[논문]Mapping of Post-Wildfire Burned Area Using KOMPSAT-3A and Sentinel-2 Imagery: The Case of Sokcho Wildfire, Korea

Nur, Arip Syaripudin; Park, Sungjae; Lee, Kwang-Jae; Moon, Jiyoon; Lee, Chang-Wook

doi:10.7780/kjrs.2020.36.6.2.6

Mapping of Post-Wildfire Burned Area Using KOMPSAT-3A and Sentinel-2 Imagery: The Case of Sokcho Wildfire, Korea 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.36 no.6 pt.2, 2020년, pp.1551 - 1565

Nur, Arip Syaripudin (Department of Smart Regional Innovation, Kangwon National University) , Park, Sungjae (Department of Smart Regional Innovation, Kangwon National University) , Lee, Kwang-Jae (Satellite Operation & Application Center, Korea Aerospace Research Institute) , Moon, Jiyoon (Satellite Operation & Application Center, Korea Aerospace Research Institute) , Lee, Chang-Wook (Division of Science Education, Kangwon National University)

Abstract ▼ AI-Helper

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

주제어

표/그림 (11)

그림 Fig. 1. (a) Sokcho in the eastern Korean Peninsula, (b) KOMPSAT-3A scene in natural color composite image taken on April 20, 2019.
그림 Fig. 2. (a) Post-wildfire event captured by KOMPSAT-3A on April 20, 2019, and (b) Post-wildfire by Sentinel-2 on April 20, 2019, in false color combination.
표 Table 1. Classification scheme and category definition for the study area
그림 Fig. 3. Normalized Burned Ratio (NBR) index map from Sentinel-2 on April 20, 2019.
그림 Fig. 4. Schematic depicting the outcome of the SVM algorithm (modified from Foody and Mathur, 2004).
그림 Fig. 5. Results of (a) the ANN and (b) the SVM classification from KOMPSAT-3A of Sokcho wildfire, The data were acquired on April 20, 2019.
그림 Fig. 6. Results of (a) the ANN and (b) the SVM classification from Sentinel-2 of Sokcho wildfire. The data were acquired on April 20, 2019.
표 Table 2. Error matrix on classified map of burned area from KOMPSAT-3A using ANN method
표 Table 3. Error matrix on classified map of burned area from KOMPSAT-3A using SVM method
표 Table 4. Error matrix on classified map of burned area from Sentinel-2 using ANN method
표 Table 5. Error matrix on classified map of burned area from Sentinel-2 using SVM method

AI 본문요약
AI-Helper

제안 방법

Despite the large number of studies that have been published regarding burned area mapping using various machine learning, there is a lack of research using KOMPSAT-3A. By combining high-resolution multispectral data and machine learning, this study aims to map the post-wildfire in Sokcho using high- resolution KOMPSAT-3A and Sentinel-2 imagery with the same acquisition date and band combination. Four post-wildfire maps from two satellite imageries were generated using ANN and SVM classifiers and compared the mapping results to improve fire mitigation, management, and evaluation effectiveness.
By combining high-resolution multispectral data and machine learning, this study aims to map the post-wildfire in Sokcho using high- resolution KOMPSAT-3A and Sentinel-2 imagery with the same acquisition date and band combination. Four post-wildfire maps from two satellite imageries were generated using ANN and SVM classifiers and compared the mapping results to improve fire mitigation, management, and evaluation effectiveness.

이론/모형

After the post-wildfire classification had been acquired from the Sentinel-2 imagery, an accuracy assessment was performed using the same data and method used for KOMPSAT-3A accuracy assessment. 297 points were used as the test data to generate the ANN and SVM accuracy assessment.
By collecting data from KOMPSAT-3A and Sentinel-2, post-wildfire maps were generated. Two classification methods were used to produce the post-wildfire maps: the ANN algorithm and the SVM algorithm, which is considered a novel method for mapping burned area, especially in the region of Sokcho. To reveal the accuracy of the two methods, an accuracy assessment was performed.

성능/효과

As seen in Fig. 2(a), the spectral between paddy field area and burned area was similar, so the paddy field areas were detected as burned areas (dark green) by ANN classifier. The SVM classification result did not misclassify the northern area.
Thus, this study’s results are expected to be useful for researchers, planners, and policymakers as a basis for analyzing the occurrence of wildfire in Korea and developing mitigation and evacuation plans to minimize the severity of such hazards in the future. Although the results managed to map the burned area to mitigate an evacuation, a different approach might be preferable in future studies such as optimization or deep learning algorithm to achieve the most effective mapping of the burned area, especially in Korea.
61%) for the KOMPSAT- 3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). Notably, the satellite data and classifier used influenced the results of classification and accuracy.
11%). Notably, the satellite data and classifier used influenced the results of classification and accuracy. It is crucial to map the burned area in disaster assessment after a wildfire to prevent further damage and restore the affected area.
This study demonstrates that the methods used, ANN and SVM could reveal the burned area map for the Sokcho wildfire in 2019. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT- 3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.
The ANN and SVM classifiers yielded similar post- wildfire map results. The post-wildfire map results between the ANN and SVM from the KOMPSAT-3A image were well classified. Both ANN and SVM can classify the five classes assigned from the training data.
This study demonstrates that the methods used, ANN and SVM could reveal the burned area map for the Sokcho wildfire in 2019. The SVM showed higher accuracy (overall accuracy 95.
931, respectively. Thus, the SVM yielded higher accuracy than ANN for KOMPSAT-3A image classification.

후속연구

Thus, this study’s results are expected to be useful for researchers, planners, and policymakers as a basis for analyzing the occurrence of wildfire in Korea and developing mitigation and evacuation plans to minimize the severity of such hazards in the future

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