Search Results (2)

Supraglacial lakes in Greenland are highly dynamic hydrological features in which glacial meltwater cumulates, allowing for the loss and transport of freshwater from a glacial surface to the ocean or a nearby waterbody. Standard supraglacial lake monitoring techniques, specifically image segmentation, rely heavily on a series of region-dependent thresholds, limiting the adaptability of the algorithm to different illumination and surface variations, while being susceptible to the inclusion of false positives such as shadows. In this study, a supraglacial lake segmentation algorithm is developed for Sentinel-2 images based on a deep learning architecture (U-Net) to evaluate the suitability of artificial intelligence techniques in this domain. Additionally, a deep learning-based cloud segmentation tool developed specifically for polar regions is implemented in the processing chain to remove cloudy imagery from the analysis. Using this technique, a time series of supraglacial lake development is created for the 2016 to 2022 melt seasons over Nioghalvfjerdsbræ (79°N Glacier) and Zachariæ Isstrøm in Northeast Greenland, an area that covers 26,302 km² and represents roughly 10% of the Northeast Greenland Ice Stream. The total lake area was found to have a strong interannual variability, with the largest peak lake area of 380 km² in 2019 and the smallest peak lake area of 67 km² in 2018. These results were then compared against an algorithm based on a thresholding technique to evaluate the agreement of the methodologies. The deep learning-based time series shows a similar trend to that produced by a previously published thresholding technique, while being smoother and more encompassing of meltwater in higher-melt periods. Additionally, while not completely eliminating them, the deep learning model significantly reduces the inclusion of shadows as false positives. Overall, the use of deep learning on multispectral images for the purpose of supraglacial lake segmentation proves to be advantageous. Full article

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km² area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m². Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km² and a maximum individual lake size of 30 km². With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks. Full article