ارزیابی دقت روش فرامکعب لاتین به‌منظور انتخاب موقعیت نقاط مطالعاتی برای تهیه نقشه‌ی رقومی ویژگی‌های خاک

با توجه به اینکه دقت و صحت تمام اطلاعات خاک شناسی وابسته به بهترین گمانه زنی در مورد مکان تغییرات خاک ها در قالب تعیین الگوی نمونه برداری می باشد، انتخاب روشی کارآمد که بتواند به بهترین شکل این تغییرات را رصد نماید بسیار حائز اهمیت است. تاکنون مطالعات اندکی در رابطه با بررسی تاثیر تصادفی بودن انتخاب نمونه ها در روش فرامکعب لاتین بر صحت نقشه ها انجام شده است. این مطالعه با هدف ارزیابی دقت روش فرامکعب لاتین در انتخاب موقعیت نمونه برداری به منظور انجام مطالعات نقشه‌برداری رقومی خاک در منطقه ای از شهرستان بروجن در استان چهارمحال و بختیاری انجام شد. با توجه به اینکه، چندین مرتبه نمونه برداری میدانی برای ارزیابی روش نمونه برداری خاک امری غیرمنطقی است در این پژوهش تلاش گردید تا از روش های شبیه سازی بر اساس نقشه هایی با صحت بسیار بالا برای این منظور استفاده شود. فاصله باهاتاچاریا برای کمّی سازی فاصله بین توزیع احتمال جامعه اصلی و اجراهای مختلف روش فرامکعب لاتین استفاده گردید. نقشه ویژگی‌های خاک (درصد کربنات کلسیم معادل، رس و کربن آلی) عمق سطحی (صفر تا 30 سانتی متر) با استفاده از روش ماشین‌بردار پشتیبان تهیه گردید و اعتبارسنجی شد. علاوه بر آن، انتخاب موقعیت نقاط نمونه برداری با استقاده از روش فرامکعب لاتین با تراکم 200 نقطه با 500 مرتبه اجرا انجام گردید. در هر مرحله، اعتبارسنجی برای پیش بینی ویژگی های خاک با استفاده از r2، rmse و %rmse انجام شد. نتایج نشان داد که برای تمامی ویژگی های مورد بررسی، مدل ماشین بردار پشتیبان از صحت قابل قبولی (%rmse کمتر از 40) برخوردار می باشد. از سوی دیگر، نتایج گویای آن است که خروجی های مختلف روش فرامکعب لاتین در اجراهای مختلف آن بر صحت مدلسازی تاثیرگذار است و مقادیر rmse مدل در حالت های مختلف برای درصد کربنات کلسیم معادل، رس و کربن آلی به ترتیب از 1.1، 1.1 و 0.02 تا 3.2، 2 و 0.12 متغیر است. اگرچه این موضوع متاثر از ویژگی مورد بررسی و میزان تغییرات آن در منطقه مورد مطالعه نیز می باشد.

evaluating the precision of the conditioned latin hypercube sampling method for selecting soil samples to generate digital maps of soil properties

introductionsoil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. they have a significant impact on nutrient cycling, carbon sequestration, and soil management. digital soil mapping (dsm) is a process aimed at delineating soil properties. soil sampling for dsm serves as  a fundamental  step in improving prediction accuracy and is crucial for incorporating variability in terms of environmental covariates. conditioned latin hypercube (clh) sampling is a technique utilized to generate a sample of points from a multivariate distribution conditioned on one or more covariates. numerous researchers (ramirez-lopez et al., 2014; adhikari et al., 2017; zhang et al., 2022) have endorsed this approach in their studies, following its inception by minasny and mcbratney in 2006. however, there has been limited research to date on the impact of the latin hypercube method's random sample selection process on the accuracy of resulting maps. hence, the central question remains: is the latin hypercube sampling method, which is currently widely adopted, always a dependable approach in this field? materials and methodsthe study area covers longitudes 50°35'47'' to 51°29'' east and latitudes 31°36''31'' to 32°15'48'' north in borujen city, chaharmahal, and bakhtiari province. the region, with an average elevation of 2338 meters above sea level, receives an annual rainfall of 250 millimeters and maintains an average temperature of 11.5 degrees centigrade. in this investigation, inherited data from soil studies were utilized, consisting of 250 samples distributed across the study area. in this research, the studied characteristics included percentage of equivalent calcium carbonate, clay, and soil organic carbon at a depth of 0 to 30 cm. land component variables were extracted using the alus palsar digital elevation model with a spatial resolution of 12.5 meters. in the initial stage, digital maps of equivalent calcium carbonate, clay, and soil organic carbon were generated using the support vector machine method. the modeling process proceeded until a highly accurate model was achieved, with the root mean square error percentage (rmse%) being less than 40. the latin hypercube approach was utilized for sample design, with 500 repetitions in this study. after selecting sampling points for each run using the latin hypercube method, these points were mapped onto a detailed map, and the corresponding feature values were retrieved. the final map was created based on the extracted points. subsequently, the latin hypercube approach was employed to generate soil property maps for each selected dataset. validation was conducted using criteria such as the coefficient of explanation, root mean square error, and root mean square error in multiple iterations to ensure the accuracy of the generated maps. results and discussionthe results distinctly illustrates the varied selection of sampling positions with each implementation of the latin hypercube method. it is important to note that there may be some overlaps in different implementations. consequently, the primary question arises: is a one-time execution of the latin hypercube sufficient for selecting study points? the findings indicate that the support vector machine model achieves satisfactory accuracy for all the examined characteristics. in the studied area, the environmental factors such as slope and elevation were identified as a significant predictors for estimating percentage of equivalent calcium carbonate. conclusionin the present study, the accuracy of the latin hypercube method was assessed for selecting sampling location for digital soil mapping endeavors in chaharmahal and bakhtiari province. given the impracticality of collecting numerous field samples to evaluate the soil sampling method, this research aimed to employ simulation methods based on highly accurate maps for this purpose. the results indicate that the different outputs of the latin hypercube method influence the accuracy of modeling, although this effect is also influenced by the specific feature under investigation and the extent of its variability within the study area.  considering that the latin hypercube method is based on the principle that samples are randomly selected in each class of environmental parameters, it is suggested that future studies using this method should account for this principle. adequate consideration should be given, and the selection of sampling locations should rely on multiple implementations of the bhattacharya distance method to ensure robustness and reliability.