Effects of digital elevation model (DEM) spatial resolution on soil landscape analysis (Case study Raakat watershed of Izeh, Khuzestan Province)

Document Type : Original Article

Authors

1 Soil science department, Shahid Chamran University

2 Soil Science department

Abstract

The most important factors which are effective for landscape analysis is spatial resolution of digital elevation model (DEM).In this study, the effect of spatial resolution on soil parameters and modeling of soil properties have studied. For this research 6 parameters including (height, slope gradient, slope aspect, minimal curvature, upland area and sediment transportation index) from 5 different spatial resolutions (10, 30 (base), 60,90 and 120) have originated and for modeling of soil properties (soil texture, K, P,EC,pH and soil depth) have used. The differences between mean of each parameter of spatial resolutions accomplished using Kruskal-Wallis test and multi linear regression, then the best model in each spatial resolution was selected based on AICC index. Our results depicted that with coarser DEM, the mean of slope gradient (G), sediment transportation index (STI) and the minimum curvature (Cmin) were decreased whereas the mean and minimum of upslope area (UP) was enhanced. Statistical indices of height showed the low sensivity to spatial resolution variations. Changes of mean and maximum slope aspect in different spatial resolutions have no regular trend. Only minimum curvature and upland area have significant difference relating to different spatial resolutions. With changes of DEM spatial resolution, the best combination of parameters for modeling of soil properties and AICc and R2adj will be change. Finally, our results illustrated that for an area with high variability of geomorphic conditions, there is no capability to use only one specific resolution for all soil properties.

Keywords

References

 
 
Akaike H. 1974. A new look at the statistical model identification. IEEE transactions on automatic control, 19 (6), 716-723. ‏
Bishop T. F., and Minasny B. 2016. Digital soil-terrain modeling: the predictive potential and uncertainty. In Grunwald, S. (Eds). Environmental soil-landscape modeling, CRC Press. ‏ pp. 185-213.
‏Box G. E., Jenkins G. M., Reinsel G. C., and Ljung G. M. 2016. Time Series Analysis: Forecasting and Control. 5th Ed, John Wiley and Sons. 712p.
Chaplot V., Walter C., and Curmi P. 2000. Improving soil hydromorphy prediction according to DEM resolution and available pedological data. Geoderma, 97(3): 405-422. ‏
Chang K. T., and Tsai B. W. 1991. The effect of DEM resolution on slope and aspect mapping. Cartography and Geographic Information Systems, 18(1), 69-77. ‏
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J. 2015: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991-2007, doi:10.5194/gmd-8-1991-2015.
Deng Y., Wilson J. P., and Bauer B. O. 2007. DEM resolution dependencies of terrain attributes across a landscape. International Journal of Geographical Information Science, 21(2), 187-213. ‏
Florinsky I. 2016. Digital Terrain Analysis in Soil Science and Geology. Second Ed., Academic Press, ‏ Amsterdam, pp. 7-68. ‏
Gallant J. C., and Wilson J. P. 2000. Primary Topographic Attributes. In Wilson, J. P. and Gallant, J. C. (Eds). Terrain analysis: principles and applications. John Wileys and Sons, New York, pp. 58-59.
Han X., Liu J., Mitra S., Li X., Srivastava P., Guzman S. M., and Chen X. 2018. Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach. Catena, 163, 257-275. ‏
Khanifar J., Khademalrasoul A., and Amerikhah H. 2018. Effect of Digital Elevation Model (DEM) Spatial Resolution on Geomorphometric modeling of Soil aggregate stability. The First National Conference on Sustainable Development in Agricultural Sciences and Natural Resource. Tehran, Iran. (In Persian)
Kienzle S. 2004. The effect of DEM raster resolution on first order, second order and compound terrain derivatives. Transactions in GIS, 8(1), 83-111. ‏
Koo J. Y., Yoon D. S., Lee D. J., Han J. H., Jung Y., Yang J. E., and Lim K. J. 2016. Effect of DEM Resolution in USLE LS Factor. Journal of Korean Society on Water Environment, 32(1), 89-97. ‏
Horn R.G. and Israelachvili, J.N. 1981. Direct measurement of structural forces between two surfaces in a nonpolar liquid. The Journal of Chemical Physics, 75(3), 1400-1411.
McKenzie N. J., and Ryan P. J. 1999. Spatial prediction of soil properties using environmental correlation. Geoderma, 89(1): 67-94.
Moore I. D., Grayson R. B., and Ladson A. R. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. ‏
Nath D. A. 2006. Soil landscape modeling in the Northwest Iowa Plains region of O’Brien County, Iowa, Master of Science, Iowa State University, USA.
Park S. J., and Vlek P. L. 2002a. Soil-landscape analysis as a tool for sustainable land management. Hydrology and Earth System Sciences Discussions, 36(1), 31-49. ‏
Park S. J., and Vlek P. L. G. 2002b. Environmental correlation of three-dimensional soil spatial variability: a comparison of three adaptive techniques. Geoderma, 109(1-2), 117-140. ‏
Pierson F. B., and Mulla D. J. 1990. Aggregate stability in the Palouse region of Washington: effect of landscape position. Soil Science Society of America Journal, 54(5): 1407-1412. ‏
Pradhan B., and Sameen M. I. 2017. Effects of the Spatial Resolution of Digital Elevation Models and Their Products on Landslide Susceptibility Mapping. In Pradhan, B. (Ed.). Laser Scanning Applications in Landslide Assessment. Springer, pp. 133-150.
Sørensen R., and Seibert J. 2007. Effects of DEM resolution on the calculation of topographical indices: TWI and its components. Journal of Hydrology, 347(1-2), 79-89. ‏
Sörensen R., Zinko U., and Seibert J. 2006. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences Discussions, 10(1), 101-112. ‏
Smith M. P., Zhu A. X., Burt J. E., and Stiles C. 2006. The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma, 137(1): 58-69. ‏
Sugiura N. 1978. Further analysts of the data by akaike's information criterion and the finite corrections: Further analysts of the data by akaike's. Communications in Statistics-Theory and Methods, 7(1), 13-26. ‏
Thompson J. A., Bell J. C., and Butler C. A. 2001. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma, 100(1-2), 67-89. ‏
Wilson J. P., and Gallant J. C. 2000. Digital terrain analysis. In Wilson, J. P. and Gallant, J. C. (Ed.). Terrain analysis: principles and applications. John Wileys and Sons, New York, pp. 1-22.
Wu W., Fan Y., Wang Z., and Liu H. 2008. Assessing effects of digital elevation model resolutions on soil–landscape correlations in a hilly area. Agriculture, Ecosystems and Environment, 126(3-4), 209-216. ‏
Zhang W., and Montgomery D. R. 1994. Digital elevation model grid size, landscape representation, and hydrologic simulations. Water resources research, 30(4), 1019-1028. ‏
Zhang H. Y., Shi Z. H., Fang N. F., and Guo M. H. 2015. Linking watershed geomorphic characteristics to sediment yield: Evidence from the Loess Plateau of China. Geomorphology, 234: 19-27.
Yuan L., Zhou Q., Li W., Zhang Q., and Jiang W. 2006. DEM-based watershed topographic attributes extraction and analysis. In EEE International Symposium on Geoscience and Remote Sensing, Denver CO, pp. 902-904.
Zevenbergen L. W., and Thorne C. R. 1987. Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12: 47–56.

Files

History

  • Receive Date: 11 January 2019
  • Revise Date: 12 May 2019
  • Accept Date: 14 May 2019

Share

How to cite

Statistics