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 681 - 685 of 1605self-trained semisupervised SVM approach to the remote sensing land cover classification
Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification.
ECOSEL: Multi-objective optimization to sell forest ecosystem services
ECOSEL is a voluntary market mechanism that attempts to match willing sellers of forest ecosystem services with willing buyers. The goal of this paper is to show how multi-objective programming can be used to generate minimum-cost management alternatives for a real ECOSEL auction where optimal production plans for carbon sequestration, mature forest habitat and timber revenues are to be identified. The case study is suggestive of one of the most sophisticated uses of ECOSEL that might work for some but not all forest landowners.
Uniform global deforestation patterns — An empirical analysis
The forest transition (FT) hypothesis implies that changes in a region's forest cover follow a determinable pattern of decline and later re-expansion over time, which is supposed to be similar across regions and countries. Such a uniform pattern – if empirically proven and quantified – might help in establishing REDD+ baselines (i.e., references against which reductions in the emissions from deforestation and forest degradation of developing countries could be measured, and subsequently be rewarded).
Ploughing and grazing alter the spatial patterning of surface soils in a shrub-encroached woodland
Grazing is known to affect the spatial patterning of soil resources through biologically-mediated processes such as the removal of plant biomass and deposition of dung. In dense shrublands, grazing is thought to reinforce the concentration of resources around shrubs (fertile island effect) by enhancing the movement of resources from the interspace to the shrub hummocks. Shrub removal practices such as ploughing, which is commonly used to manage dense shrub patches, has unknown impacts on the distribution of soil properties.
Groundwater modelling for the assessment of water management alternatives
Rise in groundwater level followed by waterlogging and secondary salinisation has become a serious problem in canal irrigated areas located in arid and semi-arid regions of the world. To solve the problem, the groundwater model SGMP was applied in a waterlogged area of Haryana State of India in which about 500,000ha has already waterlogged resulting in reduced crop yield and abandonment of agricultural lands. After successful calibration and validation, several scenario building exercises have been conducted. Error and sensitivity analyses of the model parameters were done.