The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments.

University, Diss--Göttingen, 2005.

Thesis (doctoral)--Universität zu Göttingen, 2003. / Includes bibliographical references (p. 139-155).

Glacier outlines during the ‘Little Ice Age’ maximum in Jotunheimen were mapped by using remote sensing techniques (vertical aerial photos and satellite imagery), glacier outlines from the 1980s and 2003, a digital terrain model (DTM), geomorphological maps of individual glaciers, and field-GPS measurements. The related inventory data (surface area, minimum and maximum altitude) and several other variables (e.g. slope, range) were calculated automatically by using a geographical information system. The length of the glacier flowline was mapped manually based on the glacier outlines at the maximum of the ‘Little Ice Age’ and the DTM. The glacier data during the maximum of the ‘Little Ice Age’ were compared with the Norwegian glacier inventory of 2003. Based on the glacier inventories during the maximum of the ‘Little Ice Age’, the 1980s and 2003, a simple parameterization after HAEBERLI & HOELZLE (1995) was performed to estimate unmeasured glacier variables, as e.g. surface velocity or mean net mass balance. Input data were composed of surface glacier area, minimum and maximum elevation, and glacier length. The results of the parameterization were compared with the results of previous parameterizations in the European Alps and the Southern Alps of New Zealand (HAEBERLI & HOELZLE 1995; HOELZLE et al. 2007). A relationship between these results of the inventories and of the parameterization and climate and climate changes was made. / Die Gletscherumrisse während des Maximalstandes der „Kleinen Eiszeit“ in Jotunheimen wurden unter der Verwendung von Fernerkundungstechniken (vertikale Luftbilder und Satellitenbilder), von Gletscherumrissen aus den 1980er Jahren und von 2003, von einem digitalen Geländemodel (DTM), von geomorphologischen Karten einzelner Gletscher und von GPS-Messungen im Gelände kartiert. Die daraus erzielten Inventardaten (Gletscherfläche, minimale und maximale Höhe) und einige andere Variablen (z.B. Hangneigung, Höhendifferenz) wurden automatisch mit einem geographischen Informationssystem berechnet. Die Länge der Gletscherfließlinie wurde basierend auf den Gletscherumrissen zum Maximum der „Kleinen Eiszeit“ und dem DTM manuell kartiert. Die Gletscherdaten zum Maximalstand der „Kleinen Eiszeit“ wurden mit dem Gletscherinventar von 2003 verglichen. Basierend auf den letscherinventaren zum Maximum der „Kleinen Eiszeit“, von den 1980er Jahren und von 2003 wurde eine einfache Parametrisierung nach HAEBERLI & HOELZLE (1995) durchgeführt, um ungemessene Gletschervariablen, wie z.B. Oberflächengeschwindigkeit oder mittlere Netto-Massenbilanz, abzuschätzen. Eingabedaten bestanden aus Gletscherfläche, minimaler und maximale Höhe und der Gletscherlänge. Die Resultate der Parametrisierung wurden mit den Ergebnissen früherer Parametrisierungen aus den Europäischen Alpen und den Southern Alps auf Neuseeland verglichen (HAEBERLI & HOELZLE 1995; HOELZLE et al. 2007). Eine Verbindung zwischen diesen Ergebnissen aus den Inventaren und der Parametrisierung und dem Klima und der Klimaänderung wurde hergestellt.

The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments.

The doctoral thesis describes methodologies and appropriate adaptations of existing solutions to model land suitability in two ways for the valley and basin areas of the South-Siberian Altai Mountains within a geo-information system (GIS) environment. Starting-point approaches are: 1) the Agricultural Soil Suitability Model „Almagra” and Land Capability Model “Cervatana”/MicroLEIS System (De la Rosa et. al 1992, 1998) developed for Mediterranean regions and a method specifically compiled by Burlakova L. M. (1988) for the Altai based on the weighted means of a factor set. 2) For comparison purposes, second, third and fourth versions of the same model are developed using three different types of Fuzzy Logic approaches. They are used to present how Gauss membership functions of particular classes can be computed as different classes and how variables taking values in ranges can be handled in a mathematical way. Furthermore, the paper presents ideas on how remote sensing might interact with the geo-information system (GIS) where - like in the present case – the required input geo-data are not fully sufficient to (i) feed the models formalising soil and climatic conditions, and (ii) to characterise the patterns of land management within the study area. Three agricultural crops (summer wheat, sunflowers and potatoes) are relevant to the Altai Region at a regional level and are, therefore considered. A rating is classified using five suitability classes according to the FAO classification (1976). For the case study the Uimon Basin was chosen. Social and economic factors are so far excluded but can be added within a further phase of development. / Diese Doktorarbeit beschreibt Methoden und geeignete Anpassungen bereits existierender Lösungen, um auf zwei verschiedenen Wegen die Landeignung für die Tal- und Beckenregionen der Südsibirischen Altaigebirges innerhalb eines Geoinformationssystems zu modellieren (GIS). Die Ausgangsmethoden sind: 1) die Bodeneignungsmodelle „Almagra" and „Cervatana“ (MicroLEIS System), entwickelt für die Mittelmeerregionen (De la Rosa et al. 1992 and 1998) und die „Gewichtsmethode“, welche Burlakova L. M. (1988) speziell für die Altairegion entwickelte. Letztgenannte Methode basiert auf den gewichteten Mitteln für eine gegebene Anzahl von Faktoren. 2) Zum Vergleich, die zweite, dritte und vierte Version des gleichen Modells mit drei unterschiedlichen Typen wurden mit Fuzzy-Logik-Methoden entwickelt. Sie werden benutzt, um darzustellen, wie unscharfe Mengen zum einen die Berechnung von Gauß-Mitgliedschaftsfunktionen bestimmter Klassen veranschaulichen können, welche zu anderen Klassen gehören, und wie die Variablen in einer mathematischen Handhabung angefasst werden können. Außerdem stellt diese Arbeit Ideen vor, wie die Fernerkundung das Geoinformationssystem (GIS) eingesetzt werden kann, wenn - wie im vorliegenden Fall - nur unzureichend Geodaten vorhanden sind, (i) um in die Modellierung der Boden- und Klimabedingungen einzugehen und (ii) um die Charakteristik des Landmanagements im Untersuchungsgebiet zu kennzeichnen. Drei landwirtschaftliche Agrarkulturen (Sommerweizen, Sonnenblumen und Kartoffeln) sind für die Altairegion auf regionaler Ebene von Bedeutung und wurden daher in die vorliegende Untersuchung einbezogen. Die Bewertung erfolgte nach fünf Eignungskategorien, entsprechend der FAO Klassifikation (1976). Das Uimon-Becken wurde als Untersuchungsgebiet ausgewählt. Soziale und ökonomische Faktoren wurden bisher ausgeschlossen, können aber innerhalb einer weiteren Entwicklungsphase hinzugenommen werden.

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 doctoral thesis describes methodologies and appropriate adaptations of existing solutions to model land suitability in two ways for the valley and basin areas of the South-Siberian Altai Mountains within a geo-information system (GIS) environment. Starting-point approaches are: 1) the Agricultural Soil Suitability Model „Almagra” and Land Capability Model “Cervatana”/MicroLEIS System (De la Rosa et. al 1992, 1998) developed for Mediterranean regions and a method specifically compiled by Burlakova L. M. (1988) for the Altai based on the weighted means of a factor set. 2) For comparison purposes, second, third and fourth versions of the same model are developed using three different types of Fuzzy Logic approaches. They are used to present how Gauss membership functions of particular classes can be computed as different classes and how variables taking values in ranges can be handled in a mathematical way. Furthermore, the paper presents ideas on how remote sensing might interact with the geo-information system (GIS) where - like in the present case – the required input geo-data are not fully sufficient to (i) feed the models formalising soil and climatic conditions, and (ii) to characterise the patterns of land management within the study area. Three agricultural crops (summer wheat, sunflowers and potatoes) are relevant to the Altai Region at a regional level and are, therefore considered. A rating is classified using five suitability classes according to the FAO classification (1976). For the case study the Uimon Basin was chosen. Social and economic factors are so far excluded but can be added within a further phase of development. / Diese Doktorarbeit beschreibt Methoden und geeignete Anpassungen bereits existierender Lösungen, um auf zwei verschiedenen Wegen die Landeignung für die Tal- und Beckenregionen der Südsibirischen Altaigebirges innerhalb eines Geoinformationssystems zu modellieren (GIS). Die Ausgangsmethoden sind: 1) die Bodeneignungsmodelle „Almagra" and „Cervatana“ (MicroLEIS System), entwickelt für die Mittelmeerregionen (De la Rosa et al. 1992 and 1998) und die „Gewichtsmethode“, welche Burlakova L. M. (1988) speziell für die Altairegion entwickelte. Letztgenannte Methode basiert auf den gewichteten Mitteln für eine gegebene Anzahl von Faktoren. 2) Zum Vergleich, die zweite, dritte und vierte Version des gleichen Modells mit drei unterschiedlichen Typen wurden mit Fuzzy-Logik-Methoden entwickelt. Sie werden benutzt, um darzustellen, wie unscharfe Mengen zum einen die Berechnung von Gauß-Mitgliedschaftsfunktionen bestimmter Klassen veranschaulichen können, welche zu anderen Klassen gehören, und wie die Variablen in einer mathematischen Handhabung angefasst werden können. Außerdem stellt diese Arbeit Ideen vor, wie die Fernerkundung das Geoinformationssystem (GIS) eingesetzt werden kann, wenn - wie im vorliegenden Fall - nur unzureichend Geodaten vorhanden sind, (i) um in die Modellierung der Boden- und Klimabedingungen einzugehen und (ii) um die Charakteristik des Landmanagements im Untersuchungsgebiet zu kennzeichnen. Drei landwirtschaftliche Agrarkulturen (Sommerweizen, Sonnenblumen und Kartoffeln) sind für die Altairegion auf regionaler Ebene von Bedeutung und wurden daher in die vorliegende Untersuchung einbezogen. Die Bewertung erfolgte nach fünf Eignungskategorien, entsprechend der FAO Klassifikation (1976). Das Uimon-Becken wurde als Untersuchungsgebiet ausgewählt. Soziale und ökonomische Faktoren wurden bisher ausgeschlossen, können aber innerhalb einer weiteren Entwicklungsphase hinzugenommen werden.

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.

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