DOI QR코드

DOI QR Code

A Study on the Recovery Rate of Vegetation in Forest Fire Damage Areas Using Sentinel-2B Satellite Images

Sentinel-2B 위성 영상을 활용한 산불 피해지역 식생 회복률에 관한 연구

  • Gumsung Cheon (Team of Ecosystem Service, National Institute of Ecology) ;
  • Kwangil Cheon (Team of Ecosystem Service, National Institute of Ecology) ;
  • Byung Bae Park (Department of Environment and Forest Resources, Chungnam National University)
  • Received : 2023.11.06
  • Accepted : 2023.11.27
  • Published : 2023.12.31

Abstract

The amount of damage and the area of damage to forest fires are increasing globally, and the effectiveness analysis of the restoration method after the damage is performed insufficient. This study calculated the area of forest fire damage was calculated using Sentinel-2B satellite images and stack map and the intensity of forest fire damage is analyzed according to the forest type. In addition, the vegetation index was calculated using various wavelength bands. Based on the results, the vegetation resilience by the restoration method was quantitatively. As results, areas with a high proportion of coniferous forests suffered high intensity forest fire damage, and areas with a relatively high ratio of mixed and broad-leaved forests tended to have low forest fire damage. Also, artificial forests showed a recovery of about 92.7% compared to before forest fires and natural forests showed a recovery of about 99.6% from the result of analyzing vegetation resilience in artificial and natural forests after forest fires. Accordingly, it was confirmed that natural forests after forest fire damage had superior vegetation resilience compared to artificial forests. It can be proposed that this study is meaningful in providing important information for efficiently restoring the affected target site and the selection criteria for trees to reduce forest fire damage through the evaluation of vegetation resilience by the intensity of forest fire damage and restoration methods.

산불에 대한 피해액과 피해 면적은 전 세계적으로 커지고 있지만 피해 후 복원 방법에 따른 효과연구는 부족한 현실이다. 본 연구는 Sentinel-2B 위성 영상과 임상도를 활용하여 산불 피해 면적을 산출하고, 임상에 따른 산불 피해 강도를 분석하였다. 또한, 다양한 파장대를 활용하여 식생지수를 계산하고, 이를 토대로 복원 방법에 따른 식생 회복력을 -1.0에서 1.0 범위 내에서 정량적으로 분석하였다. 그 결과 침엽수림 비율이 높은 지역에서 높은 강도의 산불 피해가 발생하였고, 상대적으로 혼효림과 활엽수림의 비율이 높은 지역에서 낮은 강도의 산불 피해 경향을 보였다. 산불 발생 이후 인공림과 천연림에서의 식생 회복률을 분석한 결과, 인공림은 산불 발생 이전 대비 약 92%, 천연림은 약 101% 식생을 회복하였으며 인공림보다 천연림에서 식생 회복력이 우수한 것을 확인할 수 있었다. 본 연구는 임상에 따른 산불 피해 강도를 분석하고 복원 방법에 따른 식생 회복력을 평가함으로써 산불 피해를 줄이기 위한 수목 선정에 기초자료를 제공하고 복원방법에 따른 식생회복력을 비교하는데 의의가 있다.

Keywords

Acknowledgement

본 연구는 국립생태원 생태계서비스 평가 기반 정책 결정 지원 체계 수립(NIE-고유연구-2023-3)" 및 "기후변화 적응을 위한 의사결정형 통합 영향평가 모형요소 기술개발:생태계(2022003570001)"의 지원을 받아 수행되었습니다.

References

  1. Brewer CK, Winne JC, Redmond RL, Opitz DW, Mangrich MV. 2005. Classifying and mapping wildfire severity, Photogrammetric Engineering & Remote Sensing, 71(11): 1311-1320.  https://doi.org/10.14358/PERS.71.11.1311
  2. Cho SH, Lee GS, Hwang JW. 2020. Drone-based vegetation index analysis considering vegetation vitality. Journal of the Korean Association of Geographic Information Studies, 23(2): 21-35. [Korean Literature]  https://doi.org/10.11108/KAGIS.2020.23.2.021
  3. Choi KY, Kwon WT. 2008. Current and Future Changes in the Type of Wintertime Precipitation in South Korea. Journal of the Korean Geographical Society, 43(1): 1-19. [Korean Literature] 
  4. Cocke AE, Fule PZ, Crouse JE. 2005. Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire, 14(2): 189-198.  https://doi.org/10.1071/WF04010
  5. DeBano LF, Neary DG, Ffolliott PF. 1998. Fire's effects on ecosystems, John Wiley and Sons: New York, NY. 
  6. Doerr SH, Shakesby RA, Blake WH, Chafer CJ, Humphreys GS, Wallbrink PJ. 2006. Effects of differing wildfire severities on soil wettability and implications for hydrological response. Journal of Hydrology, 319(1-4): 295-311.  https://doi.org/10.1016/j.jhydrol.2005.06.038
  7. Fassnacht FE, Schmidt-Riese E, Kattenborn T, Hernandez J. 2021. Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird's eye (UAS) perspective. International Journal of Applied Earth Observation and Geoinformation, p. 95. 
  8. Genevieve R, Mochael S, Frederic B. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55: 95-107.  https://doi.org/10.1016/0034-4257(95)00186-7
  9. Key CH, Benson NC. 2006. Landscape assessment (LA). FIREMON: Fire effects monitoring and inventory system, 164, LA-1. 
  10. Kim DH, Ko JS, Choi SW, Kim KI. 1999. A study on fire investigation and calorie analysis of main trees in Go-sung wildfire land. Korean Institute of Fire Science and Engineering, 13(1): 12-334. 
  11. Kim YH. 2022. A Study on the Recovery Rate of Vegetation in Forest Fire Damage Areas using Multi-Spectral data. Sungkyunkwan University, Master thesis, p. 97. 
  12. Korea Forest Service. 2022. Forest Fire Statistics, 128: 154. 
  13. Korea Forest Service. 2023. Statistical yearbook of Forestry, p. 160. 
  14. Kunert N, Mercado Cardenas A. 2015. Are mixed tropical tree plantations more resistant to drought than monocultures?. Forests, 6(6): 2029-2046.  https://doi.org/10.3390/f6062029
  15. Kurnaz B, Bayik C, Abdikan S. 2020. Forest fire area detection by using Landsat-8 and Sentinel-2 satellite images: A case study in Mugla. Turkey. 
  16. Lee SY, Kang YS, An SH, Oh JS. 2002. Characteristic Analysis of Forest Fire Burned Area using GIS. Journal of the Korean Association of Geographic Information Studies, 5(1): 20-26. [Korean Literature] 
  17. Li F, Miao Y, Feng G, Yuan F, Yue S, Gao X, Chen X. 2014. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 15: 111-123.  https://doi.org/10.1016/j.fcr.2013.12.018
  18. Lim JH, Kim JH, Bae SW. 2012. Natural Regeneration Patten of Pine Seedings on the Burned Forest Site in Gosung. Korea, 14(4): 222-228.  https://doi.org/10.5532/KJAFM.2012.14.4.222
  19. Morgan P, Neuenschwander LF. 1988. Shrub response to high and low severity burns following clearcutting in northern Idaho. Western Journal of Applied Forestry, 3(1): 5-9.  https://doi.org/10.1093/wjaf/3.1.5
  20. National Geographic Information Instiute. 2021. National Land Satellite Center Research Report. pp. 124-125. [in Korean) 
  21. National Institute of Forest Science. 2010. Post-Fire Restoration-To Establish a Healthy and Sustainable Forest Ecosystem, Korea Forest Service. pp. 7-60. 
  22. Noh JS, Choi JY. 2022. Normalized Difference Vegetation Index based on Landsat Images Variations between Artificial and Natural Restoration Areas after Forest Fire. Journal of the Korean Society of Environmental Restoration Technology, 25(5): 43-57. [Korean Literature]  https://doi.org/10.13087/KOSERT.2022.25.5.43
  23. Roy DP, Boschetti L, Trigg SN. 2006. Remote sensing of fire severity: assessing the performance of the normalized burn ratio, IEEE Geoscience and Remote Sensing Letters, 3(1): 112-116.  https://doi.org/10.1109/LGRS.2005.858485
  24. SentinelHub. Sentinel-hub. https://apps.sentinelhub.com/eo-browser/, 2023.11.02. 
  25. Shaver TM, Khosla R, Westfall DG. 2006. Utilizing green normalized difference vegetation indices (GNDVI) for production level management zone delineation in irrigated corn. In The 18th World Congress of Soil Science. 
  26. Silverio E, Duque-Lazo J, Navarro-Cerrillo RM, Perena F, Palacios-Rodriguez G. 2020. Resilience or vulnerability of the rear-edge distributions of Pinus halepensis and Pinus pinaster plantations versus that of natural populations, under climate-change scenarios. Forest Science, 66(2): 178-190.  https://doi.org/10.1093/forsci/fxz066
  27. Teodoro A, Amaral A. 2019. A statistical and spatial analysis of Portuguese forest fires in summer 2016 considering Landsat 8 and Sentinel 2A data. Environments, 6(3): 36. 
  28. Van Wagtendonk JW, Root RR, Key CH. 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote sensing of environment, 92(3): 397-408.  https://doi.org/10.1016/j.rse.2003.12.015
  29. Wang GG. 2002. Fire severity in relation to canopy composition within burned boreal mixed-wood stands. Forest Ecology and Management, 163(1-3): 85-92.  https://doi.org/10.1016/S0378-1127(01)00529-1
  30. White JD, Ryan KC, Key CC, Running SW. 1996. Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3): 125-136.  https://doi.org/10.1071/WF9960125
  31. Won MS, Koo KS, Lee MB. 2007. An quantitative analysis of severity classification and burn severity for the large forest fire areas using normalized burn ratio of Landsat imagery. Journal of the Korean Association of Geographic Information Studies, 10(3): 80-92. 
  32. Xingwang F, Yuanbo L. 2016. A global study of NDVI difference amongmoderate-resolution satellite sensors. Journal of Photogrammetry and Remote Sensing, 121: 177-191.  https://doi.org/10.1016/j.isprsjprs.2016.09.008
  33. Zennir R, Khallef B. 2023. Forest fire area detection using Sentinel-2 data: Case of the Beni Salah national forest Algeria. Journal of Forest Science, 69: 33-40. https://doi.org/10.17221/50/2022-JFS