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Aufgrund oftmals erhöhter Arsen- und Schwermetallgehalte in den Oberböden von Auen ist eine Verwertung von Auenböden, zum Beispiel im Rahmen von Renaturierungsmaßnahmen, oftmals schwierig, da keine auenspezifische Bewertungsgrundlage für dieser Stoffgehalte vorliegt.
Am Beispiel der Lippeaue erfolgte auf Grundlage abgeleiteter Haupteinflussfaktoren für die Konzentration von Arsen- und Schwermetallen in den Oberböden die Ableitung von Hintergrundgehalten. Die Metall- und Schwermetallgehalte in den Oberböden der Lippeaue lagen i. d. R. erwartungsgemäß oberhalb der in der Praxis verwendeten Einstufungswerte (z. B. Vorsorgewerte der BBodSchV, Hintergrundwerte von NRW), die sich allerdings auf Standorte außerhalb der Aue beziehen.
Anhand von gewonnen Daten aus Projektarbeiten im Zuge der Umsetzung der EU-Wasserrahmenrichtlinie (z. B. bodenkundliche und ingenieurgeologische Profilaufnahme, bodenmechanische Laborversuche) und ergänzend durchgeführter Untersuchungen, wie z. B. Bodentypenkartierung und mineralogische Untersuchungen, erfolgte eine detaillierte Standortcharakterisierung der Böden in der Lippeaue.
Auf Basis der abgeleiteten Hintergrundgehalte wurden unter Berücksichtigung der ermittelten Bodenkennwerte und Einflussgrößen auenspezifische Einbauregeln abgeleitet. Mit dem Ziel einer praktikablen Anwendbarkeit wurde eine auf die wesentlichen Kenngrößen reduzierte Bewertungsmatrix erarbeitet. Bei geplanten baulichen Eingriffen in den Boden kann nun mit den lippeauenspezifischen Einbauwerten für Arsen und Schwermetalle anhand konventioneller Parameter für jeden Standort ermittelt werden, ob besonders günstige oder ungünstige Bedingungen für einen potenziellen Wiedereinbau vorliegen. Die abgeleiteten Hintergrundgehalte und Einbauwerte verstehen sich dabei als – auf Basis der aktuellen Datenlage abgeleitete – Handlungsempfehlung und Orientierung zur Bewertung von Böden im Hinblick auf einen gebietsübergreifenden Wiedereinbau in der Lippeaue.
Global food security poses large challenges to a fast changing human society and has been a key topic for scientists, agriculturist, and policy makers in the 21st century. The United Nation predicts a total world population of 9.15 billion in 2050 and defines the provision of food security as the second major point in the UN Sustainable Development Goals. As the capacities of both, land and water resources, are finite and locally heavily overused, reducing agriculture’s environmental impact while meeting an increasing demand for food of a constantly growing population is one of the greatest challenges of our century. Therefore, a multifaceted solution is required, including approaches using geospatial data to optimize agricultural food production.
The availability of precise and up-to-date information on vegetation parameters is mandatory to fulfill the requirements of agricultural applications. Direct field measurements of such vegetation parameters are expensive and time-consuming. On the contrary, remote sensing offers a variety of techniques for a cost-effective and non-destructive retrieval of vegetation parameters. Although not widely used, hyperspectral thermal infrared (TIR) remote sensing has demonstrated being a valuable addition to existing remote sensing techniques for the retrieval of vegetation parameters.
This thesis examined the potential of TIR imaging spectroscopy as an important contribution to the growing need of food security. The main scientific question dealt with the extraction of vegetation parameters from imaging TIR spectroscopy. To this end, two studies impressively demonstrated the ability of extracting vegetation related parameters from leaf emissivity spectra: (i) the discrimination of eight plant species based on their emissivity spectra and (ii) the detection of drought stress in potato plants using temperature measures and emissivity spectra.
The datasets used in these studies were collected using the Telops Hyper-Cam LW, a novel imaging spectrometer. Since this FTIR spectrometer presents some particularities, special attention was paid on the development of dedicated experimental data acquisition setups and on data processing chains. The latter include data preprocessing and the development of algorithms for extracting precise surface temperatures, reproducible emissivity spectra and, in the end, vegetation parameters.
The spectrometer’s versatility allows the collection of airborne imaging spectroscopy datasets. Since the general availability of airborne TIR spectrometers is limited, the preprocessing and
data extraction methods are underexplored compared to reflective remote sensing. This counts especially for atmospheric correction (AC) and temperature and emissivity separation (TES) algorithms. Therefore, we implemented a powerful simulation environment for the development of preprocessing algorithms for airborne hyperspectral TIR image data. This simulation tool is designed in a modular way and includes the image data acquisition and processing chain from surface temperature and emissivity to the final at-sensor radiance data. It includes a series of available algorithms for TES, AC as well as combined AC and TES approaches. Using this simulator, one of the most promising algorithms for the preprocessing of airborne TIR data – ARTEMISS – was significantly optimized. The retrieval error of the atmospheric water vapor during the atmospheric characterization was reduced. As a result, this improvement in atmospheric characterization accuracy enhanced the subsequent retrieval of surface temperatures and surface emissivities intensely.
Although, the potential of hyperspectral TIR applications in ecology, agriculture, and biodiversity has been impressively demonstrated, a serious contribution to a global provision of food security requires the retrieval of vegetation related parameters with global coverage, high spatial resolution and at high revisit frequencies.
Emerging from the findings in this thesis, the spectral configuration of a spaceborne TIR spectrometer concept was developed. The sensors spectral configuration aims at the retrieval of precise land surface temperatures and land surface emissivity spectra. Complemented with additional characteristics, i.e. short revisit times and a high spatial resolution, this sensor potentially allows the retrieval of valuable vegetation parameters needed for agricultural optimizations. The technical feasibility of such a sensor concept underlines the potential contribution to the multifaceted solution required for achieving the challenging goal of guaranteeing global food security in a world of increasing population.
In conclusion, thermal remote sensing and more precisely hyperspectral thermal remote sensing has been presented as a valuable technique for a variety of applications contributing to the final goal of a global food security.
Reptiles belong to a taxonomic group characterized by increasing worldwide population declines. However, it has not been until comparatively recent years that public interest in these taxa has increased, and conservation measures are starting to show results. While many factors contribute to these declines, environmental pollution, especially in form of pesticides, has seen a strong increase in the last few decades, and is nowadays considered a main driver for reptile diversity loss. In light of the above, and given that reptiles are extremely underrepresented in ecotoxicological studies regarding the effects of plant protection products, this thesis aims at studying the impacts of pesticide exposure in reptiles, by using the Common wall lizard (Podarcis muralis) as model species. In a first approach, I evaluated the risk of pesticide exposure for reptile species within the European Union, as a means to detect species with above average exposure probabilities and to detect especially sensitive reptile orders. While helpful to detect species at risk, a risk evaluation is only the first step towards addressing this problem. It is thus indispensable to identify effects of pesticide exposure in wildlife. For this, the use of enzymatic biomarkers has become a popular method to study sub-individual responses, and gain information regarding the mode of action of chemicals. However, current methodologies are very invasive. Thus, in a second step, I explored the use of buccal swabs as a minimally invasive method to detect changes in enzymatic biomarker activity in reptiles, as an indicator for pesticide uptake and effects at the sub-individual level. Finally, the last part of this thesis focuses on field data regarding pesticide exposure and its effects on reptile wildlife. Here, a method to determine pesticide residues in food items of the Common wall lizard was established, as a means to generate data for future dietary risk assessments. Subsequently, a field study was conducted with the aim to describe actual effects of pesticide exposure on reptile populations at different levels.