Fernerkundung der Dynamik supraglazialer Seen in der Antarktis - Analyse von supraglazialen Seen in Multi-Sensor Fernerkundungsdaten mittels Methoden der Künstlichen Intelligenz

With accelerating global climate change, the Antarctic Ice Sheet is exposed to increasing ice dynamic change. During 1992 and 2017, Antarctica contributed ~7.6 mm to global sea-level-rise mainly due to ocean thermal forcing along West Antarctica and atmospheric warming along the Antarctic Peninsula (API). Together, these processes caused the progressive retreat of glaciers and ice shelves and weakened their efficient buttressing force causing widespread ice flow accelerations. Holding ~91% of the global ice mass and 57.3 m of sea-level-equivalent, the Antarctic Ice Sheet is by far the largest potential contributor to future sea-level-rise. Despite the improved understanding of Antarctic ice dynamics, the future of Antarctica remains difficult to predict with its contribution to global sea-level-rise representing the largest uncertainty in current projections. Given that recent studies point towards atmospheric warming and melt intensification to become a dominant driver for future Antarctic ice mass loss, the monitoring of supraglacial lakes and their impacts on ice dynamics is of utmost importance. In this regard, recent progress in Earth Observation provides an abundance of high-resolution optical and Synthetic Aperture Radar (SAR) satellite data at unprecedented spatial and temporal coverage and greatly supports the monitoring of the Antarctic continent where ground-based mapping efforts are difficult to perform. As an automated mapping technique for supraglacial lake extent delineation in optical and SAR satellite imagery as well as a pan-Antarctic inventory of Antarctic supraglacial lakes at high spatial and temporal resolution is entirely missing, this thesis aims to advance the understanding of Antarctic surface hydrology through exploitation of spaceborne remote sensing. In particular, a detailed literature review on spaceborne remote sensing of Antarctic supraglacial lakes identified several research gaps including the lack of (1) an automated mapping technique for optical or SAR satellite data that is transferable in space and time, (2) high-resolution supraglacial lake extent mappings at intra-annual and inter-annual temporal resolution and (3) large-scale mapping efforts across the entire Antarctic continent. In addition, past method developments were found to be restricted to purely visual, manual or semi-automated mapping techniques hindering their application to multi-temporal satellite imagery at large-scale. In this context, the development of automated mapping techniques was mainly limited by sensor-specific characteristics including the similar appearance of supraglacial lakes and other ice sheet surface features in optical or SAR data, the varying temporal signature of supraglacial lakes throughout the year as well as effects such as speckle noise and wind roughening in SAR data or cloud coverage in optical data. To overcome these limitations, this thesis exploits methods from artificial intelligence and big data processing for development of an automated processing chain for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 satellite imagery. The combination of both sensor types enabled to capture both surface and subsurface lakes as well as to acquire data during cloud cover or wind roughening of lakes. For Sentinel-1, a deep convolutional neural network based on residual U-Net was trained on the basis of 21,200 labeled Sentinel-1 SAR image patches covering 13 Antarctic regions. Similarly, optical Sentinel-2 data were collected over 14 Antarctic regions and used for training of a Random Forest classifier. Optical and SAR classification products were combined through decision-level fusion at bi-weekly temporal scale and unprecedented 10 m spatial resolution. Finally, the method was implemented as part of DLR’s High-Performance Computing infrastructure allowing for an automated processing of large amounts of data including all required pre- and postprocessing steps. The results of an accuracy assessment over independent test scenes highlighted the functionality of the classifiers returning accuracies of 93% and 95% for supraglacial lakes in Sentinel-1 and Sentinel-2 satellite imagery, respectively. Exploiting the full archive of Sentinel-1 and Sentinel-2, the developed framework for the first time enabled the monitoring of seasonal characteristics of Antarctic supraglacial lakes over six major ice shelves in 2015-2021. In particular, the results for API ice shelves revealed low lake coverage during 2015-2018 and particularly high lake coverage during the 2019-2020 and 2020-2021 melting seasons. On the contrary, East Antarctic ice shelves were characterized by high lake coverage during 2016-2019 and extremely low lake coverage during the 2020-2021 melting season. Over all six investigated ice shelves, the development of drainage systems was revealed highlighting an increased risk for ice shelf instability. Through statistical correlation analysis with climate data at varying time lags as well as annual data on Southern Hemisphere atmospheric modes, environmental drivers for meltwater ponding were revealed. In addition, the influence of the local glaciological setting was investigated through computation of annual recurrence times of lakes. Over both ice sheet regions, the complex interplay between local, regional and large-scale environmental drivers was found to control supraglacial lake formation despite local to regional discrepancies, as revealed through pixel-based correlation analysis. Local control factors included the ice surface topography, the ice shelf geometry, the presence of low-albedo features as well as a reduced firn air content and were found to exert strong control on lake distribution. On the other hand, regional controls on lake evolution were revealed to be the amount of incoming solar radiation, air temperature and wind occurrence. While foehn winds were found to dictate lake evolution over the API, katabatic winds influenced lake ponding in East Antarctica. Furthermore, the regional near-surface climate was shown to be driven by large-scale atmospheric modes and teleconnections with the tropics. Overall, the results highlight that similar driving factors control supraglacial lake formation on the API and EAIS pointing towards their transferability to other Antarctic regions.Der antarktische Eisschild erfährt angesichts der globalen Erderwärmung zunehmende eisdynamische Veränderungen. Zwischen 1992 und 2017 trug die Antarktis mit ~7.6 mm zum globalen Meeresspiegelanstieg bei, was vor allem auf die Erwärmung des Ozeans entlang der Westantarktis und die Erwärmung der Atmosphäre entlang der Antarktischen Halbinsel zurückzuführen ist. Zusammen verursachten diese Prozesse den fortschreitenden Rückgang von Gletschern und Schelfeis und schwächten ihren Rückhalteeffekt. Mit einem Anteil von ~91% an der globalen Eismasse und einem Meeresspiegeläquivalent von 57.3 m ist der antarktische Eisschild der größte potentielle Verursacher eines zukünftigen Meeresspiegelanstiegs. Trotz des verbesserten Verständnisses der antarktischen Eisdynamik kann die Zukunft der Antarktis nur schwer vorhergesagt werden. In Anbetracht der Tatsache, dass die Erwärmung der Atmosphäre und die damit einhergehende Oberflächenschmelze eine der Hauptursachen für künftige Massenverluste der Antarktis sein werden, ist die Kartierung von supraglazialen Seen von größter Bedeutung und Wichtigkeit. In dieser Hinsicht liefert die Erdbeobachtung eine Vielzahl von räumlich und zeitlich hochaufgelösten Satellitendaten für das Monitoring der Antarktis. Da eine automatisierte Methode zur Kartierung von supraglazialen Seen in Satellitendaten sowie ein großräumiges Inventar gänzlich fehlen, ist das Ziel dieser Arbeit zu einem besseren Verständnis der antarktischen Oberflächenhydrologie beizutragen. Zu diesem Zweck wurde ein neuartiges Prozessierungsverfahren für die automatisierte Kartierung von supraglazialen Seen in Sentinel-1 und Sentinel-2 Satellitenbilddaten entwickelt. Basierend auf einer umfassenden Literaturrecherche in Bezug auf die satellitengestützte Fernerkundung von antarktischen supraglazialen Seen wurden mehrere Forschungslücken identifiziert, darunter das Fehlen von (1) einem automatisierten Klassifikationsalgorithmus für optische und Radar Satellitendaten, der in Raum und Zeit übertragbar ist, (2) hochaufgelösten Kartierungen von supraglazialen Seen mit jährlicher sowie saisonaler zeitlicher Auflösung und (3) großräumigen Kartierungen über der gesamten Antarktis. Darüber hinaus wurde festgestellt, dass sich vergangene Methodenentwicklungen auf eine rein visuelle, manuelle oder halbautomatisierte Kartierungstechnik stützten, was ihre Anwendung auf multitemporale Satellitenbilder über dem gesamten Kontinent verhinderte. Die Entwicklung einer automatisierten Kartierungsmethode wurde hierbei vor allem durch sensorspezifische Merkmale eingeschränkt, darunter das ähnliche Erscheinungsbild von supraglazialen Seen und anderen Landbedeckungsklassen in optischen oder Radar Daten, die variierende zeitliche Signatur von supraglazialen Seen sowie Effekte wie SpeckleRauschen oder die Windaufrauhung von Seen in Radar Daten. Um diese Limitierungen zu überwinden, basiert der entwickelte Algorithmus zur automatisierten Kartierung von supraglazialen Seen in optischen and Radar Satellitendaten auf Methoden der künstlichen Intelligenz und der Big-Data-Analytik. Die Kombination von beiden Sensortypen ermöglicht es, sowohl supraglaziale als auch mit Schnee bedeckte Seen zu erfassen. Für Sentinel-1 wurde ein neuronales Netzwerk basierend auf „residual U-Net“ mittels 21,200 Radaraufnahmen über 13 antarktischen Regionen trainiert. In ähnlicher Weise wurden optische Sentinel-2 Daten über 14 antarktischen Regionen gesammelt und zum Trainieren eines „Random Forest“ Klassifikators verwendet. Die beiden Methoden wurden durch die Fusion von optischen und Radar Klassifikationsergebnissen kombiniert und als Teil der DLR-internen Prozessierungs-Infrastruktur auf Hochleistungsrechnern implementiert, die eine vollautomatische Verarbeitung großer Datenmengen einschließlich aller erforderlichen Vor- und Nachverarbeitungsschritte ermöglichen. Eine Fehleranalyse über unabhängigen Testszenen zeigte die Funktionalität der Algorithmen, die Genauigkeiten von 93% bzw. 95% für supraglaziale Seen in Sentinel-1 und Sentinel-2 Daten erreichten. Unter Nutzung des gesamten Archivs an Sentinel-1 und Sentinel-2 Daten im Zeitraum 2015-2021 ermöglichte die entwickelte Prozessierungs-Kette erstmals die Erfassung von saisonalen Merkmalen supraglazialer Seen über sechs großen SchelfeisRegionen. Die Ergebnisse für die Antarktische Halbinsel zeigten ein geringes Auftreten von supraglazialen Seen im Zeitraum 2015-2018 und ein stark erhöhtes Auftreten von supraglazialen Seen während der Schmelzsaison 2019-2020 und 2020-2021. Im Gegensatz dazu war die Ostantarktis durch ein stark erhöhtes Auftreten von supraglazialen Seen in den Jahren 2016-2019 sowie ein stark reduziertes Auftreten von supraglazialen Seen während der Schmelzsaison 2020-2021 gekennzeichnet. Über beiden Regionen entwickelten sich ausgeprägte Seen-Netzwerke, die ein erhöhtes Risiko für die Stabilität von Schelfeis darstellen. Durch statistische Korrelationsanalysen mit saisonalen Klimadaten sowie jährlichen Daten zu atmosphärischen Modi wurden Umwelteinflüsse auf die Entstehung von Seen analysiert. In beiden antarktischen Regionen wurde festgestellt, dass das komplexe Zusammenspiel von lokalen, regionalen und großräumigen Umweltfaktoren die Entstehung von supraglazialen Seen begünstigt. Zu den lokalen Einflussfaktoren gehören die Topographie, die Schelfeisgeometrie, das Vorhandensein von Oberflächen mit geringer Albedo sowie ein reduzierter Luftgehalt im Firn. Andererseits wurde festgestellt, dass die Sonneneinstrahlung, die Lufttemperatur und Wind die Entstehung von Seen regional beeinflussen. Während Föhnwinde über der Antarktischen Halbinsel auftreten, dominieren katabatische Winde in der Ostantarktis. Darüber hinaus wurde verdeutlicht, dass das regionale Klima von atmosphärischen Modi beeinflusst wird. Insgesamt deuten die Ergebnisse darauf hin, dass ähnliche Umweltfaktoren die Entstehung von supraglazialen Seen über beiden Regionen steuern, was Rückschlüsse auf ihre Übertragbarkeit in andere antarktische Regionen zulässt

Topographic change quantification and DEM uncertainty assessment using TanDEM-X and F-SAR DEM time series and quality maps: Application to the 2014-2015 Bárðarbunga volcanic eruption, Iceland

Topographical information is of fundamental interest for a wide range of disciplines including glaciology, agriculture, communication network planning, or hazard management. In volcanology, elevation data is of particular interest when assessing material flows throughout a volcanic system. To obtain accurate estimates of time-varying topography in volcanic active regions, high-resolution digital elevation models (DEMs) are required. Whilst ground-based GPS measurements and photogrammetry are restricted in terms of temporal and spatial resolution, the monitoring with airborne laser scanning (LiDAR) depends upon meteorological and illumination conditions and is limited by volcanic ash clouds. The use of space- and airborne synthetic aperture radar (SAR) overcomes such issues and allows the generation of medium- to high-resolution DEMs with interferometric techniques (InSAR). To monitor and evaluate topographical changes and especially volumetric gains and losses during the 2014-2015 Bárðarbunga eruption, Iceland, time sequences of TanDEM-X and F-SAR InSAR DEMs were evaluated differencing pairs of elevation models. The 2014-2015 volcanic eruption was associated with the rare event of a caldera collapse, visible on the surface of the Vatnajökull glacier, as well as a major effusive eruption in the Holuhraun plain. In order to guarantee the reliability of the topographical analysis at both locations, DEM absolute and relative height errors were investigated and raised the importance of accounting for the impact of system parameters, the SAR processing and the local environment before processing the DEMs. Acquisitions over the snow-covered Bárðarbunga caldera were especially affected by microwave penetration into snow and DEMs over the Holuhraun lava field implied large height errors due to fast moving lava flows. Considering the TanDEM-X dataset, the topographical analysis over the Bárðarbunga caldera revealed a total volume loss of approximately -1.40 ± 0.13 km3 and the volume gain of the generated lava field was computed at +1.44 ± 0.03 km3. Taking into account the calculation of rates, the temporal development of caldera collapse and lava effusion was found to exhibit a near-exponential decrease. The ratio between subsidence and lava volume moreover indicated the coupling between the processes. The quality of the applied workflow and achieved results was finally verified comparing the results to other reference data and revealed the overall excellent agreement

Geomorphometric analysis of the 2014-2015 Bárðarbunga volcanic eruption, Iceland

Topographical information is of fundamental interest for a wide range of disciplines including glaciology, agriculture, communication network planning, or hazard management. In volcanology, elevation data are of particular importance when assessing material flows throughout a volcanic system. To obtain accurate estimates of time-varying topography in volcanic active regions, high-resolution digital elevation models (DEMs) are required. To monitor and evaluate topographical changes and especially volumetric gains and losses during the 2014-2015 Bárðarbunga eruption, Iceland, multi-temporal TanDEM-X DEM sequences were evaluated. The 2014-2015 volcanic eruption was associated with the rare event of a caldera collapse, visible on the surface of the Vatnajökull glacier, as well as major lava effusion in the Holuhraun plain. Before investigating topographical change at the two study areas, the TanDEM-X DEMs were analysed for absolute and relative height errors resulting from the radar system parameters, the SAR processing or the local environment. The uncertainty investigation determined that acquisitions over the snow-covered Bárðarbunga caldera were primarily affected by microwave penetration into snow and DEMs over the Holuhraun lava field exhibited increased height errors due to active lava flows and dynamic outwash plain. The topographical analysis of the 2014-2015 Bárðarbunga eruption revealed a maximum vertical displacement of approximately -65 m and +43 m at the study area of the Bárðarbunga caldera and Holuhraun lava field respectively. With a total subsidence volume of -1.40 ± 0.13 km3 and a dense-rock equivalent (DRE) lava volume of +1.36 ± 0.07 km3, known uncertainties in volume were decreased by approximately 35% and 77% accordingly. Taking into account the calculation of rates, the temporal development of caldera collapse and lava effusion was found to exhibit a near-exponential decrease. The ratio between subsidence and DRE lava volume moreover indicated the coupling between piston collapse and magma drainage

Geodetic exploitation of the 2014-2015 Bardarbunga unrest: volume variation history and lava tube evolution

On August 31, 2014, the main effusive eruption started at Holuhraun in the on the Flædur flood plain (central Iceland), located ~6km north of Vatnajökull glacier and 47 km north east of the Bardarbunga ice-covered caldera, source of the Holuhraun eruption. The activity was declared finished on February 27, 2015, thus lasting for about 6 months. During these months the dyke feeding the eruption kept extracting the magma from the chamber located below the caldera causing the rare event of a gradual caldera collapse. In this context and because of the difficult access conditions of the Icelandic highlands, TanDEM-X remote sensing data is of particular interest. By producing high-resolution and accurate elevation models, TanDEM-X data allow quantification of elevation and volume changes observed within the volcanic system during the eruption. This study focuses on the space-time evolution of the Bardarbunga caldera collapse and the evolution of closed lava pathways in the Holuhraun lava field. It provides a unique opportunity to better characterize and understand the physical processes behind these topographical changes. A stack of thirteen DEMs is employed for the caldera monitoring and imaging of the northwestern portion of the Vatnajökull glacier. The caldera volume loss has been temporally tracked, up to the final measured loss of about 1.4 cubic kilometers. Moreover, the dyke propagation from Bardarbunga to the Holuhraun lava field has been derived and a graben structure with a width of up to 1 km and a sinking of a few meters has been measured. A second stack of nine DEMs over the Holuhraun area has also been generated. This data stack covers the last stage of the eruption, allowing the characterization of a lava field, which was dominated by the gradual development of closed lava pathways. The areas of uplift have been precisely localized and the volume estimates for this phase of the eruption have been derived, thus getting a more detailed picture of the closure of the eruption and its space-time evolution up to a few months after the end of the activity. Finally, the two stacks allow the derivation of the ratio between the caldera volume loss and the lava volume, which has been temporally tracked