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Journal of Mountain Science

, Volume 14, Issue 10, pp 2053–2063 | Cite as

Watershed classification by remote sensing indices: A fuzzy c-means clustering approach

  • Bahram ChoubinEmail author
  • Karim Solaimani
  • Mahmoud Habibnejad Roshan
  • Arash Malekian
Article

Abstract

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean (FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups. The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently, the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

Keywords

Karkheh watershed Fuzzy c-means clustering Watershed classification Homogeneous sub-watersheds 

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References

  1. Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of irrigation and drainage engineering 133: 380–394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)CrossRefGoogle Scholar
  2. Bastiaanssen WG (1998) Remote sensing in water resources management: The state of the art. International Water Management Institute.Google Scholar
  3. Bezdek JC (1973) Cluster validity with fuzzy sets. Journal of Cybernetics 3: 58–73. https://doi.org/10.1080/01969727308546047CrossRefGoogle Scholar
  4. Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York.CrossRefGoogle Scholar
  5. Blöschl G, Sivapalan M, Wagener T, et al. (2013) Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales. Cambridge University Press.CrossRefGoogle Scholar
  6. Carrillo G, Troch PA, Sivapalan M, et al. (2011) Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient. Hydrology and Earth System Sciences 15: 3411–3430. https://doi.org/10.5194/hess-15-3411-2011CrossRefGoogle Scholar
  7. Castellarin A, Burn DH, Brath A (2008) Homogeneity testing: How homogeneous do heterogeneous cross-correlated regions seem? Journal of Hydrology 360: 67–76. DOI: http://orcid.org/10.1016/j.jhydrol.2008.07.014CrossRefGoogle Scholar
  8. Chang NB, Makkeasorn A (2010) Optimal site selection of watershed hydrological monitoring stations using remote sensing and grey integer programming. Environmental modeling & assessment 15: 469–486. https://doi.org/10.1007/s10666-009-9213-7CrossRefGoogle Scholar
  9. Choubin B, Khalighi-Sigaroodi S, Malekian A, et al. (2014) Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach. Journal of Mountain Science 11(6): 1593–1605. https://doi.org/10.1007/s11629-014-3020-6CrossRefGoogle Scholar
  10. Choubin B, Khalighi-Sigaroodi S, Malekian A, et al. (2016a) Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal 61(6): 1001–1009. https://doi.org/10.1080/02626667.2014.966721CrossRefGoogle Scholar
  11. Choubin B, Malekian A (2017a) Combined gamma and M-testbased ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environmental Earth Sciences 76:538. https://doi.org/10.1007/s12665-017-6870-8CrossRefGoogle Scholar
  12. Choubin B, Malekian A, Golshan M (2016b) Application of several data-driven techniques to predict a standardized precipitation index. Atmósfera 29(2): 121–128. https://doi.org/10.20937/ATM.2016.29.02.02CrossRefGoogle Scholar
  13. Choubin B, Malekian A, Samadi S, et al. (2017b) An ensemble forecast of semi-arid rainfall using large-scale climate predictors. Meteorological Applications. https://doi.org/10.1002/met.1635Google Scholar
  14. Dodangeh E, Soltani S, Sarhadi A, et al. (2014) Application of L-moments and Bayesian inference for low-flow regionalization in Sefidroud basin, Iran. Hydrological Processes 28: 1663–1676. https://doi.org/10.1002/hyp.9711CrossRefGoogle Scholar
  15. Dunn JC (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3: 32–57. https://doi.org/10.1080/01969727308546046CrossRefGoogle Scholar
  16. Garambois PA, Roux H, Larnier K, et al. (2015) Parameter regionalization for a process-oriented distributed model dedicated to flash floods. Journal of Hydrology 525: 383–399. https://doi.org/10.1016/j.jhydrol.2015.03.052CrossRefGoogle Scholar
  17. Goodwin NR, Coops NC, Wulder MA, et al. (2008) Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote sensing of environment 112: 3680–3689. https://doi.org/10.1016/j.rse.2008.05.005CrossRefGoogle Scholar
  18. Hall DK, Riggs GA (2007) Accuracy assessment of the MODIS snow products. Hydrological Process 21: 1534–1547. https://doi.org/10.1002/hyp.6715CrossRefGoogle Scholar
  19. Hall DK, Riggs GA (2016) MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 6. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A2.006Google Scholar
  20. Hély C, Braconnot P, Watrin J, et al. (2009) Climate and vegetation: simulating the African humid period. Comptes Rendus Geoscience 341: 671–688. https://doi.org/10.1016/j.crte.2009.07.002CrossRefGoogle Scholar
  21. Huete AR (1988) A soil−adjusted vegetation index (SAVI). Remote sensing of environment 25: 295–309. https://doi.org/10.1016/0034-4257(88)90106-XCrossRefGoogle Scholar
  22. JAMAB (1999) Comprehensive Assessment of National Water Resources: Karkheh River Basin. JAMAB Consulting Engineers in Association with Ministry of Energy, Iran.Google Scholar
  23. Julien Y, Sobrino JA, Mattar C, et al. (2011) Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001. International Journal of Remote Sensing 32: 2057–2068. https://doi.org/10.1080/01431161003762363CrossRefGoogle Scholar
  24. Kult J (2013) Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal variability (Doctoral dissertation, The University of Wisconsin-Milwaukee).Google Scholar
  25. Masih I, Uhlenbrook S, Maskey S, et al. (2010) Regionalization of a conceptual rainfall–runoff model based on similarity of the flow duration curve: A case study from the semi-arid Karkheh basin, Iran. Journal of Hydrology 391: 188–201. https://doi.org/10.1016/j.jhydrol.2010.07.018CrossRefGoogle Scholar
  26. McDonnell JJ, Woods R (2004) On the need for catchment classification. Journal of Hydrology 299: 2–3. https://doi.org/10.1016/j.jhydrol.2004.09.003CrossRefGoogle Scholar
  27. Nester T, Kirnbauer R, Parajka J, et al. (2012) Evaluating the snow component of a flood forecasting model. Hydrology Research 43: 762–779. https://doi.org/10.2166/nh.2012.041CrossRefGoogle Scholar
  28. Nruthya K, Srinivas VV (2015) Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study. Aquatic Procedia 4: 641–648. https://doi.org/10.1016/j.aqpro.2015.02.083CrossRefGoogle Scholar
  29. Parajka J, Naeimi V, Blöschl G, et al. (2009) Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria. Hydrology and Earth System Sciences 13: 259–271. https://doi.org/10.5194/hess-13-F259-2009CrossRefGoogle Scholar
  30. Patil SD (2011) Information transfer for hydrologic prediction in engaged river basins (Doctoral dissertation, Georgia Institute of Technology). https://doi.org/10.5194/hess-15-989-2011Google Scholar
  31. Patil SD, Stieglitz M (2015) Comparing spatial and temporal transferability of hydrological model parameters. Journal of Hydrology 525: 409–417. https://doi.org/10.1016/j.jhydrol.2015.04.003CrossRefGoogle Scholar
  32. Raju KS, Kumar DN (2011) Classification of microwatersheds based on morphological characteristics. Hydro-environment Research 5: 101–109. https://doi.org/10.1016/j.jher.2010.09.002CrossRefGoogle Scholar
  33. Razavi T, Coulibaly P (2013) Classification of Ontario watersheds based on physical attributes and streamflow series. Journal of Hydrology 493: 81–94. https://doi.org/10.1016/j.jhydrol.2013.04.013CrossRefGoogle Scholar
  34. Saghafian B, Davtalab R (2007) Mapping snow characteristics based on snow observation probability. International Journal of Climatology 27: 1277–1286. https://doi.org/10.1002/joc.1494CrossRefGoogle Scholar
  35. Sawicz K, Wagener T, Sivapalan M, et al. (2011) Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrology and Earth System Sciences 15: 2895–2911. https://doi.org/10.5194/hess-15-2895-2011CrossRefGoogle Scholar
  36. Sawicz KA (2013) Catchment classification (Doctoral dissertation, The Pennsylvania State University).Google Scholar
  37. Sigaroodi SK, Chen Q, Ebrahimi S, et al. (2014) Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran. Hydrology and Earth System Sciences 18(5): 1995–2006. https://doi.org/10.5194/hess-18-1995-2014CrossRefGoogle Scholar
  38. Sivakumar B, Singh VP (2012) Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework. Hydrology and Earth System Sciences 16: 4119–4131. https://doi.org/10.5194/hess-16-4119-2012CrossRefGoogle Scholar
  39. Sivakumar B, Singh VP, Berndtsson R, et al. (2014) Catchment classification framework in hydrology: challenges and directions. Journal of Hydrologic Engineering 20: A4014002(1-12). https://doi.org/10.1061/(ASCE)HE.1943-5584.0000837CrossRefGoogle Scholar
  40. Sivapalan M, Schaake J, Sapporo J (2003) PUB science and implementation plan. V5. Online available at: http://pub.iwmi.org/UI/Images/PUBScience Plan, 5.Google Scholar
  41. Ssegane H, Tollner EW, Mohamoud YM, et al. (2012) Advances in variable selection methods II: Effect of variable selection method on classification of hydrologically similar watersheds in three Mid-Atlantic ecoregions. Journal of Hydrology 438: 26–38. https://doi.org/10.1016/j.jhydrol.2012.01.035CrossRefGoogle Scholar
  42. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150. https://doi.org/10.1016/0034-4257(79)90013-0CrossRefGoogle Scholar
  43. Wagener T, Sivapalan M, Troch P, et al. (2007) Catchment classification and hydrologic similarity. Geography Compass 1: 901–931. https://doi.org/10.1111/j.1749-8198.2007.00039.xCrossRefGoogle Scholar
  44. Waters R, Allen R, Bastiaanssen W, et al. (2002) Surface energy balance algorithms for land, Idaho implementation, advanced training and user’s manual. NASA, USA.Google Scholar
  45. Watson DJ (1947) Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties and within and between years. Annals of Botany 11: 41–76. http://www.jstor.org/stable/42907002CrossRefGoogle Scholar
  46. Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 80: 385–396. https://doi.org/10.1016/S0034-4257(01)00318-2CrossRefGoogle Scholar
  47. Wolock DM, Winter TC, McMahon G (2004) Delineation and evaluation of hydrologic-landscape regions in the United States using geographic information system tools and multivariate statistical analyses. Environmental Management 34: 71–88. https://doi.org/10.1007/s00267-003-5077-9CrossRefGoogle Scholar
  48. Yadav M, Wagener T, Gupta H (2007) Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Advances in Water Resources 30: 1756–1774. https://doi.org/10.1016/j.advwatres.2007.01.005CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Watershed ManagementSari University of Agricultural Sciences and Natural ResourcesSariIran
  2. 2.Department of Reclamation of Arid and Mountainous RegionsUniversity of TehranKarajIran

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