Journal of Mountain Science

, Volume 9, Issue 6, pp 731–741 | Cite as

Estimation of non-CO2 GHGs emissions by analyzing burn severity in the Samcheok fire, South Korea

  • Myoung Soo Won
  • Kyo Sang Koo
  • Myung Bo Lee
  • Woo Kyun Lee
  • Kyu-Young KangEmail author


This study was performed to estimate the emission of non-CO2 greenhouse gases (GHGs) from biomass burning at a large fire area. The extended methodology adopted the IPCC Guidelines (2003) equation for use on data from the Samcheok forest fire gathered using 30 m resolution Landsat TM satellite imagery, digital forest type maps, and growing stock information per hectare by forest type in 1999. Normalized burn ratio (NBR) technique was employed to analyze the area and severity of the Samcheok forest fire that occurred in 2000. The differences between NBR from pre- and post-fire datasets are examined to determine the extent and degree of change detected from burning. The results of burn severity analysis by dNBR of the Samcheok forest fire area revealed that a total of 16,200 ha of forest were burned. The proportion of the area characterized by a ‘Low’ burn severity (dNBR below 152) was 35%, with ‘Moderate’ (dNBR 153–190) and ‘High’ (dNBR 191–255) areas were at 33% and 32%, respectively. The combustion efficiency for burn severity was calculated as 0.43 for crown fire where burn severity was ‘High’, as 0.40 for ‘Moderate’ severity, and 0.15 for ‘Low’ severity surface fire. The emission factors for estimating non-CO2 GHGs were separately applied to CO 130, CH4 9, NOx 0.7 and N2O 0.11. Non-CO2 GHGs emissions from biomass burning in the Samcheok forest fire area were estimated to be CO 44.100, CH4 3.053, NOx 0.238 and N2O 0.038 Gg.


Biomass burning Non-CO2 GHGs Normalized burn ratio Combustion efficiency Emission factor Landsat TM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allen AG, Miguel AH (1995) Biomass burning in the Amazon: Characterization of the ionic component of aerosols generated from flaming and smoldering rainforest and savannah. Environmental Science and Technology 29(2): 486–493.CrossRefGoogle Scholar
  2. Andreae MO (1993) The influence of tropical biomass burning on climate and the atmospheric environment. In: Oremland RS (Ed.) Biogeochemistry of global change: Radiatively active trace gases. Chapman and Hall, New York, USA. pp 113–150.CrossRefGoogle Scholar
  3. Andreae MO, Browell EV, Garstang M, et al. (1988) Biomass burning emissions and associated haze layers over Amazonia. Journal of Geophysical Research 93(D2): 1509–1527. DOI: 10.1029/JD093iD02p01509CrossRefGoogle Scholar
  4. Andreae MO, Merlet P (2001) Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15(4): 955–966. DOI: 10.1029/2000GB001382CrossRefGoogle Scholar
  5. Baird C (1999) Environmental Chemistry. W.H. Freeman, New York, USA.Google Scholar
  6. Bigler C, Kulakowski D, Veblen TT (2005) Multiple disturbance interactions and drought influence fire severity in Rocky Mountain subalpine forests. Ecology 86(11): 3018–3029. DOI: 3018-3029. DOI: 10.1890/05-0011CrossRefGoogle Scholar
  7. Brewer CK, Winne JC, Redmond RL, et al. (2005) Classifying and mapping wildfire severity: A comparison of methods. Photogrammetric Engineering and Remote Sensing 71(11): 1311–1320.Google Scholar
  8. Cardille JA, Ventura SJ (2001) Occurrence of wildfire in the northern Great Lakes region: effects of land cover and land ownership assessed at multiple scales. International Journal of Wildland Fire 10(2): 145–154. DOI: 10.1071/WF01010CrossRefGoogle Scholar
  9. Chafer CJ, Noonan M, Macnaught E (2004) The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires. International Journal of Wildland Fire 13(2): 227–240. DOI: 10.1071/WF03041CrossRefGoogle Scholar
  10. Clark J, Bobbe T (2006) Using remote sensing to map and monitor fire damage in forest ecosystems. In: Wulder M, Franklin S (Eds.) Understanding forest disturbance and spatial pattern: Remote sensing and GIS approaches. Taylor and Francis, Boca Raton, FL, USA. pp 113–131.Google Scholar
  11. 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. DOI: 10.1071/WF04010CrossRefGoogle Scholar
  12. Cofer WR III, Winstead EL, Stocks BJ, et al. (1998) Crown fire emissions of CO2, CO, H2, CH4, and TNMHC from a dense jack pine boreal forest fire. Geophysical Research Letters 25(21): 3919–3922. DOI: 10.1029/1998GL900042CrossRefGoogle Scholar
  13. Collins BM, Kelly M, van Wagtendonk JW, et al. (2007) Spatial patterns of large natural fires in Sierra Nevada wilderness areas. Landscape Ecology 22(4): 545–557. DOI: 10.1007/s10980-006-9047-5CrossRefGoogle Scholar
  14. Crutzen PJ, Andreae MO (1990) Biomass burning in the tropics: Impact on atmospheric chemistry and biogeochemical cycles. Science 250(4988): 1669–1678. DOI: 10.1126/science. 250.4988.1669CrossRefGoogle Scholar
  15. Crutzen PJ, Delaney AC, Greenburg J, et al. (1985) Tropospheric chemical composition measurements in Brazil during the dry season. Journal of Atmospheric Chemistry 2(3): 233–256. DOI: 10.1007/BF00051075CrossRefGoogle Scholar
  16. Delmas R, Lacaux JP, Brocard D (1995) Determination of biomass burning emission factors: Methods and results. Environmental Monitoring and Assessment 38(2–3): 181–204. DOI: 10.1007/BF00546762CrossRefGoogle Scholar
  17. Duffy PA, Epting J, Graham JM, et al. (2007) Analysis of Alaskan burn severity patterns using remotely sensed data. International Journal of Wildland Fire 16(3): 277–284. DOI: 10.1071/WF06034CrossRefGoogle Scholar
  18. Epting J, Verbyla D, Sorbel B (2005) Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment 96: 328–339. DOI: 10.1016/J.RSE.2005.03.002CrossRefGoogle Scholar
  19. Fearnside PM (1990) Fire in the tropical rain forest of the Amazon Basin. In: Goldammer JG (Ed.) Fire in the tropical biota. Ecological Studies 84. Springer-Verlag, New York, USA. pp 106–116.CrossRefGoogle Scholar
  20. Farina A (1998) Principles and Methods in Landscape Ecology. Chapman & Hall, London, UK.Google Scholar
  21. Hall RJ, Freeburn JT, de Groot WJ, et al. (2008) Remote sensing of burn severity: experience from western Canada boreal fires. International Journal of Wildland Fire 17(4): 476–489. DOI: 10.1071/WF08013CrossRefGoogle Scholar
  22. Hammill KA, Bradstock RA (2006) Remote sensing of fire severity in the Blue Mountains: Influence of vegetation type and inferring fire intensity. International Journal of Wildland Fire 15(2): 213–226. DOI: 10.1071/WF05051CrossRefGoogle Scholar
  23. Houghton RA (1990) The global effects of tropical deforestation. Environmental Science and Technology 24(4): 414–422. DOI: 10.1021/es00074a001CrossRefGoogle Scholar
  24. Intergovernmental Panel on Climate Change (IPCC) (1994) Climate Change 1994: Radiative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios.Google Scholar
  25. IPCC National Greenhouse Gas Inventories Programme (2003) Good Practice Guidance for Land Use, Land-Use Change and Forestry.Google Scholar
  26. Jensen JR (2000) Remote Sensing of the Environment: An Earth Resources Perspective, 2nd Ed. Prentice Hall, NJ, USA. p 204.Google Scholar
  27. Kauffman JB, Cummings DL, Ward DE, et al. (1995) Fire in the Brazilian Amazon: 1. Biomass, nutrient pools, and losses in slashed primary forests. Oecologia 104(4): 397–408. DOI: 10.1007/BF00341336CrossRefGoogle Scholar
  28. Key CH, Benson NC (2006) Landscape assessment (LA): Sampling and analysis methods. USDA Forest Service, RockyGoogle Scholar
  29. Mountain Research Station, General Technical Report No. RMRS-GTR-164-CD. Ogden, UT, USA.Google Scholar
  30. Korea Forest Service (2005) Statistical yearbook of forestry 2005 No. 35. Daejeon, Korea. pp 38–39. (In Korean)Google Scholar
  31. Korea Forest Service (2006) Forest fire statistics, 2006 Annual Report. Daejeon, Korea. pp 26–27. (In Korean)Google Scholar
  32. Lee SW, Lee MB, Lee YG, et al. (2009) Relationship between landscape sturcuture and burn severity at the landscape and class levels in Samcheok, South Korea. Forest Ecology and Management 258: 1591–1604. DOI: 10.1016/j.foreco.2009.07.017CrossRefGoogle Scholar
  33. Lentile LB, Holden ZA, Smith AMS, et al. (2006) Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15(3): 319–345. DOI: 10.1071/WF05097CrossRefGoogle Scholar
  34. Li H, Wu J (2004) Use and misuse of landscape indices. Landscape Ecology 19(4): 389–399. DOI: 10.1023/ B:LAND.0000030441.15628.d6CrossRefGoogle Scholar
  35. Lloret F, Calvo E, Pons X, et al. (2002) Wildfires and landscape patterns in the Eastern Iberian Peninsula. Landscape Ecology 17(8): 745–759. DOI: 10.1023/A:1022966930861CrossRefGoogle Scholar
  36. Miettinen J, Andreas L, Florian S (2007) Burnt area estimation for the year 2005 in Borneo using multi-resolution satellite imagery. International Journal of Wildland Fire 16(16): 45–53. DOI: 10.1071/ WF06053CrossRefGoogle Scholar
  37. National Wildfire Coordination Group (2005) Glossary of wildland fire terminology. US National Wildfire Coordination Group Report No. PMS-205. Ogden, UT, USA.Google Scholar
  38. Nunes, MCS, Vasconcelos MJ, Pereira JMC, et al. (2005) Land cover type and fire in Portugal: Do fires burn land cover selectively? Landscape Ecology 20(6): 661–673. DOI: 10.1007/s10980-005-0070-8CrossRefGoogle Scholar
  39. 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. DOI: 10.1109/LGRS.2005.858485CrossRefGoogle Scholar
  40. Ryu SR, Chen J, Zheng D, et al. (2007) Relating surface fire spread to landscape structure: an application of FARSITE in a managed forest landscape. Landscape and Urban Planning 83: 275–283. DOI: 10.1016/j.landurbplan.2007.05.002CrossRefGoogle Scholar
  41. Son YM, Kim JC, Lee KH, et al. (2007) Forest biomass assessment in Korea. Korea Forest Research Institute, Research Paper No. 07-22. Seoul, Korea.Google Scholar
  42. Stephens SL (2001) Fire history differences in adjacent Jeffrey pine and upper montane forests in the eastern Sierra Nevada. International Journal of Wildland Fire 10(2): 161–176. DOI: 10.1071/WF01008CrossRefGoogle Scholar
  43. Bonnicksen TM (2008) Greenhouse gas emissions from four California wildfires: Opportunities to prevent and reverse environmental and climate impacts. FCEM Report No.2. p 2.Google Scholar
  44. van der Werf GR, Randerson JT, Giglio L, et al. (2010) Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics 10: 16153–16230. DOI: 10.5194/acpd-10-16153-2010Google Scholar
  45. 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. DOI: 10.1016/j.rse.2003.12.015CrossRefGoogle Scholar
  46. Ward DE (1990) Factors influencing the emissions of gases and particulate matter from biomass burning. In: Goldammer JG (Ed.) Fire in the tropical biota. Springer-Verlag, New York, USA. pp 418–436.CrossRefGoogle Scholar
  47. Ward DE, Susott RA, Kauffman JB, et al. (1992) Smoke and fire characteristics for Cerrado and deforestation burns in Brazil: BASE-B experiment. Journal of Geophysical Research 97(D13): 14601–14619. DOI: 10.1029/92JD01218CrossRefGoogle Scholar
  48. Ward DE, Hardy CC (1984) Advances in the characterization and control of emissions from prescribed fire. Presented at the 77th Annual Meeting of the Air Pollution Control Association, June 24–29, 1984, San Francisco, CA, USA.Google Scholar
  49. White JD, Ryan KC, Key CC, et al. (1996) Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire 6(3): 125–136. DOI: 10.1071/WF9960125CrossRefGoogle Scholar
  50. Wimberly MC, Reilly MJ (2007) Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery. Remote Sensing of Environment 108(2): 189–197. DOI: 10.1016/j.rse.2006.03.019CrossRefGoogle Scholar
  51. Won MS, Koo KS, Lee MB (2006) Analysis of forest fire occurrence hazards by changing temperature and humidity of ten-day intervals for 30 years in spring. Korean Journal of Agricultural and Forest Meteorology 8(4): 250–259. (In Korean with English abstract)Google Scholar
  52. 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 GIS Association of Korea 10(3): 80–92. (In Korean with English abstract)CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Myoung Soo Won
    • 1
  • Kyo Sang Koo
    • 1
  • Myung Bo Lee
    • 1
  • Woo Kyun Lee
    • 2
  • Kyu-Young Kang
    • 3
    Email author
  1. 1.Division of Forest Disaster ManagementKorea Forest Research InstituteSeoulRepublic of Korea
  2. 2.Faculty of Environmental Science and Ecological EngineeringKorea University-SeoulSeoulRepublic of Korea
  3. 3.Department of Biological and Environmental ScienceDongguk University-SeoulSeoulRepublic of Korea

Personalised recommendations