한국환경복원기술학회지 (Journal of the Korean Society of Environmental Restoration Technology)

  • 제27권5호
  • /
  • Pages.13-25
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  • 2024
  • /
  • 1229-3032(pISSN)
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  • 2733-5011(eISSN)
한국환경복원기술학회 (The Korea Society of Environmental Restoration Technology)
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NDVI 평균-분산 도표를 활용한 산불피해지 복원 방법별 식생 회복 경향 분석

Analysis of Vegetation Recovery Trends by Restoration Method in Wildfire-Damaged Areas Using NDVI Mean-Variance plot

  • 김인화 (고려대학교 환경생태공학과) ;
  • 김윤지 (고려대학교 오정리질리언스센터) ;
  • 정혜인 (고려대학교 오정리질리언스센터) ;
  • 신유진 (고려대학교 환경생태공학과) ;
  • 이상욱 (고려대학교 환경생태공학과) ;
  • 정다영 (고려대학교 환경생태공학과) ;
  • 전성우 (고려대학교 환경생태공학부)
  • Kim, In-hwa (Dept. of Environmental Science & Ecological Engineering, Korea University) ;
  • Kim, Yoon-Ji (Ojeong Resilience Institute, Korea University) ;
  • Chung, Hye-In (Ojeong Resilience Institute, Korea University) ;
  • Shin Yu-jin (Dept. of Environmental Science & Ecological Engineering, Korea University) ;
  • Lee, Sang-Wook (Dept. of Environmental Science & Ecological Engineering, Korea University) ;
  • Jeong, Da-yong (Dept. of Environmental Science & Ecological Engineering, Korea University) ;
  • Jeon, Seong-Woo (Dept. of Environmental Science & Ecological Engineering, Korea University)
  • 투고 : 2024.08.13
  • 심사 : 2024.09.25
  • 발행 : 2024.10.31
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초록

With the increasing wildfire damage driven by climate change, it is crucial to assess the effectiveness of restoration efforts on a large scale. The majority of forests in Korea are situated in rugged mountainous regions, making it challenging to monitor large-scale wildfires. Consequently, establishing methodologies that use satellite imagery to evaluate restoration effectiveness is essential. This study aims to assess the recovery trends of ecosystems in wildfire-affected areas using NDVI mean-variance plots, which monitor changes in NDVI mean and variance over time through satellite imagery and visually represent the restoration process. The analysis of NDVI mean-variance plots for different restoration methods revealed that landscape restoration had the slowest recovery. This slower recovery is likely due to reduced growth from the complete removal of damaged trees. In contrast to High Severity (HS) areas, Moderate High Severity (MHS) areas showed that commercial afforestation, revegetation, ecological forest treatment led to a more stable recovery state post-disturbance, suggesting that areas with lower wildfire severity may recover more quickly. Furthermore, the recovery trends between artificial and natural restoration showed no significant difference, indicating that natural restoration can have similar restoration effects to artificial restoration in appropriate areas. Therefore, the study emphasizes the need to expand natural restoration areas, considering ecological and economic benefits such as increased biodiversity and genetic resource conservation. This research provides critical baseline data for the formulation and implementation of restoration policies in large-scale wildfire-affected regions and is expected to contribute significantly to the development of effective management strategies and monitoring techniques.

키워드

과제정보

본성과는 환경부의 재원을 지원받아 한국환경산업기술원 "신기후체제 대응 환경기술개발사업"의 연구개발을 통해 창출되었습니다. (2022003570003)

참고문헌

  1. Cheon GS.Cheon K and Park BB. 2023. A Study on the Recovery Rate of Vegetation in Forest Fire Damage Areas Using Sentinel-2B Satellite Images. Environ. Impact Assess. 32(6): 463-472. 
  2. Cui X.Gibbes C.Southworth J and Waylen P. 2013. Using remote sensing to quantify vegetation change and ecological resilience in a semi-arid system. Land. 2(2):108-130. 
  3. Djalante R. 2019. Key assessments from the IPCC special report on global warming of 1.5 C and the implications for the Sendai framework for disaster risk reduction. Progress in Disaster Science. 1:100001. 
  4. East Coast Wildfire Damage Joint Investigation Team. 2000. Detailed Investigation Report on the East Coast Wildfire Area for Establishing Plans for Healthy Ecosystem Recovery and Permanent Forest Restoration. Volume 1. Korea Forest Service. National Institute of Forest Science. 
  5. East Coast Wildfire Damage Joint Investigation Team. 2000. Detailed Investigation Report on the East Coast Wildfire Area for Establishing Plans for Healthy Ecosystem Recovery and Permanent Forest Restoration. Volume 2. Korea Forest Service. National Institute of Forest Science. 
  6. Garcia ML and Caselles V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1): 31-37. 
  7. Hwang YI.Kang WS.Park KH.Lee KC.Han SG and Kweon HK. 2022. Monitoring of Vegetation Recovery According to Natural and Artificial Restoration Methods After Forest Fire Damage Using Satellite Imagery. Practical Agriculture and Fisheries Research. 24(3) 33-43. 
  8. Jones MW.Smith AJ.Betts R․Canadell JG. Prentice IC and Le QC. 2020. Climate change increases the risk of wildfires. ScienceBrief Review. pp.116-117. 
  9. Kang HM.Choi SH.Kim DH and Song JT. 2017. A Study on the Restoration Effects of Vegetation Restoration Types. Korean J. Environ. Ecol. 31(2):174-187. 
  10. Kim SI.Ahn DS and Kim SC. 2021. RGB Composite Technique for Post Wildfire Vegetation Monitoring Using Sentinel-2 Satellite Data. Korean Journal of Remote Sensing. 37(5): 939-946. 
  11. Korea Forest Service. 2019. 2018 Annual Wildfire Statistics Report. Korea Forest Service.
  12. Korea Forest Service. . 2023. https://www.forest.go.kr/kfsweb/kfi/kfs/frfr/selectFrfrStats.do?mn=AR04_01_03 
  13. Lee GS.Jeong YS.Kim SC.Shin SS.No CH and Park SD. 2004. Development of vegetation structure over the years following a wildfire in the East Coast affected area. Korean J. Ecology. 27(2): 99-106. 
  14. Lim JH.Kim JH and Bae SW. 2012. Natural Regeneration Patten of Pine Seedlings on the Burned Forest Site in Gosung, Korea. Korean Journal of Agricultural and Forest Meteorology. 14(4): 222-228. 
  15. Lin WT.Lin CY and Chou WC. 2006. Assessment of vegetation recovery and soil erosion at landslides caused by a catastrophic earthquake: a case study in Central Taiwan. Ecological Engineering. 28(1):79-89. 
  16. Mutti PR.Lucio PS.Dubreuil V and Bezerra BG. 2020. NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots. International Journal of Remote Sensing. 41(7): 2759-2788. 
  17. National Institute of Forest Science. 2006. Wildfire Damage Restoration Manual for Healthy Ecosystem Recovery and Permanent Forest Restoration. Korea Forest Service. 
  18. National Institute of Forest Science. 2010. Map of Wildfire Occurrences from 1991 to 2009. Korea Forest Service. 
  19. Noh JS and Choi JY. 2022. Normalized Difference Vegetation Index based on Landsat Images Variations between Artificial and Natural Restoration Areas after Forest Fire. Korean Env. Res. Tech. 25(5):3-57. 
  20. Ryu JH.Han KS.Hong KS.Park NW.Lee YW and Cho JI. 2018. Satellite-based evaluation of the post-fire recovery process from the worst forest fire case in South Korea. Remote Sensing, 10(6), 918. 
  21. Pickup G and Foran BD. 1987. The use of spectral and spatial variability to monitor cover change on inert landscapes. Remote Sensing of Environment, 23(2), 351-363. 
  22. Pricope NG.Gaughan AE.All JD․Binford MW and Rutina LP. 2015. Spatio-temporal analysis of vegetation dynamics in relation to shifting inundation and fire regimes: disentangling environmental variability from land management decisions in a southern african transboundary watershed. Land. 4(3): 627-655. 
  23. Rezaei R and Ghaffarian S. 2021. Monitoring forest resilience dynamics from very high-resolution satellite images in case of multi-hazard disaster. Remote Sensing, 13(20): 4176. 
  24. Seong SY.Lee DG and Kim JY. 2015. Monitoring vegetation changes in the East Coast wildfire area using Landsat imagery based on restoration methods. Proceedings of the Korean Institute of Landscape Architecture Conference. 15-16. 
  25. Shin YS.Song SH.Yang AR.Hwang JH and Park YM. 2014. Growth Characteristics of Trees following Different Types of Cutting in Quercus acutissima Stand. Journal of Environmental Science International, 23(6): 1075-1083. 
  26. Song YK. 2018. Analysis of Urban Green Areas using NDVI and Development of a Model to Analyze Bird Diversity in Urban Parks. Korean Env. Res. Tech. 21(1): 73-82. 
  27. Washington-Allen RA.Ramsey RD.West NE and Norton BE. 2008. Quantification of the Ecological Resilience of Drylands Using Digital Remote Sensing. Ecology and Society. 13(1). 
  28. Won MS.Koo KS and 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. 
  29. Xue J and Su B. 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of sensors. 2017(1): 1353691. 
  30. Youn HJ Y and Jeong JC. 2020. Classification of forest fire damage grade using machine learning and Sentinel-2. The Korea Spatial Planning Review. 106: 107-117. 
  31. Zhang X.Wang M.Liu K.Xie J and Xu H. 2018. Using NDVI time series to diagnose vegetation recovery after major earthquake based on dynamic time warping and lower bound distance. Ecological indicators. 94:52-61