(2016). Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content. , 30(1), 95-104. doi: 10.22092/ijsr.2016.106323
. "Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content". , 30, 1, 2016, 95-104. doi: 10.22092/ijsr.2016.106323
(2016). 'Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content', , 30(1), pp. 95-104. doi: 10.22092/ijsr.2016.106323
Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content. , 2016; 30(1): 95-104. doi: 10.22092/ijsr.2016.106323

Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content

پژوهش های خاک
Article 9, Volume 30, Issue 1, June 2016, Pages 95-104    PDF (1.22 M)
Document Type: Research Paper
DOI: 10.22092/ijsr.2016.106323
Abstract
Although a better understanding and quantitative knowledge of digital elevation model scale will help to improve soil predictions, the influence of pixel size has not been investigated in detail. The aim of this study was to investigate the role of spatial scale on soil clay content prediction by empirically testing the interaction between pixel resolution and window size with regression tree model. In two different areas in terms of their geomorphology and soil (area 1, Maybod located in Yazd province covered 400 km2; area 2, Iasokand located in Kurdistan province covered 400km2), 120 surface soil samples (0-30 cm) were taken and their clay contents were measured. From 121 digital elevation models representing different scales, 22 attribute were extracted and used for soil clay content prediction. Results showed that Maybod area had the minimum RMSE (9.0%) and maximum R2 (0.47) and dependence of tree model on pixel size was significant for clay prediction[H1] ; however, in Iasokand area, the minimum RMSE (5.65%) and maximum R2 (0.77) were obtained and window size was significant for clay prediction.



 [H1]از چکیده فارسی مطلب این گونه ترجمه شده. لطفا کنترل شود.
Keywords
Regression tree; Wrapper algorithm; Geomorphometry
References
  1. Addiscott, T.M. 1998. Modeling concepts and their relation to the scale of the problem. Nutrient Cycling in Agroecosystems. Nutrient Cycling in Agroecosystems. 50: 239-245.
  2. Atkinson, P.M., and Tate, N.J. 2000. Spatial scale problems and geostatistical solutions: a review. The Professional Geographer. 52: 607-623.
  3. Behrens, T., Zhu, A., Schmidt, K., and Scholten, T. 2010. Multi-scale digital terrain analysis and features selection for digital soil mapping. Geoderma. 155: 175-185.
  4. Cavazzi, S., Corstanje, R., Mayr, T., Hannam, J., and Fealy, R. 2013. Are fine resoulation digital models always the best choice in digital soil mapping?. Geoderma. 195: 111-121.
  5. Corstanje, R., Kirk, G.J.D., and Lark, R.M. 2010. The behaviour of soil process models of ammonia volatilization at contrasting spatial scales. European Journal of Soil Science. 59: 1271-1283.
  6. Florinsky, I.V., and Kuryakova, G.A. 2000. Determination of grid size for digital terrain modeling in landscape investigations-exemplified by soil moisture distribution at a micro-scale. International Journal of Geographical Information Science. 14: 815-832.
 

 

  1. Florinsky, I.V., Eilers, R.G., Manning, G.R., and Fuller, L.G. 2002. Prediction of soil properties by digital terrain modelling. Environmental Modelling & Software. 17: 295-311.
  2. Gallant, J.C., and Hutchinson, M.F. 1997. Scale dependence in terrain analysis. Mathematics and Computers in Simulation. 43: 313-321.
  3. Gee, G. W. and Bauder, J. W. 1986. Particle size analysis. p. 383-411. In: A. Klute. (ed), Methods of Soil Analysis. Part 1, American Society of Agronomy Madison, WI.
  4. Grinand, C., Arrouays, D., Laroche, B., and Martin, M.P. 2008. Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context. Geoderma. 143: 180- 190.
  5. Gruijter, J.J., McBratney, A.B., Minasny, B., Wheeler, I., Malone, B.P., and Stockmann, U. 2016. Farm-scale soil carbon auditing. Geoderma. 265: 120- 130.
  6. Heung, B., Ho, C.H., Zhang, J., Knudby, A., Bulmer, C.E., and Schmidt, M. 2016. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma. 265: 62- 77.
  7. Hudson, B.D. 1992. The soil survey as paradigm-based science. Soil Science Society of America Journal. 56: 836-841.
  8. Marceau, D.J., and Hay, G.J. 1999. Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing. 25. 357-366.
  9. McBratney, A.B., Santos, M.L.M., and Minasny, B. 2003. On digital soil mapping. Geoderma. 117: 3-52.
  10. 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.
  11. Malone, B.P., McBratney, A.B., and Minasny, B. 2011. Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes. Geoderma. 160: 614-626.
  12. Minasny, B., McBratney, A.B., and Salvador-Blanes, S. 2008. Quantitative models for pedogenesis — a review. Geoderma. 144: 140-157.
  13. Mitchell, T.M. 1997. Machine Learning. Springer.
  14. Quinlan, J.R. 2001. Cubist: An Informal Tutorial. http://www.rulequest.com.
  15. Pain, C.F. 2005. Size does matter: relationships between image pixel size and landscape process scales. MODSIM 2005 International Congress on Modeling and Simulation. 1430-1436.
  16. Roecker, S.M., and Thompson, J.A. 2008. Scale Effects on Terrain Attribute Calculation and Their Use as Environmental Covariates for Digital Soil Mapping. Proceedings of the 3rd Global Workshop on Digital Soil Mapping, Logan.
  17. Smith, M.P., Zhu, A., Burt, J.E., and Stiles, C. 2008. The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma. 137: 58- 69.
  18. Thompson, A.J., Bell, J.C., and Butler, C.A. 2001. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma. 100: 67– 89.
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