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[I] The uncertainty in carbon einissions fi-om fire was esti~nated for the boreal region of Alaska over the 50 years of recorded wildfire. Building on previous work where carbon emissions were estimated using a geographic infonnation systems-based model, the uncertainty attached to the different parameters of the basic equation was assessed and propagated(More)
This paper reviews the main applications of geostatistics to the description and modeling of the spatial variability of microbiological and physico-chemical soil properties. First, basic geostatistical tools such as the correlogram and semivariogram are introduced to characterize the spatial variability of each attribute separately as well as their spatial(More)
OBJECTIVE Arsenic in drinking water has been linked with the risk of urinary bladder cancer, but the dose-response relationships for arsenic exposures below 100 microg/L remain equivocal. We conducted a population-based case-control study in southeastern Michigan, USA, where approximately 230,000 people were exposed to arsenic concentrations between 10 and(More)
The use of surrogate models or metamodeling has lead to new areas of research in simulation-based design optimization. Metamodeling approaches have advantages over traditional techniques when dealing with the noisy responses and=or high computational cost characteristic of many computer simulations. This paper focuses on a particular algorithm, Efficient(More)
This paper presents a methodology to incorporate both hyper-spectral properties and spatial coordinates of pixels in maximum likelihood classification. Indicator kriging of ground data is used to estimate, for each pixel, the prior probabilities of occurrence of classes which are then combined with spectral-based probabilities within a Bayesian framework.(More)
This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž. attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž. locations multiple-point or spatial uncertainty. Two approaches are presented: kriging-based and Ž. simulation-based techniques that can be implemented within(More)
BACKGROUND Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk(More)
Unusual difficulties are encountered when characterizing the spatial distribution of the properties that collectively define the state of estuaries. Due to the variability of these estuarine conditions, greater sampling efforts are often necessary to describe estuarine environments, as compared to other aquatic systems. That is why in coastal management(More)