A systematic exploration of satellite radar coherence  methods for rapid landslide detection

Burrows, Katy; Walters, R. J.; Milledge, David; Densmore, Alexander L.

doi:10.5194/nhess-20-3197-2020

Cited by 32 publications

(33 citation statements)

References 52 publications

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“…CC BY 4.0 License. aftermath of major triggering events [Mondini 2017, Adriano et al 2020, Burrows et al, 2020, Jung & Yun 2020. The most widely used technique is to map landslides directly from optical imagery, either from Unmanned Aerial Vehicle (UAV) imagery [Casagli et al 2017, aerial photography [Harp et al 2004] or satellite observations [Casagli et al 2017, Martha et al 2012, Behling et al 2014.…”

Section: Landslide Inventoriesmentioning

confidence: 99%

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Emberson

Kirschbaum²,

Amatya³

et al. 2021

Preprint

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Abstract. Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide headscarp location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.

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Section: Landslide Inventoriesmentioning

confidence: 99%

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Emberson

Kirschbaum²,

Amatya³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The mean slope angle is 10° ± 8° (± 1 standard deviation) with a maximum slope of 84°. This EOI was also previously investigated using SAR change detection methods by Adriano et al, (2020), Burrows et al (2020) and Jung and Yun (2020). These studies showed that both coherence change and backscatter intensity change detection methods work to characterize the landslides, however the backscatter based methods outperform SAR coherence change detection in the vegetated mountains (Jung and Yun, 2020).…”

Section: Earthquake-triggered Landslides Hokkaido Japanmentioning

confidence: 76%

“…In this way, the landslide density heatmap is a proxy for potential landslide occurrence and not a heatmap of observed landslides classified using other methods. Several recent studies have applied a landslide density map approach to identify critically damaged areas, rather than focus on the location of individual landslides (e.g., Bessette-Kirton et al 2019;Burrows et al, 2020;Rosi et al, 2018). Our landslide density heatmaps are similar to these other density maps, however instead of counting individual landslides, we calculate the density of individually detected pixels over a fixed area.…”

Section: Landslide Density Heatmapsmentioning

confidence: 99%

“…However, most studies have focused on interferometric SAR (InSAR), which measures the radar phase change between two acquisitions in order to quantify ground surface deformation (e.g., Handwerger et al, 2019;Huang et al, 2017b;Intrieri et al, 2017;Schlögel et al, 2015). SAR radar backscatter intensity-and coherence-based change detection can also be used to detect landslides, floods, and other types of natural hazards (Burrows et al, 2020;DeVries et al, 2020;Jung and Yun, 2020;Mondini et al, 2019;Rignot and Van Zyl, 1993;Tay et al, 2020;Yun et al, 2015). Changes in backscatter and coherence occur when there are changes in ground surface properties (e.g., reflectance, roughness, dielectric properties) before and after landslide events.…”

mentioning

confidence: 99%

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Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

Handwerger

Jones

Amatya

et al. 2021

Preprint

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Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change detection approach that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to detect areas with high landslide density using the cloud-based Google Earth Engine (GEE). Importantly, our approach does not require downloading a large volume of data to a local system or specialized processing software. We provide strategies, including a landslide density heatmap approach, that can aid in rapid response and landslide detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries, and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

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“…Sentinel-1 ICFs might therefore play a larger role in empirical models based in arid environments than they did here. Sentinel-1 InSAR coherence has been used to map erosion in arid environments (Cabré et al, 2020), indicating that it could also be applied to landslides. However, it is also more difficult to derive landslide inventories from optical satellite images in less vegetated areas.…”

Section: Possible Application In Other Settingsmentioning

confidence: 99%

Integrating empirical models and satellite radar can improve landslide detection for emergency response

Burrows

Milledge

Walters

et al. 2021

Nat. Hazards Earth Syst. Sci.

Self Cite

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Abstract. Information on the spatial distribution of triggered landslides following an earthquake is invaluable to emergency responders. Manual mapping using optical satellite imagery, which is currently the most common method of generating this landslide information, is extremely time consuming and can be disrupted by cloud cover. Empirical models of landslide probability and landslide detection with satellite radar data are two alternative methods of generating information on triggered landslides that overcome these limitations. Here we assess the potential of a combined approach, in which we generate an empirical model of the landslides using data available immediately following the earthquake using the random forest technique and then progressively add landslide indicators derived from Sentinel-1 and ALOS-2 satellite radar data to this model in the order they were acquired following the earthquake. We use three large case study earthquakes and test two model types: first, a model that is trained on a small part of the study area and used to predict the remainder of the landslides and, second, a preliminary global model that is trained on the landslide data from two earthquakes and used to predict the third. We assess model performance using receiver operating characteristic analysis and r2, and we find that the addition of the radar data can considerably improve model performance and robustness within 2 weeks of the earthquake. In particular, we observed a large improvement in model performance when the first ALOS-2 image was added and recommend that these data or similar data from other L-band radar satellites be routinely incorporated in future empirical models.

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A systematic exploration of satellite radar coherence methods for rapid landslide detection

Cited by 32 publications

References 52 publications

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

Integrating empirical models and satellite radar can improve landslide detection for emergency response

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