کاربرد شاخص موران در تشخیص داده‌های پرت مکانی و ارزیابی اثر آن‌ها بر برآورد توزیع مکانی ماده آلی خاک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشگاه ارومیه

2 دانشجوی کارشناسی ارشد دانشگاه ارومیه

چکیده

ماده آلی خاک به‌عنوان شاخصی کلیدی از درجه تخریب خاک‌ها و قابلیت ترسیب کربن در آن‌ها بوده و تعیین الگوی پراکنش مکانی آن در یک منطقه از اقدامات اساسی برای تدوین استراتژی‌های مؤثر مدیریت خاک و اکوسیستم محسوب می‌شود. در این خصوص، روش‌های زمین‌آماری مانند کریجینگ به‌طورگسترده‌ای به‌منظور تعیین الگوی پراکنش مکانی کربن آلی و سایر ویژگی‌های خاک به‌کار برده شده است. ویژگی‌های آماری داده‌های اولیه تأثیر بسیار زیادی بر کیفیت نقشه‌های تولیدشده داشته و از این‌رو تشخیص و حذف داده‌های پرت کلی و مکانی به‌عنوان یک گام اساسی اولیه در تهیه نقشه‌های دقیق‌تر از ماده آلی محسوب می‌شود که در پژوهش‌های پیشین کمتر مورد توجه قرار گرفته است. بنابراین، این پژوهش باهدف ارزیابی اثر داده‌های پرت مکانی بر چگونگی توزیع ماده آلی خاک در حوزه آبخیز روضه‌چای واقع در شهرستان ارومیه، استان آذربایجان غربی، صورت گرفت. برای این منظور 89 نمونه خاک سطحی (10-0 سانتی‌متری) بر اساس روش نمونه‌برداری تصادفی نظارت‌شده از حوزه تهیه شد. داده‌های پرت کلی مربوط به ماده آلی با استفاده از نمودار جعبه‌ای و پس از نرمال کردن توزیع مقادیر حذف شد. به‌منظور حذف داده‌های پرت مکانی از شاخص‌های موران کلی و محلی استفاده شد. کورلوگرام شاخص موران کلی در فاصله900 متری حداکثر همبستگی مکانی را نشان داد که این فاصله به‌عنوان مبنای تعیین نقاط پرت مکانی با استفاده از شاخص موران محلی در نظر گرفته شد. نقشه خوشه‌بندی به‌دست‌ آمده از شاخص موران محلی، چهار داده پرت مکانی را در منطقه نشان داد که با حذف آن‌ها مقادیر ضرایب کارایی نقشه­کریجینگ شامل MAE و RMSE به ترتیب از 97/0 و 31/1 به 85/0 و 12/1 کاهش یافته و در نتیجه دقت نقشه در مقایسه با شرایط عدم حذف این داده‌ها 5/13 درصد افزایش یافت. نتایج کلی، مؤید کارایی استفاده از شاخص موران برای تشخیص داده‌های پرت مکانی و اثر مثبت حذف این داده‌ها در افزایش دقت نقشه کریجینگ ماده آلی در منطقه بود.

کلیدواژه‌ها

عنوان مقاله [English]

Identification of Spatial Outliers by Moran’s Index and Evaluation of Their Effects on the Spatial Distribution of Soil Organic Matter

نویسندگان [English]

  • F. Asadzadeh 1
  • M. Rahmati 2
  • H. Asgarzadeh 1
چکیده [English]

Soil organic matter (SOM) is a key index in evaluation of the soil degradation and soil carbon sequestration. Therefore, determination of the SOM spatial patterns is essential for developing the suitable strategies of soil and ecosystem management. Geostatistical methods such as kriging have been widely employed to investigate the spatial pattern of different soil properties like SOM. The quality of the spatial maps is significantly influenced by the statistical properties of the raw data. Thus, identification and elimination of spatial outlier data, which has rarely been considered in previous works, is an important step in preparation of accurate and suitable maps of soil organic matter. The aim of the present study was to evaluate the effect of spatial outliers on the spatial pattern of soil organic carbon at the Rozeh-Chay watershed, Urmia, west Azarbayjan province. A total of 89 surface soil samples (0-10 cm) were collected based on the stratified random sampling scheme. After the normalization of the raw SOM data, global outliers were eliminated by box plot method. Spatial outliers were identified by the global and local Moran’s I indices. Spatial correlogram of the global Moran’s I showed the highest spatial autocorrelation at distance of 900 m, which was used as a distance band for preparation of spatial clusters map by local Moran’s I index. Cluster map of the local Moran’s I index showed four spatial outliers which were eliminated to assess their effects on the accuracy of kriging map of SOM. With the elimination of the spatial outliers, the MAE and RMSE of the SOM map were decreased from 0.97 and 1.31 to 0.85 and 1.12, respectively. Therefore, the accuracy of the kriging map increased by 13.5 percent. Generally, it can be concluded that the combination of Moran’s I index and kriging method improves the efficiency of organic matter map in the study area.

کلیدواژه‌ها [English]

  • Global outlier
  • Kriging
  • spatial structure
  • Urmia

مراجع

  1. Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27: 93–115.
  2. Ayoubi, S., Karchegani, P.M., Mosaddeghi, M.R., and Honarjoo, N. 2012. Soil aggregation and organic carbon as affected by topography and land use change in western Iran. Soil and Tillage Research, 121: 18-26.
  3. Bameri, A., Khormali, F., Kiani, F., and Dehghani, A.A. 2015. Spatial variability of soil organic carbon in different hillslope positions in Toshan area, Golestan Province, Iran: Geostatistical approaches. Journal of Mountain Science, 12(6): 1422-1433.
  4. Bhunia, G.S., Shit, P.K., and Maiti, R. 2016. Spatial variability of soil organic carbon under different land use using radial basis function (RBF). Modeling Earth Systems and Environment, 2(1):1-8.
  5. Brody, S.D., Highfield, W.E., and Thornton, S. 2006. Planning at the urban fringe: an examination of the factors influencing nonconforming development patterns in southern Florida. Environment and Planning B: Planning and Design, 33(1): 75-96.
  6. Cambardella, C.A., Moorman, T.B., Parkin, T.B., Karlen, D.L., Novak, J.M., Turco, R.F., and Konopka, A.E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil science society of America journal, 58(5):1501-1511.
  7. Cotrufo, M. F., Conant, R. T., and Paustian, K. 2011. Soil organic matter dynamics: land use, management and global change. Plant and soil, 338(1): 1-3.
  8. Dawson, R. 2011. How significant is a boxplot outlier. Journal of Statistics Education, 19(2): 1-12.
  9. Fu, W., Tunney, H., and Zhang, C. 2010. Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil and Tillage Research, 106(2):185-193.
  10. Havaee, S., Mosaddeghi, M.R., and Ayoubi, S. 2015. In situ surface shear strength as affected by soil characteristics and land use in calcareous soils of central Iran. Geoderma, 237: 137-148.
  11. Huo, X.N., Li, H., Sun, D.F., Zhou, L.D., and Li, B.G. 2012. Combining geostatistics with Moran’s I analysis for mapping soil heavy metals in Beijing, China. International journal of environmental research and public health, 9(3): 995-1017.
  12. Ishiokh, F., Kurihara, K., Suito, H., Horikawa, Y., and Ono, Y. 2007. Detection of hotspots for three-dimensional spatial data and its application to environmental pollution data. Journal of Environmental Science for Sustainable Society, 1:15-24.
  13. Kerry, R., and Oliver, M. A. 2007. Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood. Geoderma, 140: 383–396.
  14. Lal, R. 2004. Soil carbon sequestration impacts on global climate change and food security. Science, 304(5677):1623-1627.
  15. Lalor, G.C., and Zhang, C. 2001. Multivariate outlier detection and remediation in geochemical databases. Science of the total environment, 281(1): 99-109.
  16. Liu, L., Wang, H., Dai, W., Lei, X., Yang, X., and Li, X. 2014. Spatial variability of soil organic carbon in the forestlands of northeast China. Journal of Forestry Research, 25(4): 867-876.
  17. Liu, Z., Shao, M.A., and Wang, Y., 2011. Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region, China. Agriculture, Ecosystems & Environment, 142(3): 184-194.
  18. Liu, D., Wang, Z., Zhang, B., Song, K., Li, X., Li, J., Li, F., and Duan, H., 2006. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agriculture, ecosystems & environment, 113(1):73-81.
  19. McGrath, D., and Zhang, C. S. 2003. Spatial distribution of soil organic carbon concentrations in grassland of Ireland. Applied Geochemistry, 18:1629–1639.
  20. Mirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H., and Asadzadeh, F. 2016. Spatial variability of soil organic matter using remote sensing data. Catena, 145:118-127.
  21. Mondal, B., Das, D. N., and Dolui, G. 2015. Modeling spatial variation of explanatory factors of urban expansion of Kolkata: a geographically weighted regression approach. Modeling Earth Systems and Environment, 1(4): 1-13.
  22. Overmars, K.P., De Koning, G.H.J., and Veldkamp, A. 2003. Spatial autocorrelation in multi-scale land use models. Ecological Modelling, 164(2): 257-270.
  23. Pariente, S., and Lavee, H., 2003. Soil organic matter and degradation. In Briefing Papers of the 1st Scape workshop in Alicante (ES): 83-88.
  24. Sadeghi, A., Graff, C. D., Starr, J., Mccarty, G., Codling, E., and Sefton, K. 2006. Spatial variability of soil phosphorous levels before and after poultry litter application. Soil Science, 171: 850–857.
  25. Rowell, D. L. 1994. Soil science: Methods & applications. John Wiley & Sons, New York, 350 p.
  26. Salame, C. W., Queiroz, J. C. B., de Miranda Rocha, G., Amin, M. M., and da Rocha, E. P. 2016. Use of spatial regression models in the analysis of burnings and deforestation occurrences in forest region, Amazon, Brazil. Environmental Earth Sciences, 75(3): 1-12.
  27. Stockmann, U., Adams, M.A., Crawford, J.W., Field, D.J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A.B., de Courcelles, V.D.R., Singh, K., and Wheeler, I. 2013. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, 164: 80-99.
  28. Tu, J., and Xia, Z.G. 2008. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. Science of the total environment, 407(1): 358-378.
  29. Wang, Z.M., Zhang, B., Song, K.S., Lui, D.W., and Ren., C.Y. 2010. Spatial variability of soil organic carbon under maize monoculture in the Song-Nen Plain, Northeast China. Pedosphere, 20(1): 80-89.
  30. Wilding, L.P. 1985. Spatial variability: its documentation, accommodation and implication to soil surveys. In: Nielsen, D.R., Bouma, J. (Eds.), Soil Spatial Variability. Pudoc, Wageningen, The Netherlands, pp. 166–194.
  31. Zhang, P., and Shao, M.A. 2014. Spatial variability and stocks of soil organic carbon in the Gobi desert of Northwestern China. PloS one, 9(4): p.e93584; 1-12.
  32. Zhang, C., Luo, L., Xu, W., and Ledwith, V. 2008. Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of the total environment, 398(1): 212-221.
  33. Zhang, C., Tang, Y., Luo, L. and Xu, W. 2009. Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land. Environmental pollution, 157(11): 3083-3090.
  34. Zhao, K., Fu, W., Liu, X., Huang, D., Zhang, C., Ye, Z. and Xu, J. 2014. Spatial variations of concentrations of copper and its speciation in the soil-rice system in Wenling of southeastern China. Environmental Science and Pollution Research, 21(11):7165-7176.
پژوهش های خاک
دوره 30، شماره 3
آذر 1395
صفحه 281-293

فایل ها

هم رسانی

ارجاع به این مقاله

آمار