Comparing Maximum Likelihood and Fuzzy Classification Methods for Mapping Land Cover (Case Study: Behshahr urban Catchment)
Subject Areas : landuseKebriya jafari 1 , marziyeh alikhah-asl 2 , Yahya Kooch 3
1 - M.S. student, Environmental field (trend: assessment and land use planning), Payame Noor university, Tehran, Iran.
2 - Assistant professor, Department of Agriculture and Natural Resources, Payame Noor University, Tehran, Iran. *(Corresponding Author)
3 - Assistant professor of forestry, Natural resources faculty, Tarbiat Modares university, Noor, Iran.
Keywords: Behshahr, Maximum likelihood, Fuzzy, Remote Sensing.,
Abstract :
Background and Objective: Land use maps are one of the most important maps for land use planning which is being prepared using satellite images. Land use classification of satellite images is one of remote sensing utilities and Now days, different classification methods have been expanded for mapping land cover that have different accuracies. The aim of this research is to Compare Maximum Likelihood and Fuzzy Classification Methods for mapping land use in Behshar urban catchment located in Mazandaran province.
Material and Methodology: First OLI image for the year 2015 was prepared and after geometric and elevation corrections, image was classified using maximum likelihood and Fuzzy methods in Erdas Imagine software. Kappa and overall accuracy indexes were used to calculate classification accuracy. Findings: Based on results, in both methods Broad-leaved tree area involve most part of the area while urban area is the lowest. Also results showed Maximum likelihood method with kappa 0.87 and overall accuracy 89.52% in comparison with Fuzzy method with kappa 0.86 and overall accuracy 88.57% has better classification accuracy.
Discussion and Conclusion: Results show despite high capability of Landsat images in mapping land use, it is necessary to land mapping using the best classification method resulted from comparing different classification accuracies.
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