Geostatistical modelling of spatial uncertainty using p-field simulation with conditional probability fields
Geostatistical modelling of spatial uncertainty using p-field simulation with conditional probability fields
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DOI：
10.1080/13658810110099125
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发表时间：
2002-03-01
影响因子：
5.7
通讯作者：
Goovaerts, P
中科院分区：
文献类型：
--
作者：
Goovaerts, P
本文提出了一个变种的p场模拟，允许通过采样的一组条件概率分布函数（ccdf）的概率值集，称为p场的空间实现。而在该算法的常见实现中，p场是具有均匀边缘分布的随机函数的无条件实现，而在这里，它们以数据位置处的0.5概率值为条件，这需要对这些位置周围的ccdf的中心部分进行优先采样。使用随机抽样（近红外通道的200个观测值）SPOT场景的半落叶热带森林的方法说明。结果表明，使用条件概率场提高了再现统计，如直方图和半变异函数，同时产生更准确的预测反射率值比常见的p-字段的实现或更CPU密集型的顺序指示模拟。然后，根据模拟的反射率值是否超过给定的阈值，将像素值分类为森林或萨凡纳。在这种情况下，所提出的方法导致一个更精确和准确的预测的面积连续覆盖的大草原比其他两个模拟算法。
This paper presents a variant of p-field simulation that allows generation of spatial realizations through sampling of a set of conditional probability distribution functions (ccdf) by sets of probability values, called p-fields. Whereas in the common implementation of the algorithm the p-fields are nonconditional realizations of random functions with uniform marginal distributions, they are here conditional to 0.5 probability values at data locations, which entails a preferential sampling of the central part of the ccdf around these locations. The approach is illustrated using a randomly sampled ( 200 observations of the NIR channel) SPOT scene of a semi-deciduous tropical forest. Results indicate that the use of conditional probability fields improves the reproduction of statistics such as histogram and semivariogram, while yielding more accurate predictions of reflectance values than the common p-field implementation or the more CPU-intensive sequential indicator simulation. Pixel values are then classified as forest or savannah depending on whether the simulated reflectance value exceeds a given threshold value. In this case study, the proposed approach leads to a more precise and accurate prediction of the size of contiguous areas covered by savannah than the two other simulation algorithms.