GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs

Landslides in glacial environments are high-magnitude, long-runout events, believed to be increasing in frequency as a paraglacial response to ice retreat and thinning and, arguably, due to warming temperatures and degrading permafrost above current glaciers. However, our ability to test these assumptions by quantifying the temporal sequencing of debris inputs over large spatial and temporal extents is limited in areas with glacier ice. Discrete landslide debris inputs, particularly in accumulation areas, are rapidly “lost”, being reworked by motion and icefalls and/or covered by snowfall. Although large landslides can be detected and located using their seismic signature, smaller (M≤5.0) landslides frequently go undetected because their seismic signature is less than the noise floor, particularly supraglacially deposited landslides, which feature a “quiet” runout over snow. Here, we present GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new free-to-use tool leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE outputs maps of new supraglacial debris additions within user-defined areas and time ranges, providing a user with a reference map, from which large debris inputs such as supraglacial landslides (>0.05 km2) can be rapidly identified. We validate the effectiveness of GERALDINE outputs using published supraglacial rock avalanche inventories, and then demonstrate its potential by identifying two previously unknown, large (>2 km2) landslide-derived supraglacial debris inputs onto glaciers in the Hayes Range, Alaska, one of which was not detected seismically. GERALDINE is a first step towards a complete global magnitude–frequency of landslide inputs onto glaciers over the 38 years of Landsat Thematic Mapper imagery.

GERALDINE (Google earth Engine supRaglAciaL Debris INput dEtector) – A new Tool for Identifying and Monitoring Supraglacial Landslide Inputs

Rock avalanches, a high-magnitude, long runout form of bedrock landslide, are thought to increase in frequency as a paraglacial response to ice-retreat/thinning, and arguably, due to warming temperatures/degrading permafrost above current glaciers. However, our ability to test these assumptions by quantifying the temporal sequencing of debris inputs over large spatial and temporal extents is limited in areas with glacier ice. Discrete landslide debris inputs, particularly in accumulation areas are rapidly ‘lost’, being reworked by motion and icefalls, and/or covered by snowfall. Although large landslides can be detected and located using their seismic signature, small to medium-sized landslides, particularly supraglacially deposited landslides which feature a quiet runout over snow, frequently go undetected because their seismic signature is less than the noise floor. Here, we present GERALDINE (Google earth Engine supRaglAciaL Debris INput dEtector): a new open-source tool leveraging Landsat 4–8 satellite imagery and Google Earth Engine. GERALDINE outputs maps of new supraglacial debris additions within user-defined areas and time ranges, providing a user with a reference map, from which large debris inputs such as supraglacial rock avalanches can be rapidly identified. We validate the effectiveness of GERALDINE outputs using published rock-avalanche inventories, then demonstrate its potential by identifying two previously unknown, large (> 2 km2) supraglacial debris inputs onto glaciers in the Hayes Range, Alaska, one of which was not detected seismically. GERALDINE is a first step towards a revised global magnitude-frequency of rock avalanche inputs onto glaciers over the 37 years of Landsat Thematic Mapper imagery.

Rapid Evaluation of Arid Lands (REAL)

Management of desert grasslands requires rapid, low technology, coarse assessment methods that provide a triage-like prioritization for the manager. Such approaches necessitate the ability to quickly and effectively identify coarse-scale plant communities that provide guidance for this prioritization. Complex, computer intensive digital image classification of Landsat TM data, while marginally successful, requires time, equipment, and expertise not always available in such environments. This study identifies landform boundaries in the Armendaris Ranch, New Mexico by visual inspection of Landsat-7 Enhanced Thematic Mapper imagery and topographic maps using traditional photoreconnaissance techniques. Employing predetermined hierarchical landform classifications, it was possible to map plant communities using ecological relationships that exist between the general physiographic and vegetation settings in the area and representative geomorphic landform-mapping units. The authors’ field work verified the plant community map using a random walk approach and visual inspection. This synthetic expert opinion-based approach proved successful and is repeatable in other arid rangeland settings.