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Remote sensing for disease risk profiling: a spatial analysis of schistosomiasis in West Africa
(2014)
Global environmental change leads to the emergence of new human health risks. As a consequence, transmission opportunities of environment-related diseases are transformed and human infection with new emerging pathogens increase. The main motivation for this study is the considerable demand for disease surveillance and monitoring in relation to dynamic environmental drivers. Remote sensing (RS) data belong to the key data sources for environmental modelling due to their capabilities to deliver spatially continuous information repeatedly for large areas with an ecologically adequate spatial resolution.
A major research gap as identified by this study is the disregard of the spatial mismatch inherent in current modelling approaches of profiling disease risk using remote sensing data. Typically, epidemiological data are aggregated at school or village level. However, these point data do neither represent the spatial distribution of habitats, where disease-related species find their suitable environmental conditions, nor the place, where infection has occurred. As a consequence, the prevalence data and remotely sensed environmental variables, which aim to characterise the habitat of disease-related species, are spatially disjunct.
The main objective of this study is to improve RS-based disease risk models by incorporating the ecological and spatial context of disease transmission. Exemplified by the analysis of the human schistosomiasis disease in West Africa, this objective includes the quantification of the impact of scales and ecological regions on model performance.
In this study, the conditions that modify the transmission of schistosomiasis are reviewed in detail. A conceptual underpinning of the linkages between geographical RS measures, disease transmission ecology, and epidemiological survey data is developed. During a field-based analysis, environmental suitability for schistosomiasis transmission was assessed on the ground, which is then quantified by a habitat suitability index (HSI) and applied to RS data. This conceptual model of environmental suitability is refined by the development of a hierarchical model approach that statistically links school-based disease prevalence with the ecologically relevant measurements of RS data. The statistical models of schistosomiasis risk are derived from two different algorithms; the Random Forest and the partial least squares regression (PLSR). Scale impact is analysed based on different spatial resolutions of RS data. Furthermore, varying buffer extents are analysed around school-based measurements. Three distinctive sites of Burkina Faso and Côte d’Ivoire are specifically modelled to represent a gradient of ecozones from dry savannah to tropical rainforest including flat and mountainous regions.
The model results reveal the applicability of RS data to spatially delineate and quantitatively evaluate environmental suitability for the transmission of schistosomiasis. In specific, the multi-temporal derivation of water bodies and the assessment of their riparian vegetation coverage based on high-resolution RapidEye and Landsat data proofed relevant. In contrast, elevation data and water surface temperature are constraint in their ability to characterise habitat conditions for disease-related parasites and freshwater snail species. With increasing buffer extent observed around the school location, the performance of statistical models increases, improving the prediction of transmission risk. The most important RS variables identified to model schistosomiasis risk are the measure of distance to water bodies, topographic variables, and land surface temperature (LST). However, each ecological region requires a different set of RS variables to optimise the modelling of schistosomiasis risk. A key result of the hierarchical model approach is its superior performance to explain the spatial risk of schistosomiasis.
Overall, this study stresses the key importance of considering the ecological and spatial context for disease risk profiling and demonstrates the potential of RS data. The methodological approach of this study contributes substantially to provide more accurate and relevant geoinformation, which supports an efficient planning and decision-making within the public health sector.
The glaciers in Norway exert a strong influence on Norwegian economy and society. Unlike many glaciers elsewhere and despite ongoing climate change and warming, many of them showed renewed advances and positive net mass changes in the 1980's and 1990's, followed by rapid retreats and mass losses since 2000. This difference in behaviour may be attributed to differences and shifts in the glaciological regime - the differences in the magnitude of impacts of climatic and non-climatic geographical factors on the glacier mass.
This study investigates the influence of various atmospheric variables on mass balance changes of a selection of glaciers in Norway by means of Pearson correlation analyses and cross-validated stepwise multiple regression analyses. The analyses are carried out for three time periods (1949-2008, 1949-1988, 1989-2008) separately in order to take into consideration the possible shift in the glaciological regime in the 1980's. The atmospheric variables are constructed from ERA40 and NCEP/NCAR re-analysis datasets and include regional means of seasonal air temperature and precipitation rates and atmospheric circulation indices. The multiple regression models trained in these time periods are then applied to predictors reconstructed from the CMIP3 climate model dataset to generate an estimate for mass changes from the year 1950 to 2100. The temporal overlap of estimates and observations is used for calibration. Finally, observed atmospheric states in seasons that are characterised by a particularly positive or negative mass balance are categorised into time periods of modelled climate by the application of a Bayesian classification procedure.
The strongest influence on winter mass balance is exerted by different indices of the North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and precipitation. The correlation coefficients and explained variances determined from the multiple regression analyses reveal an East-West gradient, suggesting a weaker influence of the NAO and NAM on glaciers underlying a more continental regime. The highest correlation coefficients and explained variances were obtained for the 1989-2008 time period, which might be due to a strong and predominantly positive phase of the NAO. Multi-model ensemble means of the estimates show a mass loss for all three eastern glaciers, while the estimates for the more maritime glaciers are ambivalent. In general, the estimates show a greater sensitivity to the training time period than to the greenhouse gas emission scenarios according to which the climates were simulated. The average net mass change by the end of 2100 is negative for all glaciers except for the northern Engabreen. For many glaciers, the Bayesian classification of observed atmospheric states into time periods of modelled climate reveals a decrease in probability of atmospheric states favouring extremes in winter, and an increase in probability of atmospheric states favouring extreme mass loss in summer for the distant future (2071-2100). This pattern of probabilities for the ablation season is most pronounced for glaciers underlying a continental and intermediate regime.