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A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data
Gao B. B.; Pan, Y. C.; Chen, Z. Y.; Wu, F.; Ren, X. H.; Hu, M. G.
Source PublicationTransactions in Gis
2016
Volume20
Issue5
Pages735-754
Keywordconstrained optimization variogram information variables variance
AbstractFor obtaining maps of good precision by the spatial inference method, the distribution of sampling sites in geographical and feature space is very important. For a regional variable with trends, the predicting error comes from trend estimation, variogram estimation and spatial interpolation. Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and spatial interpolation. MODIS data and simulated data were used as sampling fields to draw sample sets using scLHS, cLHS, cLHS with x and y coordinates as covariates, simple random and spatial even sampling methods, and the distribution and prediction errors of sample sets from different methods were evaluated. The results showed that scLHS performed well in balancing spreading in geographic and feature space, and can generate points pairs with small distances, and the sample sets drawn by scLHS produced smaller mapping error, especially when there were trends in the target variable.
Indexed BySCI
Language英语
ISSN1361-1682
DOI10.1111/tgis.12176
Citation statistics
Document TypeSCI/SSCI论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/42942
Collection历年回溯文献
Recommended Citation
GB/T 7714
Gao B. B.,Pan, Y. C.,Chen, Z. Y.,et al. A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data. 2016.
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