DOI QR코드

DOI QR Code

Monitoring of Vegetation Recovery According to Natural and Artificial Restoration Methods After Forest Fire Damage Using Satellite Imagery

위성영상을 이용한 산불피해 이후 자연복원과 인공복원 방법에 따른 식생회복 모니터링

  • Hwang, Yeong In (Department of Landscape Architecture, Korea National College of Agriculture and Fisheries) ;
  • Kang, Won Seok (Forest Ecology Division, Nantional Institute of Forest Science) ;
  • Park, Ki Hyung (Forest Ecology Division, Nantional Institute of Forest Science) ;
  • Lee, Kyeong Cheol (Department of Landscape Architecture, Korea National College of Agriculture and Fisheries) ;
  • Han, Sang Gyun (Division of Forest Science, Kangwon National University) ;
  • Kweon, Hyeong Keun (Department of Landscape Architecture, Korea National College of Agriculture and Fisheries)
  • 황영인 (한국농수산대학교 작물산림학부) ;
  • 강원석 (국립산림과학원 산림생태연구과) ;
  • 박기형 (국립산림과학원 산림생태연구과) ;
  • 이경철 (한국농수산대학교 작물산림학부) ;
  • 한상균 (강원대학교 산림과학부) ;
  • 권형근 (한국농수산대학교 작물산림학부)
  • Received : 2022.09.08
  • Accepted : 2022.10.18
  • Published : 2022.10.20

Abstract

This study was conducted to monitor the vegetation recovery in the areas damaged by the forest fires on the east coast that occurred in April 2000. The study site was a forest fire-damaged area in Samcheok-si, Gangwon-do, and 21 monitoring areas (12 natural restoration sites, 9 artificial restoration sites) were selected to analyze the vegetation recovery trend since 1998. The vegetation recovery trend was compared by calculating the values according to the year using the difference Normalized Burn Ratio (dNBR) and Normalized Difference Vegetation Index (NDVI) based on satellite images (Landsat TM/ETM+ and Sentinel-2A). As the result of this study, all 21 sites, vegetation was recovered, and both groups showed the greatest recovery in summer. In the case of the dNBR, the artificial restored sites showed higher values than the natural restored sites, and in the case of the NDVI, the natural restored sites were higher than the artificially restored sites in summer and autumn. However, the difference between the two groups of natural and artificial restoration sites was not significant. Therefore, the direction of forest restoration after forest fire damage can be effectively restored if properly implemented for the purpose of restoration of the target site.

Keywords

Acknowledgement

본 연구는 국립산림과학원(Korea Forest Research Institute)의 연구비 지원에 의해 이루어진 것임.

References

  1. 김남균, 곽재환, 김만일. (2019). 산불 사고사례 분석을 통한 산불 대응 의사결정 트리 강화의 중요성 고찰. 2. 한국방재학회 논문집, 19, 213-220.
  2. 박성욱, 김형우, 이수진, 윤예슬, 김은숙, 임종환, 이양원. (2018). 고해상도 위성영상과 Fully Convolutional Network 를 활용한 산림재해 피해지 탐지. 한국사진지리학회지, 28(4), 87-101. https://doi.org/10.35149/JAKPG.2018.28.4.006
  3. 성선용, 이동근, 김지연. (2015). Landsat 영상을 활용한 동해안 산불지역의 복원방법에 따른 식생변화 모니터링. In Proceedings of the Korean Institute of Landscape Architecture Conference (pp. 15-16). The Korean Institute of Landscape Architecture.
  4. 신형진, 하림, 박민지, 김성준. (2010). Estimation of spatial evapotranspiration using the relationship between MODIS NDVI and morton ET-For Chungjudam watershed. Journal of the Korean Society of Agricultural Engineers, 52(1), 19-24. https://doi.org/10.5389/KSAE.2010.52.1.019
  5. 원명수, 구교상, 이명보. (2007). Landsat 영상으로부터 정규탄화지수 추출과 산불피해지역 및 피해강도의 정량적 분석. 한국지리정보학회지, 10(3), 80-92.
  6. 원명수, 김경하, 이상우. (2014). SPOT5 영상과 현장조사 자료를 융합한 대형산불지역의 피해강도 분석. Korean Journal of Agricultural and Forest Meteorology, 16(2), 114-124. https://doi.org/10.5532/KJAFM.2014.16.2.114
  7. 윤형진, 정종철. (2020). 기계학습을 이용한 Sentinel-2 산불피해등급 분류. 국토연구, 107-11 7.
  8. 이규송, 정연숙, 김석철, 신승숙, 노찬호, 박상덕. (2004). 동해안 산불 피해지에서 산불 후 경과 년 수에 따른 식생 구조의 발달. 한국생태학회지, 27(2), 99-106.
  9. 이시영, & 안상현. (2009). 지표화 산불피해지의 수종별 임목 고사율 비교분석. 한국방재학회논문집, 9(2), 39-43.
  10. 이형석, 이시영. (2011). The analysis of distribution and characteristics of forest fires damage over 30 ha in Korea. Fire Science and Engineering, 25(5), 39-46.
  11. 임주훈, 김정환, 배상원. (2012) 고성 산불피해지에서 소나무 치수의 자연복원 패턴, 한국농림기상학회지, 14(4), 222-228. https://doi.org/10.5532/KJAFM.2012.14.4.222
  12. 채희문, 엄기증, 이시영. (2011). CCGIS 를 활용한 강원도 산불의 기후변화 취약성 평가. 2. 한국방재학회논문집, 11, 123-130.
  13. 최철순, 최승필. (1997). 인공위성 TM 영상을 이용한 고성 산불 피해지역의 추적조사. 한국측량학회지, 15, 215-219.
  14. 산림청. (2021). 산불통계연보
  15. Aquino, D. D. N., Rocha Neto, O. C. D., Moreira, M. A., Teixeira, A. D. S., Andrade, E. M. D. (2018). Use of remote sensing to identify areas at risk of degradation in the semi-arid region. Revista Ciencia Agronomica, 49, 420-429.
  16. Franco, M. G., Mundo, I. A., Veblen, T. T. (2020). Field-validated burn-severity mapping in North Patagonian forests. Remote Sensing, 12(2), 214. https://doi.org/10.3390/rs12020214
  17. Garcia, M. L., Caselles, V. (1991). Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1), 31-37. https://doi.org/10.1080/10106049109354290
  18. Lentile, L. B., Holden, Z. A., Smith, A. M., Falkowski, M. J., Hudak, A. T., Morgan, P., Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3), 319-345. https://doi.org/10.1071/WF05097
  19. Jones, M. W., Smith, A., Betts, R., Canadell, J. G., Prentice, I. C., Le Quere, C. (2020). Climate change increases the risk of wildfires. ScienceBrief Review, 116, 117.
  20. Kasischke, E. S., Hoy, E. E., French, N. H. F., Turetsky, M. R. (2007). Post-fire evaluation of the effects of fire on the environment using remotely-sensed data. Towards an operational use of remote sensing in forest fire management, 38.
  21. Key, C. H., Benson, N. C. (2006). Landscape assessment (LA). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA-1-55, 164.
  22. McKenna, P., Phinn, S., Erskine, P. D. (2018). Fire severity and vegetation recovery on mine site rehabilitation using WorldView-3 imagery. Fire, 1(2), 22. https://doi.org/10.3390/fire1020022
  23. Rouse Jr, J. W., Haas, R. H., Deering, D. W., Schell, J. A., Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation NASA, (No. E75-10354).
  24. Ryu, J. H., Han, K. S., Hong, S., Park, N. W., Lee, Y. W., Cho, J. (2018). Satellite-based evaluation of the post-fire recovery process from the worst forest fire case in South Korea. Remote Sensing, 10(6), 918. https://doi.org/10.3390/rs10060918
  25. Stephens, S. L., Burrows, N., Buyantuyev, A., Gray, R. W., Keane, R. E., Kubian, R., Van Wagtendonk, J. W. (2014). Temperate and boreal forest mega-fires: Characteristics and challenges. Frontiers in Ecology and the Environment, 12(2), 115-122. https://doi.org/10.1890/120332
  26. Weirather, M., Zeug, G., Schneider, T. (2018). Automated Delineation Of Wildfire Areas Using Sentinel-2 Satellite Imagery. GI_Forum 2018,, 6, 251-262.
  27. 미국지질조사국(United States Geological Survey) https://www.usgs.gov/