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 assess the quality of the data reconstruction. Both instantaneous and aggregated indicators derived from LST maps match related meteorological indicators with statistical significance. The correlation with thermal maps from LANDSAT-TM is less strong due to different sensor resolutions and a time shift between the overpasses of the LANDSAT-TM and Terra satellites.As a result, a completely reconstructed remotely sensed thermal data set is available for parts of Northern Italy. Using temperature gradient based models which have been developed within the thesis together with high resolution elevation maps, it was also possible to increase the original resolution of the LST maps from 1,000 m to 200 m pixel size. Due to the subsequent aggregations of daily data, different derived temperature indicator data sets are now available at various temporal resolutions. In fact, more than 11,000 maps have been produced for the study area in Northern Italy. The produced maps were then applied in two case studies on disease vectors in order to understand seasonality and spatial distribution. The aggregated LST maps were used as input variables in these case studies.In the first case study on the hard tick Ixodes ricinus, time series of larvae and nymphs counts were enriched with time series of LST derived ecological indicators. Since it was demonstrated by comparison with meteorological data that the statistical significance of the LST data is high, an integration of further tick data will help to determine better temperature based models.A second case study was performed on the invasive mosquito Aedes albopictus, a species known to be spreading in Northern Italy. Only two out of 594 positive municipalities result outside of the predicted distribution area of Aedes albopictus (false negative error of 0.3%). Reconstructed MODIS LST data can be accepted as a valid proxy for analysing the temperature profile in relation to mosquito survival.

Markus Neteler received his MSc degree in Physical Geography and Landscape Ecology from the University of Hanover in Germany in 1999. He worked at the Institute of Geography as Research Scientist and teaching associate for two years. From 2001-2007, he was a researcher at FBK-irst (formerly ITC-irst), from 2005-2007 also at Centro di Ecologia Alpina. Since 2008 he is employed at Fondazione Edmund Mach (FEM), Trento, Italy, as coordinator of the GIS-Remote Sensing unit. His main research interests are remote sensing for environmental risk assessment and Free Software GIS development. He is author/co-author of two books on the Open Source Geographical Information System GRASS and various papers on applications in GIS. He is founding-member of the GRASS Anwender-Vereinigung e.V. (Germany) and the Open Source Geospatial Foundation (OSGeo.org, USA). In September 2006, he was honored with the Sol Katz Award for Geospatial Free and Open Source Software (GFOSS). In 2010, he obtained the the degree of a Doctor of Natural Science (Dr. rer.nat) in Physical Geography. Markus Neteler is head of the GIS and Remote Sensing Unit at Fondazione Edmund Mach.