Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia

Kim, Jin-Woo; Coe, Jeffrey A.; Lü, Zhong; Avdievitch, Nikita; Hults, Chad P.

doi:10.1016/j.rse.2022.113231

Cited by 12 publications

(4 citation statements)

References 61 publications

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“…So far however, most studies investigating glacial debuttressing have taken place at sites where the glaciers were land-terminating. Observations of landslides adjacent to lake-or marineterminating glaciers are much less common (an exception is given by the studies of Kim et al (2022) and Dai et al (2020)), as is documentation of accelerations at such sites. In the following, we discuss possible differences between the land-and water-terminating situations, evaluate the varying slope evolutions following destabilization, and address the limitations of our methods.…”

Section: Discussionmentioning

confidence: 99%

A regional analysis of paraglacial landslide activation in southern coastal Alaska

Walden,

Jacquemart,

Higman

et al. 2024

Preprint

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Abstract. Glaciers worldwide are retreating rapidly due to anthropogenic climate change. One consequence of glacier mass loss is the destabilization of valley walls as the support provided by the glacier changes and eventually vanishes, a process known as ''debuttressing.'' In this work, we examine the evolution of eight large, active instabilities in southern coastal Alaska, a region experiencing some of the fastest glacier retreat worldwide. At half of the sites, the glacier is still in contact with the landslide, while in the other four cases, the terminus retreated past the landslide in recent decades. One site has experienced catastrophic failure; the others have not. We use automatic and manual feature tracking on optical imagery to derive slope movement from the 1980s to present and compare this with glacier terminus retreat and thinning, precipitation, and seismic energy. We find that the majority of sites underwent a pulse of accelerated landslide motion (up to 17 times higher compared to the five years preceding the acceleration) during the studied time period and that the subsequent deformation was independent of the initial activation. In two cases, the acceleration occurred after a particularly rainy month and/or a marked increase (around two times higher than the 1960–2000 average) in glacier thinning. At two further sites, no distinct activation could be detected, though both landslides are known to be moving at velocities below the detection threshold of the methods employed here. In four cases, landslide activation coincided with the rapid retreat (up to 12 times the long term average) of a lake- or marine- terminating glacier past the instability. Our results suggest that landslides adjacent to lake- or marine- terminating glaciers may be especially susceptible to sudden activation, which we hypothesize is due to the faster retreat rates of water-terminating glaciers as well as mechanical and hydrological changes resulting from the replacement of ice with water at the landslide toe. This work shows that glacier retreat can be associated with increasing landslide hazards in various glaciological settings in Alaska, which has implications for the assessment of hazards in a warming world.

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Section: Discussionmentioning

confidence: 99%

A regional analysis of paraglacial landslide activation in southern coastal Alaska

Walden,

Jacquemart,

Higman

et al. 2024

Preprint

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“…SHAP can be considered as a substitute for the conventional method of feature importance [44]. It furnishes a visual representation to illustrate the impact of characteristics for the dependent variable and comprehensively elucidates the model's components [45]. SHAP model is a more comprehensive approach to model explainability, which allows for both global and local explanation.…”

Section: Model Explainability Analysismentioning

confidence: 99%

A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall

Gao,

He,

Chen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide susceptibility mapping (LSM) is of great significance in geohazard early warning and prevention. The existing LSM methods mostly used traditional static landslide conditioning factors (LCFs), which only considered the spatial features of single-pixel neighborhoods and could not extract the time-series dynamic features of developing landslides, resulting in low accuracy and insufficient reliability of LSM. To solve this problem, this study proposes to introduce time-series rainfall factors based on the traditional static factors and construct an integrated time-series dynamic neural network (TSDNN) model for LSM. A convolutional neural network adding time-distributed convolution (TDCNN) and a bidirectional long and short-term memory neural network (Bi-LSTM) are utilized to extract timeseries rainfall features, and a multiscale convolutional neural network (MSCNN) is utilized to extract static features of the static LCFs. In this study, multicollinearity analysis and GeoDetector are utilized to analyze the LCFs. Multiple evaluation metrics are utilized to analyze the proposed model performance. The results indicate that the overall accuracy has improved by introducing time-series rainfall factors, the susceptibility area of actual predicted is more refined. The study indicates that significant advantages of the proposed TSDNN model are over models (CNN, MSCNN, random forest (RF)) when processing combined static and rainfall data. This is notably evident that the accuracy is enhanced by 12.9%,10.7%, and 11.4% compared to CNN, MSCNN and RF models in the receiver operating characteristic curve (ROC) analysis, respectively. Moreover, two typical areas containing three recent landslide events validate the reliability of the proposed TSDNN model. The proposed network model framework for LSM considering time-series rainfall factors can provide new ideas and key technical support for landslide disaster prevention.

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“…Alaska (USA) has a history of tsunamis caused by both subaerial and submarine landslides along the Gulf shoreline (e.g., Haeussler et al., 2014; Higman et al., 2018; Miller, 1961), and abundance of narrow and deep fjords that can exacerbate their impact (e.g., Fritz et al., 2009; Yavari‐Ramshe & Ataie‐Ashtiani, 2016). Warming temperatures due to climate change have enhanced glacier retreat and permafrost degradation, increasing the abundance of potentially unstable slopes in southcentral and southeast Alaska (e.g., Bessette‐Kirton & Coe, 2020; Coe et al., 2018; Dai et al., 2020; Higman et al., 2018; Kim et al., 2022), as well as in many other parts of the world (e.g., Allen et al., 2009; Fan et al., 2022; Haeberli et al., 1997; Holm et al., 2004; Klimeš et al., 2021; Penna et al., 2023; Svennevig et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The Chugach flysch is typically a weak rock mass that ranges from very blocky to disintegrate and with variably altered joint conditions. In other parts of Alaska (e.g., Bessette‐Kirton & Coe, 2020; Kim et al., 2022) and around the world (e.g., Duman et al., 2005; Margielewski, 2006), the presence of flysch is known to be highly susceptible to rock‐slope failures. The landslide has an aerial extent of approximately 3.4 km 2 , with a width of 2.7 km between lateral margins, and a height of 1.5 km between the fjord and headscarp.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Evolution of a Large Paraglacial Landslide in the Barry Arm Fjord of Alaska

Schaefer,

Coe,

Wikstrom Jones

et al. 2023

JGR Earth Surface

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Our warming climate is adversely affecting cryospheric landscapes via glacial retreat, permafrost degradation, and associated slope destabilization. In Prince William Sound, Alaska, the rapid retreat of Barry Glacier has destabilized the slopes flanking the glacier, resulting in numerous landslides. The largest of these landslides (∼500 Mm3 in volume) is more than 2 km wide and has the potential to generate a tsunami that could affect nearby recreationists, marine traffic, infrastructure, natural and cultural resources, and the community of Whittier, located 60 km from the landslide. Here, we combine landslide structural and kinematic element mapping with data acquired from bi‐yearly airborne lidar, multi‐week satellite‐based synthetic aperture radar (SAR), sub‐hourly ground‐based SAR, and seismic monitoring from 2020 to 2022 to characterize this landslide and examine its evolution. While some methods serve as a snapshot in time that is a culmination of events, others emphasize the ever‐evolving nature of the landslide and associated hazards. Four major kinematic elements define the overall structure of the landslide, which vary in deformation type and rate, from creep (5 mm per day over several months) to episodic movement (2 m in 30 days) and landslide‐wide to localized events. In some areas of the landslide, short‐term deformation deviates from structures formed by cumulative movement, implying structural and kinematic evolution associated with glacier retreat. These insights are important for assessing landslide hazards and hazard evolution for large, slow‐moving bedrock landslides in actively deglaciating environments.

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Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia

Cited by 12 publications

References 61 publications

A regional analysis of paraglacial landslide activation in southern coastal Alaska

A regional analysis of paraglacial landslide activation in southern coastal Alaska

A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall

Kinematic Evolution of a Large Paraglacial Landslide in the Barry Arm Fjord of Alaska

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