Using the attraction model in remote sensing to evaluation of topographic wetness index (TWI)

Document Type : Research Article


1 Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Iran

2 MSc. in Remote Sensing and Geographic Information System, Tehran University, Iran

3 Department of Range and Watershed Management, College of Agriculture, University of Fasa, Iran,


The objective of this study is evaluation of topographic wetness index (TWI) using attraction model in northern of Fars province. The first attraction model was used to enhance the spatial resolution of digital elevation model (DEM). In this research to estimate of value of sub-pixels neighboring pixels, touching and quadrant neighboring models were used. After manufacturing output images for sub pixels in the 2, 3 and 4 scales with different neighborhoods, the best scale with most appropriate type of neighborhood was determined using ground control points then the values of RMSE was calculated for them. The total number of ground control points extracted from the mapping maintenance was 2118 points. The results showed that between scales with different neighborhoods, 3 scale and quadrant neighboring model have the most accuracy by the lowest value of RMSE for DEM 90 meter. Then using produced DEM from 3 scale and quadrant neighboring model, topographic wetness index for the study area was determined. The results showed that topographic wetness index (TWI) in the study area is variable between -4.45 to 6.06. Central zones of the study area have the highest values ​​of wetness. Compare of wetness index produced from attraction model (with more spatial resolution) with DEM 90 meter (with lower spatial resolution) showed that with using attraction model, more details of the amount of moisture in the study area, is visible.


Main Subjects


    1. Atkinson PM. Sub-pixel target mapping from soft-classified, remotely sensed imagery Photogram. Engineering Remote Sensing. 1997:71 (7): 839–846.
    2. Guo PT, Liu HB, Wu W. spatial prediction of soil organic matter using terrain attributes in a hilly area, International Conference on Environmental Science and Information Application Technology. China. 2009: (3) 1: 759-762.
    3. Wang QM, Wang DF. Sub-pixel mapping based on sub-pixel to sub-pixel spatial attraction model. In: Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. 2011: 593–596.
    4. Muad AM, Foody GM. Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution. Journal of Applied Earth Observation Geo information. 2012: (12) 1: 79–91.
    5. Chen CY, Chen LK, Yu FC, Lin SC, Lin YC, Lee C L, et al. Characteristics analysis for the flash flood-induced debris flows. Journal of Natural Hazards. 2008: 47(1): 245-261.
    6. Luca C, Si BC, Farrell RE. Upslope length improves spatial estimation of soil organic carbon content. Canada Journal of Soil Science. 2007: (87) 1: 291-300.
    7. Mertens JC, Verbeke LPC, Ducheyne EI, Wulf RD. Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing. 2003: (24) 21: 4241–4247.
    8. Grabs T, Seibert J, Bishop K, Laudon H. Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology. 2009: (373) 1: 15-23.
    9. Whelan MJ, Gandolfi C. Modeling of spatial controls on de-nitrification in the landscapes scales. Hydrology Process. 2002: (16) 7: 1437-1450.
    10. Tatem AT, Lewis HG, Atkinson PM. Super resolution target identification from remotely sensed images using a Hopfield neural network, IEEE Trans. Geoscience Remote Sensing. 2001: (39) 4: 781–796.
    11. Sorensen R, Zinko U, Seibert J. On the calculation of the topographic wetness index: evaluation of different methods based on field observation. Hydrology and Earth System Sciences. 2005: (10): 1-10.
    12. Zhong LP, Zhang PX, Li HF. A sub-pixel mapping algorithm based on artificial immune systems for remote sensing imagery. In: Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS: III-1007–III-1010.
    13. Xu X, Zhong Y, Zhang L. A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images. Neuron computing. 2014: (134): 79–91.
    14. Hojati M, Mokarram M. Using attraction method to landform classification. quantity geomorphology. 2016: (4) 4: 40-55.
    15. Zhang K, Wu YF, Zhong PX. A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neuron computing. 2008: (71): 2046–2054.
    16. Maleki S, khormali GH, Karemi AR. The introduction of streaming algorithms for mapping wetness index and organic carbon in the loess land, Tvshn logic Golestan Province. Journal of soil and water. 2014: 21 (1): 145-162. (In Persian)
    17. Welsch DL, Kroll CN, Mc Donnell JJ, Burns DA. Topographic controls on the chemistry of subsurface storm flow. Hydrology Process. 2001: (15) 10: 1925-1938.
    18. Gessler PE, Moore N, McKenzie J, Ryan P J. Soil landscape modeling and spatial prediction of soil attributes. International Journal of GIS. 1995: 9 (4): 421-432. 
    19. Khiavi K, Ghalami A. Application of artificial neural network in precipitation and runoff modeling case study Ghareso watershed, Ardabil. 2011, 3th national congress of Civil Engineering.
Volume 4, Issue 1
March 2017
Pages 237-245
  • Receive Date: 07 December 2016
  • Revise Date: 14 January 2017
  • Accept Date: 19 January 2017
  • First Publish Date: 21 March 2017