Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data.

Accurate and timely information about land cover pattern and change in urbanareas is crucial for urban land management decision-making, ecosystem monitoring andurban planning. This paper presents the methods and results of an object-basedclassification and post-classification change detection of multitemporal high-spatialresolution Emerge aerial imagery in the Gwynns Falls watershed from 1999 to 2004. TheGwynns Falls watershed includes portions of Baltimore City and Baltimore County,Maryland, USA. An object-based approach was first applied to implement the land coverclassification separately for each of the two years. The overall accuracies of theclassification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. Following theclassification, we conducted a comparison of two different land cover change detectionmethods: traditional (i.e., pixel-based) post-classification comparison and object-basedpost-classification comparison. The results from our analyses indicated that an objectbasedapproach provides a better means for change detection than a pixel based methodbecause it provides an effective way to incorporate spatial information and expertknowledge into the change detection process. The overall accuracy of the change mapproduced by the object-based method was 90.0%, with Kappa statistic of 0.854, whereasthe overall accuracy and Kappa statistic of that by the pixel-based method were 81.3% and0.712, respectively.