Comparison of different map classification techniques for LU/LC mapping change analysis of the Konya province in Turkey

Information on Land Use (LU) and Land Cover (LC) in the form of maps is significant component in understanding the influences of the human activities with the environment and thus it is vital to monitor and detect the changes to maintain to sustainable environment. In this study, Remote Sensing (RS), Geographic information system (GIS) and (ENVI 4.7) were carried out in order to study classification of land use (LU) and land cover (LC) changes. The urban area is changing due to various human activities, natural conditions and development activities. The objective of this study is to understand the causes of land use (LU) and land cover (LC) change to the (Konya Province of Turkey) among the years (2002 to 2014). A combination of remote sensing data, Landsat (ETM 7, TM 5 and OLI 8) images were used, the images used were from (JUNE, 2002, ETM 7), (August, 2006, ETM 7), (September, TM 5, 2010), (August, 2014, OLI 8). The Landsat data images were classified using supervised and unsupervised classification, for the classification purpose, generally the land use (LU) and land cover (LC) of the study area were classified into six classes that included (Water, Road, Building, Tree, Agriculture, Open Land) for accuracy assessment classification was done and change detection between the images for all the land use (LU) and land cover (LC) were computed. Different image processing approaches applied for Landsat data such as the Minimum Noise Fraction (MNF) and Principle Component Analysis (PCA). They were used to improve the image classification provided that the better accuracy assessment was the Maximum Likelihood image (MLH) %86.55. Knowledge on urban area growth, land use (LU) and land cover (LC) change study is very useful to local government and urban area planners for the betterment plans of maintainable evolvement of the city.