Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2637-2022
© Author(s) 2022. 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-22-2637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2022
Research article |
| 17 Aug 2022
| 17 Aug 2022
Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
Odin Marc
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
Dominique Remy
Géosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/CNES/UPS, Observatoire Midi-Pyrénées, Toulouse, France
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Cited articles
Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y.: Earthquake-Induced
Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using
PALSAR-2 Data, Remote Sensing, 11, 2351, https://doi.org/10.3390/rs11202351, 2019. a
Baghdadi, N., Choker, M., Zribi, M., Hajj, M. E., Paloscia, S., Verhoest,
N. E., Lievens, H., Baup, F., and Mattia, F.: A new empirical model for radar
scattering from bare soil surfaces, Remote Sensing, 8, 920, https://doi.org/10.3390/rs8110920, 2016. a, b
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location
of shallow rainfall-induced landslides using a model for transient,
unsaturated infiltration, J. Geophys. Res.-Ea. Surf.,
115, F03013, https://doi.org/10.1029/2009JF001321, 2010. a
BBC News: Cyclone Idai: Zimbabwe school hit by landslide,
https://www.bbc.com/news/world-africa-47602399 (last access: 5 November 2021), news report 17 March 2019, 2019. a
Bekaert, D. P., Handwerger, A. L., Agram, P., and Kirschbaum, D. B.:
InSAR-based detection method for mapping and monitoring slow-moving
landslides in remote regions with steep and mountainous terrain: An
application to Nepal, Remote Sens. Environ., 249, 111983, https://doi.org/10.1016/j.rse.2020.111983, 2020. a
Belenguer-Plomer, M. A., Tanase, M. A., Fernandez-Carrillo, A., and Chuvieco,
E.: Burned area detection and mapping using Sentinel-1 backscatter
coefficient and thermal anomalies, Remote Sens. Environ., 233,
111345, https://doi.org/10.1016/j.rse.2019.111345, 2019. a
Bell, R., Fort, M., Götz, J., Bernsteiner, H., Andermann, C., Etzlstorfer,
J., Posch, E., Gurung, N., and Gurung, S.: Major geomorphic events and
natural hazards during monsoonal precipitation 2018 in the Kali Gandaki
Valley, Nepal Himalaya, Geomorphology, 372, 107451, https://doi.org/10.1016/j.geomorph.2020.107451, 2021. a, b
Bernard, T. G., Lague, D., and Steer, P.: Beyond 2D landslide inventories and their rollover: synoptic 3D inventories and volume from repeat lidar data, Earth Surf. Dynam., 9, 1013–1044, https://doi.org/10.5194/esurf-9-1013-2021, 2021. a
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018. a
Burrows, K.: KABurrows/Supplement-to-nhess-2022-21: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6984291, 2022. 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 Surf.
Proc. Landf., 45, 2091–2106, https://doi.org/10.1002/esp.4868, 2020. a
Copernicus: Copernicus Sentinel data, Copernicus [data set], https://scihub.copernicus.eu/dhus/#/home, last access: August 2022. a
Dahal, R. K. and Hasegawa, S.: Representative rainfall thresholds for
landslides in the Nepal Himalaya, Geomorphology, 100, 429–443, 2008. a
Dubois, P. C., Van Zyl, J., and Engman, T.: Measuring soil moisture with
imaging radars, IEEE T. Geosci. Remote, 33,
915–926, 1995. a
Emberson, R., Kirschbaum, D. B., Amatya, P., Tanyas, H., and Marc, O.: Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories, Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, 2022. a, b, c, d
Esposito, G., Marchesini, I., Mondini, A. C., Reichenbach, P., Rossi, M., and Sterlacchini, S.: A spaceborne SAR-based procedure to support the detection of landslides, Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, 2020. a
Filipponi, F.: Sentinel-1 GRD preprocessing workflow, in: Multidisciplinary
Digital Publishing Institute Proceedings, MDPI, vol. 18, p. 11, https://doi.org/10.3390/ECRS-3-06201, 2019. a
Franceschini, R., Rosi, A., Catani, F., and Casagli, N.: Exploring a landslide
inventory created by automated web data mining: the case of Italy,
Landslides, 19, 841–853, 2022. a
Gabet, E. J., Burbank, D. W., Putkonen, J. K., Pratt-Sitaula, B. A., and Ojha,
T.: Rainfall thresholds for landsliding in the Himalayas of Nepal,
Geomorphology, 63, 131–143, 2004. 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
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds
for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, 2007. a
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I.,
Rossi, M., and Melillo, M.: Geographical landslide early warning systems,
Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020. a
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
Hashimoto, R., Tsuchida, T., Moriwaki, T., and Kano, S.: Hiroshima Prefecture
geo-disasters due to Western Japan Torrential rainfall in July 2018, Soils
and Foundations, 60, 283–299, 2020. a
Hernandez, N. D., Pastrana, A. A., Garcia, L. C., de Leon, J. C. V., Alvarez,
A. Z., Morales, L. D., Nemiga, X. A., and Posadas, G. D.: Co-seismic
landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca,
Mexico, based on rapid mapping method using high and medium resolution
synthetic aperture radar (SAR) images, Landslides, 18, 3833–3844, 2021. a
Hoekman, D. H. and Reiche, J.: Multi-model radiometric slope correction of SAR
images of complex terrain using a two-stage semi-empirical approach, Remote
Sens. Environ., 156, 1–10, 2015. a
Hu, X., Bürgmann, R., Lu, Z., Handwerger, A. L., Wang, T., and Miao, R.:
Mobility, thickness, and hydraulic diffusivity of the slow-moving Monroe
landslide in California revealed by L-band satellite radar interferometry,
J. Geophys. Res.-Sol. Ea., 124, 7504–7518, 2019. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci.
Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour.
Res., 36, 1897–1910, 2000. a
Kang, Y., Lu, Z., Zhao, C., Xu, Y., Kim, J.-w., and Gallegos, A. J.: InSAR
monitoring of creeping landslides in mountainous regions: A case study in
Eldorado National Forest, California, Remote Sens. Environ., 258,
112400, https://doi.org/10.1016/j.rse.2021.112400, 2021. a
Kirschbaum, D. and Stanley, T.: Satellite-based assessment of
rainfall-triggered landslide hazard for situational awareness, Earth's
Future, 6, 505–523, 2018. a
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A.: A global
landslide catalog for hazard applications: method, results, and limitations,
Nat. Hazards, 52, 561–575, 2010. a
Konishi, T. and Suga, Y.: Landslide detection using COSMO-SkyMed images: a
case study of a landslide event on Kii Peninsula, Japan, Eur. J.
Remote Sens., 51, 205–221, 2018. 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, in: Proc. of SPIE Vol, SPIE digital library, vol. 11154, 111540H–1, https://doi.org/10.1117/12.2531695, 2019. a
Ma, T., Li, C., Lu, Z., and Bao, Q.: Rainfall intensity–duration thresholds
for the initiation of landslides in Zhejiang Province, China, Geomorphology,
245, 193–206, 2015. a
Marc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., and Chiang, S.-H.: Initial insights from a global database of rainfall-induced landslide inventories: the weak influence of slope and strong influence of total storm rainfall, Earth Surf. Dynam., 6, 903–922, https://doi.org/10.5194/esurf-6-903-2018, 2018. a, b
Marc, O., Behling, R., Andermann, C., Turowski, J. M., Illien, L., Roessner, S., and Hovius, N.: Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides, Earth Surf. Dynam., 7, 107–128, https://doi.org/10.5194/esurf-7-107-2019, 2019a. a, b, c
Marc, O., Gosset, M., Saito, H., Uchida, T., and Malet, J.-P.: Spatial patterns
of storm-induced landslides and their relation to rainfall anomaly maps,
Geophys. Res. Lett., 46, 11167–11177, 2019b. a
Masato, O., Abe, T., Takeo, T., and Masanobu, S.: Landslide detection in
mountainous forest areas using polarimetry and interferometric coherence,
Earth Planet. Space, 72, https://doi.org/10.1186/s40623-020-01191-5, 2020. 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
Ministry of Information, P. and Broadcasting, Z.: Twitter,
https://twitter.com/InfoMinZW/status/1107121417773035521 (last access: 5 November 2021), tweet
@infoMinZW, 17 March 2019, 2019. a
Mondini, A. C.: Measures of spatial autocorrelation changes in multitemporal
SAR images for event landslides detection, Remote Sens., 9, 554, https://doi.org/10.3390/rs9060554, 2017. a, b
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
Motohka, T., Shimada, M., Uryu, Y., and Setiabudi, B.: Using time series
PALSAR gamma nought mosaics for automatic detection of tropical
deforestation: A test study in Riau, Indonesia, Remote Sens.
Environ., 155, 79–88, 2014. a
OCHA: Cyclone Idai hits Zimbambe, causing flash flooding, death and
destruction of livelihoods,
https://www.unocha.org/story/cyclone-idai-hits-zimbambe-causing-flash-flooding-death-and-destruction-livelihoods (last access: 5 November 2021),
news report 17th March 2019, 2019. a
Ozturk, U., Saito, H., Matsushi, Y., Crisologo, I., and Schwanghart, W.: Can
global rainfall estimates (satellite and reanalysis) aid landslide
hindcasting?, Landslides, 18, 3119–3133, 2021. a
Petley, D.: Global patterns of loss of life from landslides, Geology, 40,
927–930, 2012. a
Pokharel, B., Alvioli, M., and Lim, S.: Assessment of earthquake-induced
landslide inventories and susceptibility maps using slope unit-based logistic
regression and geospatial statistics, Sci. Rep., 11, 1–15, 2021. a
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, U.S. Geological Survey data release [data set], https://doi.org/10.5066/F7DZ06F9, 2017. a, b, c, d, e, f, g
Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Namara, E.,
Dewitte, O., Kervyn, M., and Jacobs, L.: Can citizen scientists provide a
reliable geo-hydrological hazard inventory? An analysis of biases,
sensitivity and precision for the Rwenzori Mountains, Uganda, Environ.
Res. Lett., 17, 045011, https://doi.org/10.1088/1748-9326/ac5bb5, 2022. a, b
Solari, L., Del Soldato, M., Raspini, F., Barra, A., Bianchini, S., Confuorto,
P., Casagli, N., and Crosetto, M.: Review of Satellite Interferometry for
Landslide Detection in Italy, Remote Sens., 12, 1351, https://doi.org/10.3390/rs12081351, 2020. a
Spaans, K. and Hooper, A.: InSAR processing for volcano monitoring and other
near-real time applications, J. Geophys. Res.-Sol. Ea.,
121, 2947–2960, 2016. 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, Eng. Geol., 297, 106504, https://doi.org/10.1016/j.enggeo.2021.106504, 2022. a, b, c
Uieda, L., Tian, D., Leong, W. J., Jones, M., Schlitzer, W., Toney, L., Grund,
M., Yao, J., Magen, Y., Materna, K., Newton, T., Anant, A., Ziebarth, M.,
Quinn, J., and Wessel, P.: PyGMT: A Python interface for the Generic
Mapping Tools, Zenodo [code], https://doi.org/10.5281/zenodo.5607255, 2021. a
U.S Geological Survey: Landsat 8 imagery, U.S Geological Survey [data set], https://earthexplorer.usgs.gov/, last access: August 2022. a
Villard, L. and Borderies, P.: Backscattering Border Effects for Forests at
C-band, PIERS, 3, 731–735, 2007. a
Vollrath, A., Mullissa, A., and Reiche, J.: Angular-based radiometric slope
correction for Sentinel-1 on google earth engine, Remote Sens., 12, 1867, https://doi.org/10.3390/rs12111867,
2020. a, b
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, c
Wilson, R. C. and Wieczorek, G. F.: Rainfall Thresholds for the Initiation
of Debris Flows at La Honda, California, Environ.
Eng. Geosci. I, 1, 11–27, https://doi.org/10.2113/gseegeosci.I.1.11, 1995. a
Wu, Y.-M., Lan, H.-X., Gao, X., Li, L.-P., and Yang, Z.-H.: A simplified
physically based coupled rainfall threshold model for triggering landslides,
Eng. Geol., 195, 63–69, 2015. a
Yamada, M., Matsushi, Y., Chigira, M., and Mori, J.: Seismic recordings of
landslides caused by Typhoon Talas (2011), Japan, Geophys. Res.
Lett., 39, L13301, https://doi.org/10.1029/2012GL052174, 2012. a, b
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
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.
The locations of triggered landslides following a rainfall event can be identified in optical...
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