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

Author

Listed:
  • Zhangwen Su

    (Zhangzhou Institute of Technology
    Fujian Agriculture and Forestry University)

  • Lujia Zheng

    (Zhangzhou Institute of Technology)

  • Sisheng Luo

    (Guangdong Academy of Forestry)

  • Mulualem Tigabu

    (Fujian Agriculture and Forestry University)

  • Futao Guo

    (Fujian Agriculture and Forestry University)

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.

Suggested Citation

  • Zhangwen Su & Lujia Zheng & Sisheng Luo & Mulualem Tigabu & Futao Guo, 2021. "Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1317-1345, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04733-6
    DOI: 10.1007/s11069-021-04733-6
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    References listed on IDEAS

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    Cited by:

    1. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Ailin Cabrera & Camilo Ferro & Alejandro Casallas & Ellie Anne López-Barrera, 2024. "Wildfire Scenarios for Assessing Risk of Cover Loss in a Megadiverse Zone within the Colombian Caribbean," Sustainability, MDPI, vol. 16(8), pages 1-35, April.
    3. Chaoxue Tan & Zhongke Feng, 2023. "Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    4. Slobodan Milanović & Zoran Trailović & Sladjan D. Milanović & Eduard Hochbichler & Thomas Kirisits & Markus Immitzer & Petr Čermák & Radek Pokorný & Libor Jankovský & Abolfazl Jaafari, 2023. "Country-Level Modeling of Forest Fires in Austria and the Czech Republic: Insights from Open-Source Data," Sustainability, MDPI, vol. 15(6), pages 1-20, March.

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