Diseases associated with viruses also found in environmental samples cause major health problems in developing countries. Little is known about the frequency and pattern of viral contamination of drinking water sources in these resource-poor settings. We established a method to analyze 10 liters of water from drinking water sources in a rural area of Benin for the presence of adenoviruses and rotaviruses. Overall, 541 samples from 287 drinking water sources were tested. A total of 12.9% of the sources were positive for adenoviruses and 2.1% of the sources were positive for rotaviruses at least once. Due to the temporary nature of viral contamination in drinking water sources, the probability of virus detection increased with the number of samples taken at one test site over time. No seasonal pattern for viral contaminations was found after samples obtained during the dry and wet seasons were compared. Overall, 3 of 15 surface water samples (20%) and 35 of 247 wells (14.2%) but also 2 of 25 pumps (8%) tested positive for adenoviruses or rotaviruses. The presence of latrines within a radius of 50 m in the vicinity of pumps or wells was identified as being a risk factor for virus detection. In summary, viral contamination was correlated with the presence of latrines in the vicinity of drinking water sources, indicating the importance of appropriate decision support systems in these socioeconomic prospering regions.

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The working group for GIS & Remote Sensing at the Department of Geography at the University of Cologne has established a WebGIS called CampusGIS of the University of Cologne. The overall task of the CampusGIS is the connection of several existing databases at the University of Cologne with spatial data. These existing databases comprise data about staff, buildings, rooms, lectures, and general infrastructure like bus stops etc. These information were yet not linked to their spatial relation. Therefore, a GIS-based method is developed to link all the different databases to spatial entities. Due to the philosophy of the CampusGIS, an online-GUI is programmed which enables users to search for staff, buildings, or institutions. The query results are linked to the GIS database which allows the visualization of the spatial location of the searched entity. This system was established in 2005 and is operational since early 2006. In this contribution, the focus is on further developments. First results of (i) including routing services in, (ii) programming GUIs for mobile devices for, and (iii) including infrastructure management tools in the CampusGIS are presented. Consequently, the CampusGIS is not only available for spatial information retrieval and orientation. It also serves for on-campus navigation and administrative management.

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The marginality index for agricultural land use, which was originally defined on a global scale, was used to evaluate agricultural land resources of Benin. For the assessment of the index, several biophysical factors limiting agricultural production under low capital input are analyzed using a fuzzy logic based algorithm. For Benin, we determined the marginality index (MI) successfully in a spatial resolution of 1km x 1km using influencing factors with a higher spatial resolution and an adapted fuzzy logic based algorithm. The results of the approach proved that the chosen indicators on a global scale are also useful indicators on a national scale. The necessary modifications were slight and mostly with the aim to increase the tangibility for national decision makers. On a national scale, data derived from remote sensing like MODIS or SRTM are interesting and embolden sources to derive input data. To support national decision makers, input data and algorithms were implemented within a computer-based Spatial Decision Support System (SDSS). With the developed SDSS 'AGROLAND' the user is able to visualize and analyze agricultural land resources based on the MI. Additionally, advanced model based raster analyses as well as the possibility of user interactions during runtime are implemented.

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The main objective of this study is to derive plant nitrogen (N) status and aboveground biomass via satellite remote sensing. To understand canopy spectral reflectance, the focus of the first part was set on the analysis of spectral signatures of winter wheat during its vegetation period under different N treatments. Spectral reflectance at different phenological stages, measured by a spectroradiometer (ASD HandHeld), is related to agronomy parameters like plant N, aboveground biomass and leaf area index (LAI). For this purpose, an extensive field survey was carried out in Huimin County in the North China Plain. For detection of plant N status of winter wheat and biomass on regional scale, hyperspectral (EO-1 Hyperion) and radar (Envisat ASAR) remote sensing data were obtained. First results of preprocessing of remote sensing data are presented in this contribution.