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Natural Hazards

, Volume 81, Issue 2, pp 981–1001 | Cite as

The spatial variation in forest burn severity in Heilongjiang Province, China

  • Yu Chang
  • Zhiliang Zhu
  • Yuting Feng
  • Yuehui Li
  • Rencang Bu
  • Yuanman Hu
Original Paper

Abstract

Quantitative assessment of forest burn severity and determination of its spatial variation are important for post-fire forest restoration and forest fire management. In this paper, we assessed forest burn severity using pre- and post-fire Landsat TM/ETM+ data and field-surveyed data and explored the spatial variation in burn severity and its influencing factors. Our results showed a relatively strong linear relationship between normalized burn ratio (NBR) and composite burn index (CBI) (R 2 = 0.63), suggesting that NBR was the best spectral index and could be used to assess forest burn severity in Heilongjiang Province. The forest burn severity showed obvious spatial variation. The majority of heavily burned areas were distributed within elevation greater than 800 m, with slope between 5° and 15°, with eastern and southern slopes, and in conifers. In addition, the forest burn severity also demonstrated a north-to-south gradient. The Great Xing’an Mountains located in the north of Heilongjiang Province tended to be burned with high severity, while the Small Xing’an Mountains located in the central part with lower severity. Topographic factors (elevation, slope, aspect) and daily mean humidity had determinative influences on forest burn severities.

Keywords

Forest fire Burn severity Remote sensing NBR CBI NDVI dNBR rdNBR DCCA 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant Nos. 31470516 and 41271201) and the Strategic Priority Research Program—Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences (Grant No. XDA05050201). We Thank the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/) for providing the observational meteorological data. The DEM data were provided by Geospatial Data Cloud, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn/), and our thanks are also given to the anonymous reviewers for very helpful suggestions to improve the manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Yu Chang
    • 1
  • Zhiliang Zhu
    • 2
  • Yuting Feng
    • 1
  • Yuehui Li
    • 1
  • Rencang Bu
    • 1
  • Yuanman Hu
    • 1
  1. 1.State Key Laboratory of Forest and Soil Ecology, Institute of Applied EcologyChinese Academy of SciencesShenyangChina
  2. 2.U.S. Geological SurveyRestonUSA

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