Articles | Volume 20, issue 11
https://doi.org/10.5194/nhess-20-3197-2020
© Author(s) 2020. 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-20-3197-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
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27 Nov 2020
Research article | Highlight paper |
| 27 Nov 2020
| 27 Nov 2020
A systematic exploration of satellite radar coherence methods for rapid landslide detection
COMET, Department of Earth Sciences, Durham University, Durham, UK
Richard J. Walters
COMET, Department of Earth Sciences, Durham University, Durham, UK
David Milledge
School of Engineering, Newcastle University, Newcastle, UK
Alexander L. Densmore
Department of Geography, Durham University, Durham, UK
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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.
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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.
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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.
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This study focuses on understanding soil moisture, a key factor for evaluating hillslope stability and landsliding. In Nepal, where landslides are common, we used a computer model to better understand how rapidly soil dries out after the monsoon season. We calibrated the model using field data and found that, by adjusting soil properties, we could predict moisture levels more accurately. This helps understand where landslides might occur, even where direct measurements are not possible.
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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
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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.
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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.
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In mountain ranges, earthquake-derived landslides mobilize large amounts of organic carbon (OC) by eroding soil from hillslopes. We propose a model to explore the role of different parameters in the post-seismic redistribution of soil OC controlled by fluvial export and heterotrophic respiration. Applied to the Southern Alps, our results suggest that efficient OC fluvial export during the first decade after an earthquake promotes carbon sequestration.
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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.
Satellite radar could provide information on landslide locations within days of an earthquake or...
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