Geostatistical Analyst for Deciding Optimal Interpolation Strategies for Delineating Compact Zones

ABSTRACT

Variability maps of Hydraulic conductivity (K) were generated by using geo statistical analyst extension of ARC GIS for delineating compact zones in a farm. In the initial exploratory spatial data analysis, K data for 0 - 15 and 15 - 30 cm soil layers showed spatial dependence, anisotropy, normality on log transformation and linear trend. Outliers present in both layers were also removed. In the next step, cross validation statistics of different combinations of kriging (Ordinary, simple and universal), data transformations (none and logarithmic) and trends (none and linear) were compared. Combination of no data transformation and linear trend removal was the best choice as it resulted in more accurate and unbiased prediction. It thus, confirmed that for generating prediction maps by kriging, data need not be normal. Ordinary kriging is appropriate when trend is linear. Among various available anisotropic semivariogram models, spherical model for 0 - 15 cm and tetra spherical model for 15 - 30 cm were found to be the best with major and minor ranges between 273 - 410 m and 98 - 213 m. The kriging was superior to other interpolation techniques as the slope of the best fit line of scatter plot of predicted vs. measured data points was more (0.76) in kriging than in inverse distance weighted interpolation (0.61) and global polynomial interpolation (0.56). In the generated prediction maps, areas where K was <12 cm?day–1 were delineated as compact zone. Hence, it can be concluded that geostatistical analyst is a complete package for preprocessing of data and for choosing the optimal interpolation strategies.

Variability maps of Hydraulic conductivity (K) were generated by using geo statistical analyst extension of ARC GIS for delineating compact zones in a farm. In the initial exploratory spatial data analysis, K data for 0 - 15 and 15 - 30 cm soil layers showed spatial dependence, anisotropy, normality on log transformation and linear trend. Outliers present in both layers were also removed. In the next step, cross validation statistics of different combinations of kriging (Ordinary, simple and universal), data transformations (none and logarithmic) and trends (none and linear) were compared. Combination of no data transformation and linear trend removal was the best choice as it resulted in more accurate and unbiased prediction. It thus, confirmed that for generating prediction maps by kriging, data need not be normal. Ordinary kriging is appropriate when trend is linear. Among various available anisotropic semivariogram models, spherical model for 0 - 15 cm and tetra spherical model for 15 - 30 cm were found to be the best with major and minor ranges between 273 - 410 m and 98 - 213 m. The kriging was superior to other interpolation techniques as the slope of the best fit line of scatter plot of predicted vs. measured data points was more (0.76) in kriging than in inverse distance weighted interpolation (0.61) and global polynomial interpolation (0.56). In the generated prediction maps, areas where K was <12 cm?day–1 were delineated as compact zone. Hence, it can be concluded that geostatistical analyst is a complete package for preprocessing of data and for choosing the optimal interpolation strategies.

Cite this paper

nullK. Kamble and P. Aggrawal, "Geostatistical Analyst for Deciding Optimal Interpolation Strategies for Delineating Compact Zones,"*International Journal of Geosciences*, Vol. 2 No. 4, 2011, pp. 585-596. doi: 10.4236/ijg.2011.24061.

nullK. Kamble and P. Aggrawal, "Geostatistical Analyst for Deciding Optimal Interpolation Strategies for Delineating Compact Zones,"

References

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[2] A. W. Warrick, D. E. Myers and D. R. Nielsen, “Geostatistical Methods Applied to Soil Science,” In: A. Klute, Eds., Methods of Soil Analysis, Part I, 2nd Edition, Soil Science Society of America Book, Madison, 1986, pp. 53-82.

[3] P. Aggarwal and R. P. Gupta, “Two Dimensional Geostatistical Analysis of Soils,” In: R. P. Gupta and B. P. Gnildyal, Eds., Theory and Practice in Agrophysics Measurements, Allied Publishers, Buffalo, 1998, pp. 253-263.

[4] I. S. Dahiya, B. Kalta and R. P. Agrawal, “Kriging for Interpolation through Spatial Variability Analysis of Data,” In: R. P. Gupta, B. P. Ghildyal, Eds., Theory and Practice in Agrophysics Measurements, Allied Publishers, Buffalo, 1998, pp. 242-252.

[5] A. B. McBratney and M. J. Pringle, “Estimating Average and Proportional Variograms of Soil Properties and Their Potential use in Precision Farming,” Precision. Agriculture, Vol. 1, No. 2, 1999, pp. 219-236. doi:10.1023/A:1009995404447

[6] M. Mzuku, R. Khosla, R. Reich, D. Inman, F. Smith and L. Macdonald, “Spatial Variability of Measured Soil Properties across Site Specific Management Zones,” Soil Science. Society of America Journal, Vol. 69, No. 5, 2005, pp. 1572-1579. doi:10.2136/sssaj2005.0062

[7] A. Sarangi, C. A. Cox and C. A. Madramootoo, “Geostatistical Methods for Prediction of Spatial Variability of Rainfall in a Mountainous Region,” Transactions of ASAE, Vol. 48, No. 3, 2005, pp. 943-954.

[8] D. J. Mulla and A. B. McBratney, “Soil Spatial Variability,” In: A. W. Warrick, Ed., Soil Physics Companion, CRC Press, Boca Raton, 2002, pp. 343-370.

[9] P. Santra, U. K. Chopra and D. Chakraborty, “Spatial Variability of Soil Properties and Its Application in Predicting Surface Map of Hydraulic Parameters in an Agricultural Farm,” Current Science, Vol. 95, No. 7, 2008, pp. 937-945.

[10] K. K?l??, E. ?zg?z and F. Akba, “Assessment of Spatial Variability in Penetration Resistance as Related to Some Soil Physical Properties of Two Fluvents in Turkey,” Soil Tillage Research, Vol. 76, No. 1, 2004, pp .1-11.

[11] J. Iqbal, J. A. Thomasson , J. N. Jenkins, P. R. Owen and F. D. Whisler, “Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils,” Soil Science Society of America Journal, Vol. 69, No. 4, 2005, pp. 1338-1350. doi:10.2136/sssaj2004.0154

[12] M. Dufferra, G. W. Jeffrey and R. Weisz, “Spatial Variability of Southeastern U.S. Coastal Plain Soil Physical Properties: Implications for Site-Specific Management,” Geoderma, Vol. 137, No. 3-4, 2007, pp. 327-339. doi:10.1016/j.geoderma.2006.08.018

[13] J. M. Ver Hoef, K. Krivoruchko and N. Luca, “Using ARCGIS Geostatistical Analyst,” ESRI, St. Charles, 2001, pp. 1-306.

[14] R. P. Gupta and I. P. Abrol, “A Study of Some Tillage Practices for Sustainable Crop Production in India,” Soil & Tillage Research, Vol. 27, No. 1-4, 1993, pp. 253-273. doi:10.1016/0167-1987(93)90071-V

[15] K. Johnston, J. M. V. Hoef, K. Krivoruchko and N. Lucas, “Using Geostatatistical Analyst,” ESRI, St. Charles, 2001, pp. 1-306.

[1] W. A. Jury, “Spatial Variability of Soil Properties”, In: S. C. Hern and S. M. Melancon, Eds., Vadose Zone Modelling of Organic Pollutants, Lewis Publishers, Boca Raton, 1986, pp. 245-269.

[2] A. W. Warrick, D. E. Myers and D. R. Nielsen, “Geostatistical Methods Applied to Soil Science,” In: A. Klute, Eds., Methods of Soil Analysis, Part I, 2nd Edition, Soil Science Society of America Book, Madison, 1986, pp. 53-82.

[3] P. Aggarwal and R. P. Gupta, “Two Dimensional Geostatistical Analysis of Soils,” In: R. P. Gupta and B. P. Gnildyal, Eds., Theory and Practice in Agrophysics Measurements, Allied Publishers, Buffalo, 1998, pp. 253-263.

[4] I. S. Dahiya, B. Kalta and R. P. Agrawal, “Kriging for Interpolation through Spatial Variability Analysis of Data,” In: R. P. Gupta, B. P. Ghildyal, Eds., Theory and Practice in Agrophysics Measurements, Allied Publishers, Buffalo, 1998, pp. 242-252.

[5] A. B. McBratney and M. J. Pringle, “Estimating Average and Proportional Variograms of Soil Properties and Their Potential use in Precision Farming,” Precision. Agriculture, Vol. 1, No. 2, 1999, pp. 219-236. doi:10.1023/A:1009995404447

[6] M. Mzuku, R. Khosla, R. Reich, D. Inman, F. Smith and L. Macdonald, “Spatial Variability of Measured Soil Properties across Site Specific Management Zones,” Soil Science. Society of America Journal, Vol. 69, No. 5, 2005, pp. 1572-1579. doi:10.2136/sssaj2005.0062

[7] A. Sarangi, C. A. Cox and C. A. Madramootoo, “Geostatistical Methods for Prediction of Spatial Variability of Rainfall in a Mountainous Region,” Transactions of ASAE, Vol. 48, No. 3, 2005, pp. 943-954.

[8] D. J. Mulla and A. B. McBratney, “Soil Spatial Variability,” In: A. W. Warrick, Ed., Soil Physics Companion, CRC Press, Boca Raton, 2002, pp. 343-370.

[9] P. Santra, U. K. Chopra and D. Chakraborty, “Spatial Variability of Soil Properties and Its Application in Predicting Surface Map of Hydraulic Parameters in an Agricultural Farm,” Current Science, Vol. 95, No. 7, 2008, pp. 937-945.

[10] K. K?l??, E. ?zg?z and F. Akba, “Assessment of Spatial Variability in Penetration Resistance as Related to Some Soil Physical Properties of Two Fluvents in Turkey,” Soil Tillage Research, Vol. 76, No. 1, 2004, pp .1-11.

[11] J. Iqbal, J. A. Thomasson , J. N. Jenkins, P. R. Owen and F. D. Whisler, “Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils,” Soil Science Society of America Journal, Vol. 69, No. 4, 2005, pp. 1338-1350. doi:10.2136/sssaj2004.0154

[12] M. Dufferra, G. W. Jeffrey and R. Weisz, “Spatial Variability of Southeastern U.S. Coastal Plain Soil Physical Properties: Implications for Site-Specific Management,” Geoderma, Vol. 137, No. 3-4, 2007, pp. 327-339. doi:10.1016/j.geoderma.2006.08.018

[13] J. M. Ver Hoef, K. Krivoruchko and N. Luca, “Using ARCGIS Geostatistical Analyst,” ESRI, St. Charles, 2001, pp. 1-306.

[14] R. P. Gupta and I. P. Abrol, “A Study of Some Tillage Practices for Sustainable Crop Production in India,” Soil & Tillage Research, Vol. 27, No. 1-4, 1993, pp. 253-273. doi:10.1016/0167-1987(93)90071-V

[15] K. Johnston, J. M. V. Hoef, K. Krivoruchko and N. Lucas, “Using Geostatatistical Analyst,” ESRI, St. Charles, 2001, pp. 1-306.