Geology topics

Sabrina N. Martinez

Publications and source records attributed to Sabrina N. Martinez.

4 recordsLinked to original sources

USGS2024

Parsimonious high-resolution landslide susceptibility modeling at continental scales

Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database ( N = 613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories ( N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

AGU Advances↗

USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗

USGS2021

Landslides triggered by the August 14, 2021, magnitude 7.2 Nippes, Haiti, earthquake

The August 14, 2021, magnitude 7.2 Nippes, Haiti, earthquake triggered thousands of landslides on the Tiburon Peninsula. The landslides directly caused fatalities and damage and impeded response efforts by blocking roads and causing other infrastructure damage. Adverse effects of the landslides likely will continue for months to years. This report presents an assessment of potential postearthquake landslide-related geologic hazards for the Tiburon Peninsula and a preliminary map of the landslides triggered by the earthquake. This hazard assessment is based on an emergency analysis of the currently available, postearthquake satellite imagery. In this report, we highlight specific areas of concern that may benefit from more detailed assessment and longer-term monitoring. Our mapping efforts revealed that at least 4,893 landslides were triggered across the Tiburon Peninsula by the earthquake and subsequent rainfall from Tropical Cyclone Grace. We also observed hundreds of landslide deposits potentially restricting flow in rivers and streams. In addition, we observed landslides that likely affected roads by rendering them impassable or susceptible to subsequent damage from existing landslides. Because of the preliminary nature of this report and the limits of remote analyses, additional investigation and monitoring would be beneficial to accurately determine the threat posed by these hazards to people and infrastructure.

Nippes↗

USGS2021

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 (M w ) 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.

Alaska↗