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http://dx.doi.org/10.7780/kjrs.2019.35.3.8

Forest Vertical Structure Classification in Gongju City, Korea from Optic and RADAR Satellite Images Using Artificial Neural Network  

Lee, Yong-Suk (Department of Geoinformatics, University of Seoul)
Baek, Won-Kyung (Department of Geoinformatics, University of Seoul)
Jung, Hyung-Sup (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.35, no.3, 2019 , pp. 447-455 More about this Journal
Abstract
Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.
Keywords
forest vertical structure; Forest Survey; machine learning; Artificial Neural Network;
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Times Cited By KSCI : 3  (Citation Analysis)
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