Abstract
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.
Highlights
The November 30, 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake, triggered substantial ground failure throughout Anchorage and surrounding areas (Grant et al, 2020b; Jibson et al, 2020)
The success of the elevation differencing method at delineating soil slumps, soil slides and rapid soil flows can be attributed to the fact that they have distinct erosional or depositional signatures which increases the extent of the landslide affected area
The success of the NDVI differencing method at delineating soil slumps and rapid soil flows can be attributed to their severity and size, with the major limiting factor being that landslides need to occur in vegetated areas in order for NDVI methods to be useful
Summary
The November 30, 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake, triggered substantial ground failure throughout Anchorage and surrounding areas (Grant et al, 2020b; Jibson et al, 2020). Compiling landslide inventories after triggering events (e.g., earthquake, rainfall) is highly beneficial for landslide hazard assessment and risk reduction efforts. Recent studies have emphasized the importance of landslide inventory quality across a variety of triggering scenarios, landscapes, and climates for landslide studies and model development (Tanyas et al, 2017; Mirus et al, 2020; Tanyas and Lombardo, 2020). Limitations in data resolution or field observations result in infrequent documentation of the smallest landslides triggered by a seismic or rainfall event (Guzzetti et al, 2012). The end goals and purpose for creating landslide inventories differ between authors and across organizations, resulting in varying levels of detail and data inclusion (e.g., Mirus et al, 2020)
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