Two-dimensional Markov chain simulation of soil type spatial distribution
Li W. D. ; Zhang C. R. ; Burt J. E. ; Zhu A. X. ; Feyen J.
2004
关键词optimal sampling strategies geostatistics probability model heterogeneity information
英文摘要Soils typically exhibit complex spatial variation of multi-categorical variables such as soil types and soil textural classes. Quantifying and assessing soil spatial variation is necessary for land management and environmental research, especially for accurately assessing the water and solute transport processes in watershed scales. This study describes an efficient Markov chain model for two-dimensional modeling and simulation of spatial distribution of soil types (or classes). The model is tested through simulations of a simplified soil map. The application of the model for predictive soil mapping with parameters estimated from survey lines is explored. Analyses of both simulated maps and associated semi-variograms show that the model can effectively reproduce observed spatial patterns of soil types and their spatial autocorrelation given an adequate number of survey lines. This indicates that the model is a feasible method for modeling spatial distributions of soil types (or classes) and the transition probability matrices of soil types in different directions can adequately capture the spatial interdependency relationship of soil types. The model is highly efficient in terms of computer time and storage. The model also provides an approach for assessing the uncertainty of soil type spatial distribution in areas where detailed survey data are lacking. The major constraint on applications of the model at this stage is that the minor soil types are relatively underestimated when survey lines are too sparse.
出处Soil Science Society of America Journal
68
5
1479-1490
收录类别SCI
语种英语
ISSN号0361-5995
内容类型SCI/SSCI论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/23608]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Li W. D.,Zhang C. R.,Burt J. E.,et al. Two-dimensional Markov chain simulation of soil type spatial distribution. 2004.
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