Land use land cover analysis with pixel-based classification approach

Abstract

Rapid development in certain urban area will affect its natural features. Therefore, it is important to identify and determine the changes occur for further analysis and future development planning. This process will influence several factors such as area development, environmental issues and human social activities. The selection of remote sensing data and method will derive the accurate land use land cover maps. This research study accessed the classification accuracy of different classifier approach for land use land cover classification in urban area. The objective of this paper is to compare the accuracy of the classification for each technique used. The study was conducted in a highly urbanized area in Kuala Lumpur, Malaysia. The dataset used for this study is the multi temporal LANDSAT satellite imageries for the year of 2001,2006,2011 and 2016. The pre-processing and analysis of the dataset has been done using software ENVI 5.3. Five land use classes (Urban/built up area, Forest, Agriculture, Water Body and fallow land) were identify for classification process. The classification approach for this study is the supervised classification with two algorithms namely Maximum Likelihood (ML) and Support Vector Machine (SVM). The overall accuracy and kappa statistic of the classification indicate that support vector machine algorithm was more accurate than maximum likelihood algorithm for five different time intervals.Therefore, this classification approach is acceptable and highly recommended for mapping the changes of land cover.