نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری، دانشکده کشاورزی، دانشگاه شهرکرد
2 دانشیار، دانشکده کشاورزی، دانشگاه شهرکرد
3 دانشیار، دانشکده علوم کشاورزی، دانشگاه گیلان
4 استادیار، دانشکده کشاورزی، دانشگاه ارومیه
چکیده
آگاهی از توزیع و تغییرات مکانی فرسایش‌پذیری و ویژگی‌های خاک اهمیت زیادی در طراحی و اجرای راه‌کارهای حفاظت آب، مهار سیل و رواناب و مدیریت فرسایش خاک یا به‌طور کلی مدیریت حوزه‌های آبخیز دارد. انتخاب و استفاده از فن میانیابی مناسب برای تعیین عوامل فرسایش‌پذیری به‌وسیله مد‌ل‌های فرسایشی مانند WEPP و ویژگی‌های خاک، ضرورت دارد. هدف از این پژوهش، پهنه‌بندی فرسایش‌پذیری بین‌شیاری خاک و عوامل تاثیرگذار در آن مانند میزان رس، ماده آلی، کربنات کلسیم معادل با استفاده از تخمینگر کریجینگ و کوکریجینگ به کمک اطلاعات سنجش از دور (ماهواره Landsat 7) است. بدین‌منظور، تعداد 100 نمونه خاک از عمق صفر تا 15 سانتی‌متری به‌صورت تصادفی در بخشی از حوزه آبخیز سئلین منطقه کلیبر واقع در استان آذربایجان شرقی برداشت و میزان رس، ماده آلی، کربنات کلسیم معادل خاک و فرسایش‌پذیری بین‌شیاری مدل WEPP اندازه‌گیری شدند. تحلیل همبستگی بین ویژگی‌های یاد شده و اعداد رقومی تصویر +ETM نشان داد که میزان رس، ماده آلی، کربنات کلسیم معادل و فرسایش‌پذیری بین‌شیاری خاک به‌ترتیب با اعداد رقومی (DN) باند 7، 1، 1 و 3 تصویر +ETM بیشترین ضریب همبستگی (0.406-، 0.431-، 0.291 و 0.299) را داشت. لذا، اعداد رقومی باندهای یاد شده به‌عنوان داده‌های کمکی در تخمینگر کوکریجینگ استفاده شدند. ویژگی‌های خاک با مدل کروی برای مدل‌سازی تغییرنما بهترین برازش را داشتند. با وجود به‌کارگیری اطلاعات سنجش از دور به‌عنوان متغیر کمکی در تخمینگر کوکریجینگ تفاوت قابل توجهی بین کریجینگ و کوکریجینگ مشاهده نشد. این امر می‌تواند ناشی از همبستگی نه چندان قوی بین متغیر هدف و اطلاعات سنجش از دور باشد.
کلیدواژه‌ها
عنوان مقاله [English]
Suitable interpolation methods for zoning of interrill erodibility of WEPP model and some soil properties using geostatistics and remote sensing data
نویسندگان [English]
- Salman Mirzaee 1
- Shoja Ghorbani Dashtaki 2
- Jahangard Mohammadi 2
- Hossein Asadi 3
- Farokh Asadzadeh 4
1 PhD Student, Faculty of Agriculture, Shahrekord University, Iran
2 Associated Professor, Faculty of Agriculture, Shahrekord University, Iran
3 Associated Professor, Faculty of Agriculture, University of Guilan, Iran
4 Assistant Professor, Faculty of Agriculture, Urmia University, Iran
چکیده [English]
Understanding the spatial distribution and variability of erodibility and soil properties is essential for planning of water conservation methods, controlling of flood and runoff and managing of soil erosion or watershed. Selecting and using appropriate interpolation techniques for soil properties and erodibility mapping by erosion models such as WEPP is essential. The objective of this study was regionalization of interrill erodibility and effective factors like clay, organic matter and lime using kriging and cokriging and remote sensing data (Landsat 7). For this purpose, 100 soil samples were selected randomly from 0-15 cm depth of Selin watershed in Kaleibar region of East Azerbaijan. Interrill erodibility of WEPP model and some soil properties as clay, organic matter and lime were measured. Correlation analysis between soil properties and digital number (DN) ETM+ image showed that clay, organic matter, lime and interrill erodibility had the highest correlation with DN of Band 7, 1, 1 and 3 ETM+ image (−0.406, -0.431, 0.291 and 0.299), respectively. Therefore, the DN of these bands used as auxiliary data for cokriging estimator. The spherical model was fitted the best model to calculate variogram of interrill erodibility, clay, organic matter and lime. No significant difference were noted between kriging and cokriging despite using remote sensing data as auxiliary data. This can be attributed no strong correlation between interrill erodibility, clay, organic matter and lime and remote sensing data.
کلیدواژه‌ها [English]
- East Azerbaijan
- ETM+ image
- Kriging
- cokriging
- soil erosion
- Allison, L.E. and C.D. Moodie. 1965. Methods of soil analysis. Part 2, Chemical Methods, 2nd ed., Agronomy MonogrAph, 9, ASA, CSSA and SSSA, Madison, WI, 1379-1400.
- Alavipanah, S.K. 2003. Application of remote sensing in the earth science (soil). University of Tehran press, 125 pages (in Persian).
- Ben-Dor, E., J.R. Irons and G.F. Epema. 1999. Soil reflectance. In A.N. Rencz (ed.), Remote sensing for the earth sciences: Manual of remote sensing. Wiley, Danvers, MA, 111–188.
- Burgess, T.M. and R. Webster. 1980. Optimal interpolation and isarithm mapping of soil properties. I. The semivariogram and punctual kriging. European Journal of Soil Science, 31: 315–331.
- Cambardella, C.A., T.B. Moorman, J.M. Nocak, T.B. Parkin, D.L. Karlen, R.F. Turco and A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58: 1501–1511.
- Chen, F., D.E. Kissel, L.T. West and W. Adkins. 2000. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Science Society of America Journal, 64:746–753.
- Dayani, M., J. Mohammadi and M. Naderi. 2009. Geostatistical analysis of Pb, Zn and Cd concentration in soil of Sepahanshahr suburb (south of Isfahan). Journal of Water and Soil, 23(4): 67-76 (in Persian).
- Dayani, M., M. Naderi and J. Mohammadi. 2010. Mapping concentration of Pb, Zn and Cd soils using Landsat ETM+ data in southern Isfahan. Journal of Water and Soil, 24(2): 286-296 (in Persian).
- Elliot, W.J., A.A. Liebenow, J.M. Laflen and K.D. Kohl. 1989. A compendium of soil erodibility data from WEPP cropland soil field, erodibility experiments 1987 and 88. NSERL Report, vol. 3., Agricultural Research Service, U.S. Department of Agriculture, National Soil Erosion Research Lab., West Lafayette, Indiana, 316 pages.
10. Gee, G.H. and J.W. Bauder. 1986. Particle size analysis. Methods of soil analysis, Physical Properties. SSSA, Madison, WI 9: 383-411.
11. Istok, J.D., J.D. Smyth and A.L. Flint. 1993. Multivariate geostatistical analysis of ground-water contaminant: a case history. Ground Water, 31(1): 63–74.
12. Krishnan, P., J.D. Alexander, B.J. Butler and J.W. Hummel. 1980. Reflectance technique for predicting soil organic matter. Soil Science Society of America Journal, 44: 1282–1285.
13. Khalil Moghadam, B. and S. Ghorbani Dashtaki. 2012. Comparison of geostatistics, PTFs, SSPFs methods and their combination for estimating soil surface shear strength. Journal of Water and Soil, 26(1): 127-128 (in Persian).
14. Laflen, J.M., W.J. Elliot, J.R. Simanton, C.S. Holzey and K.D. Kohl. 1991. WEPP soil erodibility experiments for rangeland and cropland soils. Journal of Soil and Water Conservation, 46(1): 39–44.
15. Liao, K., X.U. Shaohui, W.U. Jichun and Z.H.U. Qing. 2013. Spatial estimation of surface soil texture using remote sensing data. Soil Science and Plant Nutrition, 59: 488–500.
16. Martinez-Cob, C.A. 1996. Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology, 174: 19–35.
17. Mousavifard, S.M., H.R. Momtaz and H. Khodaverdiloo. 2014. Efficiency of some geostatistical estimators for interpolation and mapping some soil quality properties. Journal of Soil and Water Resource Conservation, 2(3): 57-71 (in Persian).
18. Mohammadi, J. 2006. Pedometrics, Vol 2: Spatial statistic. Pelk press, 250 pages (in Persian).
19. Mohammadi, J. and V. Chitsaz. 2002. Comparison of geostatistical predictors and linear regression for estimating some surface soil properties using thematic mapping data. Journal of Water and Soil Science, 16(2): 196-205 (in Persian).
20. Odeh, I.O.A., A.B. McBratney and D.J. Chittleborough. 1995. Further results on prediction from terrain attributes: Heterotopic cokriging and regression kriging. Geoderma, 67: 215–236.
21. Seybold, C.A., R.B. Grossman and T.G. Reinsch. 2005. Predicting cation exchange capacity for soil survey using linear models. Soil Science Society of America Journal, 69: 856–863.
22. Slami, M., A. Jafari Ardakani and K. Kamali. 2007. Investigating of measured soil erodibility correlation with rainfall simulation, Baisal and Whishmaier graph. Watershed engineering and Science Congress with Watershed Management Subject.
23. Stenberg, B., R.A. Viscarra Rossel, A.M. Mouazen and J. Wetterlind. 2010. Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107: 163–215.
24. Sullivan, D.G., J.N. Shaw, D. Rickman, P.L. Mask and J.C. Luvall. 2005. Using remote sensing data to evaluate surface soil properties in Alabama ultisols. Soil Science, 170: 954–968.
25. Triantafilis, J., W. Ward and A. McBratney. 2001. Land suitability assessment in the Namoi valley of Australia, using a continuous model. Australian Journal of Soil Research, 39: 273–290.
26. Walkley, A. and I.A. Black. 1934. An examination of Degtgareff method for determining soil organic matter and a proposed modification of the chromic acid in soil analysis. 1. Experimental. Soil Science Society America Journal, 79: 459-465.
27. Warrington, D.N., A.I. Mamedov, A.K. Bhardwaj and G.J. Levy. 2009. Primary particle size distribution of eroded material affected by degree of aggregate slaking and seal development. European Journal of Soil Science, 60: 84–93.
28. Wu, C., J. Wu, Y. Luo, L. Zhang and S.D. DeGloria. 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society America Journal, 73: 1202–1208.
29. Wu, J., W.A. Norvell, D.G. Hopkins, D.B. Smith, M.G. Ulmer and R.M. Welch. 2003. Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity. Soil Science Society America Journal, 67: 919–927.
)