Use and performance of the Forest Fire Weather Index to model the risk of wildfire occurrence in the Alpine region

被引:5
|
作者
Beccari, Andrea [1 ]
Borgoni, Riccardo [2 ]
Cazzuli, Orietta [3 ]
Grimaldelli, Roberto [3 ]
机构
[1] Univ Milano Bicocca, Milan, Italy
[2] Univ Milano Bicocca, Dipartimento Econ Metodi Quantitat & Strategie Im, Stat, Milan, Italy
[3] UO Meteoclimatol ARPA Lombardia, Milan, Italy
来源
关键词
Canadian Forest Fire Weather Index; wildfire probability; spatial binary regression; WILDLAND-URBAN INTERFACE; REGRESSION; SCALES;
D O I
10.1177/0265813515596448
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Assessing a territory's fire proneness is fundamental when planning and undertaking effective forest protection and land management. Accurate methods to estimate the risk of fire ignition in natural environments have been proposed over the last decades and digital mapping has been used to identify critical areas. The Canadian Forest Fire Weather Index is a well-known fire danger rating index created and improved during the last 45 years by the Canadian Forest Service. The goal of this paper is twofold. Firstly, we evaluated whether the Forest Fire Weather Index is an adequate instrument to predict fire ignition in Alpine and sub-Alpine areas using quite a large dataset of meteorological and forest fire data collected in the Lombardy region (Northern Italy) between 2003 and 2011. By means of a spatial binary regression model, we demonstrated that Forest Fire Weather Index has a significant impact on the probability of fire ignition. Since this approach allows us to account for other characteristics of the territory in order to provide a more accurate estimate of the spatial wildfire dynamics at a moderately large scale, the second goal of the paper aims at creating a model to assess fire risk occurrence using the Forest Fire Weather Index and land use information. It has been found that ignition can easily occur in large forested areas whereas denser urban areas are less exposed to fire since they usually have no fuels to ignite. Nevertheless, since human activity has a direct impact on fire ignition human presence, it fosters ignition in forested areas. Finally, the model, including these spatial dimensions, has been employed to derive a probability map of fire occurrences at 1.5 km resolution, which is a fundamental instrument to develop optimal prevention and risk management policy plans for the decision maker.
引用
收藏
页码:772 / 790
页数:19
相关论文
共 50 条
  • [41] A Probabilistic Method Predicting Forest Fire Occurrence Combining Firebrands and the Weather-Fuel Complex in the Northern Part of the Daxinganling Region, China
    Sun, Ping
    Zhang, Yunlin
    FORESTS, 2018, 9 (07)
  • [42] Modelling the risk of forest to fire for the Bosomkese Forest Reserve, Ahafo Region, Ghana
    Dadzie, Adams Elias
    Mary, Antwi
    SOUTH AFRICAN JOURNAL OF GEOMATICS, 2021, 10 (01): : 60 - 74
  • [43] Optimal allocation model of forest fire detection towers in protected areas based on fire occurrence risk: Where and how to act?
    Ramalho, Antonio Henrique Cordeiro
    Fiedler, Nilton Cesar
    dos Santos, Alexandre Rosa
    Juvanhol, Ronie Silva
    Peluzio, Telma Machado de Oliveira
    Dias, Henrique Machado
    Pereira, Reginaldo Sergio
    Maffioletti, Fernanda Dalfior
    Araujo, Jamille Silva
    Aragao, Mariana de Aquino
    Guanaes, Gabriel Madeira da Silva
    Biazatti, Leonardo Duarte
    Lucas, Fernanda Moura Fonseca
    CANADIAN JOURNAL OF FOREST RESEARCH, 2024, 54 (01) : 68 - 82
  • [44] Assessment of a New Fire Risk Index for the Atlantic Forest, Brazil
    Delgado, Rafael Coll
    Wanderley, Henderson Silva
    Pereira, Marcos Gervasio
    de Almeida, Andre Quintao
    de Carvalho, Daniel Costa
    Lindemann, Douglas da Silva
    Zonta, Everaldo
    da Costa de Menezes, Sady Junior Martins
    dos Santos, Gilsonley Lopes
    de Santana, Romario Oliveira
    de Souza, Renato Sinquini
    Queiroz dos Santos, Otavio Augusto
    FORESTS, 2022, 13 (11):
  • [45] A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain
    Vilar, Lara
    Woolford, Douglas G.
    Martell, David L.
    Pilar Martin, M.
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2010, 19 (03) : 325 - 337
  • [46] Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections
    He, Yan
    Zhou, Zixuan
    Im, Eun-Soon
    Kwon, Hyun-Han
    WEATHER AND CLIMATE EXTREMES, 2025, 48
  • [47] Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation
    Woo, Hyeyoung
    Chung, Woodam
    Graham, Jonathan M.
    Lee, Byungdoo
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2017, 26 (09) : 789 - 805
  • [48] BALANCED RANDOM FOREST MODEL IS MORE SUITABLE FOR WILDFIRE RISK ASSESSMENT
    Wang, Zili
    He, Binbin
    Lai, Xiaoying
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 3596 - 3599
  • [49] Impacts of future land use/land cover on wildfire occurrence in the Madrid region (Spain)
    Marta Gallardo
    Israel Gómez
    Lara Vilar
    Javier Martínez-Vega
    Maria Pilar Martín
    Regional Environmental Change, 2016, 16 : 1047 - 1061
  • [50] Impacts of future land use/land cover on wildfire occurrence in the Madrid region (Spain)
    Gallardo, Marta
    Gomez, Israel
    Vilar, Lara
    Martinez-Vega, Javier
    Martin, Maria Pilar
    REGIONAL ENVIRONMENTAL CHANGE, 2016, 16 (04) : 1047 - 1061