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.
Research article
| 
10 Sep 2020
Research article |
| 10 Sep 2020
| 10 Sep 2020
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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Cited
15 citations as recorded by crossref.
- A new strategy to map landslides with a generalized convolutional neural network N. Prakash et al. 10.1038/s41598-021-89015-8
- Exploring event landslide mapping using Sentinel-1 SAR backscatter products M. Santangelo et al. 10.1016/j.geomorph.2021.108021
- Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti P. Amatya et al. 10.1007/s11069-023-06096-6
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- Early landslide mapping with slope units division and multi-scale object-based image analysis — A case study in the Xianshui River basin of Sichuan, China H. Gao et al. 10.1007/s11629-022-7333-6
- Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons L. Pelascini et al. 10.5194/nhess-22-3125-2022
- Monitoring Lake Levels From Space: Preliminary Analysis With SWOT A. Nair et al. 10.3389/frwa.2021.717852
- SAR data and field surveys combination to update rainfall-induced shallow landslide inventory P. Miele et al. 10.1016/j.rsase.2022.100755
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. 10.5194/nhess-22-3679-2022
- Landslide mapping using object-based image analysis and open source tools P. Amatya et al. 10.1016/j.enggeo.2021.106000
- Deep learning based landslide detection using open-source resources: Opportunities and challenges S. Das et al. 10.1007/s12145-023-01141-1
- Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future A. Mondini et al. 10.1016/j.earscirev.2021.103574
- Sediment connectivity as a key to understand geomorphic effects of the Storm Alex in two mountain catchments of the Mediterranean Alps (Italy) F. Ardizzone et al. 10.1016/j.geomorph.2024.109176
- Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. 10.5194/nhess-22-2637-2022
- Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling N. Necula et al. 10.3390/rs13193862
15 citations as recorded by crossref.
- A new strategy to map landslides with a generalized convolutional neural network N. Prakash et al. 10.1038/s41598-021-89015-8
- Exploring event landslide mapping using Sentinel-1 SAR backscatter products M. Santangelo et al. 10.1016/j.geomorph.2021.108021
- Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti P. Amatya et al. 10.1007/s11069-023-06096-6
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
- Early landslide mapping with slope units division and multi-scale object-based image analysis — A case study in the Xianshui River basin of Sichuan, China H. Gao et al. 10.1007/s11629-022-7333-6
- Finite-hillslope analysis of landslides triggered by excess pore water pressure: the roles of atmospheric pressure and rainfall infiltration during typhoons L. Pelascini et al. 10.5194/nhess-22-3125-2022
- Monitoring Lake Levels From Space: Preliminary Analysis With SWOT A. Nair et al. 10.3389/frwa.2021.717852
- SAR data and field surveys combination to update rainfall-induced shallow landslide inventory P. Miele et al. 10.1016/j.rsase.2022.100755
- Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. 10.5194/nhess-22-3679-2022
- Landslide mapping using object-based image analysis and open source tools P. Amatya et al. 10.1016/j.enggeo.2021.106000
- Deep learning based landslide detection using open-source resources: Opportunities and challenges S. Das et al. 10.1007/s12145-023-01141-1
- Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future A. Mondini et al. 10.1016/j.earscirev.2021.103574
- Sediment connectivity as a key to understand geomorphic effects of the Storm Alex in two mountain catchments of the Mediterranean Alps (Italy) F. Ardizzone et al. 10.1016/j.geomorph.2024.109176
- Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. 10.5194/nhess-22-2637-2022
- Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling N. Necula et al. 10.3390/rs13193862
Latest update: 11 Dec 2024
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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