Aller au contenu principal

page search

Community Organizations Elsevier
Elsevier
Elsevier
Publishing Company

Location

Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals.

All knowledge begins as uncommon—unrecognized, undervalued, and sometimes unaccepted. But with the right perspective, the uncommon can become the exceptional.

That’s why Elsevier is dedicated to making uncommon knowledge, common—through validation, integration, and connection. Between our carefully-curated information databases, smart social networks, intelligent search tools, and thousands of scholarly books and journals, we have a great responsibility and relentless passion for making information actionable.

Members:

Resources

Displaying 976 - 980 of 1605

Seasonal nitrous oxide emissions from different land uses and their controlling factors in a tropical riparian ecosystem

Journal Articles & Books
Décembre, 2012
Thaïlande

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.

Quantifying nutrient transfer pathways in agricultural catchments using high temporal resolution data

Journal Articles & Books
Décembre, 2012

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.

assessment of the effectiveness of a random forest classifier for land-cover classification

Journal Articles & Books
Décembre, 2012
Espagne

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

Journal Articles & Books
Décembre, 2012
Iran

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.

Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions

Journal Articles & Books
Décembre, 2012
Chine

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.