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Estimation of premature forests in Georgia (USA) using U.S. Forest Service FIA data and Landsat imagery

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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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Acknowledgements

This work was in part supported by a Georgia TIP-3 Fiber Supply Assessment grant. Special thanks to Dr. Pete Bettinger for his valuable advices in all aspects of the work.

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Author notes

  1. Hojung Kim, Chris J. Cieszewski and Roger C. Lowe have contributed equally to this research.

Authors and Affiliations

  1. Warnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green Street, Athens, GA, 30602, USA

    Hojung Kim, Chris J. Cieszewski & Roger C. Lowe

Authors
  1. Hojung Kim
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  2. Chris J. Cieszewski
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  3. Roger C. Lowe
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Corresponding author

Correspondence to Hojung Kim.

Additional information

Project funding: This work was in part supported by a Georgia TIP-3 Fiber Supply Assessment grant.

The online version is available at http://www.springerlink.com

Corresponding editor: Yu Lei.

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Kim, H., Cieszewski, C.J. & Lowe, R.C. Estimation of premature forests in Georgia (USA) using U.S. Forest Service FIA data and Landsat imagery. J. For. Res. 28, 1249–1260 (2017). https://doi.org/10.1007/s11676-017-0389-4

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  • DOI: https://doi.org/10.1007/s11676-017-0389-4

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