IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v40y2020i7p1418-1437.html
   My bibliography  Save this article

Evaluating Lightning‐Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

Author

Listed:
  • José Ramón Rodríguez‐Pérez
  • Celestino Ordóñez
  • Javier Roca‐Pardiñas
  • Daniel Vecín‐Arias
  • Fernando Castedo‐Dorado

Abstract

It is widely accepted that the relationship between lightning wildfire occurrence and its influencing factors vary depending on the spatial scale of analysis, making the development of models at the regional scale advisable. In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning‐caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning‐caused fire in any 4 × 4‐km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possible covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitude and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning‐caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long‐term forest fuel and fire management strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:7:p:1418-1437
    DOI: 10.1111/risa.13488
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.13488
    Download Restriction: no
    File URL: https://libkey.io/10.1111/risa.13488?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
    ---><---

    References listed on IDEAS

    as
    1. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    2. José Ramón González‐Olabarria & Blas Mola‐Yudego & Lluis Coll, 2015. "Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain)," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1197-1209, July.
    3. 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.
    4. Kurt J. Beron & Wim P. M. Vijverberg, 2004. "Probit in a Spatial Context: A Monte Carlo Analysis," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 8, pages 169-195, Springer.
    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. Silveira Santos, Luís & Proença, Isabel, 2019. "The inversion of the spatial lag operator in binary choice models: Fast computation and a closed formula approximation," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 74-102.
    2. Vicente Rios & Beatriz Manotas-Hidalgo & Lisa Gianmoena, 2021. "Spatial Inequality, Civil Conflict and Cells: A Dynamic Spatial Probit Approach," Documentos de Trabajo - Lan Gaiak Departamento de Economía - Universidad Pública de Navarra 2110, Departamento de Economía - Universidad Pública de Navarra.
    3. Anna Gloria Billé & Samantha Leorato, 2017. "Quasi-ML estimation, Marginal Effects and Asymptotics for Spatial Autoregressive Nonlinear Models," BEMPS - Bozen Economics & Management Paper Series BEMPS44, Faculty of Economics and Management at the Free University of Bozen.
    4. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    5. Carrión-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2018. "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 77-93.
    6. repec:rre:publsh:v:36:y:2006:i:2:p:140-62 is not listed on IDEAS
    7. repec:asg:wpaper:1048 is not listed on IDEAS
    8. Fang, Di & Richards, Timothy, 2016. "New Maize Variety Adoption in Mozambique: A Spatial Approach," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235388, Agricultural and Applied Economics Association.
    9. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    10. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    11. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian estimation of multivariate panel probits with higher‐order network interdependence and an application to firms' global market participation in Guangdong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.
    12. Ruiz-Martinez, I. & Martinetti, D. & Marraccini, E. & Debolini, M., 2022. "Modeling drivers of farming system trajectories in Mediterranean peri-urban regions: Two case studies in Avignon (France) and Pisa (Italy)," Agricultural Systems, Elsevier, vol. 202(C).
    13. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    14. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    15. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2019. "Contagious exporting and foreign ownership: Evidence from firms in Shanghai using a Bayesian spatial bivariate probit model," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 125-146.
    16. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    17. Jiang, Hai & Tang, Shenfeng & Li, Lifang & Xu, Fangming & Di, Qian, 2022. "Re-examining the Contagion Channels of Global Financial Crises: Evidence from the Twelve Years since the US Subprime Crisis," Research in International Business and Finance, Elsevier, vol. 60(C).
    18. Nobel, Anne & Lizin, Sebastien & Malina, Robert, 2023. "What drives the designation of protected areas? Accounting for spatial dependence using a composite marginal likelihood approach," Ecological Economics, Elsevier, vol. 205(C).
    19. Alivon, Fanny & Guillain, Rachel, 2018. "Urban segregation and unemployment: A case study of the urban area of Marseille – Aix-en-Provence (France)," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 143-155.
    20. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2018. "Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China," Empirical Economics, Springer, vol. 55(1), pages 193-211, August.
    21. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Other publications TiSEM d63bf400-7ff2-4a1c-8067-1, Tilburg University, School of Economics and Management.
    22. Corral, Paul & Radchenko, Natalia, 2017. "What’s So Spatial about Diversification in Nigeria?," World Development, Elsevier, vol. 95(C), pages 231-253.

    More about this item

    Statistics

    Access and download statistics

    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:wly:riskan:v:40:y:2020:i:7:p:1418-1437. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.