Publications

Publications

A Drifting and Blowing Snow Scheme in the Weather Research and Forecasting Model

Transport of snow by the wind can have high impact on local glacier mass changes as it leads to non-uniform amounts of snow on the ground. In order to simulate and better understand this process we introduce a new modeling framework that is included into the widely used atmospheric ‘Weather Research and Forecasting (WRF)’ model. Test simulations and sensitivity experiments show the physical consistency of the model. Complex interactions between different processes like snow erosion, drifting snow sublimation and the wind field show the necessity of coupling the snow and atmospheric models.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023MS004007

Publications

Supraglacial Lake Evolution over Northeast Greenland Using Deep Learning Methods

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.

https://www.mdpi.com/2072-4292/15/17/4360

Publications

Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets

The Antarctic Peninsula Ice Sheet (APIS) has become a significant contributor to rising sea levels, and accurately estimating ice discharge from its outlet glaciers is essential for assessing the mass balance of the region. This study calculates ice discharge from APIS outlet glaciers north of 70°S using five commonly used ice-thickness reconstructions, employing a consistent surface velocity field and flux gates. Results indicate a total volumetric ice discharge ranging from 45 to 141 km3 per year for 2015–2017, with a mean of 87 ± 44 km3 per year. The substantial differences in results highlight the large uncertainty in current ice-discharge estimates, emphasizing the challenge of accurately modeling the ice-thickness distribution in this complex and data-scarce region.

https://www.cambridge.org/core/journals/annals-of-glaciology/article/estimating-ice-discharge-of-the-antarctic-peninsula-using-different-icethickness-datasets/67B2F8FC77CD9342BD07DAACA41497AF

Publications

TanDEM-X reveals ice surface elevation change patterns throughout the Antarctic Peninsula

Existing mass budget estimates for the northern Antarctic Peninsula (>70° S) are affected by considerable limitations. We carried out the first region-wide analysis of geodetic mass balances throughout this region (coverage of 96.4 %) for the period 2013–2017 based on repeat pass bi-static TanDEM-X acquisitions. A total mass budget of −24.1±2.8 Gt/a is revealed. Imbalanced high ice discharge, particularly at former ice shelf tributaries, is the main driver of overall ice loss.

https://tc.copernicus.org/articles/17/4629/2023/tc-17-4629-2023.html

 

Allgemein Publications

Caffe – A Benchmark Dataset for Glacier Calving Front Extraction from Synthetic Aperture Radar Imagery

The study emphasizes the importance of understanding marine-terminating glacier dynamics in glacier projections. Deep learning methods can automate the extraction of calving front positions from satellite imagery, reducing manual effort. The “CaFFe” dataset, which includes annotated calving fronts in Synthetic Aperture Radar (SAR) imagery, offers a standardized benchmark for evaluating deep learning techniques in this area. Researchers can use CaFFe to assess the performance of upcoming deep learning models and identify promising research directions. A leaderboard of models can be found at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.

https://ieeexplore.ieee.org/abstract/document/10283406

Publications

Conditional Random Fields for Improving Deep Learning-Based Glacier Calving Front Delineations

Advancements in Deep Learning have enabled the automated identification of glacier calving fronts in satellite imagery. This study improves the accuracy of this process by incorporating a Conditional Random Field (CRF) into the post-processing of the neural network’s predictions. Experiments using the CaFFe dataset showed a 27-meter improvement in mean distance error. The code is available at https://github.com/EntChanelt/GlacierCRF.

https://ieeexplore.ieee.org/document/10282915

Publications

Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard

Research on surge-type glaciers, although they constitute a small percentage of all glaciers, significantly contributes to understanding glacier flow mechanisms. Recent studies have utilized remote sensing techniques to unravel these processes, highlighting the potential of combining high-performance computing and earth observation. While modeling surge events has gained popularity, there is a lack of comprehensive spatial and temporal data on surge timing. To address this, an algorithm has been developed that not only detects surge-type glaciers but also determines the onset of surges. The algorithm relies on time series analysis of glacier surface velocity using Sentinel-1 data, involving seasonal and trend decomposition and outlier detection via the General Studentized Extreme Deviate Test. Cluster analysis is then applied to identify outlier clusters associated with glacier surges. The method’s effectiveness was demonstrated in the Svalbard archipelago between 2015 and 2021, where 18 glacier surges and their timing were successfully identified.

https://www.mdpi.com/2072-4292/15/6/1545

Allgemein Publications

AMD-HookNet for Glacier Front Segmentation

This article discusses the importance of tracking changes in glacier calving front positions as a means of assessing glacier status. Remote sensing imagery is a valuable tool for this purpose, but manual monitoring for all global calving glaciers is impractical due to time constraints. The article introduces a novel framework called AMD-HookNet, designed for the segmentation of glacier calving fronts in synthetic aperture radar (SAR) images. AMD-HookNet enhances feature representation by leveraging an attention mechanism and interactions between low-resolution and high-resolution inputs. The experiments conducted on a benchmark dataset demonstrate that AMD-HookNet outperforms the current state of the art by achieving a mean distance error of 438 meters to the ground truth, confirming its effectiveness.

https://ieeexplore.ieee.org/document/10044700?source=authoralert

Publications

A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications

The concept of the sequential sampling is well known and still widely used in industrial automation radar applications due to its hardware simplicity, low cost and low power consumption. However, there is only a limited amount of publications that describe this concept and its variants in detail. This tutorial introduces the typical sequential sampling impulse radar concept step by step and presents several key characteristics, such as correlation properties and SNR considerations. In addition to the system theory, selected applications are presented to illustrate the attractiveness and elegance, but also the limits of this radar concept. The shown applications range from those in industrial automation to radar concepts in the areas of automotive radar, security scanners, biomedical radar systems and ground penetrating radar. The latter application is among others including the analysis of glaciers, e.g. to determine the ice thickness and stratification in order to provide a better understanding of the glacier and its behavior in a changing climate.

The corresponding paper can be found here:

A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications

Publications

Calving Fronts and Where to Find Them

A new benchmark database for the automatic mapping of glacier fronts was recently publish in Copernicus Earth System Science Data.

“Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery”

 


This work represents the first comprehensive database for training and testing machine learning algorithms for glacier calving front detection.


Autor: Nora Gourmelon