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.

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.

Capsule The bioclimatic zone and habitat type within which birds winter are the most important determinants of population trends.Aims To investigate whether regional factors on wintering grounds, phenological mismatch, or habitat on breeding or wintering grounds show relationships with population changes of Afro-Palaearctic migrant birds.Methods We modelled breeding bird survey trends of 26 species of Afro-Palaearctic migrant birds that breed in Britain, and assessed the most important variables.

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.

In the conditions of the Republic of Belarus there were presented results of development of a system of quantitative parameters, which characterized radiation safety and economic efficiency of agricultural reuse of lands which had been contaminated with radionuclides and had been withdrawn from agricultural production on evaluated lands. The parameters helped to base the possibility of use of a land plot in agricultural production according to its radiological, agronomical and technical characteristics and the distance between the plot and a potential land user.

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.

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).

The implicit hydrologic dimensions of international efforts to mitigate climate change, specifically potential impacts of the Clean Development Mechanism-Afforestation/Reforestation (CDM-AR) provisions of the Kyoto Protocol (KP) on global, regional and local water cycles, are examined. The global impact of the redistribution of water use driven by agriculture and land use change, of which CDM-AR can be a contributing factor, is a major component of ongoing global change and climate change processes.

New developments in geographic information science and technology are changing the way we apply GIS to developing-world agriculture, creating new opportunities to utilize the technology to address problems of disaster management, climate change, land use change, land degradation, crop analysis and impact assessment.GIS allows data and demographic information to be presented graphically, primarily in maps, but also in business tools such as charts, graphs and other reports.

We investigated microbial biomass and composition (lipid profile), mineral N pools and soil physicochemical parameters in the top 5-cm soils 19 years after reforestation of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) woodland with itself or a native broadleaf species, Mytilaria laosensis. The results suggested that tree species transition had a large impact on microbial biomass and a small impact on the composition of the microbial community as indicated by the relative abundance of individual lipid biomarkers.