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Displaying 976 - 980 of 1605Quantifying nutrient transfer pathways in agricultural catchments using high temporal resolution data
There are uncertainties in the definition of phosphorus (P) and nitrogen (N) transfer pathways within agricultural river catchments due to spatiotemporal variations such as water recharge and the farming calendar, or catchment soil and hydrogeological properties. This can have implications for mitigation policies. This study combined detailed pathway studies with catchment integrated studies to characterise N and P transfer pathways for four agricultural catchments with different land management, soil drainage and geology.
Seasonal nitrous oxide emissions from different land uses and their controlling factors in a tropical riparian ecosystem
An important ecological service provided by tropical riparian ecosystems is the mitigation of nutrient pollution (e.g. nitrate) from surrounding agricultural areas. However, a negative impact of this nutrient remediation may be that the ecozone also functions as a major emitter of nitrous oxide (N₂O). We hypothesized that the high inorganic nitrogen, organic carbon, and soil water content in tropical riparian ecosystems enhances N₂O production through rapid nitrification and denitrification processes.
Land degradation during the Bronze Age in Hexi Corridor (Gansu, China)
Pollen and charcoal analysis, with high resolution AMS ¹⁴C dating, on two sediment sections in the Hexi Corridor track the process of settlement development and abandonment during the Bronze Age. The evidence shows that agricultural activity during the Bronze Age caused an increase in farmland and a decrease in the abundance of Artemisia grassland in the Hexi Corridor. Land degradation is probably the main cause for decreased agricultural activity and settlement abandonment. Agriculture- induced soil fertility loss and land salinization contributed to the process of land degradation.
assessment of the effectiveness of a random forest classifier for land-cover classification
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.
Spatial analysis and mapping of malaria risk in an endemic area, south of Iran: A GIS based decision making for planning of control
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.