Papers

Research on the prediction models of forest fuel moisture content based on meteorological factors

  • YUAN Xiaoyu1 ,
  • YANG Xiaodan1* ,
  • WANG Zhongyu2
Expand
  • 1. Public Weather Service Center, China Meteorological Administration, Beijing 100081, China
    2. Department of Mathematical and Physical, North China Electric Power University, Beijing 102206, China

Received date: 2022-08-26

  Revised date: 2023-05-31

  Online published: 2023-09-08

Abstract

The prediction of forest fuel moisture content is of great significance to the forecast of forest fire risk and the protection of forest ecosystem. Based on the systematic analyses of the key meteorological factors affecting the forest fuel moisture content, the prediction models of the live and dead fuels moisture content of four different forest types, namely, Pinus yunnanensis (shady slope), Pinus yunnanensis (sunny slope), Pinus armandii and Platycladus orientalis, were established utilizing the methods of multiple regression, classification and regression tree (CART), etc. The results showed that the average relative errors of the multiple regression model in predicting the live and dead fuels moisture content in different forest types were between 5.90%~6.60% and 20.1%~36.9%, respectively. CART model was found applicable for the prediction of forest fuel moisture content based on meteorological factors. The optimal average relative errors of the prediction of live fuels moisture content (5.38%~7.00%) were significantly lower than that of dead fuels (22.88%~26.64%), which was consistent with the multiple regression model and generally had higher accuracies. Besides, the problem that the live fuel moisture content of Pinus yunnanensis (sunny slope) cannot be predicted was also solved. The research results are expected to provide some theoretical supports for the establishment and accuracy improvement of further prediction model of forest fuel moisture content and even the forecast model of forest fire risk.

Cite this article

YUAN Xiaoyu1 , YANG Xiaodan1* , WANG Zhongyu2 . Research on the prediction models of forest fuel moisture content based on meteorological factors[J]. Science & Technology Review, 2023 , 41(16) : 124 -135 . DOI: 10.3981/j.issn.1000-7857.2023.16.011

References

[1] 赵荣钦, 黄贤金, 郧文聚, 等. 碳达峰碳中和目标下自然资源管理领域的关键问题[J]. 自然资源学报, 2022, 37(5): 1123-1136.
[2] Pan Y, Birdsey R A, Phillips O L, et al. The structure, distribution, and biomass of the world's forests[J]. Annual Review of Ecology, Evolution, and Systematics, 2013, 44: 593-622.
[3] Ahlström A, Schurgers G, Arneth A, et al. Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections[J]. Environmental Research Letters, 2012, 7(4): 044008.
[4] 胡海清, 陆昕, 孙龙, 等. 大兴安岭典型林分地表死可燃物含水率动态变化及预测模型[J]. 应用生态学报, 2016, 27(7): 2212-2224.
[5] 芮淑君. 北京市妙峰山林场可燃物含水率的影响因子及预测模型研究[D]. 北京: 北京林业大学, 2017.
[6] 王生营 . 基于计算机视觉的森林火灾检测算法研究[D]. 银川: 北方民族大学, 2021.
[7] 于宏洲, 舒立福, 邓继峰, 等. 以小时为步长的大兴安岭典型林分地表死可燃物含水率模型预测及外推精度[J]. 应用生态学报, 2018, 29(12): 3959-3968.
[8] 张云林. 蒙古栎和红松凋落物含水率动态变化影响因素及预测模型研究[D]. 哈尔滨: 东北林业大学, 2019.
[9] Masinda M M, Sun L, Wang G, et al. Moisture content thresholds for ignition and rate of fire spread for various dead fuels in northeast forest ecosystems of China[J]. Journal of Forestry Research, 2021, 32: 1147-1155.
[10] 邵潇 . 北京西山主要森林类型地表枯死可燃物含水率预测模型研究[D]. 北京: 北京林业大学, 2015.
[11] 常畅, 常禹, 胡远满, 等 . 基于文献计量的森林和草原可燃物含水率研究[J]. 生态学报, 2022, 42(4): 1655-1663.
[12] Yang X, Yu Y, Hu H, et al. Moisture content estimation of forest litter based on remote sensing data[J]. Environmental Monitoring Assessment, 2018(190): 421.
[13] Chuvieco E, Cocer D, Riaño D, et al. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating[J]. Remote Sensing of Environment, 2004, 92(3): 322-331.
[14] 刘曦, 金森 . 平衡含水率法预测死可燃物含水率的研究进展[J]. 林业科学, 2007, 47(12): 126-133.
[15] Toomey M. Vierling L A. Multispectral remote sensing of landscape levelfoliar moisture[J]. Techniques and Applications For Forest Ecosystem Monitoring Canadian Joumfl of Forest Research, 2005, 35(5): 1087-1097.
[16] Nelson R M. A Method for predicting equilibrium moisture content of forest fuels[J]. Canadian Journal of Forest Research[J], 1984, 14: 597-600.
[17] 金森, 姜文娟, 孙玉英, 等 . 用时滞和平衡含水率准确预测可燃物含水率的理论算法[J]. 森林防火, 1999(4): 12-14.
[18] Catchpole E A, Catchpole W R, Viney N R. Estimating fuel response time and predicting fuel moisture content from field data[J]. International Journal of Wildland Fire, 2001, 10(2): 115-222.
[19] 邢俊景, 曲智林 . 基于混合效应模型的大兴安岭地被可燃物含水率模型[J]. 东北林业大学学报, 2017, 45(3): 58-62.
[20] 刘金波 . 昆明地区几种林型可燃物含水率预测模型的研究[D]. 哈尔滨: 东北林业大学, 2017.
[21] Quan X, He B, Yebra M, et al. Retrieval of forest fuel moisture content using a coupled radiative transfer model[J]. Environmental Modelling & Software, 2017, 95: 290-302.
[22] 贾俊平, 何晓群, 金勇进. 统计学(第五版)[M]. 北京: 中国人民大学出版社, 2012.
[23] De'ath G, Fabricius K E. Classification and regression trees: A powerful yet simple technique for ecological data analysis[J]. Ecology, 2000, 81(11): 3178-3192.
[24] 闫一凡, 刘建立, 李晓鹏, 等 . 基于聚类和分类与回归树的地力等级评价研究[J]. 土壤, 2014, 46(4): 656-661.
[25] Belart F, Leshchinsky B, Sessions J. Finite element analysis to predict in-forest stored harvest residue moisture content[J]. Forest Science, 2017, 63(4): 362-376.
[26] Chae H M, Jeon B R, Lee S Y, et al. Effects of weather factors on fuel moisture contents of forestland in chuncheon, south korea[J]. Journal of the Faculty of Agriculture, Kyushu University, 2017, 62(1): 23-29.
Outlines

/