Refine
Year of publication
Document Type
- Doctoral Thesis (787)
- Article (187)
- Book (111)
- Contribution to a Periodical (102)
- Working Paper (58)
- Part of Periodical (21)
- Conference Proceedings (17)
- Other (16)
- Review (10)
- Master's Thesis (9)
Language
- German (799)
- English (452)
- French (75)
- Multiple languages (15)
- Russian (1)
Keywords
- Deutschland (78)
- Luxemburg (51)
- Stress (39)
- Schule (34)
- Schüler (30)
- Politischer Unterricht (28)
- Fernerkundung (25)
- Modellierung (25)
- Geschichte (24)
- Demokratie (23)
Institute
- Psychologie (206)
- Geographie und Geowissenschaften (205)
- Politikwissenschaft (125)
- Universitätsbibliothek (82)
- Rechtswissenschaft (77)
- Mathematik (65)
- Wirtschaftswissenschaften (64)
- Medienwissenschaft (56)
- Fachbereich 4 (45)
- Geschichte, mittlere und neuere (45)
Behavioural traces from interactions with digital technologies are diverse and abundant. Yet, their capacity for theory-driven research is still to be constituted. In the present cumulative dissertation project, I deliberate the caveats and potentials of digital behavioural trace data in behavioural and social science research. One use case is online radicalisation research. The three studies included, set out to discern the state-of-the-art of methods and constructs employed in radicalization research, at the intersection of traditional methods and digital behavioural trace data. Firstly, I display, based on a systematic literature review of empirical work, the prevalence of digital behavioural trace data across different research strands and discern determinants and outcomes of radicalisation constructs. Secondly, I extract, based on this literature review, hypotheses and constructs and integrate them to a framework from network theory. This graph of hypotheses, in turn, makes the relative importance of theoretical considerations explicit. One implication of visualising the assumptions in the field is to systematise bottlenecks for the analysis of digital behavioural trace data and to provide the grounds for the genesis of new hypotheses. Thirdly, I provide a proof-of-concept for incorporating a theoretical framework from conspiracy theory research (as a specific form of radicalisation) and digital behavioural traces. I argue for marrying theoretical assumptions derived from temporal signals of posting behaviour and semantic meaning from textual content that rests on a framework from evolutionary psychology. In the light of these findings, I conclude by discussing important potential biases at different stages in the research cycle and practical implications.
The larval stage of the European fire salamander (Salamandra salamandra) inhabits both lentic and lotic habitats. In the latter, they are constantly exposed to unidirectional water flow, which has been shown to cause downstream drift in a variety of taxa. In this study, a closed artificial creek, which allowed us to keep the water flow constant over time and, at the same time, to simulates with predefined water quantities and durations, was used to examine the individual movement patterns of marked larval fire salamanders exposed to unidirectional flow. Movements were tracked by marking the larvae with VIAlpha tags individually and by using downstream and upstream traps. Most individuals showed stationarity, while downstream drift dominated the overall movement pattern. Upstream movements were rare and occurred only on small distances of about 30 cm; downstream drift distances exceeded 10 m (until next downstream trap). The simulated flood events increased drift rates significantly, even several days after the flood simulation experiments. Drift probability increased with decreasing body size and decreasing nutritional status. Our results support the production hypothesis as an explanation for the movements of European fire salamander larvae within creeks.
We study planned changes in protective routines after the COVID-19 pandemic: in a survey in Germany among >650 respondents, we find that the majority plans to use face masks in certain situations even after the end of the pandemic. We observe that this willingness is strongly related to the perception that there is something to be learned from East Asians’ handling of pandemics, even when controlling for perceived protection by wearing masks. Given strong empirical evidence that face masks help prevent the spread of respiratory diseases and given the considerable estimated health and economic costs of such diseases even pre-Corona, this would be a very positive side effect of the current crisis.
Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.
Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2
(2022)
Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.
The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.
Measurements of dust emissions and the modeling of dissipation dynamics and total values are related to great uncertainties. Agricultural activity, especially soil cultivation, may be an essential component to calculate and model local and regional dust dynamics and even connect to the global dust cycle. To budget total dust and to assess the impact of tillage, measurement of mobilized and transported dust is an essential but rare basis. In this study, a simple measurement concept with Modified Wilson and Cook samplers was applied for dust measurements on a small temporal and spatial scale on steep-slope vineyards in the Moselle area. Without mechanical impact, a mean horizontal flux of 0.01 g m2 min−1 was measured, while row tillage produced a mean horizontal flux of 5.92 g m2 min−1 of mobilized material and 4.18 g m2 min−1 emitted dust from site (=soil loss). Compared on this singular-event basis, emissions during tillage operations generated 99.89% of total emitted dust from the site under low mean wind velocities. The results also indicate a differing impact of specific cultivation operations, mulching, and tillage tools as well as the additional influence of environmental conditions, with highest emissions on dry soil and with additional wind impact. The dust source function is strongly associated with cultivation operations, implying highly dynamic but also regular and thus predictable and projectable emission peaks of total suspended particles. Detailed knowledge of the effects of mechanical impulses and reliable quantification of the local dust emission inventory are a basis for analysis of risk potential and choice of adequate management options.
Die Praxishefte Demokratische Schulkultur erscheinen halbjährlich und bieten Schulleitungen und Schulpersonal theoretische Grundlagen und praxisorientierte Anleitungen zur demokratiepädagogischen Schulentwicklung. Jedes Themenheft ist jeweils einer demokratiepädagogischen Bauform oder strategischen Frage der Schulentwicklung gewidmet. Die Praxishefte werden allen Luxemburger Schulen als Printausgabe zur Verfügung gestellt und online mit zusätzlichen Materialien und in französischer Fassung vorgehalten.
Forest inventories provide significant monitoring information on forest health, biodiversity,
resilience against disturbance, as well as its biomass and timber harvesting potential. For this
purpose, modern inventories increasingly exploit the advantages of airborne laser scanning (ALS)
and terrestrial laser scanning (TLS).
Although tree crown detection and delineation using ALS can be seen as a mature discipline, the
identification of individual stems is a rarely addressed task. In particular, the informative value of
the stem attributes—especially the inclination characteristics—is hardly known. In addition, a lack
of tools for the processing and fusion of forest-related data sources can be identified. The given
thesis addresses these research gaps in four peer-reviewed papers, while a focus is set on the
suitability of ALS data for the detection and analysis of tree stems.
In addition to providing a novel post-processing strategy for geo-referencing forest inventory plots,
the thesis could show that ALS-based stem detections are very reliable and their positions are
accurate. In particular, the stems have shown to be suited to study prevailing trunk inclination
angles and orientations, while a species-specific down-slope inclination of the tree stems and a
leeward orientation of conifers could be observed.
Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural
practices have been shaping the face of the earth, and today around one third of the ice-free land
mass consists of cropland and pastures. While agriculture is necessary for our survival, the
intensity has caused many negative externalities, such as enormous freshwater consumption, the
loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation.
Some of these externalities can potentially be ameliorated by careful allocation of crops and
cropping practices, while at the same time the state of these crops has to be monitored in order
to assess food security. Modern day satellite-based earth observation can be an adequate tool to
quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to
do so, in terms of input data, reference data and classification algorithms have been constantly
improving over the past 60 years, and we live now in a time where fully operational satellites
produce freely available imagery with often less than monthly revisit times at high spatial
resolution. At the same time, classification models have been constantly evolving from
distribution based statistical algorithms, over machine learning to the now ubiquitous deep
learning.
In this environment, we used an explorative approach to advance the state of the art of crop
classification. We conducted regional case studies, focused on the study region of the Eifelkreis
Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique
role in the regional agricultural system and because of their specific phenologic characteristics
we focused solely on maize fields.
In the first case study, we generated reference data for the years 2009 and 2016 in the study
region by drawing polygons based on high resolution aerial imagery, and used these in
conjunction with RapidEye imagery to produce high resolution maize maps with a random forest
classifier and a gaussian blur filter. We were able to highlight the importance of careful residual
analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in
spite of the severe limitations introduced by the restricted acquisition windows due to cloud
coverage, high quality maps could be produced for two years, and the regional development of
maize cultivation could be quantified.
In the second case study, we used these spatially explicit datasets to link the expansion of biogas
producing units with the extended maize cultivation in the area. In a next step, we overlayed the
maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction
and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type
classification in environmental protection, by producing maps of potential soil hazards, which can
be used by local stakeholders to reallocate certain crop types to locations with less associated
risk.
In our third case study, we used Sentinel-1 data as input imagery, and official statistical records
as maize reference data, and were able to produce consistent modeling input data for four
consecutive years. Using these datasets, we could train and validate different models in spatially
iv
and temporally independent random subsets, with the goal of assessing model transferability. We
were able to show that state-of-the-art deep learning models such as UNET performed
significantly superior to conventional models like random forests, if the model was validated in a
different year or a different regional subset. We highlighted and discussed the implications on
modeling robustness, and the potential usefulness of deep learning models in building fully
operational global crop classification models.
We were able to conclude that the first major barrier for global classification models is the
reference data. Since most research in this area is still conducted with local field surveys, and only
few countries have access to official agricultural records, more global cooperation is necessary to
build harmonized and regionally stratified datasets. The second major barrier is the classification
algorithm. While a lot of progress has been made in this area, the current trend of many appearing
new types of deep learning models shows great promise, but has not yet consolidated. There is
still a lot of research necessary, to determine which models perform the best and most robust,
and are at the same time transparent and usable by non-experts such that they can be applied
and used effortlessly by local and global stakeholders.