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نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه اردکان

2 دانشگاه صنعتی خاتم النبیاء بهبهان

3 دانشگاه تهران

چکیده

ظرفیت تبادل کاتیونی خاک از جمله خصوصیات مهم خاک می باشد که در پایگاه های داده ای مربوط به خاک و به عنوان ورودی در مدل های زیست محیطی و نیز مدل های مربوط به خاک به کار می رود. این مقاله از یک روش جدیدبرای پیش بینی تغییرات مکانی ظرفیت تبادل کاتیونی خاک به طور پیوسته (تا عمق یک متر) در منطقه دورود استان لرستان بهره می گیرد. در مطالعه حاضر از ترکیب معادلات عمق اسپیلاین با نواحی یکسان و تکنیک نقشه برداری رقومی خاک بهره گرفته شد تا با استفاده از تعداد محدودی نقطه (103 نقطه)، تغییرات سطحی و عمقی ظرفیت تبادل کاتیونی خاک در کل محدوده مورد مطالعه بررسی شود. جهت ارتباط دادن ظرفیت تبادل کاتیونی خاک و متغیرهای کمکی به دست آمده از تصاویر ماهواره ای و مدل رقومی ارتفاع، از مدل رگرسیون درختی استفاده شد. نتایج نشان داد که در پیش بینی پارامتر مذکور، بعضی از پارامترهای کمکی از جمله باند سه تصویر ماهواره و مساحت اصلاح شده حوزه دارای اهمیت بیشتری بودند. همچنین نتایج نشان داد که مدل درختی به خوبی متغیر هدف را در پنج عمق استاندارد با ضریب تبیین 84/0، 84/0، 84/0، 66/0، 27/0 و میانگین ریشه مربعات به ترتیب 75/1، 84/1، 84/1، 11/2، 16/2 پیش بینی کرده است که بیانگر قابل قبول بودن نتایج در همه اعماق (بجز عمق پنجم) می باشند. نتایج تحقیق حاضر نیز نشان داد که استفاده از نقشه برداری رقومی، رگرسیون درختی و معادله اسپیلاین با نواحی یکسان ابزارهایی قدرتمند جهت برآورد تغییرات مکانی ظرفیت تبادل کاتیونی خاک به صورت جانبی و عمقی می باشند.

کلیدواژه‌ها

عنوان مقاله [English]

3D Digital Mapping of Soil Cation Exchange Capacity in Dorud, Lorestan Province

نویسندگان [English]

  • R. Taghizadeh Mehrjerdi 1
  • A. Amirian Chekan 2
  • F. Sarmadian 3

1 University of Ardakan

2 Behbahan Khatamolanbia University of Technology

3 University of Tehran

چکیده [English]

There is an increasing demand for reliable large-scale soil datato meet the requirements of models for planning of land-usesystems, characterization of soil pollution, and prediction ofland degradation. Cation exchangecapacity (CEC) is among the most important soil propertiesthat are required in soil databases. This paper applied a novel method for whole-soil profile predictions of CEC (to 1 m) across Dorudlocated in LorestanProvince. At present research, we combined equal-area spline depth functions with digital soil mapping techniques to predict the vertical and lateral variations of CEC across the study area where limited soil information exists (103 soil profiles). To model the relationship between CEC and environmental factors (i.e. Representative soil forming factors), derived from a digital elevation model and Landsat imagery, a regression tree was applied. Results indicated that some auxiliary data had more influence on the prediction model (i.e. B3 and modified catchment area). Our results also confirmed the regression tree model predicted target variable at the five specific depths with coefficient of determination of 0.84, 0.84, 0.84, 0.66, 0.27 and root mean square of 1.75, 1.84, 1.84, 2.11, and 2.16, respectively. Results showed a reasonable R2 in first four depths ranged from 0.66 to 0.84; while, it decreases to 0.27 in the last depth. Our results also confirmed that the regression tree as a predictive model, digital soil mappingtechniqueand equal area splinesare powerful tools to predict lateral and vertical variation of CEC.

کلیدواژه‌ها [English]

  • Regression tree
  • Soil depth function
  • Digital soil mapping
1- Akramkhanov A., Martius C., Park S.J., and Hendrickx J.M. 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163: 55-62.
2- Amini M., Abbaspour K.C., Khademi H., Fathianpour N., Afyuni M., and Schulin R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European J. Soil Sci., 56: 551-559.
3- Baker L., and Ellison D. 2008. Optimization of pedotransfer functions using an artificial neural network ensemble method. Geoderma, 144: 212–224.
4- Bishop T.F.A., McBratney A.B., and Laslett G.M. 1999. Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91: 27–45.
5- Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. 1984. Classification and regression. Tress. Wadsworth, Belmont, CA.
6- Dehni A., and Lounis M. 2012. Remote sensing techniques for salt affected soil mapping: application to the Oran region of Algeria. Pro. Engi. 33: 188- 198.
7- Dwivedi R.S., and Sreenivas K. 1998. Delineation of salt-affected soils and waterlogged areas in the Indo-Gangetic plains using IRS-1C LISS-III data. I. J. Rem. Sens, 14: 2739- 2751.
8- Elnaggar A.A. 2007. Development of predictive mapping techniques for soil survey and salinity mapping. Ph.D. dissertation, Oregon State University, Corvallis, Oregon.
9- Erh K.T. 1972. Application of spline functions to soil science. Soil Sci., 114: 333–338.
10- Florinsky I.V., Eilers R.G., Manning G.R., and Fuller L.G. 2002. Prediction of soil properties by digital terrain modelling. Env. Modell. Soft. 17: 295– 311.
11- Ghanbarian-Alavijeh B., Taghizadeh-Mehrjardi R., and Huang G. 2012. Estimating Mass Fractal Dimension of Soil Using Artificial Neural Networks for Improved Prediction of Water Retention Curve. Soil Sci., 177: 471-479.
12- Grimm R., Behrens T., Marker M., and Elsenbeer H. 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island — digital soil mapping using Random Forests analysis. Geoderma, 146: 102–113.
13- Grunwald S. 2009. Multi-criteria characterization of recent digital soil mapping and modelling approaches. Geoderma, 152: 95–207.
14- Hengl T., Rossiter D. G., and Stein A. 2003. Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Geoderma. 120: 75–93.
15- Jenny H. 1941. Factors of Soil Formation. McGraw-Hill, New York.
16- Lagacherie P. 2008. Digital soil mapping: astate of the art. P. 3-14. In: A. E. Hartemink et al. (ed.) Digital Soil Mapping with Limited Data. Springer Science, Australia.
17- MacMillan R.A., Moon D.E., and Coupe R.A. 2007. Automated predictive ecological mapping in a forest region of B.C., Canada, 2001–2005. Geoderma, 140: 353–373.
18- Malone B.P., McBratney A.B., Minasny B., and Laslett G.M. 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154: 138–152.
19- Manrique L.A., Jones C.A., and Dyke P.T. 1991. Predicting cation-exchange capacity from soil physical and chemical properties. Soil Sci. Soc. Am. J., 55: 787-794.
20- McBratney A.B., Mendonça-Santos M.L., and Minasny B. 2003. On digital soil mapping. Geoderma, 117: 3– 52.
21- Metternicht G., and Zinck J.A. 2003. Remote sensing of soil salinity: Potentials and constraints. Rem. Sens. Env. 85: 1-20.
22- Minasny B., McBratney A.B., and Hartemink A.E. 2010. Global pedodiversity, taxonomic distance, and the World Reference Base. Geoderma, 155: 132-139.
23- Minasny B., McBratney A.B., Mendonca-Santos M.L., Odeh I.O.A., and Guyon B. 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Aus. J. Soil Res., 44: 233–244.
24- Moran C.J., and Bui E.N. 2002. Spatial data mining for enhanced soil map modelling. I. J. Geo. Info. Sci., 16: 533-549.
25- Nyssen J., Tmesgen H., Lemenih M., Zenebe A., Haregeweyn N., and Haile M. 2008. Spatial and temporal variation of soil organic carbon stocks in a lake retreat area of the Ethiopian Rift Valley. Geoderma, 146: 261–268.
26- Odeh I.O.A., and Onus A. 2008. Spatial Analysis of Soil Salinity and Soil Structural Stability in a Semiarid Region of New South Wales, Australia. Env. Manag., 42: 265–278
27- Odgers N.P., Libohova Z., and Thompson J.A. 2012. Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale. Geoderma, 190: 153–163.
28- Parasuraman K., Elshorbagym A., and Si S.C. 2008. Estimating saturated hydraulic conductivity using genetic programming. Soil Sci. Soc. Am. J., 71, 1676–1684.
29- Ponce-Hernandez R., Marriott F.H.C., and Beckett P.H.T. 1986. An improved method for reconstructing a soil-profile from analysis of a small number of samples. J. Soil Sci., 37: 455–467.
30- Quinlan J.R. 2001. Cubist: An Informal Tutorial. http://www.rulequest.com.
31- Rossiter D.G., Geomatica S., and Bogota S. 2005. Digital soil mapping: towards a multiple-use soil information system. I. J. Geo-inform. Sci.
32- Russell J.S., and Moore A.W. 1968. Comparison of different depth weightings in the numerical analysis of anisotropic soil profile data: Transactions of the 9th International Congress of Soil Science, vol. 4, pp. 205–213.
33- Ryan P.J., McKenzie N.J., O’Connell D., Loughhead A.N., Leppert P.M., Jacquier D., and Ashton L. 2000. Integrating forest soils information across scales: spatial prediction of soil properties under Australian forests. Forest Eco. Manag. 138: 139–157.
34- Scull P., Franklin J., Chadwick O.A., and McArthur D. 2003. Predictive soil mapping: a review. Prog. Physical Geog., 27: 171-197.
35- Stoorvogel J.J., Kempen B., Heuvelink G.B.M., and Bruin S. 2009. Implementation and evaluation of existing knowledge for digital soil mapping in Senegal. Geoderma, 149: 161–170.
36- Sulaeman Y., Sarwani M., Minasny B., McBratney A.B., Sutandi A., and Barus B. 2012. Soil-landscape models to predict soil pH variation in the Subang region of West Java, Indonesia. P. 317-325. In B. Minasny et al. (ed). Digital Soil Assessment and Beyond. CRC Press.
37- Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F., and Malone P.B. 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213: 15-28.
38- Vasques G.M., Grunwald S., and Sickman J.O. 2009. Modeling of soil organic carbon fractions using visible/near-infrared spectroscopy. Soil Sci. Soc. Am. J., 73: 176–184.
39- www.cgiar-csi.org/data/srtm-90m-digital-elevation-database
40- Zhu A.X., Hudson B., Burt J., Lubich K., and Simonson D. 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci. Soc. Am. J., 65: 1463–1472.
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