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.

Nutrient transport and loss in farmlands are affected by factors such as land cover, fertilization, soil type, rainfall, and management practices. We investigated the temporal and spatial changes in macronutrient transport and loss after fertilization and precipitation in manure-fertilized eggplant farmland in an alluvial plain. Upon adding topical fertilizer, concentrations of most nutrients in runoff and groundwater increased, and nitrogen runoff increased from 22.11 to 35.81 kg/ha, although eggplant yield did not increase correspondingly.

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.

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.

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.