550 Geowissenschaften
Filtern
Volltext vorhanden
- ja (165)
Gehört zur Bibliographie
- ja (165)
Erscheinungsjahr
Dokumenttyp
Sprache
- Englisch (165) (entfernen)
Schlagworte
- remote sensing (18)
- Fernerkundung (13)
- Namibia (10)
- MODIS (9)
- Klimaänderung (8)
- climate change (8)
- Geochemie (6)
- Modellierung (6)
- time series (6)
- Geomorphologie (5)
Institut
- Institut für Geographie und Geologie (107)
- Institut für Mineralogie und Kristallstrukturlehre (18)
- Institut für Geologie (17)
- Institut für Geographie (15)
- Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) (5)
- Graduate School of Science and Technology (3)
- Institut für Altertumswissenschaften (2)
- Institut für Informatik (2)
- Institut für Paläontologie (1)
- Neuphilologisches Institut - Moderne Fremdsprachen (1)
Sonstige beteiligte Institutionen
- Deutscher Akademischer Austauschdienst (DAAD) (1)
- Deutsches Zentrum für Luft & Raumfahrt (DLR) (1)
- Deutsches Zentrum für Luft- und Raumfahrt (DLR) (1)
- INAF Padova, Italy (1)
- Jacobs University Bremen, Germany (1)
- Lehrstuhl für Fernerkundung der Uni Würzburg, in Kooperation mit dem Deutschen Fernerkundungsdatenzentrum (DFD) des Deutschen Zentrums für Luft- und Raumfahrt (DLR) (1)
- South African National Biodiversity Institute (SANBI) (1)
- University of Padova, Italy (1)
- Université d'Abomey-Calavi, Benin (1)
- VIGEA, Italy (1)
Rifting and breakup of Westgondwana in the Late Jurassic/ Early Cretaceous initiated the formation of the South Atlantic and its conjugated pair of passive continental margins. The Walvis Basin offshore NW-Namibia is an Early Cretaceous to recent depositional centre with a typically wedge-shaped postrift sedimentary succession covering an area of 105000km2. A 2D model transect across the central Walvis Basin and adjacent onshore areas is used as a case study to investigate quantitatively the denudational history of the evolving passive margin and the related contemporaneous depositional postrift evolution offshore. The database for both the onshore and offshore part of the model traverse is well constrained by own field work, published data as well as by seismic and well data supported by samples. The ultimate goal of this project is to present an integrated approach towards a quantitative link between surface processes and internal processes in terms of a mass and process balance.
Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.
The explosive expansion of the population of the Metropolitan Region of Curitiba raised a high increase in the demand for water resources and the uncontrolled settlement poses a large problem for the environment. The greatest menace to the water supply sources of this region is the urban occupation (invasion) into the areas that contain these resources. This occupation continues with its slow, silent, although progressive march, threatening precious and irreplaceable resources. From this background an area in the direct vicinity north-east of Curitiba has been studied. In this area a drinking water reservoir was constructed in the time that the study took place in the Iraí-basin. The Iraí-reservoir even though an area around the lake will be protected may be polluted by two tributaries which flow through more or less densely populated areas. In the study area on the same time wells have been constructed. To estimate what the impact may be from the possibly polluted reservoir on the aquifer a groundwater flow model has been constructed. On the same time to estimate the water balance and the spatial distribution of pollution vulnerability the hydrological model MODBIL has been used. Also other methods have been used to estimate the pollution vulnerability to make a comparison and because none of the methods takes every aspect into account. With the calibrated groundwater flow model for the situation before the construction of the Iraí-reservoir and after its construction, simple particle tracking transport models are constructed as scenarios how the water of the aquifer may be influenced.
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts
(2023)
This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.