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林业资源管理 ›› 2007, Vol. 0 ›› Issue (5): 53-58.

• 科学技术 • 上一篇    下一篇

北京八达岭人工油松林地表枯死可燃物负荷量研究

郭利峰1, 牛树奎1, 阚振国2   

  1. 1.北京林业大学省部共建森林培育与保护教育部重点实验室,北京 100083;
    2.武警警种指挥学院,北京 102202
  • 收稿日期:2006-10-27 修回日期:2007-09-03 发布日期:2020-12-08
  • 通讯作者: 牛树奎(1954-),男,副教授,生态学专业,主要研究方向:林火生态与景观生态。
  • 作者简介:郭利峰(1978-),女,山西朔州人,硕士生,主要研究方向:林火生态
  • 基金资助:
    教育部重点实验室项目(GJSYS100220402);北京市科委重大科技项目“北京山区森林健康经营关键技术研究与示范(D0706001000091)”;北京市教育委员会共建重点学科项目(XK100220555)

Study on Dead Surface Fuel Loading of Pinus tabulaeformis Forest of Badaling Forest Center in Beijing

GUO Lifeng, NIU Shukui, KAN Zhenguo   

  1. The Key Laboratory for Silviculture and Forest Conservation of Ministry of Education,Beijing Forestry University Beijing 100083, Command College of Four Kinds of Armed Police Forces, Beijing 102202, China
  • Received:2006-10-27 Revised:2007-09-03 Published:2020-12-08

摘要: 地表枯死可燃物负荷量多少是森林火灾发生的基础。调查八达岭林场不同地形条件下油松林地表枯死可燃物和林分因子,取得42块样地资料;用线性相关分析法对各因子与地表枯死可燃物负荷量进行分析,平均树高、坡度和海拔与对应负荷量有显著线性关系;应用多元线性回归中的逐步回归剔除法,建立了估测模型,除平均胸径外,平均树高、密度、郁闭度、海拔、坡度被引入,经检验回归显著,该估测模型可用于其它油松林地表可燃物负荷量估测。

关键词: 可燃物负荷量, 油松林, 林分和地形因子, 相关性分析, 线性回归分析

Abstract: The amount of dead surface fuel loading is the main basic cause that forest fire occurs.42 plots of Pinus tabulaeformis forest were surveyed,including landform factors, stand factors and several types of specific forest fuel. The correlative relations among the loading and the effective factorswere analyzed through correlation analysis method.The result shows mean standheight, slope gradient, and altitude are linear correlative with fuel loading, but mean DBH, shade coverage and stand density are not.The predicting models between fuel loading and several factors are set up by linear regression analysis method. The factors except mean DBH are used in the models.The test effect is remarkable, and the predicting models are convenient to other Pinus tabulaeformisforest fuel loading calculation.

Key words: fuel loading, Pinus tabulaeformis forest, stand and landform factors, correlation analysis method, linear regression analysis method

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