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

Author

Listed:
  • Yu Chang
  • Zhiliang Zhu
  • Yuting Feng
  • Yuehui Li
  • Rencang Bu
  • Yuanman Hu

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. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Yu Chang & Zhiliang Zhu & Yuting Feng & Yuehui Li & Rencang Bu & Yuanman Hu, 2016. "The spatial variation in forest burn severity in Heilongjiang Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 981-1001, March.
    • Handle: RePEc:spr:nathaz:v:81:y:2016:i:2:p:981-1001
    DOI: 10.1007/s11069-015-2116-9
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    Cited by:

    1. Zheng, Zhong & Huang, Wei & Li, Songnian & Zeng, Yongnian, 2017. "Forest fire spread simulating model using cellular automaton with extreme learning machine," Ecological Modelling, Elsevier, vol. 348(C), pages 33-43.
    2. Xu Jia & Yong Gao & Baocheng Wei & Shan Wang & Guodong Tang & Zhonghua Zhao, 2019. "Risk Assessment and Regionalization of Fire Disaster Based on Analytic Hierarchy Process and MODIS Data: A Case Study of Inner Mongolia, China," Sustainability, MDPI, vol. 11(22), pages 1-17, November.

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