Welcome to Chinese Journal of Applied Ecology! Today is Share:

Chinese Journal of Applied Ecology ›› 2012, Vol. 23 ›› Issue (05): 1423-1434.

• Articles • Previous Articles    

Measurement model of carbon emission from forest fire: A review.

HU Hai-qing, WEI Shu-jing, JIN Sen, SUN Long   

  1. (College of Forestry, Northeast Forestry University, Harbin 150040, China)
  • Online:2012-05-18 Published:2012-05-18

Abstract: Forest fire is the main disturbance factor for forest ecosystem, and an important pathway of the decrease of vegetation and soil carbon storage. Large amount of carbonaceous gases in forest fire can release into atmosphere, giving remarkable impacts on the atmospheric carbon balance and global climate change. To scientifically and effectively measure the carbonaceous gases emission from forest fire is of importance in understanding the significance of forest fire in the carbon balance and climate change. This paper reviewed the research progress in the measurement model of carbon emission from forest fire, which covered three critical issues, i.e., measurement methods of forest fire-induced total carbon emission and carbonaceous gases emission, affecting factors and measurement parameters of measurement model, and cause analysis of the uncertainty in the measurement of the carbon emissions. Three path selections to improve the quantitative measurement of the carbon emissions were proposed, i.e., using high resolution remote sensing data and improving algorithm and estimation accuracy of burned area in combining with effective fuel measurement model to improve the accuracy of the estimated fuel load, using high resolution remote sensing images combined with indoor controlled environment experiments, field measurements, and field ground surveys to determine the combustion efficiency, and combining indoor controlled environment experiments with field air sampling to determine the emission factors and emission ratio.

Key words: forest fire, carbon emission, carbonaceous gases emission, measurement model, path selection.