A. Jafari; Norair Toomanian; R. Taghizadeh Mehrjerdi
Abstract
Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias ...
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Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias in the areas being sampled. In digital soil mapping, soil samples may be used to elaborate quantitative relationships or models between soil attributes and soil covariates. Because the relationships are based on the soil observations, the quality of the resulting soil map depends also on the soil observation quality. An appropriate sampling design for digital soil mapping depends on how much data is available and where the data is located. Some statistical methods have been developed for optimizing data sampling for soil surveys. Some of these methods deal with the use of ancillary information. The purpose of this study was to evaluate the quality of sampling of existing data.
Materials and Methods: The study area is located in the central basin of the Iranian plateau (Figure 1). The geologic infrastructure of the area is mainly Cretaceous limestone, Mesozoic shale and sandstone. Air photo interpretation (API) was used to differentiate geomorphic patterns based on their formation processes, general structure and morphometry. The patterns were differentiated through a nested geomorphic hierarchy (Fig. 2). A four-level geomorphic hierarchy is used to breakdown the complexity of different landscapes of the study area. In the lower level of the hierarchy, the geomorphic surfaces, which were formed by a unique process during a specific geologic time, were defined. A stratified sampling scheme was designed based on geomorphic mapping. In the stratified simple random sampling, the area was divided into sub-areas referred to as strata based on geomorphic surfaces, and within each stratum, sampling locations were randomly selected (Figure 2). This resulted in 191 profiles, which were then described, sampled, analyzed and classified according to the USDA soil classification system (16). The basic rationale is to set up a hypercube, the axes of which are the quantiles of rasters of environmental covariates, e.g., digital elevation model. Sampling evaluation was made using the HELS algorithm. This algorithm was written based on the study of Carre et al., 2007 (3) and run in R.
Results and Discussion: The covariate dataset is represented by elevation, slope and wetness index (Table 2). All data layers were interpolated to a common grid of 30 m resolution. The size of the raster layer is 421 by 711 grid cells. Each of the three covariates is divided into four quantiles (Table 2). The hypercube character space has 43, i.e. 64 strata (Figure 5). The average number of grid cells within each stratum is therefore 4677 grid cells. The map of the covariate index (Figure 6) shows some patterns representative of the covariate variability. The values of the covariate index range between 0.0045 and 5.95. This means that some strata are very dense compared to others. This index allows us to explain if high or low relative weight of the sampling units (see below) is due to soil sampling or covariate density. The strata with the highest density are in the areas with high geomorphology diversity. It means that geomorphology processes can cause the diversity and variability and it is in line with the geomorphology map (Figure 2). Of the 64 strata, 30.4% represent under-sampling, 60.2% represent adequate sampling and 9.4% represent over-sampling. Regarding the covariate index, most of the under-sampling appears in the high covariate index, where soil covariates are then highly variable. Actually, it is difficult to collect field samples in these highly variable areas (Figure 7). Also, most of the over-sampling was observed in areas with alow covariate index (Figure 7). We calculated the weights of all the sampling units and showed the results in Figure 8. One 64 strata out of 16 were empty of legacy sample units. Therefore, if we are going to increase the number of samples, it is better to take samples from the empty strata.
Conclusion: Since, we assume that soil attributes to be mapped can be predicted by the environmental covariates, our estimation of the sample units is based on the covariates. Then, the results are very dependent on the covariates (number and spatial resolution of the covariates and the quality of their measurement or description). Hypercube sampling provides the means to evaluate adequacy of sampling units according to the soil covariates. The main advantage of such a method is that all the sample units can be estimated according to their density in the feature space that represents soil variability. From the results, it is possible to add new sampling units in order to cover the whole feature space. Thus, in case some parts are missing, we can enhance some parts of the feature space that appear to be under-sampled.
Keywords: Environmental variables, Latin hypercube, Soil sampling, Soil survey
