Lyme borreliosis (LB) and nephropathia epidemica (NE) are zoonoses resulting from two different transmission mechanisms and the action of two different pathogens: the bacterium Borrelia burgdorferi and the Puumala virus, respectively. The landscape configuration is known to influence the spatial spread of both diseases by affecting vector demography and human exposure to infection. Yet, the connections between landscape and disease have rarely been quantified, thereby hampering the exploitation of land cover data sources to segment areas in function of risk. This study implemented a data-driven approach to relate land cover metrics and an indicator of NE/LB risk at different scales of observation of the landscape. Our results showed the suitability of the modeling approach (r² > 0.75, ρ < 0.001) and highlighted the relevance of the scale of observation in the set of landscape attributes found to influence disease risk as well as common and specific risk factors of NE and LB.