e., S2), selleck catalog SU3 (i.e., S3 + S5), SU4 (i.e., S4), SU6 (i.e., S6 + part of S7), and SU7 (i.e., S7 + S1+ part of S6). Soil series of S2, S3, and S4 were assumed to have no changes confirmed in the updated survey. The resulting new soil series distribution map (Figure 3(b)) was used as the reference soil map for checking simulated results.Figure 3The data for categorical soil map update by Markov chain cosimulation: (a) the legacy soil map; (b) the reference soil map, representing the current distribution of soil series; (c) the sample data set (646 points), including field survey data and pseudosample …Because we assumed only a few of small areas were subject to soil type changes, our limited field survey was also confined to these small areas. Thus, the survey data are insufficient and also biased for estimating the parameters (e.

g., transiogram models) used in the cosimulation. Our suggestion is to use pseudosample data, that is, sample data directly extracted from unchanged areas in the legacy soil map. Therefore, we sampled a sparse data set of 646 points (Figure 3(c)) from the reference soil map, which cover both the changed and unchanged areas. These samples are randomly distributed, not purposively arranged with respect to soil type changes. Using this data set, we examined simulated results for other points to see how well our suggested method predicted soil type characteristics, both those that were unchanged and changed compared to the legacy map.

The rationalities behind the sample data are that (1) for areas where soil types have changed, a field survey or visual observation through remote sensing is necessary to identify the changes on the map, and both methods may produce survey sample data for the update; and (2) for areas where soil types did not change, no matter how the judgment is made (from a field survey, remote sensing, or expertise), pseudosample data may be simply extracted from the legacy soil map. Pseudosample data extraction from a legacy map or from the combination of a legacy map and remotely sensed imagery can be carried out through human-computer interactions. Thus, it is not difficult to obtain sufficient sample data with a limited soil survey (i.e., a small set of real soil survey data). Experimental transiograms were estimated from the sample data to generate transiogram models for conditional simulations.

Two subsets of omnidirectional transiogram models interpolated from the experimental transiograms are provided in Figure 4 and show that cross-transiogram models have very different sills, related to their tail class proportions. Anisotropy was not considered because no identifiable anisotropic direction can characterize all soil types Carfilzomib in the whole area while partial anisotropy is difficult to account for.