نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک ، دانشکده کشاورزی دانشگاه زنجان

2 عضو هیئت علمی دانشگاه زنجان

3 بخش تشکیل و طبقه بندی خاک، موسسه تحقیقات خاک و آب ایران، کرج، ایران

چکیده

برای جهان پر از دگرگونی‎، چالش‎های بی‎شماری در نقشه‎برداری رقومی خاک وجود دارد. یکی از این چالش‎ها، روش نمونه‎برداری است که نقش مهمی در فراهم آوردن اطلاعات مناسب برای نقشه‎برداری رقومی خاک و افزایش کارآیی آن ایفا می‎کند. روش نمونه‏برداری کارآمد، با در نظر گرفتن تعداد نمونه، تغییرات مکانی و هزینه، راهی برای شناسایی مجموعه‎ای از مکان‎های پراکنده‎ نمونه‎برداری در یک فضای جغرافیایی است که پوشش مکانی مناسبی از ویژگی‎ها را همراه داشته باشد. پوشش مکانی مناسب ویژگی‎ها برآورد دقیق پارامترهای رگرسیونی را پشتیبانی و موجب می‎شود تا میان­یابی مکانی مؤثری واقع شود. در ارزیابی خاک، شمار نمونه‎های جمع‎آوری‎شده، بیشتر با محدودیت‎های زمان و هزینه روبرو است. همچنین، کمبود جاده‎های دسترسی، پوشش گیاهی انبوه و زمین‎های ناهموار، در بازدید از مناطق خود باعث محدودیت‎های بیشتری می‎شوند. این کمبودها، انگیزه به‌کارگیری روش‎های نمونه‎برداری نیرومند‎تری را ایجاد می‎کند تا بتوان تغییرات مکانی خاک و ویژگی‎های آن را برای کاهش شمار نمونه، زمان و هزینه‎ مورد نیاز، به‎خوبی فراهم کند. به‌گونه‌ای که، کیفیت پایانی نقشه‎ها پشتیبانی شود. این مقاله برخی از مهم‌ترین روش‌های مختلف نمونه‌برداری‌های آماری و هندسی که الگوی نمونه‎برداری هندسی در یک فضای جغرافیایی را بهینه‎سازی می‎کند، بررسی و نقاط قوت و ضعف این روش‎ها را با توجه به پوشش مکانی، سادگی، دقت و کارآیی بیان می­کند. نتایج نشان داد که از نظر دقت و کارایی، نمونه‎برداری تصادفی طبقه‎بندی‎شده بالاترین دقت و صحت را داشته و به‎طور گسترده استفاده ‌شده است. نمونه‎برداری پوشش مکانی، از نظر پوشش مکانی بهترین روش است. نمونه‎برداری تصادفی ساده، نمونه‎برداری شبکه‎ای و نمونه‎برداری پوشش مکانی، از نظر ‌سادگی در مراحل طراحی و پیاده‌سازی، ساده‌ترین روش‎های نمونه‎برداری هستند. در میان روش‎های نمونه‎برداری مطالعه شده، روش نمونه‎برداری مکعب لاتین مشروط، رایج‎ترین روش استفاده و بسیار توصیه‌ شده است و نمونه‎برداری تصادفی طبقه‎بندی‎شده و نمونه‎برداری پوشش مکانی، به‎عنوان کارآمدترین روش‎هایی هستند که الگوی نمونه‎برداری را در فضای جغرافیایی بهینه‎سازی می‎کنند.

کلیدواژه‌ها

عنوان مقاله [English]

Introduction of different sampling methods in digital soil mapping studies

نویسندگان [English]

  • Leila Lotfollahi 1
  • Mohammad Amir Delavar 2
  • Mohammad Jamshidi 3

1 Department of soil science, Faculty of Agriculture, University of Zanjan

2 Associate Professor

3 Department soil genesis and classification, Soil & Water Research Institute. Karaj,Iran

چکیده [English]

There are innumerable challenges for digital soil mapping in the world full of change. One of these challenges is the sampling method which plays an important role in providing appropriate information for digital soil mapping and increasing its efficiency. Sampling method efficiency, with considering the number of samples, space changes, and cost, is a way to identify a set of scattered sampling locations in geographical space that have good location coverage of features. A good space coverage of features ensures accurate estimation of regression parameters and it makes effective spatial interpolation. In soil evaluation, the number of samples collected is limited by time and cost. Also, lack of roads, dense vegetation, and rugged terrain are caused more restrictions when visiting the area. These deficiencies lead to the use of stronger sampling methods. Methods that can provide a good description of space changes of soil and its features to reduce the number of samples, time, and cost are needed. So that it supports the final quality of the maps. Here are checked several methods of statistical sampling and geometric that optimize the geometric sampling pattern in geographical space. The strengths and weaknesses these methods considering spatial coverage, simplicity, accuracy, and efficiency briefly expressed. The results showed in terms of accuracy and efficiency; classified random sampling has the highest accuracy and has been widely used. In terms of spatial coverage; spatial coverage sampling is the best method. Due to the simplicity in the design and implementation stages; Simple random sampling, network sampling, and spatial sampling are the simplest sampling methods. Among the sampling methods studied, the Latin conditional sampling method is the most common method. It is widely used and recommended, and stratified random sampling and spatial sampling are the most efficient methods that optimize the sampling pattern in geographical space.

کلیدواژه‌ها [English]

  • Classical Sampling
  • Statistical Sampling
  • Geometric Sampling
Adhikari K., and Hartemink A.E. 2015. Digital mapping of topsoil carbon content and changes in the Driftless Area of Wisconsin, USA. Soil Science Society of America Journal, 79(1), 155-164.‏
Biswas A., and Zhang Y. 2018. Sampling designs for validating digital soil maps: a review. Pedosphere, 28(1), 1-15.
Brown R.A., McDaniel P., and Gessler, P.E. 2012. Terrain attribute modeling of volcanic ash distributions in northern Idaho. Soil Science Society of America Journal, 76(1), 179-187.
Brungard C.W., and Boettinger J.L. 2010. Conditioned latin hypercube sampling: optimal sample size for digital soil mapping of arid rangelands in Utah, USA. In digital soil mapping. Springer, Dordrecht. pp. 67-75.
Brus D.J., and De Gruijter J.J. 1997. Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion). Geoderma, 80(1-2), 1-44.
Brus D.J., De Gruijter J.J., and Van Groenigen, J.W. 2006. Designing spatial coverage samples using the k-means clustering algorithm. Developments in Soil Science, 31, 183-192.
Brus D.J., and Heuvelink, G.B. 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma, 138(1-2), 86-95.
Brus D.J., and Noij, I.G. A. M. 2008. Designing sampling schemes for effect monitoring of nutrient leaching from agricultural soils. European journal of soil science, 59(2), 292-303.
Brus D.J., Kempen B., and Heuvelink G.B. M. 2011. Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3), 394-407.
Brus D.J. 2019. Sampling for digital soil mapping: A tutorial supported by R scripts. Geoderma, 338, 464-480.
Burrough P.A., van Gaans P.F., and MacMillan, R.A. 2000. High-resolution landform classification using fuzzy k-means. Fuzzy sets and systems, 113(1), 37-52.
Cambule A.H., Rossiter D.G., and Stoorvogel J.J. 2013. A methodology for digital soil mapping in poorly-accessible areas. Geoderma, 192, 341-353.
Chapron M. 2011. Classification of soil and vegetation by fuzzy k-means classification and particle swarm optimization. In: Proceedings of the International Conference on Swarm Intelligence. International Conference on Swarm Intelligence Cergy. pp. 1-7.
Clifford D., Payne J.E., Pringle M.J., Searle R., and Butler N. 2014. Pragmatic soil survey design using flexible Latin hypercube sampling. Computers and Geosciences, 67, 62-68.
De Gruijter J.J., McBratney A.B., and Taylor J. 2010. Sampling for high-resolution soil mapping. In Proximal soil sensing. Springer, Dordrecht. pp. 3-14.
De Zorzi P., Barbizzi S., Belli M., Fajgelj A., Jacimovic R., Jeran Z., and van der Perk M. 2008. A soil sampling reference site: The challenge in defining reference material for sampling. Applied Radiation and Isotopes, 66(11), 1588-1591.
Evans D.M., and Hartemink A.E. 2014. Digital soil mapping of a red clay subsoil covered by loess. Geoderma, 230, 296-304.
Falk M.G., Denham R.J., and Mengersen K.L. 2011. Spatially stratified sampling using auxiliary information for geostatistical mapping. Environmental and Ecological Statistics, 18(1), 93-108.
Fitzgerald, G. J. 2010. Response surface sampling of remotely sensed imagery for precision agriculture. In Proximal Soil Sensing. Springer, Dordrecht. pp. 121-129.
Gao B., Pan Y., Chen Z., Wu F., Ren X., and Hu, M. 2016. A spatial conditioned Latin hypercube sampling method for mapping using ancillary data. Tra nsactions in GIS, 20(5), 735-754.
Hengl T., Rossiter D.G., and Stein A. 2003. Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Soil Research, 41(8), 1403-1422.
Houlong J., Daibin W., Chen X., Shuduan L., Hongfeng W., Chao Y., and Lina G. 2016. Comparison of kriging interpolation precision between grid sampling scheme and simple random sampling scheme for precision agriculture. Eurasian Journal of Soil Science, 5(1), 62-73.
Jafari A., Finke P. A., Vande Wauw J., Ayoubi S., and Khademi H. 2012. Spatial prediction of USDA great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science, 63(2), 284-298.
Jafari. A., Khademi. H., Finke. P., Wauw. J.V.D., Ayoubi. S. 2014. Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma. 232–234, 148–163.
Jenny H. 1941. Factors of Soil Formation: A System of Quantitative Pedology. Dover Publications Inc., New York.
Karunaratne S.B., Bishop T.F.A., Baldock J.A., and Odeh I.O.A. 2014. Catchment scale mapping of measureable soil organic carbon fractions. Geoderma, 219, 14-23.
Kempen B., Brus D.J., and de Vries F. 2015. Operationalizing digital soil mapping for nationwide updating of the 1: 50,000 soil map of the Netherlands. Geoderma, 241, 313-329.
Kerry R, and Oliver M.A. 2004. Average variograms to guide soilsampling.  International Journal of Applied Earth Observation and Geoinformation. 5: 307–325.
Kerry R., Goovaerts P., Rawlins B.G., and Marchant B.P. 2012. Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma, 170, 347-358.
Kidd D., Malone B., McBratney A., Minasny B., and Webb M. 2015. Operational sampling challenges to digital soil mapping in Tasmania, Australia. Geoderma Regional, 4, 1-10.
Koszinski S., Miller B.A., Hierold W., Haelbich H., and Sommer M. 2015. Spatial modeling of organic carbon in degraded peatland soils of northeast Germany. Soil Science Society of America Journal, 79(5), 1496-1508.
Ließ M., Glaser B., and Huwe B. 2012. Uncertainty in the spatial prediction of soil texture: comparison of regression tree and Random Forest models. Geoderma, 170, 70-79.
Malone B.P., McBratney A.B., and Minasny B. 2011. Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes. Geoderma, 160(3-4), 614-626.
Markert B. 2007. Quality assurance of plant sampling and storage. In: Quevauviller P (Ed.) Qualilty Assurance in Environmental Monitoring: Sampling and Sample Preatreatment.
McBratney A.B., Odeh I.O., Bishop T.F., Dunbar M.S., and Shatar T.M. 2000. An overview of pedometric techniques for use in soil survey. Geoderma, 97(3-4), 293-327.
McBratney A.B., Santos M.M., and Minasny B. 2003. On digital soil mapping. Geoderma, 117(1-2), 3-52.
Michot D., Walter C., Adam I., and Guéro Y. 2013. Digital assessment of soil-salinity dynamics after a major flood in the Niger River valley. Geoderma, 207, 193-204.
Minasny B., and McBratney A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and geosciences, 32(9), 1378-1388.
Minasny B., and McBratney A.B. 2006. Latin hypercube sampling as a tool for digital soil mapping. Developments in soil science, 31, 153-606.
Mulder V.L., de Bruin S., and Schaepman M.E. 2013. Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21, 301-310.
Nabiollahi K., Golmohammadi. F., Taghizadeh-Mehrjardi. R., Kerry, R. 2018. Assessing the effects of slope gradient and land use change on soil quality degradation through digital mapping of soil quality indices and soil loss rate. Geoderma. 318, 482–494.
 Ng W., Minasny B., Malone B., and Filippi, P. 2018. In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra. PeerJ, 6, e5722.
Pahlavan-Rad M.R., Akbarimoghaddam. A. 2018. Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena. 160, 275–281.
Poggio L., Gimona A., and Brewer M.J. 2013. Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates. Geoderma, 209, 1-14.
Qin C.Z., Zhu A.X., Qiu W.L., Lu Y.J., Li B.L., and Pei T. 2012. Mapping soil organic matter in small low-relief catchments using fuzzy slope position information. Geoderma, 171, 64-74.
Ramirez-Lopez L., Schmidt K., Behrens T., Van Wesemael B., Demattê J. A., and Scholten, T. 2014. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 226, 140-150.
Roudier P., Beaudette D.E., and Hewitt A. 2012. A conditioned Latin hypercube sampling algorithm incorporating operational constraints. Digital soil assessments and beyond, 227-231.
Samyn K., Cerdan O., Grandjean G., Cochery R., Bernardie S., and Bitri A. 2012. Assessment of vulnerability to erosion: digital mapping of a loess cover thickness and stiffness using spectral analysis of seismic surface-waves. Geoderma, 173, 162-172.
Singh N. 2011. Radioisotopes: Applications in Physical Sciences. BoD–Books on Demand.496p.
Sun X.L., Wu S.C., Wang H. L., Zhao Y.G., Zhao Y., Zhang G.L., and Wong M.H. 2012. Uncertainty analysis for the evaluation of agricultural soil quality based on digital soil maps. Soil Science Society of America Journal, 76(4), 1379-1389.
Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F., and Malone B.P. 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
Taghizadeh Mehrjerdi R. 2015. Determining the pattern of spatial sampling using different methods (Case study: Taft city). Agricultural Engineering (Scientific Journal of Agriculture). 38 (2): 19-36. (In Persian)
Taghizadeh Mehrjardi R., Sarmadian. F., Tazeh. M., Omid. Mahmood., Toomanian. N., Rousta. MJ. And Rahimian. MH. 2015. Comparison of different sampling methods for digital soil mapping in Ardakan region. Watershed Engineering and Management. 6 (4), 353-363. (In Persian)
Taghizadeh-Mehrjardi. M., Nabiollahi. K., Minasny. B., Triantafilis. J. 2015. Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma. 253–254, 67–77.
Taghizadeh-Mehrjardi. R., Nabiollahi. K., Rasoli. L., Kerry. R., Scholten. T. 2020. Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy. 10, (573)1–20.
Thomas M., Odgers N., Ringrose-Voase A., Grealish G., Glover M., and Dowling T. 2012. Soil survey design for management-scale digital soil mapping in a mountainous southern Philippine catchment. Digital soil assessments and beyond. London, CRC Press/Balkema, 233-238.
Triantafilis J., Earl N.Y., and Gibbs I.D. 2016. Digital soil-class mapping across the Edgeroi district using numerical clustering and gamma-ray spectrometry data. Computing Ethics: A Multicultural Approach, 187.
Vašát R., Borůvka L., and Jakšík O. 2012. Number of sampling points influences the parameters of soil properties spatial distribution and kriged maps. In Digital Soil Assessments and Beyond. CRC Press London. pp. 251-256
Vasques G.M., Grunwald S., Comerford N.B., and Sickman J.O. 2010. Regional modelling of soil carbon at multiple depths within a subtropical watershed. Geoderma, 156(3-4), 326-336.
Wadoux A.M.C. and Brus. D.J. 2020. How to compare sampling designs for mapping? European Journal of Soil Science, ‏1-12.
Walvoort D.J., Brus D.J., and De Gruijter J.J. 2010. An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers and Geosciences, 36(10), 1261-1267.
Webster R., and Lark M. 2012. Field sampling for environmental science and management. Routledge.
Wheeler I., McBratney A.B., Minasny B., and De Gruijter J.J. 2012. Digital soil mapping to inform design-based sampling strategies for estimating total organic carbon stocks at the farm scale. In Digital Soil Assessments and Beyond—Proceedings of the Fifth Global Workshop on Digital Soil Mapping, Sydney Australia.pp. 257-262.
Worsham L., Markewitz D., Nibbelink N.P., and West L.T. 2012. A comparison of three field sampling methods to estimate soil carbon content. Forest Science, 58(5), 513-522.
Xu C., He H.S., Hu Y., Chang Y., Li X., and Bu R. 2005. Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation. Ecological Modelling, 185(2-4), 255-269.
Zeraatpisheh M., Ayoubi. Sh., Jafari. A. and Finke. P. 2017. Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran. Geomorphology, 285, 186–204.
Zhang J., and Zhang C. 2012. Sampling and sampling strategies for environmental analysis. International journal of environmental analytical chemistry, 92(4), 466-478.
Zhou Y., Biswas A., Ma Z., Lu Y., Chen Q., and Shi Z. 2016. Revealing the scale-specific controls of soil organic matter at large scale in Northeast and North China Plain. Geoderma, 271, 71-79.