%0 Journal Article
%T 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
%J Journal of Range and Watershed Managment
%I University of Tehran
%Z 5044-2008
%A Zakeri, Emad
%A karimzadeh, Hamidreza
%A Mousavi, Seyed Alireza
%D 2021
%\ 02/19/2021
%V 73
%N 4
%P 753-770
%! 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
%K Soil erosion
%K Cover-management factor
%K Vegetation indices
%K GIS
%K nonparametric algorithms
%K K Nearest Neighbor
%R 10.22059/jrwm.2021.247783.1199
%X 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.
%U https://jrwm.ut.ac.ir/article_80533_4086c550093a3978d8d34fad0bb46f69.pdf