TY - JOUR A1 - Ullmann, Tobias A1 - Schmitt, Andreas A1 - Roth, Achim A1 - Duffe, Jason A1 - Dech, Stefan A1 - Hubberten, Hans-Wolfgang A1 - Baumhauer, Roland T1 - Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada N2 - In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. KW - radar KW - arctic KW - tundra KW - land cover KW - classification KW - polarimetry KW - PolSAR KW - SAR KW - TerraSAR-X KW - Radarsat-2 Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-113303 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Büdel, Christian A1 - Baumhauer, Roland A1 - Padashi, Majid T1 - Sentinel-1 SAR Data Revealing Fluvial Morphodynamics in Damghan (Iran): Amplitude and Coherence Change Detection JF - International Journal of Earth Science and Geophysics N2 - The Sentinel-1 Satellite (S-1) of ESA's Copernicus Mission delivers freely available C-Band Synthetic Aperture Radar (SAR) data that are suited for interferometric applications (InSAR). The high geometric resolution of less than fifteen meter and the large coverage offered by the Interferometric Wide Swath mode (IW) point to new perspectives on the comprehension and understanding of surface changes, the quantification and monitoring of dynamic processes, especially in arid regions. The contribution shows the application of S-1 intensities and InSAR coherences in time series analysis for the delineation of changes related to fluvial morphodynamics in Damghan, Iran. The investigations were carried out for the period from April to October 2015 and exhibit the potential of the S-1 data for the identification of surface disturbances, mass movements and fluvial channel activity in the surroundings of the Damghan Playa. The Amplitude Change Detection highlighted extensive material movement and accumulation - up to sizes of more than 4,000 m in width - in the east of the Playa via changes in intensity. Further, the Coherence Change Detection technique was capable to indicate small-scale channel activity of the drainage system that was neither recognizable in the S-1 intensity nor the multispectral Landsat-8 data. The run off caused a decorrelation of the SAR signals and a drop in coherence. Seen from a morphodynamic point of view, the results indicated a highly dynamic system and complex tempo-spatial patterns were observed that will be subject of future analysis. Additionally, the study revealed the necessity to collect independent reference data on fluvial activity in order to train and adjust the change detector. KW - SAR KW - InSAR KW - coherence KW - Iran KW - Sentinel-1 KW - radar KW - geomorphology KW - change detection Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147863 VL - 2 IS - 1 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Ullmann, Tobias A1 - Schucknecht, Anne A1 - Kuenzer, Claudia T1 - Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany. KW - earth observation KW - remote sensing KW - harvests KW - cutting events KW - grazing KW - pasture KW - meadow KW - optical KW - SAR KW - PolSAR KW - InSAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267164 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Asam, Sarah A1 - Kuenzer, Claudia T1 - Remote Sensing of Grassland Production and Management - A Review JF - Remote Sensing N2 - Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far. KW - pasture KW - use intensity KW - grazing KW - mowing KW - productivity KW - biomass KW - yield KW - satellite data KW - optical KW - SAR Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207799 SN - 2072-4292 VL - 12 IS - 12 ER - TY - JOUR A1 - Ottinger, Marco A1 - Bachofer, Felix A1 - Huth, Juliane A1 - Kuenzer, Claudia T1 - Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - Asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale. KW - aquaculture KW - Asia KW - Earth observation KW - ponds KW - coastal zone KW - Sentinel-1 KW - SAR KW - time series Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252207 SN - 2072-4292 VL - 14 IS - 1 ER - TY - JOUR A1 - Nguyen, Duy Ba A1 - Kersten, Clauss A1 - Senmao, Cao A1 - Vahid, Naeimi A1 - Kuenzer, Claudia A1 - Wagner, Wolfgang T1 - Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data JF - Remote Sensing N2 - Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions. KW - band SAR data KW - SAR KW - rice KW - WSM KW - ASAR KW - Envisat KW - MODIS image KW - Southeast China KW - polarimetric SAR KW - cropping systems KW - time-series KW - paddy rice KW - radar KW - paddy KW - rice mapping KW - Vietnam KW - Mekong-Delta KW - synthetic aperture radar KW - multitemporal ALOS/PALSAR imagery KW - soil moisture retrieval Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-137554 VL - 7 IS - 12 ER - TY - JOUR A1 - Huth, Juliane A1 - Gessner, Ursula A1 - Klein, Igor A1 - Yesou, Hervé A1 - Lai, Xijun A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China JF - Remote Sensing N2 - In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers. KW - Earth observation KW - SAR KW - Sentinel–1 KW - time series KW - Dongting Lake KW - water dynamics KW - floodpath lake KW - Ramsar Convention on Wetlands Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205977 SN - 2072-4292 VL - 12 IS - 11 ER - TY - THES A1 - Baumhoer, Celia Amélie T1 - Glacier Front Dynamics of Antarctica - Analysing Changes in Glacier and Ice Shelf Front Position based on SAR Time Series T1 - Gletscherfrontdynamiken in der Antarktis - Die Analyse von Gletscher- und Eisschelffrontänderungen basierend auf SAR-Zeitreihen N2 - The Antarctic Ice Sheet stores ~91% of the global ice volume which is equivalent to a sea-level rise of 58.3 meters. Recent disintegration events of ice shelves and retreating glaciers along the Antarctic Peninsula and West Antarctica indicate the current vulnerable state of the Antarctic Ice Sheet. Glacier tongues and ice shelves create a safety band around Antarctica with buttressing effects on ice discharge. Current decreases in glacier and ice shelf extent reduce the effective buttressing forces and increase ice discharge of grounded ice. The consequence is a higher contribution to sea-level rise from the Antarctic Ice Sheet. So far, it is unresolved which proportion of Antarctic glacier retreat can be attributed to climate change and which part to the natural cycle of growth and decay in the lifetime of a glacier. The quantitative assessment of the magnitude, spatial extent, distribution, and dynamics of circum-Antarctic glacier and ice shelf retreat is of utmost importance to monitor Antarctica’s weakening safety band. In remote areas like Antarctica, earth observation provides optimal properties for large-scale mapping and monitoring of glaciers and ice shelves. Nowadays, the variety of available satellite sensors, technical advancements regarding spatial resolution and revisit times, as well as open satellite data archives create an ideal basis for monitoring calving front change. A systematic review conducted within this thesis revealed major gaps in the availability of glacier and ice shelf front position measurements despite the improved satellite data availability. The previously limited availability of satellite imagery and the time-consuming manual delineation of calving fronts did neither allow a circum-Antarctic assessment of glacier retreat nor the assessment of intra-annual changes in glacier front position. To advance the understanding of Antarctic glacier front change, this thesis presents a novel automated approach for calving front extraction and explores drivers of glacier retreat. A comprehensive review of existing methods for glacier front extraction ascertained the lack of a fully automatic approach for large-scale monitoring of Antarctic calving fronts using radar imagery. Similar backscatter characteristics of different ice types, seasonally changing backscatter values, multi-year sea ice, and mélange made it challenging to implement an automated approach with traditional image processing techniques. Therefore, the present abundance of satellite data is best exploited by integrating recent developments in big data and artificial intelligence (AI) research to derive circum-Antarctic calving front dynamics. In the context of this thesis, the novel AI-based framework “AntarcticLINES” (Antarctic Glacier and Ice Shelf Front Time Series) was created which provides a fully automated processing chain for calving front extraction from Sentinel-1 imagery. Open access Sentinel-1 radar imagery is an ideal data source for monitoring current and future changes in the Antarctic coastline with revisit times of less than six days and all-weather imaging capabilities. The developed processing chain includes the pre-processing of dual-polarized Sentinel-1 imagery for machine learning applications. 38 Sentinel-1 scenes were used to train the deep learning architecture U-Net for image segmentation. The trained weights of the neural network can be used to segment Sentinel-1 scenes into land ice and ocean. Additional post-processing ensures even more accurate results by including morphological filtering before extracting the final coastline. A comprehensive accuracy assessment has proven the correct extraction of the coastline. On average, the automatically extracted coastline deviates by 2-3 pixels (93 m) from a manual delineation. This accuracy is in range with deviations between manually delineated coastlines from different experts. For the first time, the fully automated framework AntarcticLINES enabled the extraction of intra-annual glacier front fluctuations to assess seasonal variations in calving front change. Thereby, for example, an increased calving frequency of Pine Island Glacier and a beginning disintegration of Glenzer Glacier were revealed. Besides, the extraction of the entire Antarctic coastline for 2018 highlighted the large-scale applicability of the developed approach. Accurate results for entire Antarctica were derived except for the Western Antarctic Peninsula where training imagery was not sufficient and should be included in future studies. Furthermore, this dissertation presents an unprecedented record of circum-Antarctic calving front change over the last two decades. The newly extracted coastline for 2018 was compared to previous coastline products from 2009 and 1997. This revealed that the Antarctic Ice Sheet shrank 29,618±1193 km2 in extent between 1997-2008 and gained an area of 7,108±1029 km2 between 2009-2018. Glacier retreat concentrated along the Antarctic Peninsula and West Antarctica. The only East Antarctic coastal sector primarily experiencing calving front retreat was Wilkes Land in 2009-2018. Finally, potential drivers of circum-Antarctic glacier retreat were identified by combining data on glacier front change with changes in climate variables. It was found that strengthening westerlies, snowmelt, rising sea surface temperatures, and decreasing sea ice cover forced glacier retreat over the last two decades. Relative changes in mean air temperature could not be identified as a driver for glacier retreat and further investigations on extreme events in air temperature are necessary to assess the effect of atmospheric forcing on frontal retreat. The strengthening of all identified drivers was closely connected to positive phases of the Southern Annular Mode (SAM). With increasing greenhouse gases and ozone depletion, positive phases of SAM will occur more often and force glacier retreat even further in the future. Within this thesis, a comprehensive review on existing Antarctic glacier and ice shelf front studies was conducted revealing major gaps in Antarctic calving front records. Therefore, a fully automated processing chain for glacier and ice shelf front extraction was implemented to track circum-Antarctic calving front fluctuations on an intra-annual basis. The large-scale applicability was certified by presenting two decades of circum-Antarctic calving front change. In combination with climate variables, drivers of recent glacier retreat were identified. In the future, the presented framework AntarcticLINES will greatly contribute to the constant monitoring of the Antarctic coastline under the pressure of a changing climate. N2 - Der antarktische Eisschild speichert ~91 % des globalen Eisvolumens. Ein gänzliches Abschmelzen des Eisschildes hätte global einen Meeresspiegelanstieg von 58,3 Metern zur Folge. Der aktuelle Zerfall von Eisschelfen und der Gletscherrückgang entlang der Antarktischen Halbinsel und Westantarktis verdeutlichen den vulnerablen Status des antarktischen Eisschildes. Gletscherzungen und Eisschelfe säumen die antarktische Küstenlinie und halten die Eisströme Richtung Ozean zurück. Ein Rückzug der Eisschelfe und Gletscher vermindert den Rückhalteeffekt und führt zu zunehmenden Gletscher-Fließgeschwindigkeiten in Richtung Ozean. Der dadurch verursachte Masseverlust trägt zum globalen Meeresspiegelanstieg bei. Bislang ist ungeklärt, welcher Anteil des antarktischen Gletscherrückgangs auf den Klimawandel und welcher auf den natürlichen Kalbungszyklus der Gletscher und Eisschelfe zurückzuführen ist. Aufgrund des vermehrten Zerfalls von Eisschelfen in den letzten Dekaden ist es von großer Wichtigkeit, den Gletscherrückgang zu quantifizieren und dessen Ausmaß, räumlichen Ausdehnung, Verteilung und Dynamik zirkumantarktisch zu erfassen, um mögliche Auswirkungen auf den Meeresspiegelanstieg frühzeitig zu erkennen. In abgelegenen Regionen wie der Antarktis bietet die Erdbeobachtung optimale Voraussetzungen für das großflächige Kartieren und Beobachten von Gletschern und Eisschelfen. Heute stellt die Fülle an frei-verfügbaren Satellitendaten verschiedener Sensoren, in Kombination mit technischen Neuerungen hinsichtlich der räumlichen und zeitlichen Abdeckung, eine ideale Basis für das Monitoring der Kalbungsfronten dar. Trotz der guten Datenverfügbarkeit hat ein umfassender Literaturüberblick − welcher im Rahmen dieser Dissertation durchgeführt wurde − große Lücken in der Verfügbarkeit von Gletscher- und Eisschelffrontpositionsmessungen festgestellt. Die zuvor limitierte Verfügbarkeit von Satellitendaten und die zeitaufwändige manuelle Ableitung der Küstenlinie machten eine zirkumantarktische Beurteilung des Gletscherrückgangs und die intra-annuelle Analyse von Gletscherfrontpositionen unmöglich. Für ein besseres Verständnis antarktischer Gletscherfrontveränderungen, präsentiert diese Dissertation ein neues, automatisiertes Konzept zur Kalbungsfrontextraktion und untersucht ob klimatische Faktoren für den beobachteten Kalbungsfrontenrückgang verantwortlich sind. Anhand des Literaturüberblicks konnte festgestellt werden, dass bis dato kein komplett automatisiertes Verfahren für die Gletscherfrontextraktion aus großvolumigen Radarsatellitenbildern bestand. Ähnliche Rückstreuwerte von verschiedenen Eistypen, saisonal veränderliche Rückstreuwerte, mehrjähriges Meereis und Eis-Mélange erschwerten die Entwicklung eines automatisierten Ansatzes mit traditionellen Bildverarbeitungsansätzen. Doch die Neuerungen in den Bereichen „Big Data“ und der künstlichen Intelligenz (KI) ermöglichen es, die heutige Fülle an Satellitendaten für die Ableitung von Kalbungsfronten zu nutzen. Im Rahmen dieser Dissertation wurde das neuartige Framework “AntarcticLINES” (Antarctic Glacier and Ice Shelf Front Time Series) kreiert, welches eine komplett automatisierte, KI-basierte Prozessierungskette für die Gletscherfrontenextraktion von Sentinel-1 Daten beinhaltet. Frei verfügbare Sentinel-1 Daten sind ideal, um derzeitige und zukünftige Veränderungen der antarktischen Küstenlinie zu beobachten, da die Orbitwiederholrate weniger als sechs Tage beträgt und die Bildgebung wetterunabhängig ist. Die entwickelte Prozessierungskette beinhaltet die Vorprozessierung, Maskierung und Zerlegung der Satellitenbilder in kleinere Kacheln. Es wurden 38 Sentinel-1 Szenen genutzt, um die Deep Learning Architektur U-Net für eine Bildsegmentierung zu trainieren. Die trainierten Gewichte des Neuronalen Netzes können genutzt werden, um Sentinel-1 Szenen in die Klassen Ozean und Eis zu segmentieren. Eine zusätzliche Nachprozessierung ermöglicht noch genauere Ergebnisse durch morphologisches Filtern, bevor die finale Küstenlinie zwischen den beiden Klassen extrahiert wird. Eine umfangreiche Genauigkeitsauswertung hat ergeben, dass die automatisch abgeleitete Küstenlinie im Mittel 2-3 Pixel (93 m) von einer manuell abgeleiteten Küstenlinie abweicht. Diese Genauigkeit ist im Rahmen der durchschnittlichen Abweichungen von manuell abgeleiteten Küstenlinien verschiedener Experten. Erstmals ermöglicht das Framework AntarcticLINES die automatisierte Extraktion von intra-annuellen Gletscherfrontfluktuationen, um saisonale Variationen in der Kalbungsfrontänderung zu untersuchen. Dadurch konnte beispielsweise eine erhöhte Kalbungsfrequenz des Pine-Island-Gletschers festgestellt werden. Die Extraktion der antarktischen Küstenlinie für 2018 zeigt die mögliche Anwendung der entwickelten Methodik für großräumige Gebiete. Für den Großteil der Antarktis wurden genaue Ergebnisse erzielt, lediglich entlang der westlichen Antarktischen Halbinsel fehlten Trainingsdaten, welche in zukünftigen Studien inkludiert werden sollten. Darüber hinaus präsentiert diese Dissertation einen bis dato beispiellosen Datensatz zu zirkumantarktischen Veränderungen der Kalbungsfronten über die letzten zwei Jahrzehnte. Die neu extrahierte Küstenlinie für das Jahr 2018 wurde mit früheren Küstenlinienprodukten von 2009 und 1997 verglichen. Dies hat offengelegt, dass der Antarktische Eisschild zwischen 1997 und 2008 eine Fläche von 29,618±1193 km2 verlor und zwischen 2009 und 2018 eine Fläche von 7,108±1029 km2 dazugewann. Der Gletscherrückgang konzentrierte sich entlang der Antarktischen Halbinsel und der Westantarktis. Der einzige ostantarktische Sektor, in dem sich simultaner Gletscherrückgang zeigte, war Wilkes Land in den Jahren 2009 bis 2018. Im Anschluss wurden Ursachen für den Antarktischen Gletscherrückgang durch die Korrelation mit Klimavariablen identifiziert. Zunehmende Westwinde, Schneeschmelze, ansteigende Meeresoberflächentemperaturen und zurückgehendes Meereis begünstigten den Gletscherrückgang in den letzten zwei Dekaden. Relative Veränderungen in der durchschnittlichen Lufttemperatur konnten nicht als Ursache für den Gletscherrückgang identifiziert werden und weiter Analysen zu Extremereignissen in der Lufttemperatur sind nötig um Frontveränderungen verursacht durch atmosphärischen Antrieb besser verstehen zu können. Die Verstärkung aller identifizierten Treiber ist eng mit positiven Phasen des Southern Annular Mode (SAM) verbunden. In Anbetracht ansteigender Konzentrationen von Treibhausgasen und dem Ozonrückgang werden positive Phasen des SAMs in Zukunft öfter auftreten, was in Folge den Gletscherrückgang noch weiter vorantreiben kann. Zusammengefasst wurde im Rahmen dieser Dissertation ein umfassender Literaturüberblick zu existierenden Gletscher- und Eisschelffrontstudien durchgeführt, welcher größere Lücken in Kalbungsfrontstudien aufzeigte. Es wurde eine voll-automatisierte Prozessierungskette entwickelt, um zirkumantarktische Kalbungsfrontpositionen intra-annuell beobachten zu können und die Datenlücken zu schließen. In Kombination mit Klimavariablen wurden treibende Kräfte, die den aktuellen Gletscherrückgang begünstigen, identifiziert. In Zukunft wird das präsentierte Framework AntarcticLINES zur konstanten Beobachtung der Antarktischen Küstenlinie eingesetzt, um Veränderungen in Anbetracht eines sich ändernden Klimas zu analysieren. KW - Antarctica KW - Remote Sensing KW - Glacier KW - SAR Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245814 ER -