., and Orchiston,
C.: Near-Real-Time Modeling of Landslide Impacts to Inform Rapid Response:
An Example from the 2016 Kaikōura, New Zealand, EarthquakeNear-Real-Time
Modeling of Landslide Impacts to Inform Rapid Response, B. Seismol. Soc. Am., 108, 1665–1682, 2018. a
Scott, C., Lohman, R., and Jordan, T.: InSAR constraints on soil moisture
evolution after the March 2015 extreme precipitation event in Chile, Scient. Rep., 7, 4903, https://doi.org/10.1038/s41598-017-05123-4, 2017. a
Spaans, K. and Hooper, A.: InSAR processing for volcano monitoring and other
near-real time applications, J. Geophys. Res.-Solid, 121, 2947–2960, 2016. a
Thompson, E. M., McBride, S. K., Hayes, G. P., Allstadt, K. E., Wald, L. A.,
Wald, D. J., Knudsen, K. L., Worden, C. B., Marano, K. D., Jibson, R. W., and Grant, A. R. R.: USGS near-real-time products – and their use – for the 2018 Anchorage earthquake, Seismol. Res. Lett., 91, 94–113, 2020. a, b, c
Universität Hamburg: GLiM – Global Lithological Map, available at: https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html, last access: June 2020. a
USGS: Earthquake Hazards Program, available at: https://www.usgs.gov/natural-hazards/earthquake-hazards/data-tools, last access: January 2021. a
Vajedian, S., Motagh, M., Mousavi, Z., Motaghi, K., Fielding, E., Akbari, B.,
Wetzel, H.-U., and Darabi, A.: Coseismic deformation field of the Mw 7.3 12 November 2017 Sarpol-e Zahab (Iran) earthquake: A decoupling horizon in the northern Zagros Mountains inferred from InSAR observations, Remote Sens., 10, 1589, https://doi.org/10.3390/rs10101589, 2018. a
Wessel, P. and Smith, W. H.: New, improved version of Generic Mapping Tools
released, Eos Trans. Am. Geophys. Union, 79, 579–579, 1998. a
Williams, J. G., Rosser, N. J., Kincey, M. E., Benjamin, J., Oven, K. J., Densmore, A. L., Milledge, D. G., Robinson, T. R., Jordan, C. A., and Dijkstra, T. A.: Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes, Nat. Hazards Earth Syst. Sci., 18, 185–205, https://doi.org/10.5194/nhess-18-185-2018, 2018. a, b
Worden, C., Thompson, E., Hearne, M., and Wald, D.: ShakeMap Manual Online:
technical manual, user's guide, and software guide, US Geological
Survey, available at: http://usgs.github.io/shakemap/ (last access: February 2021), 2020. 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, Seismol. Res. Lett., 86, 1549–1556, 2015. a, b, c, d, e
Zhu, J., Baise, L. G., and Thompson, E. M.: An Updated Geospatial Liquefaction Model for Global ApplicationAn Updated Geospatial Liquefaction Model for Global Application, Bull. Seismol. Soc. Am., 107, 1365–1385, 2017. a
Ziegler, A. and König, I. R.: Mining data with random forests: current
options for real-world applications, Wiley Interdisciplin. Rev.: Data
Min. Knowl. Discov., 4, 55–63, 2014. a
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.
When cloud cover obscures optical satellite imagery, there are two options remaining for...
Altmetrics
Final-revised paper
Preprint