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


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.


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


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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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