استفاده از متغیرهای محیطی برای بررسی کیفیت نمونه برداری در نقشه برداری خاک

اطلاعات خاک منبع مهم و ضروری برای تهیه نقشه خاک و واسنجی مدل‌های پیش‌بینی‌کننده خصوصیات خاک هستند. این اطلاعات از روش‌های مختلف نمونه‌برداری استخراج می‌شوند. هیچ معیار آماری برای ارزیابی نمونه برداری خاک در نقشه‌برداری خاک وجود ندارد و این موضوع به عدم تعادل در نمونه‌برداری و کاهش کیفیت نقشه خاک منجر می‌شود. نمونه ‌برداری هایپرکیوب لاتینبه عنوان یک طرح نمونه‌برداری برای نقشه‌برداری رقومی خاک پیشنهاد شده است. در این مطالعه از اصول هایپرکیوب برای ارزیابی کیفیت نمونه‌های خاک با استفاده از الگوریتم "ارزیابی هایپرکیوب نمونه‌برداری خاک "در نرم‌افزار r استفاده گردید. منطقه مورد مطالعه در فلات مرکزی ایران واقع در دره زاینده‌رود اصفهان قرار دارد. متغیرهای محیطی از مدل رقومی ارتفاع استخراج شد. هایپرکیوب لاتین از سه متغیر کمکی ارتفاع، شیب و شاخص خیسی تشکیل و براساس آن شاخص متغیر کمکی و وزن نسبی محاسبه شد. نتایج نشان داد افزایش تراکم و تغییرپذیری متغیر محیطی منجر به افزایش شاخص متغیر کمکی می‌شود. براساس شاخص وزن نسبی، نمونه‌برداری بیش از حد در مناطق با تراکم پایین شاخص متغیر کمکی و نمونه‌برداری کم در مناطق با تراکم و تغییرپذیری بالای شاخص متغیر کمکی مشاهده می‌شود.بنابراین لحاظ کردن فرایندهای ژئومرفولوژی برای بیان تغییرپذیری منطقه خشک در طراحی نمونه‌برداری خاک بسیار مفید و موثر واقع شده که این تنها با عملیات میدانی و صحرایی قابل تشخیص است. به این ترتیب، جزء لاینفک پروژه‌های نقشه‌برداری و شناسایی خاک، عملیات صحرایی و میدانی است که منجر به نمونه‌برداری دقیق‌تر برای برنامه‌های آینده همچون نقشه‌برداری رقومی خاک می‌شود.

Using Environmental Variables for Studying of the Quality of Sampling in Soil Mapping

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 sling this may lead to bias in the areas being sled. In digital soil mapping, soil sles 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 sling 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 sling 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 sling 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 sling scheme was designed based on geomorphic mapping. In the stratified simple random sling, the area was divided into sub-areas referred to as strata based on geomorphic surfaces, and within each stratum, sling locations were randomly selected (Figure 2). This resulted in 191 profiles, which were then described, sled, 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. Sling 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 sling units (see below) is due to soil sling 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-sling, 60.2% represent adequate sling and 9.4% represent over-sling. Regarding the covariate index, most of the under-sling appears in the high covariate index, where soil covariates are then highly variable. Actually, it is difficult to collect field sles in these highly variable areas (Figure 7). Also, most of the over-sling was observed in areas with alow covariate index (Figure 7). We calculated the weights of all the sling units and showed the results in Figure 8. One 64 strata out of 16 were empty of legacy sle units. Therefore, if we are going to increase the number of sles, it is better to take sles 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 sle 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 sling provides the means to evaluate adequacy of sling units according to the soil covariates. The main advantage of such a method is that all the sle 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 sling 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-sled.