IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v116y2023i1d10.1007_s11069-022-05689-x.html
   My bibliography  Save this article

Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data

Author

Listed:
  • Yongcui Lan

    (Yunnan Normal University
    Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan
    Center for Geospatial Information Engineering and Technology of Yunnan Province)

  • Jinliang Wang

    (Yunnan Normal University
    Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan
    Center for Geospatial Information Engineering and Technology of Yunnan Province)

  • Wenying Hu

    (Yunnan Normal University
    Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan
    Center for Geospatial Information Engineering and Technology of Yunnan Province)

  • Eldar Kurbanov

    (Volga State University of Technology)

  • Janine Cole

    (Council for Geoscience)

  • Jinming Sha

    (Fujian Normal University)

  • Yuanmei Jiao

    (Yunnan Normal University
    Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan
    Center for Geospatial Information Engineering and Technology of Yunnan Province)

  • Jingchun Zhou

    (Yunnan Normal University
    Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan
    Center for Geospatial Information Engineering and Technology of Yunnan Province)

Abstract

Wildfires are an important disturbance factor in forest ecosystems. Assessing the probability of forest wildfires can assist in forest wildfire prevention, control, and supervision. The logistic regression model is widely used to forecast the probability, spatial patterns, and drivers of forest wildfires. This study used logistic regression to establish a spatial prediction model for forest wildfire susceptibility, which was applied to evaluate the risk of forest wildfires in Central Yunnan Province (CYP), China. A forest wildfire risk classification was implemented for CYP using forest burn scar data for 2001 to 2020 and the logistic spatial prediction model for forest wildfire susceptibility. Climate, vegetation, topographical, human activities, and location were selected as forest wildfire prediction variables. The results showed that: (1) The distributions of temperature, vegetation coverage, distance to water bodies, distance to roads, and precipitation were positively correlated with the occurrence of forest wildfires. Elevation, relative humidity, the global vegetation moisture index, wind speed, slope, latitude, and distance to residential areas were negatively correlated with the occurrence of forest wildfires. (2) The results of the logistic spatial prediction model for forest wildfire susceptibility showed a good fit to wildfire data, with an overall simulation probability of 81.6%. The optimal threshold for spatial prediction for forest wildfire susceptibility in CYP was determined to be 0.414. A significance level of a selected model variable of

Suggested Citation

  • Yongcui Lan & Jinliang Wang & Wenying Hu & Eldar Kurbanov & Janine Cole & Jinming Sha & Yuanmei Jiao & Jingchun Zhou, 2023. "Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data," 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. 116(1), pages 565-586, March.
  • Handle: RePEc:spr:nathaz:v:116:y:2023:i:1:d:10.1007_s11069-022-05689-x
    DOI: 10.1007/s11069-022-05689-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05689-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-022-05689-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chuvieco, Emilio & Aguado, Inmaculada & Yebra, Marta & Nieto, Héctor & Salas, Javier & Martín, M. Pilar & Vilar, Lara & Martínez, Javier & Martín, Susana & Ibarra, Paloma & de la Riva, Juan & Baeza, J, 2010. "Development of a framework for fire risk assessment using remote sensing and geographic information system technologies," Ecological Modelling, Elsevier, vol. 221(1), pages 46-58.
    2. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    3. Marco Turco & Maria Llasat & Jost Hardenberg & Antonello Provenzale, 2013. "Impact of climate variability on summer fires in a Mediterranean environment (northeastern Iberian Peninsula)," Climatic Change, Springer, vol. 116(3), pages 665-678, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rui Fragoso & Conceição Rego & Vladimir Bushenkov, 2016. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 179-198, December.
    2. Nives Grasso & Andrea Maria Lingua & Maria Angela Musci & Francesca Noardo & Marco Piras, 2018. "An INSPIRE-compliant open-source GIS for fire-fighting management," 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. 90(2), pages 623-637, January.
    3. repec:jss:jstsof:33:c02 is not listed on IDEAS
    4. Guidi, Lionel & Ibanez, Frédéric & Calcagno, Vincent & Beaugrand, Grégory, 2009. "A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data," Ecological Modelling, Elsevier, vol. 220(4), pages 451-461.
    5. Juan Carlos Duque & Raúl Ramos & Jordi Suriñach, 2007. "Supervised Regionalization Methods: A Survey," International Regional Science Review, , vol. 30(3), pages 195-220, July.
    6. Juan Carlos Duque & Raúl Ramos, 2004. "Spanish unemployment: normative versus analytical regionalisation procedures," ERSA conference papers ersa04p7, European Regional Science Association.
    7. Yang Zhang & Samsung Lim & Jason John Sharples, 2017. "Wildfire occurrence patterns in ecoregions of New South Wales and Australian Capital Territory, Australia," 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. 87(1), pages 415-435, May.
    8. José Ramón Rodríguez‐Pérez & Celestino Ordóñez & Javier Roca‐Pardiñas & Daniel Vecín‐Arias & Fernando Castedo‐Dorado, 2020. "Evaluating Lightning‐Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1418-1437, July.
    9. Abolfazl Jaafari & Omid Rahmati & Eric K. Zenner & Davood Mafi-Gholami, 2022. "Anthropogenic activities amplify wildfire occurrence in the Zagros eco-region of western Iran," 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. 114(1), pages 457-473, October.
    10. Juan Carlos Duque & Raúl Ramos, 2004. "Design of homogenous territorial units: a methodological proposal," ERSA conference papers ersa04p6, European Regional Science Association.
    11. Haifeng Bian & Jun Zhang & Ruixue Li & Huanhuan Zhao & Xuexue Wang & Yiping Bai, 2021. "Risk analysis of tripping accidents of power grid caused by typical natural hazards based on FTA-BN model," 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. 106(3), pages 1771-1795, April.
    12. Jerome Apt & Dennis Epple & Fallaw Sowell, 2023. "Forest Fires: Why The Large Year-to-Year Variation in Forests Burned?," NBER Working Papers 31738, National Bureau of Economic Research, Inc.
    13. Wenliang Liu & Shixin Wang & Yi Zhou & Litao Wang & Jinfeng Zhu & Futao Wang, 2016. "Lightning-caused forest fire risk rating assessment based on case-based reasoning: a case study in DaXingAn Mountains of China," 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. 81(1), pages 347-363, March.
    14. Rafaello Bergonse & Sandra Oliveira & Ana Gonçalves & Sílvia Nunes & Carlos Câmara & José Luis Zêzere, 2021. "A combined structural and seasonal approach to assess wildfire susceptibility and hazard in summertime," 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. 106(3), pages 2545-2573, April.
    15. Olga M. Lozano & Michele Salis & Alan A. Ager & Bachisio Arca & Fermin J. Alcasena & Antonio T. Monteiro & Mark A. Finney & Liliana Del Giudice & Enrico Scoccimarro & Donatella Spano, 2017. "Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1898-1916, October.
    16. D’Urso, Pierpaolo & Manca, Germana & Waters, Nigel & Girone, Stefania, 2019. "Visualizing regional clusters of Sardinia's EU supported agriculture: A Spatial Fuzzy Partitioning Around Medoids," Land Use Policy, Elsevier, vol. 83(C), pages 571-580.
    17. De Angelis, Antonella & Bajocco, Sofia & Ricotta, Carlo, 2012. "Modelling the phenological niche of large fires with remotely sensed NDVI profiles," Ecological Modelling, Elsevier, vol. 228(C), pages 106-111.
    18. Zhenbo Wang & Xiaorui Zhang & Bo Xu, 2015. "Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity," Sustainability, MDPI, vol. 7(7), pages 1-19, July.
    19. Calkin, David C. & Finney, Mark A. & Ager, Alan A. & Thompson, Matthew P. & Gebert, Krista M., 2011. "Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making," Forest Policy and Economics, Elsevier, vol. 13(5), pages 378-389, June.
    20. Juan C. Duque & Xinyue Ye & David C. Folch, 2015. "spMorph: An exploratory space-time analysis tool for describing processes of spatial redistribution," Papers in Regional Science, Wiley Blackwell, vol. 94(3), pages 629-651, August.
    21. Maria da Conceição Rego & Rui Fragoso & Vladimir Bushenkov, 2014. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," ERSA conference papers ersa14p218, European Regional Science Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:116:y:2023:i:1:d:10.1007_s11069-022-05689-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.