Pasar al contenido 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 506 - 510 of 1605

Identification of environmental anomaly hot spots in West Africa from time series of NDVI and rainfall

Journal Articles & Books
Diciembre, 2013
África

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s–1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s.

conceptual framework and its software implementation to generate spatial decision support systems for land use planning

Journal Articles & Books
Diciembre, 2013
Bélgica

In a context where several sectors of society compete for space, land use types must be carefully designed and spatially allocated to guarantee a sufficient level of relevant ecosystem services (ES) in a territory of interest. In this respect, contemporary land use planning involves multiple, often conflicting objectives and criteria. Consequently, major benefits can be expected from spatial decision support systems (sDSS) designed to deal with complex spatial allocation problems.

role of irrigation runoff and winter rainfall on dissolved organic carbon loads in an agricultural watershed

Journal Articles & Books
Diciembre, 2013

We investigated the role of land use/land cover and agriculture practices on stream dissolved organic carbon (DOC) dynamics in the Willow Slough watershed (WSW) from 2006 to 2008. The 415km² watershed in the northern Central Valley, California is covered by 31% of native vegetation and the remaining 69% of agricultural fields (primarily alfalfa, tomatoes, and rice). Stream discharge and weekly DOC concentrations were measured at eight nested subwatersheds to estimate the DOC loads and yields (loads/area) using the USGS developed stream load estimation model, LOADEST.

LandCaRe DSS – An interactive decision support system for climate change impact assessment and the analysis of potential agricultural land use adaptation strategies

Journal Articles & Books
Diciembre, 2013

Decision support to develop viable climate change adaptation strategies for agriculture and regional land use management encompasses a wide range of options and issues. Up to now, only a few suitable tools and methods have existed for farmers and regional stakeholders that support the process of decision-making in this field. The interactive model-based spatial information and decision support system LandCaRe DSS attempts to close the existing methodical gap.

Assessing land cover and soil quality by remote sensing and geographical information systems (GIS)

Journal Articles & Books
Diciembre, 2013

Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%.