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Land use mapping is important for evaluation, management and conservation of natural resources of an area and the knowledge on the existing land use is one of the prime pre-requisites for suggesting better use of land. In this study, we examined four mapping approaches (unsupervised, supervised, fuzzy supervised and GIS postprocessing) to identify, demarcate and map the agricultural land use categories in the Northern parts of Kolhapur district, India. A fuzzy c-means clustering algorithm for supervised classification approach was applied to prepare multi-layer class map and distance map. For the accuracy assessment a random stratified sampling method was used to allocate the sample size for each land use based on its spatial extent. Finally, the extracted land use map was classified into six major groups, namely forest, cultivated land, range land, waste land, water bodies and built-up land

Current Application of Remote Sensing Techniques in Land Use Mapping: A Case Study of Northern Parts of Kolhapur District, India