Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf.more »
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 more »
- Publication Date:
- NSF-PAR ID:
- 10179552
- Journal Name:
- Remote Sensing
- Volume:
- 12
- Issue:
- 8
- Page Range or eLocation-ID:
- 1327
- ISSN:
- 2072-4292
- Sponsoring Org:
- National Science Foundation
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Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imageryAbstract Surface meltwater is becoming increasingly widespread on Antarctic ice shelves. It is stored within surface ponds and streams, or within firn pore spaces, which may saturate to form slush. Slush can reduce firn air content, increasing an ice-shelf's vulnerability to break-up. To date, no study has mapped the changing extent of slush across ice shelves. Here, we use Google Earth Engine and Landsat 8 images from six ice shelves to generate training classes using a k -means clustering algorithm, which are used to train a random forest classifier to identify both slush and ponded water. Validation using expert elicitation gives accuracies of 84% and 82% for the ponded water and slush classes, respectively. Errors result from subjectivity in identifying the ponded water/slush boundary, and from inclusion of cloud and shadows. We apply our classifier to the Roi Baudouin Ice Shelf for the entire 2013–20 Landsat 8 record. On average, 64% of all surface meltwater is classified as slush and 36% as ponded water. Total meltwater areal extent is greatest between late January and mid-February. This highlights the importance of mapping slush when studying surface meltwater on ice shelves. Future research will apply the classifier across all Antarctic ice shelves.
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Abstract Surface melting on Amery Ice Shelf (AIS), East Antarctica, produces an extensive supraglacial drainage system consisting of hundreds of lakes connected by surface channels. This drainage system forms most summers on the southern portion of AIS, transporting meltwater large distances northward, toward the ice front and terminating in lakes. Here we use satellite imagery, Landsat (1, 4 and 8), MODIS multispectral and Sentinel-1 synthetic aperture radar to examine the seasonal and interannual evolution of the drainage system over nearly five decades (1972–2019). We estimate seasonal meltwater input to one lake by integrating output from the regional climate model [Regional Atmospheric Climate Model (RACMO 2.3p2)] over its catchment defined using the Reference Elevation Model of Antarctica. We find only weak positive relationships between modeled seasonal meltwater input and lake area and between meltwater input and lake volume. Consecutive years of extensive melting lead to year-on-year expansion of the drainage system, potentially through a link between melt production, refreezing in firn and the maximum extent of the lakes at the downstream termini of drainage. These mechanisms are important when evaluating the potential of drainage systems to grow in response to increased melting, delivering meltwater to areas of ice shelves vulnerable tomore »
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Abstract Diurnal depth cycles of decimeter scale are observed in a supraglacial lake on the McMurdo Ice Shelf, Antarctica. We evaluate two possible causes: (1) tidal tilt of the ice shelf in response to the underlying ocean tide, and (2) meltwater input variation. We find the latter to be the most likely explanation of our observations. However, we do not rule out tidal tilt as a source of centimeter scale variations, and point to the possibility that other, larger supraglacial lake systems, particularly those on ice shelves that experience higher amplitude tidal tilts, such as in the Weddell Sea, may have depth cycles driven by ocean tide. The broader significance of diurnal cycles in meltwater depth is that, under circumstances where the ice shelf is thin, tidal-tilt amplitudes are high, and meltwater runoff rates are large, there may be associated flexure stresses that can contribute to ice-shelf fracture and destabilization. For the McMurdo Ice Shelf (~20–50 m thickness, ~ 1 m tidal amplitude and ~10 cm water-depth variations), these stresses amount to several 10's of kPa.
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Abstract. Surface meltwater on ice shelves can exist as slush, it can pond in lakes orcrevasses, or it can flow in surface streams and rivers. The collapse of theLarsen B Ice Shelf in 2002 has been attributed to the sudden drainage of∼3000 surface lakes and has highlighted the potential forsurface water to cause ice-shelf instability. Surface meltwater systems havebeen identified across numerous Antarctic ice shelves, although the extentto which these systems impact ice-shelf instability is poorly constrained.To better understand the role of surface meltwater systems on ice shelves,it is important to track their seasonal development, monitoring thefluctuations in surface water volume and the transfer of water acrossice-shelf surfaces. Here, we use Landsat 8 and Sentinel-2 imagery to tracksurface meltwater across the Nivlisen Ice Shelf in the 2016–2017 meltseason. We develop the Fully Automated Supraglacial-Water Tracking algorithmfor Ice Shelves (FASTISh) and use it to identify and track the developmentof 1598 water bodies, which we classify as either circular or linear. Thetotal volume of surface meltwater peaks on 26 January 2017 at 5.5×107 m3. At this time, 63 % of the total volume is held withintwo linear surface meltwater systems, which are up to 27 km long, areorientated along the ice shelf's north–south axis, and follow themore »
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.3390/rs12081327
