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

Abstract

Abstract. 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.

Mass-Movements in Cold and Polar Climates

“…Dufresne et al (2019) reported that that passive sliding on snow atop of a glacier caused the Lamplugh rock avalanche to slide "silently", or aseismically. This mechanism has been inferred to be the origin of many "silent" supraglacial landslides not detected by seismometers but instead in optical remote sensing (Smith et al, 2020).…”

“…Understanding the magnitude-frequency of this type of landslide is important for understanding their role in landscape evolution at geological timescales, yet this is remarkably hard to quantify as deposits are usually unrecognisable in the landscape due to glacial reworking (e.g., Schleier et al, 2015). Debris that falls in the accumulation zone can be hidden by snowfall then rapidly sequestered into glaciers on the order of months (e.g., Dunning et al, 2015;Smith et al, 2020). Hence, deposits that fall on the ablation zone are transported down-glacier and contribute to the glacial deposits which are reworked beyond recognition at geological timescales.…”

Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska

“…Here we formally hypothesise that if accumulation zones RAs are under detected, then use of the Google Earth Engine supraglacial debris input detector (GERALDINE) (Smith et al, 2020) will correct this issue. GERALDINE is a new, free-to-use, cloud-based tool that utilises the computing capabilities of Google Earth Engine (Gorelick et al, 2017) to eliminate the disadvantages of manual RA detection and streamline the detection process.…”

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

“…Such combinations were also recently used in west Greenland to map a large number of avalanches after an unprecedented snow event (Abermann et al, 2019). To our knowledge, only one recent study automated the detection of avalanches using remote sensing products and an open-access scripting approach (Smith et al, 2020). This study downloaded avalanches annually for a given region of interest using available Landast-8 images and computed NDSI for each image.…”

“…Thus, we used optical data to detect avalanches on a long-term basis and built an open-access script in Google Engine interface: Snow Avalanche Frequency Estimation (SAFE). Landsat-5, 7 and 8 products were used as their resolution (30 m, i.e., minimum detectible size of 900 m²) is sufficient to detect larger avalanches (Abermann et al, 2019;Eckerstorfer et al, , 2014Hafner et al, 2021;Singh et al, 2019Singh et al, , 2020Smith et al, 2020;Yariyan et al, 2020). Our objective is to automatically map annual avalanche occurrence over the past 32 years using Landast-5, 7 and 8 image archives in the Amu Panj basin of Afghanistan.…”