Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake
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.
Citation Information
| Publication Year | 2021 |
|---|---|
| Title | Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake |
| DOI | 10.3389/feart.2021.673137 |
| Authors | Sabrina N. Martinez, Lauren N. Schaefer, Kate E. Allstadt, Eric M. Thompson |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Frontiers Earth Science Journal |
| Index ID | 70222443 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Geologic Hazards Science Center |
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Initial Observations of Landslides triggered by the 2018 Anchorage, Alaska earthquake
This data release provides the locations of 43 landslides that occurred during the 2018 Anchorage, Alaska earthquake mapped from high-resolution lidar (1-m). Lidar data can be accessed via the Alaska Division of Geological and Geophysical Surveys elevation portal (https://elevation.alaska.gov). Each landslide is represented as a point corresponding to the approximate location of the mid-point of t - Connect
Kate E Allstadt, Ph.D.
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