https://repos.hcu-hamburg.de:443 repOS captures, stores, indexes, preserves, and distributes digital research material. Thu, 16 Apr 2026 03:30:09 GMT 2026-04-16T03:30:09Z https://repos.hcu-hamburg.de:443/handle/hcu/1221 Title: Analysis of deep-sea sulphide mounds using high-resolution MBES bathymetry and backscatter data. A case study in the Indian Ocean including optimisation of the processing workflow and sulphide mound detection using deep learning. Authors: Dufek, Tanja Abstract: Sulfidvorkommen in der Tiefsee sind oft als Sulfidhügel an der Meeresbodenoberfläche erkennbar. Sie entstehen an Austrittsstellen hydrothermaler Fluide und sind allgemein als "Schwarze Raucher" bekannt. Die Sulfidvorkommen sind als zukünftige Quelle metallischer Rohstoffe von Interesse. Typische Methoden zur Erkundung hydrothermaler Vorkommen basieren auf der Detektion chemischer Anomalien im Wasser, die von austretenden hydrothermalen Fluiden herrühren. Im Allgemeinen sind erloschene Vorkommen für die Exploration von größerem Interesse, da sie in der Regel älter sind und daher mehr Zeit hatten, um Metallsulfide zu akkumulieren. Im Rahmen dieser Arbeit wird ein Workflow entwickelt, mit dem Sulfidhügel nicht nur von aktiven, sondern auch von erloschenen Hydrothermalquellen detektiert werden können. Als Datenbasis dienen hochaufgelöste Fächerecholotdaten (Bathymetrie 2 m, Rückstreuintensitäten 1 m) eines tiefgeschleppten Systems. Das Untersuchungsgebiet liegt im Bereich des Zentral- sowie Südostindischen Rückens, hat eine Ausdehnung von 782 km2, und umfasst sieben Hydrothermalfelder mit insgesamt 88 aktiven und erloschenen hydrothermalen Vorkommen. DerWorkflow beinhaltet zunächst eine optimierte Nachbearbeitung der Fächerecholotdaten. Anschließend erfolgt auf Basis der Bathymetrie (und ihrer Ableitungen) eine Segmentierung und damit eine "Hügeldetektion" mit Hilfe eines Convolutional Neural Networks (CNN). Abschließend werden die Ergebnisse des CNN, die Bathymetrie, und die Rückstreuintensitäten verwendet, um die Anzahl der detektierten Hügel zu reduzieren. Die Untersuchung des entwickelten Ansatzes zeigt, dass das CNN 55% (6.441 Hügel) der zuvor manuell annotierten Hügel richtig segmentiert. Da nicht alle Hügel im Rahmen der Erkundung untersucht werden können, erfolgt eine Reduktion ihrer Anzahl durch Ausschluss der Hügel, die vermutlich vulkanischen Ursprungs sind. Je nach gewählter Methode kann so die Anzahl der Explorationsziele auf 43% bzw. 20% der ursprünglich detektierten Hügel reduziert werden. Der Vergleich mit den bekannten Hydrothermalfeldern zeigt, dass durch diese Reduktion der Anteil der Sulfidhügel an den verbleibenden, für die Exploration als interessant eingestuften Hügeln, erhöht werden kann. Ihr relativer Anteil kann um den Faktor drei auf 3% erhöht werden. Die Arbeit bestätigt, dass ein automatisierter Prozess zur Erkennung von Sulfidhügeln eingesetzt werden kann. Zur Erhöhung der Erfolgsquote sollte das Deep-Learning-Modell und die Methode zur Unterscheidung der Hügelarten noch weiter optimiert werden. Des Weiteren kann die Klassifizierung durch Daten zusätzlicher Sensoren (zum Beispiel Magnetik) optimiert werden. Insgesamt kann gezeigt werden, dass es sich um einen vielversprechenden Ansatz zur Eingrenzung interessanter Gebiete für weitere Explorationsarbeiten handelt.; Deep-sea sulphide mounds and associated sulphide deposits form at hydrothermal venting sites, which are commonly known as black smokers. These deposits are of economic interest as potential resources because they contain a high concentration of metals. Current exploration methods focus mainly on active sites, as the plume of discharging hot hydrothermal fluid can be detected as a water column anomaly. However, inactive sites are of greater exploration interest, as they are likely to be older than active sites and therefore their deposits are thought to have accumulated more ore over time. A workflow is developed in this study to detect and identify sulphide mounds not only at active but also at inactive hydrothermal sites using only bathymetry (2m resolution) and backscatter (1m resolution) data collected with a deep towed multibeam echo sounder (MBES) system. The MBES data has a coverage of about 782 km2 and covers seven hydrothermal fields with 88 sites in the area of the Central and Southeast Indian Ocean Ridges. The developed workflow consists firstly of improving the MBES bathymetry and backscatter processing workflow to reduce errors and artefacts. Secondly, a convolutional neural network (CNN) is used for segmentation to detect mound structures based on MBES bathymetry derivatives. Finally, the detected mounds are distinguished by their origin (i.e., volcanic or sulphide) based on the combination of MBES backscatter data, MBES bathymetry, and the prediction result obtained by the CNN using deep learning. The evaluation of this approach shows that the CNN detects about 55% (6,441 mounds) of the manually labelled mounds. As these are too many for exploration, their number needs to be reduced by excluding mounds that are most likely volcanic domes. Depending on the method used, the remaining exploration targets can be reduced to about 43% or 20% of the originally identified possible targets. A comparison with known sulphide occurrences shows that the reduction increases the relative proportion of the sulphide mounds within these exploration targets by up to a factor of three to about 3%. The results validate existing studies and show that an automated approach can be applied for sulphide mound detection. Furthermore, the deep learning model and the method for distinguishing mound types need to be further improved. In addition, the integration of data from other sensors (e.g., magnetics) is recommended for an increase in the success rate. Anyhow, it is a promising concept for reducing the amount of potential exploration targets to specific areas of greater exploration interest. Thu, 09 Apr 2026 07:47:31 GMT https://repos.hcu-hamburg.de:443/handle/hcu/1221 2026-04-09T07:47:31Z https://repos.hcu-hamburg.de:443/handle/hcu/1227 Title: Year-Round Modeling of Evaporation and Substrate Temperature of Two Distinct Green Roof Systems Authors: Gößner, Dominik Abstract: This paper presents a novel model for the year-round simulation of evapotranspiration (ET) and substrate temperature on two fundamentally different extensive green roof types: a conventional drainage-based “Economy Roof” and a retention-optimized “Retention Roof” featuring capillary water redistribution. The main scope is to bridge the gap in urban climate adaptation by providing a modeling tool that captures both hydrological and thermal functions of green roofs throughout all seasons, notably including periods with dormancy and low vegetation activity. A key novelty is the explicit and empirically validated integration of core physical processes—water storage layer coupling, explicit rainfall interception, and vegetation cover dynamics—with the latter strongly controlled by plant area index (PAI). The PAI, here quantified as the plant surface area per unit ground area using digital image analysis, directly determines interception capacity and vegetative transpiration rates within the model. This process-based representation enables a more realistic simulation of seasonal fluctuations and physiological plant responses, a feature often neglected in previous green roof models. The model, which can be fully executed without high computational power, was validated against comprehensive field measurements from a temperate climate, showing high predictive accuracy (R2 = 0.87 and percentage bias = −1% for ET on the Retention Roof; R2 = 0.91 and percentage bias = −8% for substrate temperature on the Economy Roof). Notably, the layer-specific coupling of vegetation, substrate, and water storage advances ecological realism compared to prior approaches. The results illustrate the model’s practical applicability for urban planners and researchers, offering a user-friendly and transparent tool for integrated assessments of green infrastructure within the context of climate-resilient city design. Thu, 02 Apr 2026 08:12:08 GMT https://repos.hcu-hamburg.de:443/handle/hcu/1227 2026-04-02T08:12:08Z https://repos.hcu-hamburg.de:443/handle/hcu/1219 Title: Navigating conflictual cooperation: Temporary power coalitions in the planning and approval of large-scale Chinese green technology projects in Eastern Germany Authors: Langguth, Hannes Abstract: The article examines the multifaceted conflicts that emerge during the planning and approval procedures of two large-scale Chinese green technology projects and their associated manufacturing, logistics, and energy infrastructures in Thuringia and Saxony-Anhalt, Eastern Germany. The novelty, complexity, and scale of these projects, coupled with divergent conceptions, interests, and cultural norms in Sino-German cooperation, place significant pressure on planning, administration, and policy professionals, particularly at the local level. By conceptualising planning conflicts as dynamic formations of the political, the article investigates how such conflicts arise, are navigated, and managed, at the same time shedding light on the institutional frameworks that guide professionals’ interactions. The findings indicate that, although both cases encountered similar areas of conflict, the responses differed, shaped by varying power coalitions. In Thuringia, intense state intervention and a coalition with Chinese investors facilitated successful project implementation. Conversely, Saxony-Anhalt experienced resistance, despite state efforts, resulting in a coalition characterised by scepticism towards Chinese involvement. The article thus leverages planning conflicts to analyse the dynamic nature of underlying interests and power relations among professionals and their cooperation partners in large-scale green energy projects. Wed, 01 Apr 2026 09:57:19 GMT https://repos.hcu-hamburg.de:443/handle/hcu/1219 2026-04-01T09:57:19Z https://repos.hcu-hamburg.de:443/handle/hcu/1224 Title: The polysemous nature of the German Verkehrswende—Exploring the role of floating signifiers in shaping mobility futures Authors: Ertelt, Sophie-Marie; Hawxwell, Tom Abstract: The German transportation sector's negative contribution to climate change amongst broader social, environmental, and economic problems is applying evermore pressure to the prevailing automobility regime to bring about its transformation. However, the vision of this transition, referred to as the Verkehrswende or Mobilitätswende, is highly contested, with varying conceptions of different actors about the future of mobility in Germany. A discourse network analysis (DNA) is performed to examine the development of the related policy debate, identify key problem and solution framings and analyse the overall discourse evolution from 2018 to mid-2023. The findings highlight how recent exogenous events shape and reframe the discourse, inciting debates around viable mobility futures. Further, our analysis uncovers a novel discursive strategy termed repugnostic framing, through which incumbent actors aim to oppose the framings of other discursive agents, leading to increased lines of conflict and polarisation, thus possibly hindering effective policy implementation. Tue, 31 Mar 2026 12:39:03 GMT https://repos.hcu-hamburg.de:443/handle/hcu/1224 2026-03-31T12:39:03Z