Extraction of Forest Fire Scars Based on Sentinel-2 Data — Take Xiaozhushan Mountain Fire Apr.23 2020 as an Example
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Extraction of Forest Fire Scars Based on Sentinel-2 Data — Take Xiaozhushan Mountain Fire Apr.23 2020 as an Example

Zhiyuan Zhao 1* Yiting Liu 2, Wenyang Li 3, Zhiyao Wu 4
1 China University of Petroleum
2 Wuhan University
3 The Affiliated International School of Shenzhen University
4 The Experimental High School Attached to Beijing Normal University
*Corresponding author: zhaozhiyuan200302@163.com
Published on 19 November 2025
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ACE Vol.208
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-547-9
ISBN (Online): 978-1-80590-548-6
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Abstract

Using Sentinel satellite data, particularly the red-edge bands and shortwave infrared bands that are sensitive to vegetation, we addressed the lack of research on extracting post-disaster burn scars by selecting Sentinel-2 satellite data before and after the forest fire on April 23, 2020, in Xiaozhushan, Qingdao, Shandong Province. Different extraction methods were employed to explore the potential of identifying burn scars, and a comparative study was conducted. The experimental results indicated that NBR2 was the least affected by smoke and achieved the highest extraction accuracy. The other methods were relatively more affected by smoke. The supervised classification method could effectively avoid the influence of water body changes. The study demonstrated that among various methods, NBR2 could accurately extract burn scars quickly and with high precision.

Keywords:

Sentinel-2 image, burned area, vegetation index analysis, supervised classification

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Zhao,Z.;Liu,Y.;Li,W.;Wu,Z. (2025). Extraction of Forest Fire Scars Based on Sentinel-2 Data — Take Xiaozhushan Mountain Fire Apr.23 2020 as an Example. Applied and Computational Engineering,208,69-79.

References

[1]. Chéret, V. and Denux, J.-P. (2011) 'Analysis of MODIS NDVI Time Series to Calculate Indicators of Mediterranean Forest Fire Susceptibility’, GIScience & Remote Sensing, 48(2), pp. 171–194. doi: 10.2747/1548-1603.48.2.171.

[2]. WANG Qiankun, YU Xinfang, and SHU Qingtai. (2017). Forest burned scars area extraction using time series remote sensing data. Journal of Natural Disasters, 26(1), 1 - 10. (in Chinese)

[3]. Liu, Y.X., Zhang, J., Tang, Y., et al. (2023) 'Comparative Study of Rapid Extraction Methods of Burned Area Based on Sentinel-2 Images’, Forest Inventory and Planning, 48(6), pp.1-6. doi: 10. 3969/ j. issn. 1671-3168. 2023. 06. 001

[4]. Cui, Z.Y., Zhao, F.J., Zhao, S.  et al.  'Research on information extraction of forest fire damage based on multispectral UAV and machine learning', Journal of Natural Disasters, 2024, 33(01): 99-108.DOI: 10.13577/j.jnd.2024.0109.

[5]. Avazov, K., Hyun, A. E., Sami, S. A. A., Khaitov, A., Abdusalomov, A. B., & Cho, Y. I. (2023). Forest Fire Detection and Notification Method Based on AI and IoT Approaches. Future Internet, 15(2), 61. https: //doi.org/10.3390/fi15020061.

[6]. LI, M. Z., KANG, X. R., & FAN, W. Y. (2017). Burned area extraction in Huzhong forests based on remote sensing and the spatial analysis of the burned severity. Scientia Silvae Sinicae, 53(3), 163–174.

[7]. Rao, Y. M. (2020). Research on Forest Fire Monitoring and Canopy Water Content Inversion Method of Burned Area Based on Multi-source Remote Sensing Data [D/OL]. Beijing Forestry University.

[8]. Dai, W. X., Zhu, T., Wu, T., Zhu, Y. J., & Chen, K. W. (2024). Forest Fire Loss Assessment Based on Multi-Source Remote Sensing Data. Technology Innovation and Application, 24, 10 - 14.

[9]. Huang, C.Y. (2023) 'Burned Area Mapping and Fire Severity Analyse of Muli Forest Fire Based on Remote Sensing Images and Multiple Machine Learning Algorithms’, doi: 0.27345/d.cnki.gsnyu.2023.000873

[10]. Zhao Shumei, Zheng Xilai, ** Lingling and Zhang Xiaohui, 2006. Study on the Water Environmental Capacity of Xiaozhushan Reservoir in Qingdao. Journal of Ocean University of China: Natural Science Edition, 36(6), pp. 971 - 974.

[11]. Wang Yao. (2016). Yingke Center of Vanke Xiaozhushan in Qingdao. Architecture Technique, 8, 58 - 63. DOI: 10.3969/j.issn.1674 - 6635.2016.08.009.

[12]. Key, C. H., & Benson, N. C. (1999). The normalized burn ratio (NBR): A landsat TM radiometric measure of burn severity. Bozeman, MT: United States Geological Survey, Northern Rocky Mountain Science Center.

[13]. Lutes, D. C., Keane, R. E., Caratti, J. F., et al. (2006). FIREMON: Fire effects monitoring and inventory system. Colorado: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

[14]. Stroppiana, D., Boschetti, M., Zaffaroni, P., et al. “Analysis and interpretation of spectral indices for soft multicriteria burned-area mapping in Mediterranean regions.” IEEE Geoscience Remote Sensing Letters, vol. 6, 2009, pp. 499–503.

[15]. Chuvieco, E., Martín, M. (1998). 'Cartografía de grandes incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR’, Serie Geográfica, 7, pp.109–128.

[16]. Chuvieco, E., Martín, M.P. and Palacios, A. (2002) 'Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination’, International Journal of Remote Sensing, 23(23), pp. 5103–5110. doi: 10.1080/01431160210153129.

[17]. Joachims T. SVMLight: Support Vector Machine [J]. 1999. .

[18]. Jiang, X., Lu, W. X., Yang, Q. C., et al. Evaluation of soil environmental quality by applying support vector machine [J]. China Environmental Science, 2014, 34(5): 1229-1235.

[19]. Kraemer, H.C. (2015) 'Kappa coefficient’, Wiley StatsRef: Statistics Reference Online, pp. 1–4. doi: 10.1002/9781118445112.stat00365.pub2.

References

[1]. Chéret, V. and Denux, J.-P. (2011) 'Analysis of MODIS NDVI Time Series to Calculate Indicators of Mediterranean Forest Fire Susceptibility’, GIScience & Remote Sensing, 48(2), pp. 171–194. doi: 10.2747/1548-1603.48.2.171.

[2]. WANG Qiankun, YU Xinfang, and SHU Qingtai. (2017). Forest burned scars area extraction using time series remote sensing data. Journal of Natural Disasters, 26(1), 1 - 10. (in Chinese)

[3]. Liu, Y.X., Zhang, J., Tang, Y., et al. (2023) 'Comparative Study of Rapid Extraction Methods of Burned Area Based on Sentinel-2 Images’, Forest Inventory and Planning, 48(6), pp.1-6. doi: 10. 3969/ j. issn. 1671-3168. 2023. 06. 001

[4]. Cui, Z.Y., Zhao, F.J., Zhao, S.  et al.  'Research on information extraction of forest fire damage based on multispectral UAV and machine learning', Journal of Natural Disasters, 2024, 33(01): 99-108.DOI: 10.13577/j.jnd.2024.0109.

[5]. Avazov, K., Hyun, A. E., Sami, S. A. A., Khaitov, A., Abdusalomov, A. B., & Cho, Y. I. (2023). Forest Fire Detection and Notification Method Based on AI and IoT Approaches. Future Internet, 15(2), 61. https: //doi.org/10.3390/fi15020061.

[6]. LI, M. Z., KANG, X. R., & FAN, W. Y. (2017). Burned area extraction in Huzhong forests based on remote sensing and the spatial analysis of the burned severity. Scientia Silvae Sinicae, 53(3), 163–174.

[7]. Rao, Y. M. (2020). Research on Forest Fire Monitoring and Canopy Water Content Inversion Method of Burned Area Based on Multi-source Remote Sensing Data [D/OL]. Beijing Forestry University.

[8]. Dai, W. X., Zhu, T., Wu, T., Zhu, Y. J., & Chen, K. W. (2024). Forest Fire Loss Assessment Based on Multi-Source Remote Sensing Data. Technology Innovation and Application, 24, 10 - 14.

[9]. Huang, C.Y. (2023) 'Burned Area Mapping and Fire Severity Analyse of Muli Forest Fire Based on Remote Sensing Images and Multiple Machine Learning Algorithms’, doi: 0.27345/d.cnki.gsnyu.2023.000873

[10]. Zhao Shumei, Zheng Xilai, ** Lingling and Zhang Xiaohui, 2006. Study on the Water Environmental Capacity of Xiaozhushan Reservoir in Qingdao. Journal of Ocean University of China: Natural Science Edition, 36(6), pp. 971 - 974.

[11]. Wang Yao. (2016). Yingke Center of Vanke Xiaozhushan in Qingdao. Architecture Technique, 8, 58 - 63. DOI: 10.3969/j.issn.1674 - 6635.2016.08.009.

[12]. Key, C. H., & Benson, N. C. (1999). The normalized burn ratio (NBR): A landsat TM radiometric measure of burn severity. Bozeman, MT: United States Geological Survey, Northern Rocky Mountain Science Center.

[13]. Lutes, D. C., Keane, R. E., Caratti, J. F., et al. (2006). FIREMON: Fire effects monitoring and inventory system. Colorado: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

[14]. Stroppiana, D., Boschetti, M., Zaffaroni, P., et al. “Analysis and interpretation of spectral indices for soft multicriteria burned-area mapping in Mediterranean regions.” IEEE Geoscience Remote Sensing Letters, vol. 6, 2009, pp. 499–503.

[15]. Chuvieco, E., Martín, M. (1998). 'Cartografía de grandes incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR’, Serie Geográfica, 7, pp.109–128.

[16]. Chuvieco, E., Martín, M.P. and Palacios, A. (2002) 'Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination’, International Journal of Remote Sensing, 23(23), pp. 5103–5110. doi: 10.1080/01431160210153129.

[17]. Joachims T. SVMLight: Support Vector Machine [J]. 1999. .

[18]. Jiang, X., Lu, W. X., Yang, Q. C., et al. Evaluation of soil environmental quality by applying support vector machine [J]. China Environmental Science, 2014, 34(5): 1229-1235.

[19]. Kraemer, H.C. (2015) 'Kappa coefficient’, Wiley StatsRef: Statistics Reference Online, pp. 1–4. doi: 10.1002/9781118445112.stat00365.pub2.

Cite this article

Zhao,Z.;Liu,Y.;Li,W.;Wu,Z. (2025). Extraction of Forest Fire Scars Based on Sentinel-2 Data — Take Xiaozhushan Mountain Fire Apr.23 2020 as an Example. Applied and Computational Engineering,208,69-79.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 5th International Conference on Materials Chemistry and Environmental Engineering

ISBN: 978-1-80590-547-9(Print) / 978-1-80590-548-6(Online)
Editor: Ömer Burak İSTANBULLU
Conference website: https://2025.confmcee.org/
Conference date: 12 January 2026
Series: Applied and Computational Engineering
Volume number: Vol.208
ISSN: 2755-2721(Print) / 2755-273X(Online)

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