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.

Outward expansion of urban lands in the developing nations is often associated with a substantial loss of environmental resources such as forests, wetlands, freshwater and cash crop fields. Yet, determining how different aspects of urbanisation – such as city population size and spread pattern of built-up lands – contribute to the cumulative loss of resources remains controversial. In this study, data sets were constructed describing changes to land cover across 65,200 grid cells at 1 km² spatial resolution for China's Changjiang Delta Region over the past 60 years.

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.

We evaluated the response of male Reeves's pheasants Syrmaticus reevesii to different forest edges in a fragmented forest landscape in central China using radio-telemetry. Our fieldwork was carried out from April 2000 to August 2003 in the Dongzhai National Nature Reserve within the Dabie Mountains, China. We identified four major types of forest edges: shrub, farmland, road and residential edge.

Arctic ecosystems at the forest-tundra ecotone are particularly sensitive to climate-driven vegetation changes. Many recent studies have observed shifts in vegetation cover, particularly an increase in shrub growth. Here, vegetation changes were assessed at the local scale near Umiujaq, northern Quebec (Canada, 56.55°N, 76.55°W) using colour aerial photographs (1994 and 2010). By applying semi-automated image classification methods and change-detection analysis, we were able to detect and map the dominant vegetation cover changes.

Context Coyotes (Canis latrans) have adapted successfully to human landscape alteration in the past 150 years and in recent decades have successfully moved into urban areas. While this causes concern about human–wildlife conflicts, research also suggests that coyotes tend to avoid humans and human activity in urban areas. For improving management, a better understanding of space use by coyotes is needed. Aims To study how coyote social behaviour influences fine-scale space use in urban areas we present results from an extensive, multi-year GPS telemetry study (2005–13).

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca²⁺, Mg²⁺, Na⁺, Cl⁻, HCO₃⁻, pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea.

Spatially distributed air temperature is desired for various scientific studies, including climatalogical, hydrological, agricultural, environmental and ecological studies. In this study, empirical models with regard to land cover and spatial scale were introduced and compared to estimate air temperature from satellite-derived land surface temperature and other environmental parameters. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data obtained throughout 2005 in the Yangtze River Delta were adopted to develop statistical algorithms of air temperature.