Sub-Annual to Annual Dynamics of Alaskan Ice-Marginal Lakes from Automated Image Classification Using Google Earth Engine

Abstract:

Ice-marginal lakes (proglacial and ice-dammed lakes) play an important role in glacier dynamics and downstream hydrology. Proglacial lakes may alter glacial mass loss by enabling submarine melt and by enabling iceberg calving. These lakes also trap sediment and damp downstream variations in water temperature and discharge. Further, ice-marginal lakes modify the hydrologic conditions at the glacier bed and play a critical role in the generation of cyclic outburst floods. 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 to characterize how short-term variability may affect longer-term analysis and to obtain temporal resolution adequate to resolve the processes underlying lake change.

Here, we develop an automated routine implemented in Google Earth Engine to investigate ice-marginal lake area changes across southern Alaska over the Landsat 8 era (2013-present). We create monthly estimates lake area using a supervised Mahalanobis minimum-distance land cover classifier. 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 F1 score from 0.33 in the most challenging test regions to 0.77 at best.

We then interrogate timeseries of lake area time series to: 1) assess how recent short term rates of lake area have changed relative to longer-term estimates; 2) characterize the uncertainty in lake area associated with sparse temporal sampling, and; 3) explore the physical mechanisms by which ice-marginal lake area is changing. These data yield insight into the dynamics underlying ice-marginal lake evolution and provide short-term context for multi-decadal studies.