Skip to main content

Advertisement

SpringerLink
Log in

Assessing spatial variability in soil characteristics with geographically weighted principal components analysis

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

Dimensionality reduction methods such as principal components analysis (PCA) provide a means of identifying trends in soil characteristics which may be represented by a wide range of variables. However, these characteristics may be highly spatially variable and so the results from PCA represent, in some sense, an “average” of locally distinct characteristics. One approach to account for these local differences is to introduce a geographical weighting scheme into the PCA process. In this paper, such an approach is assessed in the exploration of soil characteristics in the state of Pennsylvania, USA. Data from 878 georeferenced soil profiles which include different soil parameters (n = 12) were extracted from the National Soil Survey Center database. Where data are parts of compositions (e.g., percentages of sand, silt, and clay), analysis using raw data is not appropriate and such data were transformed using log ratios (specifically, balances). Single variables (i.e., those which are not parts of compositions) were logged. The first two principal components explain over 50% of the variance. The mapped values suggest marked spatial variation in soil characteristics, but it is not possible to assess which of these variables explain most variation in particular regions from the simple maps of raw variables. Geographically weighted PCA (GWPCA) provides additional information which is obscured by PCA, and it also provides a set of component scores and loadings at all data locations. The soil variable with the largest loading at most locations of Pennsylvania is the logged base saturation (BSln), and this supports the findings of the conventional PCA analysis. While BSln loads most highly in most of the eastern third, the middle and the south west of the state, the northwest is less spatially consistent in terms of the variables which explain most variation. For GWPC 1, the variable with the second largest loading at most locations (i.e., primarily the south and west) is CEC.B1 (the log ratio of Ca, Mg, and Na to K and EXACID), while CEC.B2 (the log ratio of Ca and Mg to Na), pHln (logged pH) and BSln dominate in other areas. The GWPCA results suggest that there is marked spatial variation in multivariate soil characteristics across Pennsylvania state and that results from standard PCA obscure this considerable variation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdul-Wahab, S.A., Bakheit, C.S., Al-Alawi, S.M.: Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Modell. Softw. 20(10), 1263–1271 (2005)

    Article  Google Scholar 

  2. Aitchison, J.: The Statistical Analysis of Compositional Data. Chapman & Hall, London (1986)

    Book  MATH  Google Scholar 

  3. Arrouays, D., et al.: Large trends in French topsoil characteristics are revealed by spatially constrained multivariate analysis. Geoderma 161, 107–114 (2011)

    Article  Google Scholar 

  4. Batjes, N.H.: Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 47(2), 151–163 (1996)

    Article  Google Scholar 

  5. Bohn, H.L.: Estimate of organic carbon in world soils: II. Soil Sci. Soc. Am. J. 46, 1118–1119 (1982)

    Article  Google Scholar 

  6. Bourgault, G., Marcotte, D., Legendre, P.: The multivariate (co)variogram as a spatial weighting function in classification methods. Math. Geol. 24, 463–478 (1992)

    Article  Google Scholar 

  7. Charlton, M., Brunsdon, C., Demšar, U., Harris, P., Fotheringham, A.S.: Principal components analysis: from global to local. In: 13th AGILE International Conference on Geographic Information Science, Guimarães, Portugal, pp. 1–10 (2010)

  8. Egozcue, J.J., Pawlowsky-Glahn, V.: Groups of parts and their balances in compositional data analysis. Math. Geol. 37, 795–828 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Eswaran, H., Van Den Berg, E., Reich, P.: Organic carbon in soils of the world. J. Arid Environ. 57, 192–194 (1993)

    Google Scholar 

  10. Fotheringham, A.S., Brunsdon, C., Charlton, M.: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester, UK (2002)

    Google Scholar 

  11. Goderya, F.S.: Field scale variations in soil properties for spatially variable control: a review. J. Soil Contam. 7(2), 243–264 (1998)

    Google Scholar 

  12. Gonzalez, C., et al.: Applying multivariate methods to soil-solution interactions in carbonate media. Geoderma 137(3–4), 352–359 (2007)

    Article  Google Scholar 

  13. Hu, W., Shao, M.G., Wang, Q.J., Fan, J., Horton, R.: Temporal changes of soil hydraulic properties under different land uses. Geoderma 149(3–4), 355–366 (2009)

    Article  Google Scholar 

  14. Hur, J., Jung, M.C.: The effects of soil properties on the turbidity of catchment soils from the Yongdam dam basin in Korea. Environ. Geochem. Hlth. 31(3), 365–377 (2009)

    Article  Google Scholar 

  15. Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River, New Jersey (1998)

    Google Scholar 

  16. Kern, J.S.: Spatial patterns of soil organic carbon in the contiguous United States. Soil Sci. Soc. Am. J. 58, 439–455 (1994)

    Article  Google Scholar 

  17. Kimble, J.M., Eswaran, H., Cook, T.: Organic carbon on a volume basis in tropical and temperate soils. In: Trans. Int. Congr. Soil Sci., 14th, Comm. 5. Int SOC Soil Science, Kyoto, Japan, vol. 5, pp. 248–253 (1991)

  18. Li, W., Wang, Q.J., Wei, S.P., Shao, M.A., Yi, L.: Soil desiccation for Loess soils on natural and regrown areas. Forest Ecol. Manag. 255(7), 2467–2477 (2008)

    Article  Google Scholar 

  19. Li, Y.S., Huang, M.B.: Pasture yield and soil water depletion of continuous growing alfalfa in the Loess Plateau of China. Agr. Ecosyst. Environ. 124(1–2), 24–32 (2008)

    Article  Google Scholar 

  20. Liu, D.W., et al.: Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agr. Ecosyst. Environ. 113(1–4), 73–81 (2006)

    Article  Google Scholar 

  21. Lloyd, C.D.: Analysing population characteristics using geographically weighted principal components analysis: a case study of Northern Ireland in 2001. Comput. Environ. Urban. 34(5), 389–399 (2010)

    Article  MathSciNet  Google Scholar 

  22. Lloyd, C.D.: Local Models for Spatial Analysis, 2nd edn. CRC, Boca Raton (2011)

    MATH  Google Scholar 

  23. Mandal, U.K., et al.: Evaluating impact of irrigation water quality on a calcareous clay soil using principal component analysis. Geoderma 144(1–2), 189–197 (2008)

    Article  MathSciNet  Google Scholar 

  24. Mesdaghi, M.: Vegetation Description and Analysis: A Practical Approach. Jehad Daneshgahi of Mashhad, Mashhad, pp. 161–179 (2001)

  25. NRCS: Natural Resources Conservation Service. Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin. USDA Handbook (2006)

  26. Oliver, M.A., Webster, R.: A geostatistical basis for spatial weighting in multivariate classification. Math. Geol. 21, 15–35 (1989)

    Article  Google Scholar 

  27. Onweremadu, E.U., Akamigbo, F.O.R.: Spatial changes in the distribution of exchangeable cations in soils of a forested hilly landcape. J. Forest. Res. 1(2), 55–67 (2007)

    Article  Google Scholar 

  28. Park, S.J., Vlek, P.L.G.: Environmental correlation of three-dimensional soil spatial variability: a comparison of three adaptive techniques. Geoderma 109(1–2), 117–140 (2002)

    Article  Google Scholar 

  29. Pawlowsky-Glahn, V., Egozcue, J.J.: Compositional Data Analysis: An Introduction. Compositional Data Analysis in the Geosciences: From Theory to Practice. Geological Society Special Publications No. 264. London: Geological Society, pp. 1–10, London (2006)

    Google Scholar 

  30. Pearson, K.: On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc. R. Soc. Lond. 60, 489–498 (1897)

    MATH  Google Scholar 

  31. Pearson, K.: On lines and planes of closest fit to systems of points in space. Phil. Mag. 2(7–12), 559–572 (1901)

    Google Scholar 

  32. Pérez-Sirvent, C., Martínez-Sánchez, M.J., Vidal, J., Sánchez, A.: The role of lowquality irrigationwater in the desertification of semi-arid zones in Murcia, SE Spain. Geoderma 113(1–2), 109–125 (2003)

    Article  Google Scholar 

  33. Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G.: Soil carbon pools and life zones. Nature 298, 156–159 (1982)

    Article  Google Scholar 

  34. Post, W.M., Pastor, J., Zinke, P.J., Stangenberger, A.G.: Global patterns of soil nitrogen storage. Nature 317, 613–616 (1985)

    Article  Google Scholar 

  35. Post, W.M., et al.: The global carbon cycle. Am. Sci. 78, 310–326 (1990)

    Google Scholar 

  36. Qiu, Y., Fu, B.J., Wang, J., Chen, L.D.: Spatial variability of soil moisture content and its relation to environmental indices in a semi-arid gully catchment of the Loess Plateau, China. J. Arid Environ. 49(4), 723–750 (2001)

    Article  Google Scholar 

  37. Ranney, R.W., Ciolkosz, E.J., Petersen, G.W., Matelski, R.P., Cunningham, R.L.: The pH base-saturation relationship in b and c horizons of Pennsylvania soils. Soil Sci. 118(4), 247–253 (1974)

    Article  Google Scholar 

  38. Reeve, A.S., Siegel, D.I., Glaser, P.H.: Geochemical controls on peatland pore water from the Hudson Bay Lowland: a multivariate statistical approach. J. Hydrol. 181(1–4), 285–304 (1996)

    Article  Google Scholar 

  39. Sombroek, W.G., Nachtergaele, F.O., Hebel, A.: Amounts, dynamics and sequestering of carbon in tropical and subtropical soils. Ambio 22, 417–426 (1993)

    Google Scholar 

  40. Vestin, J.L.K., Nambu, K., van Hees, P.A.W., Bylund, D., Lundstrom, U.S.: The influence of alkaline and non-alkaline parent material on soil chemistry. Geoderma 135, 97–106 (2006)

    Article  Google Scholar 

  41. Wang, S., et al.: Vertical distribution of soil organic carbon in China. Environ. Manage. 33, 200–209 (2004)

    Article  Google Scholar 

  42. Wang, Y.Q., Zhang, X.C., Huang, C.Q.: Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma 150(1–2), 141–149 (2009)

    Article  MathSciNet  Google Scholar 

  43. Western, A.W., et al.: Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. J. Hydrol. 286(1–4), 113–134 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Carbon Management & Sequestration Center, School of Environment & Natural Resources, The Ohio State University, 2021 Coffey Road, 414A Kottman Hall, Columbus, OH, 43210-1085, USA

    Sandeep Kumar & Rattan Lal

  2. School of Geography, Archaeology and Palaeoecology, Queen’s University, Belfast, BT7 1NN, Northern Ireland, UK

    Christopher D. Lloyd

Authors
  1. Sandeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rattan Lal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher D. Lloyd
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandeep Kumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, S., Lal, R. & Lloyd, C.D. Assessing spatial variability in soil characteristics with geographically weighted principal components analysis. Comput Geosci 16, 827–835 (2012). https://doi.org/10.1007/s10596-012-9290-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-012-9290-6

Keywords

Search

Navigation