Eight change detection procedures and a hybrid forest type classification procedure were tested for their ability to detect forest land cover change in east-central Mississippi. The best performing method was change vector analysis using vegetation indices with an image segmentation classification. This was based on using an overall accuracy (82.50%) and overall Kappa (0.7900) calculated from error matrices. The hybrid forest type classification had an overall accuracy of 77.08% for 1997 and 71.25% for 2002. The results of this study were compared to a prior pilot inventory study for the same study area in east-central Mississippi. There was considerable disagreement between the two studies in terms of number of acres in the age classes and the forest type classes, most likely attributed to the difference in methods for determining forest type classes. Further extrapolation showed that the classification effect on timber volume estimates was also significant.
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