Mapping periglacial and glacial landforms within ice-free areas in a permafrost environment in southern Livingston Island (northern Antarctic Peninsula region) using space-borne SAR data
Bibliographic record
Abstract
The ice-free areas of the South Shetland Islands are dominated by periglacial and glacial processes and landforms, closely associated with deposits of fluvial and coastal origin that are characteristic within the northern Antarctic Peninsula region. These areas are influenced by cold maritime climatic conditions and have experienced the greatest warming within the southern hemisphere over the past sixty years. As a result, freeze-thaw cycles are favoured implying dynamic processes in the active layer of the permafrost, which lead to the formation of different types of surface features. Studies have shown that these ice-free areas have often contained fragile ecosystems with a high biodiversity where many different plant and animal species are concentrated and an active hydrologic cycle during the Austral summer period. It is therefore important to monitor present-day active periglacial and glacial processes linked to recent global changes. Furthermore, determining the spatial distribution of diverse periglacial landforms will be key to detect the presence of permafrost. The objective of this work is to determine and monitor the spatial distribution of landforms of different origin, with special focus on those connected with periglacial processes and the presence of permafrost within ice-free areas. The studied area is located along the southern coast of Livingston Island between Hannah Point and Bernard Point using satellite-borne single and fully polarimetric Synthetic Aperture Radar (SAR) data and techniques. A fully polarimetric fine RADARSAT-2 image as Single Look Complex data was acquired through the Canadian Space Agency within the framework of the Science and Operational Applications Research Program (Project SOAR-5169) on the 29th of March 2014. Single polarimetric SENTINEL-1 images of 2017 and 2018 were obtained through the European Space Agency. Pre-processing of the SAR data includes radiometric correction, using a speckle noise filter, developed specifically within the team for both SAR single channel images and fully polarimetric images. A digital terrain model is used to carry out the terrain and geometric corrections. Polarimetric parameters are extracted from the RADARSAT-2 data and related to the different surface covers according to the backscattering characteristics received by the sensor within selected field sites. Furthermore, backscattering characteristics are also determined for the single polarimetric SENTINEL-1 data. Several field campaigns carried out during 2013, 2017 and 2018 provided reference for determining the different surface covers. A supervised classification is carried out using Random Forest (RF), a decision tree-based classifier using the different SAR data as well as DEM information. The detailed field observations are further used as reference for the classifier where the importance of each polarimetric parameter is determined in characterizing a particular surface cover. A final step is the validation of the mapping results obtained for the different ice-free areas using both field data and existing maps that have been compiled by the authors team. Initial results show that periglacial and glacial landforms such as patterned ground, stone fields, block glaciers, ice fronts, moraines as well as raised beach systems and abundant rock outcrops are well identified with the polarimetric RADARSAT-2 and SENTINEL-1 data. The elevation data is also of importance, as this nicely differentiates the areas above certain altitudes in which permafrost exist. Using RF, a variable ranking of the polarimetric parameters for individual terrain classes can be obtained. This is useful when trying to distinguish different classes that bear resemblance to each other such as patterned ground and stone fields. The final mapping is therefore based using the most relevant variables obtained from the different data. The distribution of surface covers obtained with this approach can then be used in time series analysis for future monitoring of changes in ice-free areas of Livingston Island. This would be a novel approach to monitor the active periglacial and glacial processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".