An evaluation of land use land cover (LULC) classification for urban applications with Quickbird and WorldView2 data

Monitoring and analysis of the land and rapid environmental change, leads to the use of Land Use and Land Cover (LULC) classification approaches from remote sensing data. The main focus of this aper is to illustrate the practical approach to analysis and mapping of land use and land cover features using high resolution satellite images. The study is carried out for two different places, Basel and Tel Aviv. For this purpose, Quickbird satellite imagery is used for Basel and WorldView2 imagery for Tel Aviv. The classification method chosen for the Quickbird image is Support Vector Machine (SVM) classifier and Maximum Likelihood method for the WordView2 satellite imagery. Both of the methods are applied using ENVI 5.0 Remote Sensing software. An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels.