Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future

Mondini, Alessandro Cesare; Guzzetti, Fausto; Chang, Kang‐Tsung; Monserrat, Oriol; Martha, Tapas R.; Manconi, Andrea

doi:10.1016/j.earscirev.2021.103574

Cited by 152 publications

(104 citation statements)

References 196 publications

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“…Despite the many advantages of using SAR sensors for mapping surface changes, SAR ACD methods were not effective at mapping landslides in this study due to several geomorphologic, radiometric, and image processing factors (see Mondini et al, 2021 for a recent review). During image pre-processing, both horizontal-horizontal (HH) and horizontal-vertical (HV) polarization scenes were considered.…”

Section: Challenges To Overcomementioning

confidence: 95%

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

et al. 2021

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Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

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Section: Challenges To Overcomementioning

confidence: 95%

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

et al. 2021

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“…

Landslides are different from other natural hazards. Unlike volcanoes, they do not threaten human civilization (Papale and Marzocchi, 2019). Unlike tsunamis, they do not affect simultaneously several thousands of kilometres of coastline -although a submarine landslide in Norway caused a tsunami to hit Scotland (Dawson et al, 1988).

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confidence: 99%

“…Therefore, one way to assess where they are expected is to map past and new landslides. The technology is mature for regional and even global landslide detection and mapping services based on the automatic or semi-automatic processing of aerial and satellite imagery: optical, SAR and lidar data (Guzzetti et al, 2012;Mondini et al, 2021). An alternative -and complementary -way is through susceptibility modelling, an approach for which there is no shortage of data-driven methods but rather of suitable environmental and landslide data .…”

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confidence: 99%

“…The increasing availability of satellite imagery, some of which repeated over time and free of charge (Aschbacher, 2017), opens up unprecedented opportunities to prepare event and multi-temporal inventory maps covering very large areas, which are essential to build space-time prediction models (Lombardo et al, 2020), to investigate the legacy of old landslides on new ones (Samia et al, 2017;Temme et al, 2020), to obtain accurate thematic data for sus- ceptibility modelling , and to validate geographical landslide early warning systems (Piciullo et al, 2018;Guzzetti et al, 2020). However, the literature reveals a systematic lack of standards for constructing, validating, and ranking the quality of landslide maps and prediction models (Guzzetti et al, 2012;Mondini et al, 2021;. This reduces the credibility of the maps and models -a gap that urgently needs to be bridged .…”

mentioning

confidence: 99%

“…For both, multi-temporal inventories and landslide catalogues are essential to build innovative predictive models. To construct the records, systematic efforts are needed for landslide detection and mapping (Mondini et al, 2021). For susceptibility (where), the challenge is to prepare reliable regional, continental, or global assessments (Stanley and Kirschbaum, 2017;Broeckx et al, 2018;Wilde et al, 2018;Mirus et al, 2020).…”

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confidence: 99%

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Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future

Cited by 152 publications

References 196 publications

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake

Invited perspectives: Landslide populations – can they be predicted?

Comparative Study on Satellite-Based SAR Image Representation for Detection of Landslides and Sediment Disasters Caused by Rainfall

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