Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan

Document Type : Research Paper


1 Ph.D. Range Management, Department of Natural Resources and Watershed Management of North Khorasan province, Iran

2 University of Isfahan Tecnology

3 Department of Natural Resources Isfahan /University of Technology /]Isfahan/Iran


Cover-management factor (C) is one of the most important influential factor on soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model. C-factor is challenging to determine based on the proposed procedures due to lack of accurate information. Vegetation cover map can be used to estimate C-factor, but preparing a suitable mapping of vegetation cover is challenging in many situations. Therefore, in this study vegetation cover map was prepared and compared using the k Nearest Neighbor (k-NN) algorithm, linear regression (LR) and linear stepwise regression (LSR) in the study area. In regression methods, 17 vegetation and environmental indices were prepared and their relationships were investigated. The results of comparing the three methods showed that the k-NN method has better results than other regression methods due to its highest overall accuracy (83.3%) and kappa coefficient (75.9%) therefore, it was used to produce C-factor map. Results showed that the k-NN was very promising for mapping vegetation canopy cover in the arid and semi-arid areas. The results showed that among vegetation indices NDVI had the highest correlation (0.82) with percentage vegetation cover. Also, in the k-NN method, the Euclidean distance metrics in k = 9 has better results than the other two Fuzzy and Mahalanobis distances and can be used to estimation of vegetation cover map.


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Volume 73, Issue 4
March 2021
Pages 753-770
  • Receive Date: 11 December 2017
  • Revise Date: 10 March 2021
  • Accept Date: 08 February 2021
  • First Publish Date: 19 February 2021