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Abstract:Understanding the processes of land use and land cover changes (LUCC) and the associated driving forces is important for achieving sustainable development. This paper presents the LUCC in Portugal at the regional level (NUTS II) from 1995 to 2010 and discusses the main driving forces and implications associated with these LUCC. The main objectives of this work are: (a) to quantify the land use and land cover (LUC) types (level I of LUC cartography) by NUT II in Portugal for the years 1995, 2007 and 2010; (b) to assess the spatio-temporal LUCC; and (c) to identify and discuss the main driving forces of LUCC and corresponding implications based on correlations and Principal Components Analysis. The results revealed large regional and temporal LUCC and further highlighted the different and sometimes opposite time trends between neighboring regions. By associating driving forces to LUCC, different influences at the regional level were observed, namely LUCC into agriculture land derived from the construction of dams (Alentejo region), or the conversion of coniferous forest into eucalypt forest (Centre region) associated with increased gross value added (GVA) and employment in industry and forestry. Temporal differentiation was also observed, particularly in the settlements that expanded between 1995 and 2007 due to the construction of large infrastructures (e.g., highways, industrial complexes, or buildings), which is reflected on employment in industry and construction and respective GVA. However, certain LUCC have implications, particularly in energy consumption, for which different behavior between regions can be highlighted in this analysis, but also on land-use sustainability.

The Zika virus, one of the new epidemic diseases, is reported to have affected millions of people in the past year. The suitable climate conditions of the areas where Zika virus has been reported, especially in areas with a high population density, are the main cause of the current outbreak and spread of the disease. Indeed, the suitable climatic conditions of certain territories constitute perfect breading nest for the propagation and outbreak of worldwide diseases. The main objective of this research is to analyze the global distribution and predicted areas of both mosquitoes Ae. aegypti and Ae. albopictus which are the main vectors of Zika virus. Physical (SRTM) and climatic variables (WorldClim) were used to obtain the susceptibility maps based on the optimum conditions for the development of these mosquitoes. The susceptibility model was developed using a Species Distribution Model - correlative model, namely the Maximum Entropy, that used as input the spatial references of both vectors (Dryad Digital Repository). The results show the most important classes of each independent variable used in assessing the presence of each species of mosquitoes and the areas susceptible to the presence of these vector species. It turns out that Ae. aegypti has greater global dispersion than the Ae. albopictus specie, although two common regions stand out as the most prone to the presence of both mosquito species (tropical and subtropical zones). The crossing of these areas of greater susceptibility with areas of greater population density (e.g. India, China, Se of USA and Brazil) shows some agreement, and these areas stand out due to the presence of several records of Zika virus (HealthMap Project). In this sense, through the intersection of susceptibility and human exposure the areas with increased risk of development and spread of Zika virus are pinpointed, suggesting that there may be a new outbreak of this virus in these places, if preventive measures are not adopted.

Abstract. This work evaluates the influence of land use and land cover (LUC) data with different properties on the landslide susceptibility zonation of the road network in the Zêzere watershed (Portugal). The information value method was used to assess the landslide susceptibility using two models: one including detailed LUC data (the Portuguese Land Cover Map – COS) and the other including more generalized LUC data (the CORINE Land Cover – CLC). A set of fixed independent layers was considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope-over-area ratio, soil, and lithology) while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as a dependent layer, including 259 shallow landslides obtained from the photointerpretation of orthophotos from 2005, and further validated in three sample areas. The landslide susceptibility maps were assigned to the road network data and resulted in two landslide susceptibility road network maps. The models' performance was evaluated with prediction and success rate curves and the area under the curve (AUC). The landslide susceptibility results obtained in the two models present a high accuracy in terms of the AUC (>90 %), but the model with more detailed LUC data (COS) produces better results in the landslide susceptibility zonation on the road network with the highest landslide susceptibility.