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.

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance.

Extensive land-use and land-cover change, triggered by rapid development of tourism and the expansion of townships, has occurred in the area surrounding the Changbai Mountain Biosphere Reserve (CMBR) in northeast China, a reservoir for distinctive ecosystems and biological diversity. The objective of this study was to examine the land-use changes surrounding the reserve in the context of forest and nature reserve management with the aid of maps from Landsat MSS imagery of 1977 and Landsat TM imagery of 1991 and 2007.

That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification.

Most localities of the critically endangered species Gagea bohemica (early star-of-Bethlehem) known in the Czech Republic were surveyed using the Braun-Blanquet approach. Based on formal definitions of the expert system for Czech non-forest vegetation, 69% of the 255 samples analysed were classified as already described pioneer plant communities on shallow soils. Samples unsorted by the expert system exhibit local or transient species composition.

An important consideration when interpreting eddy-covariance (EC) flux-tower measurements is the spatial distribution of forest land surface cover and soil type within the EC flux-tower footprint. At many EC flux-tower sites, there is a range of geospatial data available with the ability to estimate the spatial distribution of forest land cover and soils. Developing methods that utilize multiple geospatial data sets will result in more thorough estimates of ecosystem C stock distributions.

This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed 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.