Comparative Study of Multiple Supervised Classification Methods to Map Land Use in Local and Regional Scales (Case Study:Kan and Karaj Rivers Basin)

Land management leading to sustainable development requires reliable and update data on land cover/use and mapping its changes at various spatial and temporal scales. In this aspect, water resources management also needs to assess land use and its changes across the basin to maintain water quality for a variety of uses. Thus, the primary goal of this study is to evaluate the effectiveness of various spectral-based supervised classification methods of Operational Land Imager (OLI) data for mapping land use across the Kan and Karaj Rivers basin. At the Anderson Level 1 and 2, the basin’s land use was mapped in five and nine classes, respectively using a broad range of different supervised classification methods, including Parallelepiped, Minimum Distance, Mahalanobis Distance, Maximum Likelihood, Spectral Angle, Binary Encoding, Spectral Information Divergence, Neural Net and Support Vector Machine. All classification methods were verified using the Google Earth images and accurate ground control points, in which the Maximum Likelihood method of both levels with Kappa coefficient of 0.77 and 0.76 and overall accuracy of 84.94 and 80.70 percent, identified as the optimum method to map the land use at the local and regional scales respectively. In addition, following the named method, the Neural Net, Support Vector Machine and the Mahalanobis Distance methods also showed acceptable accuracy indicating that like the choice of classification method, precision in procedures and accuracy assessment of land use classification map is very important and could affect the results.