Journal of Earth System Science

, Volume 125, Issue 7, pp 1463–1472 | Cite as

Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

Article
First Online:
Received:
Revised:
Accepted:

Abstract

Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha−1R−1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r 2= 0.561) followed by slope (r 2= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r 2) and RMSE were computed to be 0.76 and 0.07 t ha−1R−1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.

Keywords

Soil erodibilty DTM terrain parameters soil erosion watershed. 
This is a preview of subscription content, log in to check access.

Notes

References

  1. Behera P, Rao K D and Das K K 2005 Soil erosion modeling using MMF model – A remote sensing and GIS perspective; J. Indian Soc. Remote Sens. 33 165–176.CrossRefGoogle Scholar
  2. Bell J C, Cunningham R L and Havens M W 1994 Soil drainage class probability mapping using a soil landscape model; Soil Sci. Soc. Am. J. 58 464–470.CrossRefGoogle Scholar
  3. Beven K J and Kirkby M J 1979 A physically based, variable contributing area model of basin hydrology; Hydrol. Sci. Bull. 24 43–69.CrossRefGoogle Scholar
  4. Bhattacharyya T, Sarkar D, Sehgal J L, Velaytham M, Gajbhiye K S, Nagar A P and Nimkhedkar S S 2009 Soil Taxonomic Database of India and the States (1:250,000 scale), NBSSLUP Publ. 143, National Bureau of Soil Survey and Land Use Planning, Nagpur, 266p.Google Scholar
  5. Christianson D D 2012 Variability of soil erodibility: Its relationship to topography and soil properties in cultivated landscape; Master of Science (M.Sc.), Geography, University of Saskatchewan.Google Scholar
  6. Crave A and Gascuel-Odoux C 1997 The influence of topography on time and space distribution of soil surface water content; Hydrol. Process. 11 (2) 203–210.CrossRefGoogle Scholar
  7. Dabral P P, Baithuri N and Pandey A 2008 Soil erosion assessment in a hilly catchment of north-eastern India using USLE, GIS and remote sensing; Water Res. Manag. 22 1783–1798.CrossRefGoogle Scholar
  8. Dobos E, Carré F, Hengl T, Reuter H I and Tóth G 2006 Digital Soil Mapping as a Support to Production of Functional Maps; EUR 22123 EN; Office for Official Publications of the European Communities, Luxemburg, 68p.Google Scholar
  9. Gee G W and Bauder J W 1986 Particle-size analysis; In: Methods of soil analysis. Part 1, 2nd edn, Agron. Monogr. 9. Am. Soc. Agron. and Soil Sci. Soc. Am., Madison, pp. 383–411.Google Scholar
  10. Hartemink A E, McBratney A and Mendonça-Santos M D L 2008 Digital Soil Mapping with Limited Data; Springer, Dordrecht, The Netherlands, 445p.CrossRefGoogle Scholar
  11. Hengl T 2007 A Practical Guide to Geo-statistical Mapping of Environmental Variables; European Communities, Luxemburg.Google Scholar
  12. Ismail J and Ravichandran S 2008 RUSLE2 model application for soil erosion assessment using remote sensing and GIS; Water Res. Manag. 22 83–102.CrossRefGoogle Scholar
  13. Jain S K, Kumar S and Varghese J 2001 Estimation of soil erosion for Himalayan watershed using GIS technique; Water Res. Manag. 15 41–54.CrossRefGoogle Scholar
  14. Jetten V, Govers G and Hessel R 2003 Erosion models: Quality and spatial predictions; Hydrol. Process. 17 887–900.CrossRefGoogle Scholar
  15. Jiang B 2013 GIS-based time series study of soil erosion risk using the Revised Universal Soil Loss Equation (RUSLE) model in a micro-catchment on Mount Elgon, Uganda; Student thesis series INES.Google Scholar
  16. Krishnamurthy Y V N, Srinivasa Rao S, Rao P. and Jayaraman V 2008 Analysis of DEM generated using Cartosat-1 stereo data over mausanne les alpiles cartosat scientific appraisal programme (Csap Ts-5), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B1, Beijing, pp. 1343–1348.Google Scholar
  17. Lagacherie P, McBratney A B and Voltz M 2007 Digital soil mapping: An introductory perspective; Dev. Soil Sci. 31 600.Google Scholar
  18. Lal R 2001 Soil degradation by erosion; Land Degrad. Dev. 12 519–539.CrossRefGoogle Scholar
  19. Luca C, Si B C and Farrell R E 2007 Upslope length improves spatial estimation of soil organic carbon content; Canadian J. Soil Sci. 87 291–300.CrossRefGoogle Scholar
  20. Ma J, Lin G, Chen J and Yang L 2010 An improved topographic wetness index considering topographic position; In: Geoinformatics, 18th International Conference on IEEE, pp. 1–4.CrossRefGoogle Scholar
  21. Manchanda M L, Kudrat M and Tiwari A K 2002 Soil survey and mapping using remote sensing; Tropical Ecol. 43 (1) 61–74.Google Scholar
  22. McBratney A B, Odeh I O A, Bishop T F A, Dunbar M S and Shatar T M 2000 An overview of pedometric techniques for use in soil survey; Geoderma 97 293–327.CrossRefGoogle Scholar
  23. McBratney A B, Santos M L M and Minasny B 2003 On digital soil mapping; Geoderma 117 3–52.CrossRefGoogle Scholar
  24. Moore I D, Grayson R B and Ladson A R 1991 Digital terrain modelling: A review of hydrological, geomorphological and biological applications; Hydrol. Process. 5 3–30.CrossRefGoogle Scholar
  25. Moore I D, Gessler P E, Nielsen G A and Peterson G A 1993 Soil attribute prediction using terrain analysis; Soil Sci. Am. J. 57 443–452.CrossRefGoogle Scholar
  26. Moore I D, Lewis A and Gallant J C 1993 Terrain attributes: Estimation method and scale effects; In: Modeling change in environmental systems (eds) A K Jakeman et al., John Wiley & Sons, New York, pp. 30–38.Google Scholar
  27. Moore I D and Wilson J P 1992 Length-slope factors for the revised universal soil loss equation: Simplified method of estimation; J. Soil Water Conserv. 47 423–428.Google Scholar
  28. Muralikrishnan S, Pillai A, Narender B, Reddy P S, Venkataraman V R and Dadhwal V K 2013 Validation of Indian National DEM from Cartosat-1 data; J. Indian Soc. Remote Sens. 41 (1) 1–13.CrossRefGoogle Scholar
  29. Page A L, Miller R H and Keeney M 1992 Methods of soil analysis. Part II: Chemical and mineralogical properties; 2nd edn, SSSA Publ. 1159.Google Scholar
  30. Park S, Oh C, Jeon S, Jung H and Choi C 2011 Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation; J. Hydrol. 399 263–273.CrossRefGoogle Scholar
  31. Qin C, Zhu A X, Yang L, Li B and Pei T 2006 Topographic Wetness Index Computed Using Multiple Flow Direction algorithm and Local Maximum Downslope Gradient; In: 7th International Workshop of Geographical Information System.Google Scholar
  32. Renard K G, Foster G R, Weesies G A, McCool D K and Yoder D C 1997 Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE); Agricultural Handbook, US Department of Agriculture, Washington (DC), 703p.Google Scholar
  33. Schaetzl R J and Anderson S 2005 Soils: Genesis and Geomorphology; Cambridge University Press, Cambridge 13 817.CrossRefGoogle Scholar
  34. Shabani A F, Gorji B M, Heidari B A and Esmeili C A 2010 Effects of landuse types at different slopes on soil erodibility factor (A case study from Amol area, north of Iran); In: Proceedings of the 19th World Congress of Soil Science: Soil Solutions for a Changing World, Brisbane, Australia, pp. 14–17.Google Scholar
  35. Shi Z H, Cai C F, Ding S W, Li Z X, Wang T W and Sun Z C 2002 Assessment of erosion risk with the RUSLE and GIS in the middle and lower reaches of Hanjiang River; In:12th ISCO Conference Beijing, China.Google Scholar
  36. Tirkey A S, Pandey A C and Nathawat M S 2013 Use of Satellite Data, GIS and RUSLE for estimation of average annual soil loss in Daltonganj Watershed of Jharkhand (India); J. Remote Sens. Technol. 1 20.CrossRefGoogle Scholar
  37. Toy T J, Foster G R and Renard K G 2002 Soil erosion: Processes, prediction, measurement, and control; John Wiley & Sons, 338p.Google Scholar
  38. Voltz M, Lagacherie P and Louchart X 1997 Predicting soil properties over a region using sample information from a mapped reference area; European J. Soil Sci. 48 19–30.CrossRefGoogle Scholar
  39. Wang G, Gertner G, Liu X and Anderson A 2001 Uncertainty assessment of soil erodibility factor for revised universal soil loss equation; Catena 46 1–14.CrossRefGoogle Scholar
  40. Wilson J P and Gallant J C 2000 Secondary topographic attributes; In: Terrain Analysis: Principles and Applications; John Wiley and Sons, New York, pp. 87–131.Google Scholar
  41. Wischmeier W H and Smith D D 1978 Predicting rainfall erosion losses: A guide to conservation planning; USDA Handbook, 537p.Google Scholar
  42. Wu S, Li J and Huang G H 2007 Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation; Environ. Model. Softw. 22 1250–1260.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2016

Authors and Affiliations

  1. 1.Indian Institute of Remote SensingIndian Space Research OrganizationDehradunIndia

Personalised recommendations