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Sensors 2008, 8(3), 1613-1636; doi:10.3390/s8031613
Article

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

1,* , 1
 and
2
1 Rubenstein School of Environment and Natural Resources, University of Vermont, George D. Aiken Center, 81 Carrigan Drive, Burlington, VT 05405, USA 2 Northeastern Research Station, USDA Forest Service, South Burlington, VT 05403, USA
* Author to whom correspondence should be addressed.
Received: 29 January 2008 / Accepted: 28 February 2008 / Published: 10 March 2008
(This article belongs to the Special Issue Sensors for Urban Environmental Monitoring)
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Abstract

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.
Keywords: Object-based image analysis; post-classification change detection; high-spatial resolution image; urban landscape; Baltimore; LTER Object-based image analysis; post-classification change detection; high-spatial resolution image; urban landscape; Baltimore; LTER
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Zhou, W.; Troy, A.; Grove, M. Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data. Sensors 2008, 8, 1613-1636.

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