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RapidEye 영상을 활용한 대형산불피해지의 온실가스 배출량 추정

Estimation on Greenhouse Gases(GHGs) Emission of Large Forest Fire Area in 2013

  • 원명수 (국립산림과학원 산림방재연구과) ;
  • 김유승 (국립산림과학원 산림방재연구과) ;
  • 김경하 (국립산림과학원 산림방재연구과)
  • Won, Myoung-Soo (Division of Forest Disaster Management, Korea Forest Research Institute) ;
  • Kim, You-Seung (Division of Forest Disaster Management, Korea Forest Research Institute) ;
  • Kim, Kyong-Ha (Division of Forest Disaster Management, Korea Forest Research Institute)
  • 투고 : 2014.05.31
  • 심사 : 2014.07.29
  • 발행 : 2014.09.30

초록

본 연구는 RapidEye 영상을 활용하여 2013년 발생한 대형산불 피해지역(울주, 포항, 봉화)을 대상으로 온실가스 배출량 추정하였다. 온실가스 배출량 추정은 2006 IPCC(Intergovernmental Panel on Climate Change) 가이드라인에서 제시하는 추정식을 이용하였다. 본 연구에서는 최대 우도법을 기반으로 한 감독분류를 실시하여, 산불피해지역의 강도등급 및 피해면적을 산출하였으며, 현장정보와 비교하여 정확도 검증을 실시하였다. 산불피해 등급별 정확도 평가 결과는 평균적으로 전체정확도 73.93%과 Kappa 계수 0.67로 나타났다. 2013년 대형산불피해지의 온실가스 배출량 추정은 울주지역 $CO_2$ 63,260, CO 5.207, $CH_4$ 360, $N_2O$ 28.0, $NO_x$ $4.4g/kg^{-1}{\cdot}ha^{-1}$, 포항지역 $CO_2$ 28,675, CO 2.359, $CH_4$ 163, $N_2O$ 12.7, $NO_x$ $1.9g/kg^{-1}{\cdot}ha^{-1}$ 그리고 봉화지역 $CO_2$ 53,086, CO 1,655, $CH_4$ 114, $N_2O$ 23.5, $NO_x$ $3.6g/kg^{-1}{\cdot}ha^{-1}$로 나타났다.

This study was performed to estimate Greenhouse gases(GHGs) emissions from biomass burning at large forest fire(Ulju, Pohang and Bonghwa) in 2013. The extended methodology to estimate GHGs adopted the IPCC(Intergovermental Panel on Climate Change) Guidelines(2006) equation. For classifying fire damaged area and analyzing burn severity of total three large-fire area damaged, this study used post-fire imagery from Rapideye imagery to compute the Maximum Likelihood Classifiction (MLC). The result of accuracy assessment on burn severity from imagery showed that average overall accuracy was 75.93% and Kapp coefficient was 0.67 Finally, GHGs emissions from biomass burning in the three large-fire area 2013 were estimated as follows: Ulju $CO_2$ 63,260, CO 5.207, $CH_4$ 360, $N_2O$ 28.0 and $NO_x$ $4.4g/kg^{-1}{\cdot}ha^{-1}$, Pohang $CO_2$ 28,675, CO 2.359, $CH_4$ 163, $N_2O$ 12.7 and $NO_x$ $1.9g/kg^{-1}{\cdot}ha^{-1}$ and Bonghwa $CO_2$ 53,086, CO 1,655, $CH_4$ 114, $N_2O$ 23.5 and $NO_x$ $3.6g/kg^{-1}{\cdot}ha^{-1}$.

키워드

참고문헌

  1. Choromanska, U. and T.H. DeLuca. 2002. Microbial activity and nitrogen mineralization in forest mineral soils following heating: evaluation of post-fire effects. Soil Biology & Biochemistry 34:263-271. https://doi.org/10.1016/S0038-0717(01)00180-8
  2. Cocke, A.E., P.Z. Fule and J.E. Crouse. 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
  3. Duffy, D.A., J. J.M. Epting, T.S. Graham, Rupp and A.D. McGuire. 2007. Analysis of Alaskan burn severity patterns using remotely sensed data. International Journal of Wildland Fire 16(3):277-284. https://doi.org/10.1071/WF06034
  4. Ice, G.G., D.G. Neary and P.W. Adams. 2004. Effects of wildfire on soils and watershed processes. Journal of Forestry 102(6):16-20.
  5. Key, C.H. and N.C. Benson. 2002. Fire effects monitoring and inventory protocol-landscape assessment. USDA Forest Service Fire Science Laboratory, Missoula, MT. pp.1-16.
  6. Key, C.H. and N.C. Benson. 2006. Landscape assessment. In: D.C. Lutes et al.(eds.). FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD, pp. LA-155.
  7. KFRI(Korea Forest Research Institute). 2010. Research report in 2010 : forest preservation. 248-249pp (국립산림과학원. 2010. 2010년도 연구사업 보고서 : 산림보전분야. 248-249쪽).
  8. KFRI(Korea Forest Research Institute). 2013. A study on damage characteristics and development of burn severity evaluation methods. 66-88pp (국립산림과학원. 2013. 산불피해강도의 정량적 평가 기법 개발 및 피해특성 구명. 66-88쪽).
  9. KFRI(Korea Forest Research Institute). 2013. A prediction of forest disaster change on climate change and establishment of counter-strategy. 185-190pp (국립산림과학원. 2013. 기후변화 대응 산림재해 변화 예측 및 대응전략 개발. 185-190쪽).
  10. Lee, B.D., M.S. Won, K.M. Jang and M.B. Lee. 2008. Analysis of the relationship between landform and forest fire severity. Journal of the Korean Association of Geographic Information Studies 11(1):58-67 (이병두, 원명수, 장광민, 이명보. 2008. 지형과 산불피해도와의 관계 분석. 한국지리정보학회지 11(1):58-7).
  11. Lee, H.J., J.M. Lee, M.S. Won and S.W. Lee. 2012. Development and validation of Korean Composit Burn Index(KCBI). Journal Of Korean Forest Society 101(1):163-174 (이현주, 이주미, 원명수, 이상우. 2012. 한국형 산불피해강도지수 (KCBI) 개발 및 검증. 한국임학회지 101(1):163-174).
  12. Lee, J.M., M.S. Won, J.H. Lim and S.W. Lee. 2012. Effect of edge area burn severity on early vegetation regeneration in damaged area. Journal Of Korean Forest Society 101(1):121-129 (이주미, 원명수, 임주훈, 이상우. 2012. 가장자리와 산불피해강도가 산불피해지역 초기식생재생에 미치는 영향. 한국임학회지 101(1): 121-129).
  13. Lentile, L.B., Z.A. Holden, A.M.S. Smith, M.J. Falkowski, A.T. Hudak, P. Morgan, S.A. Lewis, P.E. Gessler and N.C. Benson. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15:319-345. https://doi.org/10.1071/WF05097
  14. McHugh, C. and T.E. Kolb. 2003. Ponderosa pine mortality following fire in northern Arizona. International Journal of Wildland Fire 12(1):7-22. https://doi.org/10.1071/WF02054
  15. Miettinen, J., L. Andreas and S. Florian. 2007. Burnt area estimation for the year 2005 in Borneo using multiresolution satellite imagery. International Journal of Wildland Fire 16(1):45-53. https://doi.org/10.1071/WF06053
  16. Morgan, P., C.C. Hardy, T. Swetnam, M.G. Rollins and L.G. Long. 2001. Mapping fire regimes across time and space: understanding coarse and fine-scale fire patterns. International Journal of Wildland Fire 10(3):329-342. https://doi.org/10.1071/WF01032
  17. Park, N.W., H.Y. Yoo, Y.H. Kim and S.Y. Hong. 2012. Classification of remote sensing data using random selection of training data and multiple classifiers. Korean Journal of Remote Sensing 28(5):489-499 (박노욱, 유희영, 김이현, 홍석영. 2012. 훈련자료의 임의선택과 다중 분류자를 이용한 원격탐사 자료의 분류. 대한원격탐사학회지 28(5):489-499). https://doi.org/10.7780/kjrs.2012.28.5.2
  18. Perez, B. and J.M. Moreno. 1998. Methods for quantifying fire severity in shrublandfires. Plant Ecology 139(1): 91-101. https://doi.org/10.1023/A:1009702520958
  19. Ryu, G.S., B.D. Lee, M.S. Won and K.H. Kim. 2014. Development of crown fire propagation probability equation using logistic regression model. Journal of the Korean Association of Geographic Information Studies 17(1):1-12 (유계선, 이병두, 원명수, 김경하. 로지스틱 회귀모형을 이용한 수관화확산확률식의 개발. 한국지리정보학회지 17(1):1-12). https://doi.org/10.11108/kagis.2014.17.1.001
  20. Spencer, C.N,. K.O. Gabel and F.R. Hauer. 2003. Wildfire effects on stream food webs and nutrient dynamics in Glacier National Park, USA. Forest Ecology and Management. 178(1):141-153. https://doi.org/10.1016/S0378-1127(03)00058-6
  21. Won, M.S. 2013. Analysis of burn severity in large-fire area using satellite imagery. Ph.D. Thesis, Univ. of Korea. Seoul, Korea. 6-9pp (원명수. 2013. 위성영상을 이용한 대형산불지역의 피해강도 분석 연구. 고려대학교 대학원 박사학위논문. 6-9쪽).
  22. Won, M.S., K.S. Koo, and M.B. Lee. 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 (원명수, 구교상, 이명보. 2007. Landsat 영상으로부터 정규탄화지수 추출과 산불피해지역 및 피해강도의 정량적 분석. 한국지리정보학회지 10(3): 80-92).
  23. Won, M.S., K.S. Koo, M.B. Lee and Y.M. Son. 2008. Forest fire, biomass burning, Non-$CO_{2}$ GHGs, normalized burn ratio, combustion efficiency, emission factor, Landsat TM. Korean Journal of Agricultural and Forest Meteorology 10(1):17-24 (원명수, 구교상, 이명보, 손영모. 2008. Landsat TM 영상자료를 활용한 삼척 대형산불피해지의 비이산화탄소 온실가스 배출량 추정. 한국농림기상학회지 10(1):17-24). https://doi.org/10.5532/KJAFM.2008.10.1.017