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Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation
Updating categorical soil maps is necessary for providing current, higher-quality soil data to agricultural and environmental management but may not require a costly thorough field survey because latest legacy maps may only need limited corrections. This study suggests a Markov chain random field (M...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3762187/ https://www.ncbi.nlm.nih.gov/pubmed/24027447 http://dx.doi.org/10.1155/2013/587284 |
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author | Li, Weidong Zhang, Chuanrong Dey, Dipak K. Willig, Michael R. |
author_facet | Li, Weidong Zhang, Chuanrong Dey, Dipak K. Willig, Michael R. |
author_sort | Li, Weidong |
collection | PubMed |
description | Updating categorical soil maps is necessary for providing current, higher-quality soil data to agricultural and environmental management but may not require a costly thorough field survey because latest legacy maps may only need limited corrections. This study suggests a Markov chain random field (MCRF) sequential cosimulation (Co-MCSS) method for updating categorical soil maps using limited survey data provided that qualified legacy maps are available. A case study using synthetic data demonstrates that Co-MCSS can appreciably improve simulation accuracy of soil types with both contributions from a legacy map and limited sample data. The method indicates the following characteristics: (1) if a soil type indicates no change in an update survey or it has been reclassified into another type that similarly evinces no change, it will be simply reproduced in the updated map; (2) if a soil type has changes in some places, it will be simulated with uncertainty quantified by occurrence probability maps; (3) if a soil type has no change in an area but evinces changes in other distant areas, it still can be captured in the area with unobvious uncertainty. We concluded that Co-MCSS might be a practical method for updating categorical soil maps with limited survey data. |
format | Online Article Text |
id | pubmed-3762187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37621872013-09-11 Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation Li, Weidong Zhang, Chuanrong Dey, Dipak K. Willig, Michael R. ScientificWorldJournal Research Article Updating categorical soil maps is necessary for providing current, higher-quality soil data to agricultural and environmental management but may not require a costly thorough field survey because latest legacy maps may only need limited corrections. This study suggests a Markov chain random field (MCRF) sequential cosimulation (Co-MCSS) method for updating categorical soil maps using limited survey data provided that qualified legacy maps are available. A case study using synthetic data demonstrates that Co-MCSS can appreciably improve simulation accuracy of soil types with both contributions from a legacy map and limited sample data. The method indicates the following characteristics: (1) if a soil type indicates no change in an update survey or it has been reclassified into another type that similarly evinces no change, it will be simply reproduced in the updated map; (2) if a soil type has changes in some places, it will be simulated with uncertainty quantified by occurrence probability maps; (3) if a soil type has no change in an area but evinces changes in other distant areas, it still can be captured in the area with unobvious uncertainty. We concluded that Co-MCSS might be a practical method for updating categorical soil maps with limited survey data. Hindawi Publishing Corporation 2013-08-20 /pmc/articles/PMC3762187/ /pubmed/24027447 http://dx.doi.org/10.1155/2013/587284 Text en Copyright © 2013 Weidong Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Li, Weidong Zhang, Chuanrong Dey, Dipak K. Willig, Michael R. Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title | Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title_full | Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title_fullStr | Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title_full_unstemmed | Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title_short | Updating Categorical Soil Maps Using Limited Survey Data by Bayesian Markov Chain Cosimulation |
title_sort | updating categorical soil maps using limited survey data by bayesian markov chain cosimulation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3762187/ https://www.ncbi.nlm.nih.gov/pubmed/24027447 http://dx.doi.org/10.1155/2013/587284 |
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