Spatio-temporal reconstruction of satellite-based temperature maps and their application to the prediction of tick and mosquito disease vector distribution in Northern Italy

High temporal resolution data from remote sensing are of great relevance to the modelling of disease transmitting ectoparasites since they allow an assessment of vector and disease distribution and their potential spread. However, despite its potential, up to now, remote sensing has been used far below the expectations expressed in epidemiological literature. In the present thesis, an innovative approach has been proposed for reconstructing incomplete time series of the new MODIS Land Surface Temperature (LST) sensor onboard the Terra and Aqua satellites. MODIS data are generated at daily resolution and freely available usually less than one week after image acquisition on a NASA server. Unfortunately, the satellite maps produced by this sensor are incomplete because cloud cover "contaminates" the data, and the maps also contain other pixel dropouts. Completion of these maps is essential for an efficient GIS based time series modelling, since these models can only be developed with complete data sets. The MODIS LST map reconstruction was executed by performing an automated data download, reprojection to a commonly used map projection system, data format conversion for the GIS import, and a complex procedure to eliminate temperature outliers and to reconstruct the LST datum in areas with no data. For this last procedure, temperature gradient based models were used. Input data points were subsequently interpolated with volumetric splines to obtain complete LST maps. Subsequently, these reconstructed daily LST maps were aggregated with various ecological indicators and were also thresholded to be able to search for signals relevant to tick and mosquito related ecological processes (e.g., onset of ticks activity in spring; mosquito moulting between life stages, etc.). The obtained daily and aggregated LST maps were also compared to meteorological temperature measurements (instantaneous and aggregated measures) as well as to thermal maps from LANDSAT-TM in order to asses