Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: a case study of the 2018 Anchorage, Alaska earthquake
Citation
Martinez, S. N., Schaefer, L. N., Allstadt, K. E., Thompson, E. M. (2021). Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: a case study of the 2018 Anchorage, Alaska earthquake. Frontiers in Earth Science, 9: 673137. Link to paper
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 (for example, clouds and water vapor) or snow and vegetation cover. With these challenges in mind, this study uses 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 (about 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. Using this manually compiled inventory, the authors investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. 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 field observations. This work highlights the importance of rapid field observations and provides guidance for future mappers on which techniques, or combination of techniques, are most effective at remotely mapping landslides in a subarctic and urban environment.