Updating categorical soil maps using limited survey data by Bayesian Markov chain cosimulation.

Weidong Li,Michael R Willig,Dipak K Dey,Chuanrong Zhang

doi:10.1155/2013/587284

Abstract

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.

Highlights

Soil is an important natural resource and is an essential component of ecosystems
The optimal prediction map of the soil series and the corresponding maximum occurrence probability map (Figure 5) were estimated from simulated realizations generated by Co-MCRF sequential simulation (MCSS), conditioned on both the sample data and the legacy soil map
Comparing with the legacy soil map and the reference map (Figure 3) shows that the unchanged S2 and S4 were exactly reproduced as SU2 and SU4, respectively, and that the S3, which was merged with a minor soil series (S5) without other changes, was exactly reproduced as SU3 in the optimal prediction map (Figure 5(a))

Summary

Introduction

Soil is an important natural resource and is an essential component of ecosystems. Categorical soil maps are widely used in ecological and agricultural studies and provide crucial information for natural resource and environmental management. Because existing soil maps may be of low quality or too outdated to reflect current soil distributions, map update is necessary for providing current, more accurate, or more detailed information to meet the requirements of applications. If an existing soil map is of sufficient quality and appropriately scaled, updating may not require a new fullcoverage soil survey for a revised soil map because the types of soils at most places in the legacy map may not have changed. We may be able to update a legacy soil map with only limited new survey data on soil distribution. Other reasons of using legacy soil maps and survey data together to create current categorical soil maps include that: (1) historical field survey data were not well kept or

Methods

Results

Conclusion