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Nonparametric multivariate analysis of variance for affecting factors on the extent of forest fire damage in Jilin Province, China

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Abstract

Forest fires are influenced by several factors, including forest location, species type, age and density, date of fire occurrence, temperatures, and wind speeds, among others. This study investigates the quantitative effects of these factors on the degree of forest fire disaster using nonparametric statistical methods to provide a theoretical basis and data support for forest fire management. Data on forest fire damage from 1969 to 2013 was analyzed. The results indicate that different forest locations and types, fire occurrence dates, temperatures, and wind speeds were statistically significant. The eastern regions of the study area experienced the highest fire occurrence, accounting for 85.0% of the total number of fires as well as the largest average forested area burned. April, May, and October had more frequent fires than other months, accounting for 78.9%, while September had the most extensive forested area burned (63.08 ha) and burnt area (106.34 ha). Hardwood mixed forest and oak forest had more frequent fires, accounting for 31.9% and 26.0%, respectively. Hardwood-conifer mixed forest had the most forested area burned (50.18 ha) and burnt area (65.09 ha). Temperatures, wind speeds, and their interaction had significant impacts on forested area burned and area burnt.

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References

  • Archibald S, Lehmann CER, Gómez-Dans JL, Bradstock RA (2013) Defining pyromes and global syndromes of fire regimes. Proc Natl Acad Sci USA 110:6442–6447

    Article  CAS  Google Scholar 

  • Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carson JM, Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, Werf GR, Pyne SJ (2009) Fire in the earth system. Science 324:481–484

    Article  CAS  Google Scholar 

  • Brown MB, Forsythe AB (1974) Robust tests for the equality of variances. J Am Stat Assoc 69:364–367

    Article  Google Scholar 

  • Calvin D (2011) Choosing and using statistics: a biologist’s guide, 3rd edn. Wiley, Hoboken, pp 175–177

    Google Scholar 

  • Chas-Amil ML, Prestemon JP, McClean CJ, Touza J (2015) Human-ignited wildfire patterns and responses to policy shifts. Appl Geogr 56:164–176

    Article  Google Scholar 

  • Duff PA, Walsh JE, Graham JM, Mann DH, Rupp TS (2005) Impacts of large-scale atmospheric-ocean variability on Alaskan fire season severity. Ecol Appl 15:1317–1330

    Article  Google Scholar 

  • Feng M (2005) The teacher gives one-way analysis of variance of influence degree on the teaching quality. Math Pract Theory 35:59–64

    Google Scholar 

  • Flannigan MD, Logan KA, Amiro BD, Skinner WR, Stocks BJ (2005) Future area burned in Canada. Clim Change 72:1–16

    Article  CAS  Google Scholar 

  • Girardin MP, Ali AA, Carcaillet C, Gauthier S, Hély C, Goff HL, Terrier A, Bergeron Y (2013) Fire in managed forests of eastern Canada: risks and options. For Ecol Manag 294:238–249

    Article  Google Scholar 

  • Guo FT, Wang GY, Innes JL, Ma XQ, Sun L, Hu HQ (2015) Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China. J For Res 26:545–555

    Article  CAS  Google Scholar 

  • Hantson S, Pueyo S, Chuvieco E (2016) Global fire size distribution: from power law to log-normal. Int J Wildland Fire 25:403–412

    Article  Google Scholar 

  • John HM (2008) Handbook of biological statistics. Sparky House Publishing Baltimore, Maryland, p 173

    Google Scholar 

  • Kou XJ, Baker WL (2006) Accurate estimation of mean fire interval for managing fire. Int J Wildland Fire 15:489–495

    Article  Google Scholar 

  • Krawchuk MA, Moritz MA, Parisien M-A, Van Dorn J, Hayhoe K (2009) Global pyrogeography: the current and future distribution of wildfire. PLoS ONE 4:e5102

    Article  Google Scholar 

  • Li J, Shan YL, Yu SX, Zhang ZZ (2017) Influencing factors of extremely large forest fires in Jilin Province with nonparametric test methods. J Northeast For Univ 45:61–64

    Google Scholar 

  • Marlier ME, DeFries RS, Voulgarakis A, Kinney PL, Randerson JT, Shindell DT, Chen Y, Faluvegi G (2012) El Niño and health risks from landscape fire emissions in Southeast Asia. Nat Clim Change 3:131–136

    Article  Google Scholar 

  • Marlon JR, Bartlein PJ, Carcaillet C, Gavin DG, Harrison SP, Higuera PE, Joos F, Power MJ, Prentice IC (2008) Climate and human influences on global biomass burning over the past two millennia. Nat Geosci 1:697–702

    Article  CAS  Google Scholar 

  • McCoy VM, Burn CR (2005) Potential alteration by climate change of the forest-fire regime in the boreal forest of central Yukon territory. Arctic 58:276–285

    Google Scholar 

  • NIFC (Naitional Interagency Fire Center) (2004) Urban-wildland and wildland fire statistics. National Interagency Fire Center, Boise

    Google Scholar 

  • O’Brien RG (1979) A general ANOVA method for robust tests of additive models for variances. J Am Stat Assoc 74:877–880

    Article  Google Scholar 

  • Pan XP, Ni ZZ, Yin F (2002) A robust method for homogeneity of variance. Mod Prev Med 29:774–776

    Google Scholar 

  • Roth AJ (1983) Robust trend tests derived and simulated: analogs of the Welch and Brown Forsythe tests. J Am Stat Assoc 78:972–980

    Article  Google Scholar 

  • Scheirer CJ, Ray WS, Hare N (1976) The analysis of ranked date derived from completely randomized factorial designs. Biometrics 32:429–434

    Article  CAS  Google Scholar 

  • Shan YL, Zhang J (2009) Estimation of carbon emission from forest fires in Jilin Province from 1969 to 2014. Sci Silv Sin 45:84–89

    CAS  Google Scholar 

  • Shan YL, Sun PY, Guan S, Sun JS, Zhang YW (2014) Applying Canadian forest fire weather index system in Jilin Province. J Northeast For Univ 42:134–136

    Google Scholar 

  • Shan YL, Wang YH, Flannigan M, Tang SY, Sun PY, Du FG (2017) Spatiotemporal variation in forest fire danger from 1996 to 2010 in Jilin Province, China. J For Res 28:983–996

    Article  Google Scholar 

  • Shen XP, Qi HP, Liu XN, Ren XW, Li JS (2013) Realization of two factor nonparametric ANOVA in SPSS. Chin J Health Stat 30:913–914

    Google Scholar 

  • Shu LF, Tian XR, Li H (1998) Status of international forest fire in last decade. World For Res 11:41–47

    Google Scholar 

  • Sokal RR, Rohlf FJ (1995) Biometry: the principles and practice of statistics in biological research. WH Freeman and Company, New York

    Google Scholar 

  • Spiller SA, Fitzsimons GJ, Lynch JR, John G, McClelland GH (2013) Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. J Mark Res 50:277–288

    Article  Google Scholar 

  • Streets DG, Yarber KF, Woo JH, Carmichael GR (2003) Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions. Global Biogeochem Cycles 17:1099

    Article  Google Scholar 

  • Sun YQ (2003) Research on the developmental mode of forest fire prevention system in Jilin Province. For Fire Prev 21:16–19

    Google Scholar 

  • Tian XR, Shu LF, Zhao FJ, Wang MY, Douglas JM (2011) Future impacts of climate change on forest fire danger in northeastern China. J For Res 22:437–446

    Article  Google Scholar 

  • Toothaker LE, Chang HS (1980) On “the analysis of ranked date derived from completely randomized factorial designs”. J Educ Behav Stat 5:169–176

    Google Scholar 

  • Westerling AL, Turner MG, Smithwick EAH, Romme WH, Ryan MG (2011) Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proc Natl Acad Sci USA 108:13165–13170

    Article  CAS  Google Scholar 

  • Zhou X, Zhang Y (2014) Statistical analysis of forest fire risk in China. Stat Inf Forum 29:34–39

    CAS  Google Scholar 

  • Zou QC, Di XY, Yang G (2010) Evaluation on forest fire suppression capacity in Jilin Province. J Northeast For Univ 38:63–65

    Google Scholar 

Download references

Acknowledgments

This study was supported financially by the National Key Research and Development Plan (2018YFD0600205), China’s National Foundation of Natural Sciences (31470497), and the Project of Jilin Province Department of Education (JJKH20180347KJ).

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Correspondence to Yanlong Shan or Long Sun.

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Project funding: This study was supported financially by the National Key Research and Development Plan (2018YFD0600205), China’s National Foundation of Natural Sciences (31470497), and the Project of Jilin Province Department of Education (JJKH20180347KJ).

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

Corresponding editor: Chai Ruihai

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Li, J., Shan, Y., Yin, S. et al. Nonparametric multivariate analysis of variance for affecting factors on the extent of forest fire damage in Jilin Province, China. J. For. Res. 30, 2185–2197 (2019). https://doi.org/10.1007/s11676-019-00958-1

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