DOI: 10.4018/IJAGR.2019010102 International Journal of Applied Geospatial Research Volume 10 • Issue 1 • January-March 2019 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 31 Optimal Methodology for Detecting Land Cover Change in a Forestry, Lakeside Environment Using NAIP Imagery Xiaomin Qiu, Department of Geography, Geology, and Planning, Missouri State University, Springfeld, USA Dexuan Sha, Department of Geography and Geo-Information Science, George Mason University, Fairfax, USA Xuelian Meng, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, USA ABSTRACT Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using one- meter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments. KEywORDS Change Detection, Land Cover Classification, NAIP, Object-Based, Random Tree INTRODUCTION Land use and land cover (LULC) change reflects complex relationships and interactions between human activities and natural environment. Knowing and modeling LULC change can help develop related policies and satisfy important social needs, e.g. transportation planning, land management, forest conservation, ecological assessment, and urban growth management. Although land cover changes can be monitored through field survey, remote sensing imagery and methods have been widely adopted due to the capability of acquiring up-to-date information over large areas promptly. In the past, remotely sensed imagery used for LULC mapping included landsat thematic mapper (TM), Satellite Probatoired’ Obsevation de la Terre (SPOT), advanced very high resolution radiometer (AVHRR), and new generation aerial photography, such as digital orthophoto quarter quads (DOQQs). Past research has proposed and tested a variety of methods and techniques for LULC change detection (Singh, 1989; Lu et al., 2004; Hussain et al., 2013). Due to the complexity of the phenomena under