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

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Vol14 No.10: 2053-2063

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

Author】Bahram CHOUBIN1*; Karim SOLAIMANI1; Mahmoud HABIBNEJAD ROSHAN1; Arash MALEKIAN2

Addresses】1 Department of Watershed Management, Sari University of Agricultural Sciences and Natural Resources, P.O. Box 737 Sari, Iran; 2 Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, 31585-3314, Iran

Corresponding author】bahram.choubin@ut.ac.ir

Citation】ChoubinB, Solaimani K, Habibnejad Roshan M, et al. (2017) Watershed classification by remote sensing indices: A fuzzy c-means clustering approach. Journal of Mountain Science 14(10). https://doi.org/s11629-017-4357-4

DOI】https://doi.org/s11629-017-4357-4

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-meansclustering; Watershed classification; Homogeneous sub-watersheds