Abandoned mines present considerable environmental risks through the release of toxic metals, particularly into adjacent water bodies. The implementation of reliable geotechnical cut-off walls containing bentonite stands as a crucial strategy in mitigating environmental groundwater contamination. This study evaluates the viability of employing the methylene blue titration method to quantify bentonite content in till samples, with the objective of verifying the assumed bentonite content (3.5%) and exploring the impact of sample preparation on bentonite content determined through titrations. Bentonite, known for its swelling properties and high cation exchange capacity, was introduced into till samples both in laboratory settings and on-site. The methylene blue titration method was utilized to measure bentonite content in these samples. Laboratory results indicated significant variations in bentonite content depending on the sample preparation method, with methods employing soil homogenization proving more effective in quantifying bentonite accurately. The study's findings support the hypothesis that the methylene blue method can reliably quantify bentonite in till soil samples with the correct preparation in a laboratory environment. On-site analysis exhibited larger variations and deviations in bentonite content quantifications compared to laboratory-based incorporations. Additionally, on-site incorporated samples demonstrated a statistically significant difference in bentonite content compared to laboratory-incorporated samples, highlighting the method's sensitivity and need for future research. This research contributes valuable insights into leveraging methylene blue titration for quantifying bentonite in till soils, crucial for establishing geotechnical barriers to control toxic metal output from mines.

This paper describes a unique method for the semi-automatic detection of historic charcoal production sites in LiDAR-derived digital elevation models. Intensified iron production in the early 17th century has remarkably influenced ways of how the land in Sweden was managed. Today, the abundance of charcoal kilns embedded in the landscape survives as cultural heritage monuments that testify about the scale forest management for charcoal production has contributed to the uprising iron manufacturing industry. An arbitrary selected study area (54 km2) south west of Gävle city served as an ideal testing ground, which is known to consist of already registered as well as unsurveyed charcoal kiln sites. The proposed approach encompasses combined morphometric classification methods being subjected to analytical image processing, where an image that represents refined terrain morphology was segmented and further followed by Hough Circle transfer function applied in seeking to detect circular shapes that represent charcoal kilns. Sites that have been identified manually and using the proposed method were only verified within an additionally established smaller validation area (6 km2). The resulting outcome accuracy was measured by calculating harmonic mean of precision and recall (F1-Score). Along with indication of previously undiscovered site locations, the proposed method showed relatively high score in recognising already registered sites after post-processing filtering. In spite of required continual fine-tuning, the described method can considerably facilitate mapping and overall management of cultural resources.

Lake Tanganyika is a complex, tropical ecosystem in East Africa, harboring an estimated 2,100 species. Extensive watershed deforestation threatens the biodiversity and ecological integrity of the lake. In this dissertation, ecological and paleoecological methods were employed to study the distribution of invertebrate biodiversity through space and time, with particular emphasis on linkages between biodiversity and land –use patterns. Ecological surveys of fish, mollusc, and ostracod crustacean diversity at sites in northern Lake Tanganyika representing different levels of watershed disturbance revealed a negative correlation between biodiversity and intensity of watershed disturbance. To elucidate the long -term relationship between disturbance and biodiversity, paleoecological records of invertebrates offshore from watersheds experiencing different degrees of anthropogenic disturbance were examined. Life, death, and fossil assemblages of ostracod valves were compared to assess the reliability and natural variability inherent to the paleoecological record. These comparisons indicated that paleoecological (i.e. death and fossil) assemblages reliably preserve information on species richness, abundance, and occurrence frequency at comparable -to- annual resolution. Unlike life assemblages, species composition of paleoecological assemblages reflects input of species from multiple habitat types. Ostracod paleoecological assemblages are characterized by spatiotemporal averaging that renders them representative of larger areas and longer time spans than life assemblages. Thus, paleoecological assemblages provide an efficient means of characterizing longer -term, site -average conditions. Natural variability in ostracod fossil assemblages from a sediment core representing the Late Glacial to the present indicates that abundance of individual ostracod species is highly variable. Ostracod assemblages were preserved in only the most recent 2,500 years of sediment. Species composition of ostracod assemblages reflects lake water depth. Core geochemical data indicate that the coring site may have been below the oxycline for ~2,000 years, inhibiting ostracod survival and preservation. Paleoecological, sedimentological, and stable isotope data revealed differences in biodiversity and watershed disturbance through time offshore from a pair of sites. The protected site is offshore from Gombe Stream National Park (Tanzania), the other offshore from a deforested watershed outside the park. Offshore from the deforested watershed, sedimentation rates increased, and turnover in ostracod species composition occurred during the past 50 years. Comparable changes were not observed offshore from the park.

Diese Arbeit untersucht, inwiefern Visual Analytics die Analyse von Prozessen in geowissenschaftlichen raum-zeitlichen Daten unterstützen kann. Hierzu wurden drei neuartige Visual Analytics Ansätze entwickelt. Jeder Ansatz addressiert eine wichtige Analyseperspektive. Der erste Ansatz erlaubt es, wichtige räumliche Zustände in den Daten sowie deren auftreten in der Zeit zu untersuchen. Mittels hierarchischem Clustering werden alle in den Daten enthaltenen räumlichen Zustände in einer Clusterhierarchie verortet. Interaktive visuelle Analyse ermöglicht es, verschiedene räumliche Zustände aus den Daten zu extrahieren und die dazugehörigen raum-zeitlichen Muster zu interpretieren und zu bewerten. Der zweite Ansatz unterstützt die systematische Analyse des in den Daten zu beobachtenden zeitlichen Verhaltens sowie dessen Auftreten im geographischen Raum mittels einer Kombination aus Cluster Ensembles und interaktiver visueller Exploration. Der dritte Ansatz gestattet die Detektion und Analyse von zeitlichen Zusammenhängen in den Daten. Hierzu wurde eine etablierte Methode zur Analyse von zeitlichen Zusammenhängen zwischen zwei einzelnen Zeitreihen, gefensterte Kreuzkorrelation, durch Visual Analytics auf den Vergleich von Zeitreihenensembles erweitert. Dadurch ist es nicht nur möglich, Zusammenhänge zwischen Zeitreihen zu untersuchen, sondern auch Unsicherheiten in den Daten zu berücksichtigen. Alle Ansätze wurden anhand einer nutzer- und aufgabenorientierten Methodik entwickelt und erfolgreich in Anwendungsfällen aus der Erdsystem-Modellierung, der Ozeanmodellierung, der Paläoklimatologie und sogar den Kognitionswissenschaften eingesetzt. Diese Dissertation zeigt, dass Visual Analytics einen wertvollen Ansatz zur Analyse von Prozess-bezogenen Mustern in raum-zeitlichen Daten darstellt. Es kann die Grenzen existierender Analysemethoden erweitern und ermöglicht Geowissenschaftlern neue, aufschlussreiche Sichtweisen auf Daten und die darin beschriebenen Prozesse. / This thesis studied how visual analytics can facilitate the analysis of processes in geoscientific spatiotemporal data. Three novel visual analytics solutions were developed, each addressing an important analysis perspective. The first solution addresses the analysis of prominent spatial situations in the data and their occurrence over time. Hierarchical clustering is used to arrange all spatial situations in the data in a hierarchy of clusters. The combination with interactive visual analysis enables geoscientists to explore and alter the resulting hierarchy, to extract different sets of representative spatial situations, and to interpret and assess the corresponding spatiotemporal patterns. The second solution supports geoscientists in the analysis of prominent types of temporal behavior and their location in geographic space. Cluster ensembles are integrated with interactive visual exploration to enable users to systematically detect and interpret various types of temporal behavior in different data sets and to use this information for assessment of simulation model output. The third solution enables geoscientists to detect and analyze interrelations of temporal behavior in the data. Windowed cross-correlation, a technique for comparison of two individual time series, was extended to the comparison of entire ensembles of time series through visual analytics. This not only allows scientists to study interrelations, but also to assess how much these interrelations vary between two ensembles. All visual analytics solutions were developed following a rigorous user- and task-centered methodology and successfully applied to use cases in Earth system modeling, ocean modeling, paleoclimatology, and even cognitive science. The results of this thesis demonstrate that visual analytics successfully addresses important analysis perspectives and that it is a valuable approach to the analysis of process-related patterns in geoscientific spatiotemporal data.

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