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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Artificial Intelligence, Geospatial modeling, Disaster monitoring and management, Climate change

Forest Burned Area Detection Using Landsat 8/9 and Sentinel-2 A/B Imagery with Various Indices: A Case Study of Uljin

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Forest Burned Area Detection Using Landsat 8/9 and Sentinel-2 A/B Imagery with Various Indices: A Case Study of Uljin
Other Titles
Landsat 8/9 및 Sentinel-2 A/B를 이용한 울진 산불 피해 탐지: 다양한 지수를 기반으로 다시기 분석
Kim, ByeongcheolLee, KyungilPark, SeonyoungIm, Jungho
Issue Date
Korean Journal of Remote Sensing, v.38, no.5, pp.765 - 779
This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.
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