Articles | Volume 20, issue 9
https://doi.org/10.5194/nhess-20-2379-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-2379-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A spaceborne SAR-based procedure to support the detection of landslides
Giuseppe Esposito
CORRESPONDING AUTHOR
National Research Council, Research Institute for Geo-Hydrological
Protection (CNR-IRPI), Rende (CS), 87036, Italy
Ivan Marchesini
National Research Council, Research Institute for Geo-Hydrological
Protection (CNR-IRPI), Perugia, 06128, Italy
Alessandro Cesare Mondini
National Research Council, Research Institute for Geo-Hydrological
Protection (CNR-IRPI), Perugia, 06128, Italy
Paola Reichenbach
National Research Council, Research Institute for Geo-Hydrological
Protection (CNR-IRPI), Perugia, 06128, Italy
Mauro Rossi
National Research Council, Research Institute for Geo-Hydrological
Protection (CNR-IRPI), Perugia, 06128, Italy
Simone Sterlacchini
National Research Council, Research Institute of Environmental Geology and Geoengineering (CNR-IGAG), Milan, 20126, Italy
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Cited articles
Alvioli, M., Mondini, A. C., Fiorucci, F., Cardinali, M., and Marchesini, I.: Topography-driven satellite imagery analysis for landslide mapping, Geomat. Nat. Haz. Risk, 9, 544–567, https://doi.org/10.1080/19475705.2018.1458050, 2018.
Blong, R. J.: Natural hazards in the Papua New Guinea highlands, Mt. Res. Dev., 6, 233–246, https://doi.org/10.2307/3673393, 1986.
Bornaetxea, T., Rossi, M., Marchesini, I., and Alvioli, M.: Effective surveyed area and its role in statistical landslide susceptibility assessments, Nat. Hazards Earth Syst. Sci., 18, 2455–2469,
https://doi.org/10.5194/nhess-18-2455-2018, 2018.
Calò, F., Calcaterra, D., Iodice, A., Parise, M., and Ramondini, M.:
Assessing the activity of a large landslide in southern Italy by ground-monitoring and SAR interferometric techniques, Int. J. Remote Sens.,
33, 3512–3530, https://doi.org/10.1080/01431161.2011.630331, 2012.
Calvello, M., Peduto, D., and Arena, L.: Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides, Landslides, 14, 473–489, https://doi.org/10.1007/s10346-016-0722-6, 2017.
Casagli, N., Frodella, W., Morelli, S., Tofani, V., Ciampalini, A., Intrieri, E., Raspini, F., Rossi, G., Tanteri, L., and Lu, P.: Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning, Geoenviron. Disast., 4, 1–23, https://doi.org/10.1186/s40677-017-0073-1, 2017.
Cigna, F., Bianchini, S., and Casagli, N.: How to assess landslide activity
and intensity with persistent scatterer interferometry (PSI): the PSI-based
matrix approach, Landslides, 10, 267–283, https://doi.org/10.1007/s10346-012-0335-7,
2013.
Colesanti, C. and Wasowski, J.: Investigating landslides with space-borne
Synthetic Aperture Radar (SAR) interferometry, Eng. Geol., 88, 173–199, https://doi.org/10.1016/j.enggeo.2006.09.013, 2006.
Comaniciu, D. and Meer, P.: Mean shift: A robust approach toward feature
space analysis, IEEE T. Pattern Anal. Mach. Intel., 24, 603–619,
https://doi.org/10.1109/34.1000236, 2002.
Copernicus: Open Access Hub, https://scihub.copernicus.eu, last access: 24 January 2020.
Dai, K., Li, Z., Tomás, R., Liu, G., Yu, B., Wang, X., Cheng, H., Chen,
J., and Stockamp, J.: Monitoring activity at the Daguangbao mega-landslide
(China) using Sentinel-1 TOPS time series interferometry, Remote Sens. Environ., 186, 501–513, https://doi.org/10.1016/j.rse.2016.09.009, 2016.
El-Darymli, K., McGuire, P., Gill, E., Power, D., and Moloney, C.: Understanding the significance of radiometric calibration for synthetic
aperture radar imagery, in: Can. Conf. Electr. Comput. Eng., May 2014,
Toronto, ON, Canada, https://doi.org/10.1109/CCECE.2014.6901104, 2014.
ESA: Thermal Denoising of Products Generated by the S-1 IPF, available at:
https://sentinels.copernicus.eu/documents/247904/2142675/Thermal-Denoising-of-Products-Generated-by-Sentinel-1-IPF
(last access: 4 January 2020), 2017.
ESA: Level-1 SLC Products, available at:
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1
(last access: 4 January 2020), 2018.
Fiorucci, F., Cardinali, M., Carlà, R., Rossi, M., Mondini, A. C., Santurri, L., Ardizzone, F., and Guzzetti, F.: Seasonal landslides mapping
and estimation of landslide mobilization rates using aerial and satellite
images, Geomorphology, 129, 59–70, https://doi.org/10.1016/j.geomorph.2011.01.013, 2011.
Frost, V. S., Stiles, J. A., Shanmugan, K. S., and Holtzman, J. C.: A Model
for Radar Images and Its Application to Adaptive Digital Filtering of
Multiplicative Noise, IEEE T. Pattern Anal. Mach. Intel., PAMI-4, 157–166, 1982.
Gabriel, A. K., Goldstein, R. M., and Zebker, H. A.: Mapping small elevation
changes over large areas: differential radar interferometry, J. Geophys. Res., 94, 9183–9191, https://doi.org/10.1029/JB094iB07p09183, 1989.
Guzzetti, F., Cardinali, M., Reichenbach, P., and Carrara, A.: Comparing
landslide maps: a case study in the upper Tiber River Basin, Central Italy,
Environ. Manage., 25, 247–363, https://doi.org/10.1007/s002679910020, 2000.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K. T.: Landslide inventory maps: New tools for an old problem,
Earth-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
IFRC – International Federation of Red Cross and Red Crescent Societies:
Emergency Plan of Action Operation Final Report – Papua New Guinea:
earthquake, available at:
https://reliefweb.int/sites/reliefweb.int/files/resources/MDRPG008dfr_0.pdf (last access: 24 January 2020), 2018.
Intrieri, E., Raspini, F., Fumagalli, A., Lu, P., Del Conte, S., Farina, P.,
Allievi, J., Ferretti, A., and Casagli, N.: The Maoxian landslide as seen
from space: detecting precursors of failure with Sentinel-1 data, Landslides, 15, 123–133, https://doi.org/10.1007/s10346-017-0915-7, 2018.
Kersten, Valgur, M., Marcel, W., Jonas, Delucchi, L., unnic, Kinyanjui, L.
K., Schlump, martinber, Baier, G., Keller, G., and Castro, C.: sentinelsat/sentinelsat: v0.12.2 (Version v0.12.2), Zenodo,
https://doi.org/10.5281/zenodo.1293758, 2018.
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, https://doi.org/10.1080/22797254.2017.1418185, 2018.
Li, N., Wang, R., Deng, Y., Liu, Y., Li, B., Wang, C., and Balz, T.:
Unsupervised polarimetric synthetic aperture radar classification of
large-scale landslides caused by Wenchuan earthquake in hue-saturation-intensity color space, J. Appl. Remote Sens., 8,
083595, https://doi.org/10.1117/1.JRS.8.083595, 2014.
Ma, H. R., Cheng, X., Chen, L., Zhang, H., and Xiong, H.: Automatic
identification of shallow landslides based on Worldview2 remote sensing images, J. Appl. Remote Sens., 10, 016008, https://doi.org/10.1117/1.JRS.10.016008, 2016.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide
inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, https://doi.org/10.1002/esp.1064, 2004.
Marchesini, I., Ardizzone, F., Alvioli, M., Rossi, M., and Guzzetti, F.:
Non-susceptible landslide areas in Italy and in the Mediterranean region,
Nat. Hazards Earth Syst. Sci., 14, 2215–2231, https://doi.org/10.5194/nhess-14-2215-2014, 2014.
Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., and Kumar, K. V.:
Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis, IEEE T. Geosci. Remote, 49, 4928–4943, https://doi.org/10.1109/TGRS.2011.2151866, 2011.
McCue, K., Gibson, G., and Love, D.: The Mainshock of 25 February 2018 and
Aftershocks in the Central Highlands of Papua New Guinea, in: Australian
Earthquake Engineering Society 2018 Conference, 16–18 November 2018, Perth, WA, 2018.
Mergili, M., Marchesini, I., Alvioli, M., Metz, M., Schneider-Muntau, B., Rossi, M., and Guzzetti, F.: A strategy for GIS-based 3-D slope stability
modelling over large areas, Geosci. Model Dev., 7, 2969–2982,
https://doi.org/10.5194/gmd-7-2969-2014, 2014a.
Mergili, M., Marchesini, I., Rossi, M., Guzzetti, F., and Fellin, W.: Spatially distributed three-dimensional slope stability modelling in a
raster GIS, Geomorphology, 206, 178–195, https://doi.org/10.1016/j.geomorph.2013.10.008, 2014b.
Momsen, E. and Metz, M.: i.segment, available at:
https://grass.osgeo.org/grass74/manuals/i.segment.html (last access:
7 January 2020), 2017.
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.
Mondini, A. C., Chang, K. T., and Yin, H. Y.: Combining multiple change
detection indices for mapping landslides triggered by typhoons,
Geomorphology, 134, 440–451, https://doi.org/10.1016/j.geomorph.2011.07.021, 2011.
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, 1–25, https://doi.org/10.3390/rs11070760, 2019.
Petley, D.: An emerging crisis? Valley blocking landslides in the Papua New
Guinea highlands, available at:
https://blogs.agu.org/landslideblog/2018/02/28/papua-new-guinea-crisis/
(last access: 24 January 2020), 2018a.
Petley, D.: Papua New Guinea earthquake – continued landslide impacts,
available at:
https://blogs.agu.org/landslideblog/2018/03/14/papua-new-guinea-earthquake-3/
(last access: 24 January 2020), 2018b.
Planet Team: Planet Application Program Interface: In Space for Life on
Earth, San Francisco, CA, available at: https://api.planet.com (last access: 24 January 2020), 2017.
Plank, S.: Rapid damage assessment by means of multi-temporal SAR – A
comprehensive review and outlook to Sentinel-1, Remote Sens., 6, 4870–4906,
https://doi.org/10.3390/rs6064870, 2014.
Plank, S., Twele, A., and Martinis, S.: Landslide mapping in vegetated areas
using change detection based on optical and polarimetric SAR data, Remote
Sens., 8, 307, https://doi.org/10.3390/rs8040307, 2016.
Raspini, F., Bardi, F., Bianchini, S., Ciampalini, A., Del Ventisette, C.,
Farina, P., Ferrigno, F., Solari, L., and Casagli, N.: The contribution of
satellite SAR-derived displacement measurements in landslide risk management
practices, Nat. Hazards, 86, 327–351, https://doi.org/10.1007/s11069-016-2691-4, 2017.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A
review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Robbins, J. C. and Petterson, M. G.: Landslide inventory development in a data sparse region: spatial and temporal characteristics of landslides in Papua New Guinea, Nat. Hazards Earth Syst. Sci. Discuss., 3, 4871–4917, https://doi.org/10.5194/nhessd-3-4871-2015, 2015.
Robbins, J. C., Petterson, M. G., Mylne, K., and Espi, J. O.: Tumbi Landslide, Papua New Guinea: Rainfall induced?, Landslides, 10, 673–684,
https://doi.org/10.1007/s10346-013-0422-4, 2013.
Rossi, M. and Reichenbach, P.: LAND-SE: a software for statistically based
landslide susceptibility zonation, version 1.0, Geosci. Model Dev., 9,
3533–3543, https://doi.org/10.5194/gmd-9-3533-2016, 2016.
Rossi, M., Ardizzone, F., Cardinali, M., Fiorucci, F., Marchesini, I., Mondini, A. C., Santangelo, M., Ghosh, S., Riguer, D. E. L., Lahousse, T.,
Chang, K. T., and Guzzetti, F.: A tool for the estimation of the distribution of landslide area in R, Geophys. Res. Abstr., 14, EGU2012-9438-1, 2012.
Salvi, S., Stramondo, S., Funning, G. J., Ferretti, A., Sarti, F, and Mouratidis, A.: The Sentinel-1 mission for the improvement of the scientific
understanding and the operational monitoring of the seismic cycle, Remote
Sens. Environ., 120, 164–174, https://doi.org/10.1016/j.rse.2011.09.029, 2012.
Santangelo, M., Marchesini, I., Bucci, F., Cardinali, M., Fiorucci, F., and
Guzzetti, F.: An approach to reduce mapping errors in the production of
landslide inventory maps, Nat. Hazards Earth Syst. Sci., 15, 2111–2126,
https://doi.org/10.5194/nhess-15-2111-2015, 2015.
Schellenberger, T., Ventura, B., Zebisch, M., and Notarnicola,
C.: Wet Snow Cover Mapping Algorithm Based on Multitemporal
COSMO-SkyMed X-Band SAR Images, IEEE J. Sel.
Top. Appl., 5, 1045–1053,
https://doi.org/10.1109/JSTARS.2012.2190720, 2012.
Schlögel, R., Malet, J.-P., Reichenbach, P., Remaître, A., and Doubre, C.: Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study, Nat. Hazards Earth Syst. Sci., 15, 2369–2389, https://doi.org/10.5194/nhess-15-2369-2015, 2015.
Shimada, M., Watanabe, M., Kawano, N., Ohki, M., Motooka, T., and Wada, Y.:
Detecting Mountainous Landslides by SAR Polarimetry: A Comparative Study
Using Pi-SAR-L2 and X-band SARs, Trans. Japan Soc. Aeronaut. Sp. Sci.
Aerosp. Technol. Japan, 12, Pn_9–Pn_15, https://doi.org/10.2322/tastj.12.pn_9, 2014.
Skriver, H.: Crop classification by multitemporal C- and L-band single- and
dual-polarization and fully polarimetric SAR, IEEE T. Geosci. Remote, 50, 2138–2149, https://doi.org/10.1109/TGRS.2011.2172994, 2012.
Stark, C. P. and Hovius, N.: The characterization of landslide size
distributions, Geophys. Res. Lett., 28, 1091–1094, https://doi.org/10.1029/2000GL008527, 2001.
Steger, S., Brenning, A., Bell, R., Petschko, H., and Glade, T.: Exploring
discrepancies between quantitative validation results and the geomorphic
plausibility of statistical landslide susceptibility maps, Geomorphology,
262, 8–23, https://doi.org/10.1016/j.geomorph.2016.03.015, 2016.
Tessari, G., Floris, M., and Pasquali, P.: Phase and amplitude analyses of SAR data for landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-Alps, Environ. Earth Sci., 76, 1–11,
https://doi.org/10.1007/s12665-017-6403-5, 2017.
Twele, A., Cao, W., Plank, S., and Martinis, S.: Sentinel-1-based flood mapping: a fully automated processing chain, Int. J. Remote Sens., 37, 2990–3004, https://doi.org/10.1080/01431161.2016.1192304, 2016.
USGS – United States Geological Survey: Event page of the M 7.5 Papua New
Guinea earthquake occurred on February 25, 2018, available at:
https://earthquake.usgs.gov/earthquakes/eventpage/us2000d7q6/executive#executive
(last access: 7 January 2020), 2018.
van Westen, C. J., van Asch, T. W. J., and Soeters, R.: Landslide hazard and
risk zonation – why is it still so difficult?, B. Eng. Geol. Environ., 65,
167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Wilson, A. M. and Jetz, W.: Remotely Sensed High-Resolution Global Cloud
Dynamics for Predicting Ecosystem and Biodiversity Distributions, PLoS Biol., 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415, 2016.
Wu, K. L. and Yang, M. S.: Mean shift-based clustering, Pattern Recognit.,
40, 3035–3052, https://doi.org/10.1016/j.patcog.2007.02.006, 2007.
Yamaguchi, Y.: Disaster Monitoring by Fully Polarimetric SAR Data Acquired
With ALOS-PALSAR, Proc. IEEE, 100, 2851–2860, https://doi.org/10.1109/JPROC.2012.2195469, 2012.
Zhao, C., Lu, Z., Zhang, Q., and De La Fuente, J.: Large-area landslide
detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA, Remote Sens. Environ., 124, 348–359,
https://doi.org/10.1016/j.rse.2012.05.025, 2012.
Zhao, C., Zhang, Q., Yin, Y., Lu, Z., Yang, C., Zhu, W., and Li, B.: Pre-, co-, and post- rockslide analysis with ALOS/PALSAR imagery: a case study of the Jiweishan rockslide, China, Nat. Hazards Earth Syst. Sci., 13, 2851–2861, https://doi.org/10.5194/nhess-13-2851-2013, 2013.
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
In this article, we present an automatic processing chain aimed to support the detection of...
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