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http://dx.doi.org/10.7780/kjrs.2018.34.6.3.7

Classification of Natural and Artificial Forests from KOMPSAT-3/3A/5 Images Using Artificial Neural Network  

Lee, Yong-Suk (Department of Geoinformatics, University of Seoul)
Park, Sung-Hwan (Department of Geoinformatics, University of Seoul)
Jung, Hyung-Sup (Department of Geoinformatics, University of Seoul)
Baek, Won-Kyung (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.34, no.6_3, 2018 , pp. 1399-1414 More about this Journal
Abstract
Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.
Keywords
Artificial Forest; Natural Forest; Forest Survey; Kompsat; Machine Learning; Artificial Neural Network; WEKA;
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Times Cited By KSCI : 2  (Citation Analysis)
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