Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification

Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product

MABEL photon-counting laser altimetry data in Alaska for ICESat-2 simulations and development

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in late 2017 and will carry the Advanced Topographic Laser Altimeter System (ATLAS) which is a photon-counting laser altimeter and represents a new approach to satellite determination of surface elevation. Given the new technology of ATLAS, an airborne instrument, the Multiple Altimeter Beam Experimental Lidar (MABEL), was developed to provide data needed for satellite-algorithm development and ICESat-2 error analysis. MABEL was deployed out of Fairbanks, Alaska, in July 2014 to provide a test dataset for algorithm development in summer conditions with water-saturated snow and ice surfaces. Here we compare MABEL lidar data to in situ observations in Southeast Alaska to assess instrument performance in summer conditions and in the presence of glacier surface melt ponds and a wet snowpack. Results indicate the following: (1) based on MABEL and in situ data comparisons, the ATLAS 90m beam-spacing strategy will provide a valid assessment of across-track slope that is consistent with shallow slopes (< 1) of an ice-sheet interior over 50 to 150m length scales; (2) the dense along-track sampling strategy of photon counting systems can provide crevasse detail; and (3) MABEL 532 nm wavelength light may sample both the surface and subsurface of shallow (approximately 2m deep) supraglacial melt ponds. The data associated with crevasses and melt ponds indicate the potential ICESat-2 will have for the study of mountain and other small glaciers.Acknowledgements. Funding for this project was through the NASA ICESat-2 Project Science Office. Funding for J. M. Amundson was provided by NSF-PLR 1303895. We acknowledge the considerable efforts of the Project, Science, and Instrument teams of NASA’s ICESat-2 and MABEL missions. We thank the following people: Eugenia De Marco (ASRC Aerospace Corp., NASA/GSFC) and Dan Reed (Sigma Space Corp., NASA/GSFC) for MABEL instrument support; Scott Luthcke (NASA/GSFC), David Hancock (NASA/WFF), and Jeff Lee (NASA/WFF) for MABEL data calibration; Scott McGee and Ya’ Shonti Bridgers (JIRP) for GPS field data collection and data processing support; and NASA/AFRC (specifically ER-2 pilots Tim Williams and Denis Steele) for Alaska airborne support. WorldView imagery was provided by the Polar Geospatial Center at the University of Minnesota, which is supported by NSF-PLR 1043681. GPS receivers for the survey of the terminus of the Lower Taku Glacier were provided by UNAVCO. GPS receivers for the JIRP survey were provided by Werner Stempfhuber of the Beuth University of Applied Sciences. And, finally, we thank two anonymous reviewers for their highly constructive suggestions.Ye