摘要
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(k NN)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 k NN 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.
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(k NN)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 k NN 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.
基金
supported by a Georgia TIP-3Fiber Supply Assessment grant
