Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Aimaiti, Yusupujiang; Liu, Wen; Yamazaki, Fumio; Maruyama, Yoshihiro

doi:10.3390/rs11202351

Cited by 45 publications

(46 citation statements)

References 41 publications

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“…Although this method was developed for detecting damage to buildings, Yun et al (2015) tested it on the 2015 Gorkha earthquake and noted that landslides in the Langtang Valley corresponded spatially to areas of coherence decrease in the ARIA surface. Coherence decrease between pre-event and co-event interferograms has since been used as an input in the landslide detection methods of Aimaiti et al (2019) and Jung and Yun (2019) applied to the 2018 Hokkaido earthquake.…”

Section: Co-event Coherence Loss (Cecl)mentioning

confidence: 99%

“…In this study, we did not attempt to map SAR classification surface values directly to landslide areal density values as this has not been attempted in previous studies (e.g. Aimaiti et al, 2019;Jung and Yun, 2019;Yun et al, 2015) and may not be possible due to differences in viewing geometry, land cover and, particularly with the ALOS-2 data used here, differences in temporal baseline between events. Thus, a binary ground truth was preferable, which we generated by assigning aggregate pixels as "landslide" if they were composed of over 25 % landslide by area according to the rasterised landslide inventories we used for verification.…”

Section: Data Processingmentioning

confidence: 99%

“…Recently, there have been several attempts to develop similar SAR-based change detection methods for rapid landslide mapping based on SAR amplitude Suga, 2018, 2019;Mondini et al, 2019), coherence Olen and Bookhagen, 2018;Yun et al, 2015) or some combination of these (Aimaiti et al, 2019;Jung and Yun, 2019) or based on polarimetric SAR methods (e.g. Yamaguchi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, with the exception of Mondini et al (2019), who used a global selection of landslides, these studies are generally tested on a single landslide event and use a single radar sensor. For example, Aimaiti et al (2019), Konishi and Suga (2019), Jung and Yun (2019), and Yamaguchi et al (2019) tested their methods using ALOS-2 imagery of the 2018 Hokkaido earthquake. If such methods are to be applied in future events, wider testing is needed.…”

Section: Introductionmentioning

confidence: 99%

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

Burrows

Walters

Milledge

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Emergency responders require information on the distribution of triggered landslides within 2 weeks of an earthquake or storm. Useable satellite radar imagery is acquired within days of any such event worldwide. Recently, several landslide detection methods that use these data have been developed, but testing of these methods has been limited in each case to a single event and satellite sensor. Here we systematically test five methods using ALOS-2 and Sentinel-1 data across four triggering earthquakes. The best-performing method was dependent on the satellite sensor. For three of our four case study events, an initial ALOS-2 image was acquired within 2 weeks, and with these data, co-event coherence loss (CECL) is the best-performing method. Using a single post-event Sentinel-1 image, the best-performing method was the boxcar–sibling (Bx–S) method. We also present three new methods which incorporate a second post-event image. While the waiting time for this second post-event image is disadvantageous for emergency response, these methods perform more consistently and on average 10 % better across event and sensor type than the boxcar–sibling and CECL methods. Thus, our results demonstrate that useful landslide density information can be generated on the timescale of emergency response and allow us to make recommendations on the best method based on the availability and latency of post-event radar data.

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Section: Co-event Coherence Loss (Cecl)mentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Burrows

Walters

Milledge

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…These accuracies indicate that the proposed methodology can exhibit an excellent performance when mapping coseismic landslides. Aimaiti et al [61] also detected the coseismic landslide caused by this event using a larger remote sensing dataset and a decision-tree classifier approach; in addition to the same descending-track SAR dataset used in this study, they employed an additional set of ascending-track PALSAR-2 data acquired before and after the earthquake. Their best classification results were 54.8% and 61.8% for the user and producer accuracies, respectively.…”

Section: Coseismic Landslide Detectionmentioning

confidence: 99%

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

et al. 2020

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The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.Keywords: landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery

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Field surveys of September 2018 landslide-generated waves in the Apporo dam reservoir, Japan: combined hazard from the concurrent occurrences of a typhoon and an earthquake

et al. 2022

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We report and analyze a case study of landslide-generated waves that occurred in the Apporo dam reservoir (Hokkaido, Japan) culminating from the rare incident of hazard combination from the September 2018 Typhoon Jebi and Hokkaido earthquake (Mw 6.6 on 5 September 2018). The typhoon and earthquake were concurrent and produced thousands of landslides in the area by the combined effects of soil saturation and ground acceleration. Here, we report the results of our field surveys of the landslides that occurred around the Apporo dam and generated damaging waves in the reservoir. We identified six landslides at a close distance to the dam body; the largest one has a length of 330 m, a maximum width of 140 m and a volume of 71,400 m3. We measured wave runup at a single point with height of 5.3 m for the landslide-generated wave in the reservoir and recorded the damage made to the revetments at the reservoir banks. By considering the locations of the landslides and their potential propagation paths, we speculate that possibly three of the six surveyed landslides contributed to the measured wave runup. The surveyed runup was reproduced by inputting landslide parameters into two independent empirical equations; however, other independent empirical relationships failed to reproduce the observed runup. Our field data from the Apporo dam can be used to improve the quality of predictions made by empirical equations and to encourage further research on this topic. In addition, our field data serves as a call for strengthening dams’ safety to landslide-generated waves in reservoirs.

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Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Cited by 45 publications

References 41 publications

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

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

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

Field surveys of September 2018 landslide-generated waves in the Apporo dam reservoir, Japan: combined hazard from the concurrent occurrences of a typhoon and an earthquake

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