Remote sensing is a useful technology for monitoring the spatial distribution and expansion of built-up and bare land areas. One effective approach, known as the normalized difference built-up index (NDBI), has been promoted for identifying built-up areas based on Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data.

Climate change is causing range shifts in many species, and impacts are predicted to be highest in mountain regions. The effectiveness of protected areas in preserving suitable habitats for target species in the face of climate change is particularly concerning, as many protected sites may lose suitable conditions for those species. We investigate the potential effect of temperature increase on the regional distribution of pygmy and boreal owl, mountain forest specialists in the Italian Alps, and the relative effectiveness of the Natura 2000 network at the regional level.

We measured bidirectional arthropod fluxes at 12 river reaches distributed across an urban‐rural gradient of riparian land use and land cover in the Scioto River system of Ohio (U.S.A.). For the terrestrial‐to‐aquatic arthropod flux (i.e. inputs of terrestrial arthropods to the river from the land), urban development was positively related to density of inputs but negatively related to biomass, partially explained by shifts in community composition and body size. Riparian grassland, typical of rural (i.e.

In order to investigate spatial and temporal characteristics of the purely grazing-based livestock husbandry system in southwestern Madagascar, individual animals from 12 cattle and 12 goat herds that were equally distributed across four villages were fitted with GPS tracking collars and their behaviour during pasturing was directly observed to identify seasonal variations in land use and movement patterns along the regional altitude and vegetation gradient.

Soil contamination by heavy metals has become a widespread dangerous problem in many parts of the world, including the Mediterranean environments. This is closely related to the increase irrigation by waste waters, to the uncontrolled application of sewage sludge, industrial effluents, pesticides and fertilizers, to the rapid urbanization, to the atmospheric deposition of dust and aerosols, to the vehicular emissions and to many other negative human activities.

Regional climate is influenced by land surface processes through energy exchange between land and atmosphere at various scales. The performance of climate model simulation is largely influenced by land cover parameterization, especially over areas that experience rapid change of land surface characterization. Accurate land cover datasets suited for climate modeling are urgently needed to improve model parameterization for better simulation.

While diverse, native riparian vegetation provides important functions, it remains unclear to what extent these assemblages can persist in urban areas, and under what conditions. We characterized forested riparian vegetation communities across an urbanizing metropolitan area and examined their relationships with surrounding land cover. We hypothesized that native and hydrophilic species assemblages would correlate with forest cover in the landscape.

To preserve land quality and mitigating land degradation represent an important task for regional planning and environmental management of the Mediterranean region. Since land cover dynamics directly affect the landscape characteristics, remote sensing represents an effective tool for land quality assessment at large scale.

From June 2005 to March 2007, we investigated wolf presence in an area of 1000km² in central northern Portugal by scat surveys along line transects. We aimed at predicting wolf presence by developing a habitat model using land cover classes, livestock density and human influence (e.g. population and road density). We confirmed the presence of three wolf packs by kernel density distribution analysis of scat location data and detected their rendezvous sites by howling simulations. Wolf habitats were characterized by lower human presence and higher densities of livestock.

Bashagard district is one of the important malaria endemic areas in southern Iran. From this region a total of 16,199 indigenous cases have been reported in recent years. The aim of this study was to determine the situation of the disease and provide the risk map for the area. ArcGIS9.2 was used for mapping spatial distribution of malaria incidence. Hot spots were obtained using evidence-based weighting method for transmission risk.

Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in 1year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques.

Marked spatiotemporal variabilities in mosquito infection of arboviruses require adaptive strategies for determining optimal field-sampling timeframes, pool screening, and data analyses. In particular, the error distribution and aggregation patterns of adult arboviral mosquitoes can vary significantly by species, which can statistically bias analyses of spatiotemporal-sampled predictor variables generating misinterpretation of prolific habitat surveillance locations.