Skip to main content

Advertisement

SpringerLink
Log in

Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The tropics is an area with high incidence of wildfire all over the world in recent years, and the forest ecosystem in the tropics is extremely fragile. Thus, it is very important to identify drivers of wildfire in the tropics for developing effective fire management strategy. In this paper, global logistic regression (GLR) and geographically weighted regression (GWLR) models were employed to analyze the spatial distribution and drivers of tropical wildfires in Xishuangbanna and Leizhou Peninsula in tropical China from 2001 to 2018. The results show that the overall distribution of wildfire in Xishuangbanna and Leizhou Peninsula from 2001 to 2018 was spatially aggregated. In these tropical seasonal forest ecosystems, wildfire was mainly driven by meteorological factors, particularly by daily temperature range and precipitation. In Xishuangbanna (inland) peninsula, the impact of driving factors tended to be global, and the GLR model predicted the probability of wildfire occurrence better than the GWLR model. Drivers of wildfire in Leizhou Peninsula (coastal area) had clear spatial variation, and the GWLR model better explained the relationship. Furthermore, wildfire in Leizhou was more driven by human activities, especially management of agricultural lands. Our results demonstrate that effective forest management practice needs to adopt fire management practices with regional characteristics. The forest management strategy and traditional agriculture production system should pay more attention to changes in these driving factors and their relationship with wildfire.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The current research uses SAS statistical software, and the related model code is open source. If there are reasonable requirements, they can be obtained from the correspondent author.

References

  • Agudelo J, Arias PA, Vieira SC, Martínez JA (2019) Influence of longer dry seasons in the Southern Amazon on patterns of water vapor transport over northern South America and the Caribbean. Clim Dyn 52(5–6):2647–2665

    Article  Google Scholar 

  • Andela N, van der Werf GR (2014) Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat Clim Chang 4(9):791–795

    Article  Google Scholar 

  • Archibalda S, Roy DP, van Wilgen BW, Scholes RJ (2009) What limits fire? An examination of drivers of burnt area in Southern Africa. Glob Change Biol 15(3):613–630

    Article  Google Scholar 

  • Bradstock RA (2010) A biogeographic model of fire regimes in Australia: current and future implications. Glob Ecol Biogeogr 19(2):145–158

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304

    Article  Google Scholar 

  • Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: part 1: theoretical approach. Remote Sens Environ 82(2):188–197

    Article  Google Scholar 

  • Chen VY, Deng W, Yang T, Matthews SA (2012) Geographically weighted quantile regression (GWQR): an application to U.S. mortality data. Geog Anal 44(2):134–150

    Article  Google Scholar 

  • China National Statistical Bureau (2018) China Statistical Yearbook 2018. China’s Statistics Press, Beijing, China

    Google Scholar 

  • Chuvieco E, Cocero D, Riaño D, Martin P et al (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92(3):322–331

    Article  Google Scholar 

  • Cochrane MA (2003) Fire science for rainforests. Nature 421(6926):913–919

    Article  Google Scholar 

  • Collins BM, Kelly M, van Wagtendonk JW, Stephens SL (2007) Spatial patterns of large natural fires in Sierra Nevada wilderness areas. Landscape Ecol 22(4):545–557

    Article  Google Scholar 

  • Collins L, McCarthy G, Mellor A, Newell G, Smith L (2020) Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sens Environ 245:111839

    Article  Google Scholar 

  • Dillon GK, Holden ZA, Morgan P, Crimmins MA, Heyerdahl EK, Luce CH (2011) Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006. Ecosphere 2(12):t130–t133

    Article  Google Scholar 

  • Du X (2006) Research on vegetation leaf water monitoring by remote sensing and spatio-temporai character analysis (In Chinese). Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing

    Google Scholar 

  • Freitas WK, Gois G, Pereira ER, Oliveira Junior JF, Magalhães LMS, Brasil FC, Sobral BS (2020) Influence of fire foci on forest cover in the Atlantic Forest in Rio de Janeiro. Brazil Ecol Indicators 115:106340

    Article  Google Scholar 

  • Gabban A, San-Miguel-Ayanz J, Viegas DX (2008) A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment. Int J Remote Sens 29(19):5677–5687

    Article  Google Scholar 

  • Garcia CV, Woodardl PM, Titus SJ, Adamowicz WL, Let BS (1995) A logit model for predicting the daily occurrence of human caused forest fires. Int J Wildland Fire 2(5):101–111

    Article  Google Scholar 

  • Giglio L, Randerson JT, van der Werf GR (2013) Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J Geophys Res Biogeosci 118(1):317–328

    Article  Google Scholar 

  • Guo F, Selvalakshmi S, Lin F, Wang G, Wang W, Su Z, Liu A (2016) Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest. Can J For Res 46(4):582–594

    Article  Google Scholar 

  • Guo F, Su Z, Wang G, Sun L, Tigabu M, Yang X, Hu H (2017) Understanding fire drivers and relative impacts in different Chinese forest ecosystems. Sci Total Environ 605:411–425

    Article  Google Scholar 

  • Hamilton R, Penny D, Hall TL (2020) Forest, fire & monsoon: investigating the long-term threshold dynamics of south-east Asia’s seasonally dry tropical forests. Quatern Sci Rev 238:106334

    Article  Google Scholar 

  • Husseini R, Aboah DT, Issifu H (2020) Fire control systems in forest reserves: an assessment of three forest districts in the Northern region. Ghana Sci Afr 7:e245

    Google Scholar 

  • Ichoku C, Ellison LT, Willmot KE, Matsui T, Dezfuli AK, Gatebe CK, Wang J, Wilcox EM, Lee J, Adegoke J, Okonkwo C, Bolten J, Policelli FS, Habib S (2016) Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa. Environ Res Lett 11(9):95005

    Article  Google Scholar 

  • Jiménez-Valverde A (2012) Insights into the area under the receiver operating characteristic curve (auc) as a discrimination measure in species distribution modelling. Glob Ecol Biogeogr 21:498–507

    Article  Google Scholar 

  • Jiménez-Muñoz JC, Mattar C, Barichivich J, Santamaría-Artigas A, Takahashi K, Malhi Y, Sobrino JA, Schrier GVD (2016) Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci Rep 6(1):33130

    Article  Google Scholar 

  • Juan JO, José S, Antonio P, Luis G, José M, Pablo M (2009) Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: case study of PROBA/CHRIS data over an agricultural area. Sensors 9(2):768–793

    Article  Google Scholar 

  • Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sens Environ 83(1):244–262

    Article  Google Scholar 

  • Kountouris Y (2020) Human activity, daylight saving time and wildfire occurrence. Sci Total Environ 727:138044

    Article  Google Scholar 

  • Koutsias N, Martínez-Fernández J, Allgower B (2010) Do factors causing wildfires vary in space? evidence from geographically weighted regression. GISci Remote Sens 47(2):221–240

    Article  Google Scholar 

  • Li D (2011) A study class division of meteorological forest fire risk at Xishuangbanna(in Chinese). J Yunnan Univ 33(S1):49–54

    Google Scholar 

  • Liu Z, Yang J, Chang Y, Weisberg PJ, He HS (2012) Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Glob Change Biol 18(6):2041–2056

    Article  Google Scholar 

  • Makowski K, Jaeger EB, Chiacchio M, Wild M, Ewen T, Ohmura A (2009) On the relationship between diurnal temperature range and surface solar radiation in Europe. J Geophys Res 114:D7D

    Google Scholar 

  • Martell DL, Otukol S, Stocks BJ (1987) A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Can J For Res 17(5):394–401

    Article  Google Scholar 

  • Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage 90(2):1241–1252

    Article  Google Scholar 

  • Martínez-Fernández J, Chuvieco E, Koutsias N (2013) Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazard 13(2):311–327

    Article  Google Scholar 

  • Miranda BR, Sturtevant BR, Stewart SI, Hammer RB (2012) Spatial and temporal drivers of wildfire occurrence in the context of rural development in northern Wisconsin. Int J Wildland Fire 21(2):141–154

    Article  Google Scholar 

  • Moritz MA, Batllori E, Bradstock RA, Gill AM, Handmer J, Hessburg PF, Leonard J, McCaffrey S, Odion DC, Schoennagel T, Syphard AD (2014) Learning to coexist with wildfire. Nature 515(7525):58–66

    Article  Google Scholar 

  • Murthy KK, Sinha SK, Kaul R, Vaidyanathan S (2019) A fine-scale state-space model to understand drivers of forest fires in the Himalayan foothills. For Ecol Manage 432:902–911

    Article  Google Scholar 

  • Ning Z, Jin-Ho Y, Jose AM, Ajit S, Carlos AN, Annarita M (2008) Causes and impacts of the 2005 Amazon drought. Environ Res Lett 3(1):14002

    Article  Google Scholar 

  • Nunes AN, Lourenço L, Meira ACC (2016) Exploring spatial patterns and drivers of forest fires in Portugal (1980–2014). Sci Total Environ 573:1190–1202

    Article  Google Scholar 

  • Oliveira S, Pereira JMC, San-Miguel-Ayanz J, Lourenço L (2014) Exploring the spatial patterns of fire density in Southern Europe using geographically weighted regression. Appl Geogr 51:143–157

    Article  Google Scholar 

  • Purevdorj TS, Tateishi R, Ishiyama T (1998) Relationships between percent vegetation cover and vegetation indices. Int J Remote Sens 19(18):3519–3535

    Article  Google Scholar 

  • Rodrigues M, de la Riva J, Fotheringham S (2014) Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl Geogr 48:52–63

    Article  Google Scholar 

  • Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, Woolmer G (2002) The human footprint and the last of the wild. Bioscience 52:891–904

    Article  Google Scholar 

  • Savadogo P, Zida D, Sawadogo L, Tiveau D, Tigabu M, Odén PC (2007) Fuel and fire characteristics in savanna - woodland of West Africa in relation to grazing and dominant grass type. Int J Wildland Fire 16(5):531–539

    Article  Google Scholar 

  • Sow M, Mbow C, Hély C, Fensholt R, Sambou B (2013) Estimation of herbaceous fuel moisture content using vegetation indices and land surface temperature from MODIS data. Remote Sens (Basel, Switzerland) 5(6):2617–2638

    Google Scholar 

  • Stetler KM, Venn TJ, Calkin DE (2010) The effects of wildfire and environmental amenities on property values in northwest Montana, USA. Ecol Econ 69(11):2233–2243

    Article  Google Scholar 

  • Su Z, Tigabu M, Cao Q, Wang G, Hu H, Guo F (2019) Comparative analysis of spatial variation in forest fire drivers between boreal and subtropical ecosystems in China. For Ecol Manage 454:117669

    Article  Google Scholar 

  • Suazo-Ortuño I, Benítez-Malvido J, Marroquín-Páramo J, Soto Y, Siliceo H, Alvarado-Díaz J (2018) Resilience and vulnerability of herpetofaunal functional groups to natural and human disturbances in a tropical dry forest. For Ecol Manage 426:145–157

    Article  Google Scholar 

  • Tao CJ, Huang SS, Brown G (2019) The impact of festival participation on ethnic identity: the case of Yi torch festival. Event Manage 4(4):527–536

    Article  Google Scholar 

  • Tien Bui D, Le HV, Hoang N (2018) GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Eco Inform 48:104–116

    Article  Google Scholar 

  • van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, DeFries RS, Jin Y, van Leeuwen TT (2010) Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys 10(215):11707–11735

    Article  Google Scholar 

  • Viedma O, Chico F, Fernández JJ, Madrigal C, Safford HD, Moreno JM (2020) Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain. Remote Sens Environ 247:111891

    Article  Google Scholar 

  • Vilar del Hoyo L, Martín IM, Martínez Vega F (2011) Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data. Eur J Forest Res 130:983–996

    Article  Google Scholar 

  • Whitmore TC (1990) Tropical rain forests. Oxford University Press, USA

    Google Scholar 

  • Wu W, Zhang LJ (2013) Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in northeastern Puerto Rico. Appl Geogr 37:52–62

    Article  Google Scholar 

  • Xiong Q, Luo X, Liang P, Xiao Y, Xiao Q, Sun H, Pan K, Wang L, Li L, Pang X (2020) Fire from policy, human interventions, or biophysical factors? Temporal–spatial patterns of forest fire in southwestern China. For Ecol Manage 474:118381

    Article  Google Scholar 

  • Xu X, Jia G, Zhang X, Riley WJ, Xue Y (2020) Climate regime shift and forest loss amplify fire in Amazonian forests. Glob Change Biol 26(10):5874–5885

    Article  Google Scholar 

  • Xue J, Zhong W, Xie L, Unkel I (2015) Millennial-scale variability in biomass burning covering the interval ~ 41,000–7050 cal BP in the tropical Leizhou Peninsula (south China). Palaeogeogr Palaeoclimatol Palaeoecol 438:344–351

    Article  Google Scholar 

  • Yu Y, Mao J, Thornton PE, Notaro M, Wullschleger SD, Shi X, Hoffman FM, Wang Y (2020) Quantifying the drivers and predictability of seasonal changes in African fire. Nat Commun 11(1):2893

    Article  Google Scholar 

  • Zhang Y, Niu SJ, Wu DP (2006) The weather disaster and the scheme against the disaster on the Peninsula of Leizhou(in Chinese). Marine Forecast 23(S1):27–33

    Google Scholar 

  • Zhang X, Liu C, Nepal S, Yang C, Dou W, Chen J (2014a) A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud. J Comput Syst Sci 80(5):1008–1020

    Article  Google Scholar 

  • Zhang HJ, Qi PC, Guo GM (2014b) Improvement of fire danger modelling with geographically weighted logistic model. Int J Wildland Fire 23(8):1130–1146

    Article  Google Scholar 

  • Zumbrunnen T, Pezzatti GB, Menéndez P, Bugmann H, Bürgi M, Conedera M (2011) Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. For Ecol Manage 261(12):2188–2199

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project (2018YFE0207800); Young and Middle-aged Teacher Education Research Project of Fujian Province (JAT201273).

Funding

This research was funded by the National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project (2018YFE0207800); Young and Middle-aged Teacher Education Research Project of Fujian Province (JAT201273).

Author information

Authors and Affiliations

  1. Zhangzhou Institute of Technology, Zhangzhou, 363000, China

    Zhangwen Su & Lujia Zheng

  2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Zhangwen Su, Mulualem Tigabu & Futao Guo

  3. Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou, 510520, Guangdong, China

    Sisheng Luo

Authors
  1. Zhangwen Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lujia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sisheng Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mulualem Tigabu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Futao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZS conceived and designed the experiments; FG and ZS analyzed the data of experiments; ZS, LZ, and SL collected the data; ZS wrote the original draft; MT and FG critically reviewed and edited the manuscript; FG supervised the whole research and writing process; FG and ZS provided the support of the funding.

Corresponding author

Correspondence to Futao Guo.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs.

Fig. 7
figure 7

Ripley’s K-function for each year wildfire ignitions from 2001 to 2018 in Xishuangbanna Peninsula is calculated and compared with the theoretical Ripley’s K-function

Full size image

7,

Fig. 8
figure 8

Ripley’s K-function for each year wildfire ignitions from 2001–2018 in Leizhou peninsula is calculated and compared with the theoretical Ripley’s K-function

Full size image

8,

Fig. 9
figure 9

Ripley’s K-function for each month wildfire ignitions in Xishuangbanna Peninsula is calculated and compared with the theoretical Ripley’s K-function

Full size image

9,

Fig. 10
figure 10

Ripley’s K-function of each month wildfire ignitions in Leizhou peninsula is calculated and compared with the theoretical Ripley’s K-function

Full size image

10 and Table

Table 7 Independent variables included in wildfire model development
Full size table

7.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Su, Z., Zheng, L., Luo, S. et al. Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression. Nat Hazards 108, 1317–1345 (2021). https://doi.org/10.1007/s11069-021-04733-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-021-04733-6

Keywords

Search

Navigation