Arid regions, that have a terrestrial share of 30 %, heavily rely on groundwater for do-mestic, industrial and irrigation purposes. The reliance on groundwater has partly turned into a dependency in areas where the increasing population number and the expansion of irrigated agricultural areas demand more groundwater than is naturally replenished. Yet, spatial and temporal information on groundwater are often scarce induced by the facts that groundwater is given a low priority in many national budgets and numerous (semi-) arid regions in the world encompass large and inaccessible areas. Hence, there is an urgent need to provide low-cost alternatives that in parallel cover large spatial and temporal scales to gain information on the groundwater system. Remote sensing holds a tremendous potential to represent this alternative. The main objective of this thesis is the improvement of existing and the development of novel remote sensing applications to infer information on the scarce but indispensable resource groundwater at the example of the Dead Sea. The background of these de-velopments relies mainly on freely available satellite data sets. I investigate 1) the pos-sibility to infer potential groundwater flow-paths from digital elevation models, 2) the applicability of multi-temporal thermal satellite data to identify groundwater discharge locations, 3) the suitability of multi-temporal thermal satellite data to derive information on the long-term groundwater discharge behaviour, and 4) the differences of thermal data in terms of groundwater discharge between coarse-scaled satellite data and fine-scaled airborne data including a discharge quantification approach. 1) I develop a transparent, reproducible and objective semi-automatic approach us-ing a combined linear filtering and object based classification approach that bases on a medium resolution (30 m ground sampling distance) digital elevation model to extract lineaments. I demonstrate that the obtained lineaments have both, a hydrogeological and groundwater significance, that allow the derivation of potential groundwater flow-paths. These flow-paths match results of existing groundwater flow models remarkably well that validate the findings and shows the possibility to infer potential groundwater flow-paths from remote sensing data. 2) Thermal satellite data enable to identify groundwater discharge into open water bodies given a temperature contrast between groundwater and water body. Integrating a series of thermal data from different periods into a multi-temporal analysis accounts for the groundwater discharge intermittency and hence allows obtaining a representa-tive discharge picture. I analyse the constraints that arise with the multi-temporal anal-ysis (2000-2002) and show that ephemeral surface-runoff causes similar thermal anomalies as groundwater. To exclude surface-runoff influenced data I develop an au-tonomously operating method that facilitates the identification. I calculate on the re-maining surface-runoff uninfluenced data series different statistical measures on a per pixel basis to amplify groundwater discharge induced thermal anomalies. The results reveal that the range and standard deviation of the data series perform best in terms of anomaly amplification and spatial correspondence to in-situ determined spring dis-charge locations. I conclude on the reason that both mirror temperature variability that is stabilized and therefore smaller at areas where spatio-temporal constant groundwater discharge occurs. 3) The application of the before developed method on a thermal satellite data set spanning the years 2000 to 2011 enables to localise specific groundwater discharge sites and to semi-quantitatively analyse the temporal variability of the thermal anomalies (termed groundwater affected area - GAA). I identify 37 groundwater discharge sites along the entire Dead Sea coastline that refine the so far coarsely given spring areas to specific locations. All spatially match independent in-situ groundwater discharge observations and additionally indicate 15 so far unreported discharge sites. Comparing the variability of the GAA extents over time to recharge behaviour reveals analogous curve progressions with a time-shift of two years. This observation suggests that the thermally identified GAAs directly display the before only assumed groundwater discharge volume. This finding provides a serious alternative to monitor groundwater discharge over large temporal and spatial scales that is relevant for different scientific communities. From the results I furthermore conclude to observe the before only assumed and modelled groundwater discharge share from flushing of old brines during periods with an above average Dead Sea level drop. This observation implies the need to not only consider discharge from known terrestrial and submarine springs, but also from flushing of old-brines in order to calculate the total Dead Sea water budget. 4) I present a complementary airborne thermal data set recorded in 01/2011 over the north-western part of the Dead Sea coast. The higher spatial resolution allows to refine the satellite-based GAA to 72 specific groundwater discharge sites and even to specify the so far unknown abundance of submarine springs to six sites with a share of <10 % to the total groundwater discharge. A larger contribution stems from newly iden-tified seeping spring type (24 sites) where groundwater emerges diffusively either ter-restrial or submarine close to the land/water interface with a higher share to the total discharge than submarine springs provide. The major groundwater contribution origi-nates from the 42 identified terrestrial springs. For this spring type, I demonstrate that 93 % of the discharge volume can be modelled with a linear ordinary least square re-gression (R2=0.88) based on the thermal plume extents and in-situ measured discharge volumes from the Israel Hydrological Service. This result implies the possibility to determine discharge volumes at unmonitored sites along the Dead Sea coast as well that can provide a complete physically-based picture of groundwater discharge magni-tude to the Dead Sea for the first time.:1 Introduction 1.1 Remote sensing applications on groundwater 1.1.1 Classical aspects 1.1.2 Modern aspects 1.2 Motivation and main objectives 1.3 Why the western catchment of the Dead Sea? 1.4 Overview 2 The western catchment of the Dead Sea 2.1 Geological and Structural Overview 2.2 Groundwater system 2.3 Groundwater inputs 2.4 Dead Sea 3 Groundwater flow-paths 3.1 Prologue 4 Method development for groundwater discharge identification 4.1 Prologue 5 Localisation and temporal variability of groundwater discharge 5.1 Prologue 6 Qualitative and quantitative refinement of groundwater discharge 6.1 Prologue 7 Conclusion and Outlook 7.1 Main results and implications 7.2 Outlook References Appendix

Knowledge about land use and land cover represents an important information basis for various planning applications. In particular, urban and suburban regions are subject to a high dynamic development. The detection and identification of changes is therefore an important instrument to follow and accompany the developments by planning. Here, aerial photography and, increasingly, satellite images serve as an important basis for information. The recognition and mapping of changes is still a time-consuming and cost-intensive matter which is mostly realized by visual interpretation of aerial photography and to an increasing degree of high- and ultra-high-resolution satellite images. Within the scope of the present work a new, robust and largely automated process based on a statistical change analysis is developed and presented. Basis for the data are multitemporal high-resolution satellite image data. The generated suspect areas, respectively areas of change, are supposed to function as clues in order to facilitate the process of the visual interpretation of multitemporal image datasets with regard to change mapping, since only marked areas of change have to undergo further examination. Consequently, this process can be used as a tool to ease and accelerate the updating of planning bases in general and maps in particular so far realised by visual interpretation. However, the automation of the process is not only supposed to serve the purpose of saving time and cost but also to bring the interpretation process to a higher level of objectivity. In order to improve the quality of the whole process, for the preprocessing of the image data selected methods of image processing have been integrated. Through the use of additional geo-information reference data for the automated calculation of the areas of change, a further refinement of the results can be reached. The obtained results in the first time-cut (1997-1998) can be proved and verified by a different data-take (1997-2000). To reach a convenient use and a good distribution of the developed method, the process has been implemented by means of the widespread image processing software ERDAS IMAGINE. This allows to make the developed method available for other users, since it can easily be integrated into the working environment of ERDAS IMAGINE. / Das Wissen um die Landnutzung und Landbedeckung ist für planerische Anwendungsgebiete eine wichtige Informationsgrundlage. Gerade urbane und suburbane Regionen unterliegen einer hohen Entwicklungsdynamik. Das Erkennen und Aufzeigen von Veränderungen ist somit ein wichtiges Instrument um Entwicklungen zu verfolgen und planerisch zu begleiten. Luft- und zunehmend Satellitenbilder dienen hierfür als wichtige Informationsgrundlage. Das Erkennen und Kartieren von Veränderungen ist nach wie vor eine zeitaufwändige und kostenintensive Angelegenheit, die überwiegend durch visuelle Interpretation von Luft- und zunehmend auch mit hoch- und höchstauflösenden Satellitenbildern realisiert wird. In dieser Arbeit wird ein neues, robustes, weitgehend automatisiertes, auf einem statistischen Ansatz beruhendes Verfahren der Veränderungsanalyse entwickelt und vorgestellt. Die Datengrundlage bilden multitemporale, hoch auflösende Satellitenbilddaten. Die generierten Verdachts- bzw. Veränderungsflächen sollen als Anhaltspunkte fungieren, um den Prozess der visuellen Interpretation von multitemporalen Bilddatensätzen in Hinsicht auf eine Veränderungskartierung zu erleichtern, da nur als Veränderungsflächen markierte Areale einer weiteren Untersuchung unterzogen werden müssen. Das Verfahren kann somit als Werkzeug dienen, die durch visuelle Interpretation realisierte Aktualisierung von Planungsgrundlagen bzw. Kartenwerken zu erleichtern und zu beschleunigen. Die Automatisierung des Verfahrens soll jedoch nicht allein dem Zweck der Zeit- und Kostenersparnis dienen, sondern auch den Interpretationsprozess objektiver gestalten. Um die Qualität des Verfahrens zu erhöhen, werden ausgewählte Methoden der Bildverarbeitung für die Vorverarbeitung der Bilder in das Verfahren integriert. Durch das Einbinden zusätzlicher Geobasisdaten in die automatisierte Berechnung der Veränderungsflächen kann eine weitere Verbesserung der Ergebnisse erzielt werden. Die Ergebnisse, der im ersten Zeitschnitt (1997-1998) untersuchten Datensätze, werden mit Hilfe eines weiteren Zeitschnitts (1997-2000) überprüft und verifiziert. Um eine unkomplizierte Anwendung und Verbreitung der Methode zu erreichen, wurde das Verfahren mit Hilfe der weit verbreiteten Bildverarbeitungssoftware ERDAS IMAGINE realisiert. Dies ermöglicht, das Verfahren auch anderen Nutzern zur Verfügung zu stellen, da es problemlos in die Arbeitsumgebung des Bildverarbeitungssystems ERDAS IMAGINE integriert werden kann

The phenomenon of induced seismicity is caused by anthropogenic activity such as: underground and opencast mining, extraction of conventional and unconventional hydrocarbons, construction of water reservoirs and production of geothermal energy. In recent years, interest in induced seismicity increased due to the fact that it causes increasingly stronger earthquakes, even above 4 on the Richter scale. Thus, it poses a threat to people, technical and urban infrastructure. This study analyzed the seismic event of M = 4.6, which occurred on the 15 September 2018 in the Rudna copper mine area in SW Poland. For this purpose, Sentinel 1 satellite data and DInSAR processing method were used to determine the ground movement values in the satellite line of sight. Based on the results for four image pairs, the area disturbed by the seismic event was determined. The maximum values of subsidence ranged from -65 mm to -75 mm depending on the analysed dataset and the area of deformation was determined at approx. 4 km sq. The results indicate the usefulness of the adopted method to determine ground deformation caused by induced seismicity in an underground mining area.