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Estimation of gases emitted by forest fires based on remote sensing data

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Abstract

Forest fire, an important agent for change in many forest ecosystems, plays an important role in atmospheric chemical cycles and the carbon cycle. The primary emissions from forest fire, CO2, CO, CH4, long-chained hydrocarbons and volatile organic oxides, however, have not been well quantified. Quantifying the carbonaceous gas emissions of forest fires is a critical part to better understand the significance of forest fire in calculating carbon balance and forecasting climate change. This study uses images from Enhanced Thematic Mapper Plus (ETM+) on the Earth-observing satellite LANDSAT-7 for the year 2005 to estimate the total gases emitted by the 2006 Kanduhe forest fire in the Daxing’an Mountains. Our results suggest that the fire emitted approximately 149,187.66 t CO2, 21,187.70 t CO, 1925.41 t C x H y , 470.76 t NO and 658.77 t SO2. In addition, the gases emitted from larch forests were significantly higher than from both broadleaf-needle leaf mixed forests and broadleaf mixed forests.

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Acknowledgments

This study was supported by Fundamental Research Funds for Central Universities (No. DL13BA02), National Natural Science Foundation of China (Grant No. 31400552), the Twelfth 5-Year National Science And Technology Project in Rural Areas (No. 2011BAD37B0104), and the Forestry Industry Research Special Funds for Public Welfare Project (No. 201004003-6).

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Correspondence to Futao Guo.

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Project Funding: This study was supported by Fundamental Research Funds for Central Universities (No. DL13BA02), National Natural Science Foundation of China (Grant No. 31400552), the Twelfth 5-Year National Science and Technology Project In Rural Areas (No. 2011BAD37B0104), and the Forestry Industry Research Special Funds For Public Welfare Project (No. 201004003-6).

The online version is available at http://www.springerlink.com

Corresponding editor: Chai Ruihai

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Wang, Q., Guo, F., Hu, H. et al. Estimation of gases emitted by forest fires based on remote sensing data. J. For. Res. 27, 369–378 (2016). https://doi.org/10.1007/s11676-015-0162-5

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