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http://dx.doi.org/10.7780/kjrs.2022.38.5.1.9

The Study of DMZ Wildfire Damage Area Detection Method Using Sentinel-2 Satellite Images  

Lee, Seulki (Department of Smart Regional Innovation, Kangwon National University)
Song, Jong-Sung (Division of Science Education, Kangwon National University)
Lee, Chang-Wook (Division of Science Education, Kangwon National University)
Ko, Bokyun (Division of Science Education, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_1, 2022 , pp. 545-557 More about this Journal
Abstract
This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.
Keywords
DMZ; Sentinel-2; Wildfire; SVM;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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