Sub-Annual To Annual Dynamics Of Alaskan Ice-Marginal Lakes From Automated Image Classification Using Google Earth Engine

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Anthony Matthew Hengst (Creator)
Appalachian State University (ASU )
Web Site:
William Armstrong

Abstract: Ice-marginal lakes play an important role in glacier dynamics and downstream hydrology. Proglacial lakes may alter glacial mass loss by enabling submarine melt and by providing a body of water into which glaciers may calve, and provide a basin which traps glacial sediment. Ice-dammed lakes play a critical role in the generation of outburst floods and must be monitored for human safety in downstream environments. Observation of ice-marginal lakes from satellite imagery provides valuable insight into these remote systems because in-situ data are difficult to obtain over a large study area. However, even large-scale remote sensing of these lakes is difficult due to their varied spectral appearance and the complex interface between sediment-laden, iceberg filled lakes and their adjacent crevassed and water-covered glaciers. Previous remote sensing studies feature coarse temporal sampling of lake behavior over a multi-decadal timescale. We seek to investigate how ice-marginal lakes evolve over sub-annual to annual timescales. Ice-marginal lakes are intimately connected to glacial systems, which can vary over seasonal cycles and longer-term cycles in the case of some surging glaciers. We develop a robust remote sensing method to provide observations of ice-marginal lakes across Alaska, a region whose ice-marginal lakes have received comparatively little attention.We develop an automated routine implemented in Google Earth Engine to investigate short- term glacial lake area changes across southern Alaska over the Landsat 8 era (2013-present). We create monthly estimates of ice-marginal lake area by applying a supervised Mahalanobis minimum-distance land cover classifier to Landsat 8 imagery. We optimize image processing parameters by running a suite of classifications and selecting the parameters that minimize error against a set of manually-delineated lakes and achieve an F-score from 0.33 in the most challenging test regions to 0.77 at best. In an exploration using Monte Carlo simulations, we interrogate our data to characterize the uncertainty in lake area associated with sparse temporal sampling. These data provide short- term context for multi-decadal studies and yield insight into the uncertainty inherent in remote- sensing studies of ice-marginal lake.

Additional Information

Honors Project
Hengst, A. (2020). Sub-Annual To Annual Dynamics Of Alaskan Ice-Marginal Lakes From Automated Image Classification Using Google Earth Engine. Unpublished Honors Thesis. Appalachian State University, Boone, NC.
Language: English
Date: 2020
remote sensing, monte carlo, cryosphere, ice-marginal, proglacial

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