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

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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.

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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.

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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).

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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

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  1. Zhangwen Su
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  2. Lujia Zheng
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  3. Sisheng Luo
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  4. Mulualem Tigabu
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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.

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Correspondence to Futao Guo.

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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

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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

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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

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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

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10 and Table

Table 7 Independent variables included in wildfire model development
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7.

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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

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

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