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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)
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: Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern(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)
BACKGROUND Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in(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)