Abstract
We used geographic information system applications and statistical analyses to classify young, premature forest areas in southeastern Georgia using combined data from Landsat TM 5 satellite imagery and ground inventory data. We defined premature stands as forests with trees up to 15 years old. We estimated the premature forest areas using three methods: maximum likelihood classification (MLC), regression analysis, and k-nearest neighbor (kNN) modeling. Overall accuracy (OA) of classifying the premature forest using MLC was 82% and the Kappa coefficient of agreement was 0.63, which was the highest among the methods that we have tested. The kNN approach ranked second in accuracy with OA of 61% and a Kappa coefficient of agreement of 0.22. Regression analysis yielded an OA of 57% and a Kappa coefficient of 0.14. We conclude that Landsat imagery can be effectively used for estimating premature forest areas in combination with image processing classifiers such as MLC.
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