TY  - JOUR
A1  - Dirscherl, Mariel
A1  - Dietz, Andreas J.
A1  - Kneisel, Christof
A1  - Kuenzer, Claudia
T1  - Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach
JF  - Remote Sensing
N2  - Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.
KW  - Antarctica
KW  - Antarctic ice sheet
KW  - supraglacial lakes
KW  - surface melt
KW  - hydrology
KW  - ice sheet dynamics
KW  - sentinel-2
KW  - remote sensing
KW  - random forest
KW  - machine learning
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203735
SN  - 2072-4292
VL  - 12
IS  - 7
ER  - 
TY  - JOUR
A1  - Kunz, Julius
A1  - Kneisel, Christof
T1  - Glacier − Permafrost Interaction at a Thrust Moraine Complex in the Glacier Forefield Muragl, Swiss Alps
JF  - Geosciences
N2  - The internal structures of a moraine complex mostly provide information about the manner in which they develop and thus they can transmit details about several processes long after they have taken place. While the occurrence of glacier–permafrost interactions during the formation of large thrust moraine complexes at polar and subpolar glaciers as well as at marginal positions of former ice sheets has been well understood, their role in the formation of moraines on comparatively small alpine glaciers is still very poorly investigated. Therefore, the question arises as to whether evidence of former glacier–permafrost interactions can still be found in glacier forefields of small alpine glaciers and to what extent these differ from the processes in finer materials at larger polar or subpolar glaciers. To investigate this, electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) surveys were carried out in the area of a presumed alpine thrust moraine complex in order to investigate internal moraine structures. The ERT data confirmed the presence of a massive ice core within the central and proximal parts of the moraine complex. Using GPR, linear internal structures were detected, which were interpreted as internal shear planes due to their extent and orientation. These shear planes lead to the assumption that the moraine complex is of glaciotectonic origin. Based on the detected internal structures and the high electrical resistivity values, it must also be assumed that the massive ice core is of sedimentary or polygenetic origin. The combined approach of the two methods enabled the authors of this study to detect different internal structures and to deduce a conceptual model of the thrust moraine formation.
KW  - mountain permafrost
KW  - glacier–permafrost interaction
KW  - thrust moraine
KW  - near surface geophysics
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205830
SN  - 2076-3263
VL  - 10
IS  - 6
ER  -