A markov chain-based probability vector approach for modeling spatial uncertainties of soil classes

Due to our imperfect knowledge of soil distributions acquired from techniques may represent an optimal guess based on field surveys, spatial uncertainties inevitably arise in mapping soils at unobserved locations.Providing spatial uncertaintyinformation along the dataset and the interpolation method used, but does with survey maps is crucial for risk assessment and decision-making. not reflect the real spatial variation characteristics beThis paper introduces a novel probability vector approach for spatial cause of uneven smoothing effects (Goovaerts, 1997, uncertainty modeling of soil classes based on an existing two-dimen- p. 369–370). As Journel (1997, p. viii) pointed out: “The sional Markov chain model for conditional simulation. The objective very reason for geostatistics and the future of the disis to find an accurate and efficient way to represent spatial uncertaint- cipline lie in the modeling of uncertainty, at each node ies that arise in mapping soil classes. Joint conditional probability through conditional distribution and globally through distribution (JCPD) represented by a set of occurrence probability stochasticimages(conditional simulations).”Therefore, vectors (PVs) of soil classes is directly calculated from conditional soil survey maps should be accompanied by related spaMarkov transition probabilities, rather than the conventional approxitial uncertainty information. Data reflecting spatial un

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