Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification

• Main Authors: Halberstadt, Anna Ruth W., Gleason, Colin J., Moussavi, Mahsa S., Pope, Allen, Trusel, Luke D.
• Format: Text
• Language: unknown
• Published: ScholarWorks@UMass Amherst 2020
• Subjects: supraglacial lakes; surface meltwater; supervised classification; Landsat-8; Antarctic ice shelves; ice shelf stability; sea level rise; Antarctic; The Antarctic; Antarc*; Antarctica; Ice Sheet; Ice Shelf; Ice Shelves
• Online Access: https://scholarworks.umass.edu/cee_faculty_pubs/840; https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1839&context=cee_faculty_pubs

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.