Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain

References

• Abbott KN, Leblon B, Staples GC, Maclean DA, Alexander ME. 2007. Fire danger monitoring using RADARSAT‐1 over northern boreal forests. Int J Remote Sens. 28(6):1317–1338. https://doi.org/10.1080/01431160600904956
   Google Scholar
• Agencia Estatal de Meteorología (AEMET). 2012. Informe mensual climatológico. Octubre de 2012. Available at:http://www.aemet.es/documentos/es/serviciosclimaticos/vigilancia_clima/resumenes_climat/mensuales/2012/res_mens_clim_2012_10.pdf
   Google Scholar
• Allgöwer B, Carlson JD, van Wagtendonk JW. 2003. Introduction to fire danger rating and remote sensing — will remote sensing enhance wildland fire danger rating? In: Chuvieco E, editor. Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific Publishing; pp. 1–19. https://doi.org/10.1142/9789812791177_0001
   Google Scholar
• Amatulli G, Rodrigues MJ, Trombetti M, Lovreglio R. 2006. Assessing long-term fire risk at local scale by means of decision tree technique. J Geophys Res Biogeosci. 111:G04S05. https://doi.org/10.1029/2005JG000133
   Google Scholar
• Archibald S, Lehmann CER, Gomez-Dans JL, Bradstock RA. 2013. Defining pyromes and global syndromes of fire regimes. Proc Natl Acad Sci USA. 110(16):6442–6447. https://doi.org/10.1073/pnas.1211466110
   Google Scholar
• Bar Massada A, Syphard AD, Stewart SI, Radeloff VC. 2013. Wildfire ignition-distribution modelling: a comparative study in the Huron? Manistee National Forest, Michigan, USA. Int J Wildland Fire. 22(2):174. https://doi.org/10.1071/WF11178
   Google Scholar
• Calderón Cortés D, Mateo Fernández JF. 2017. Prevención de incendios en labores de recolección de cereal en Castilla-La Mancha. 7° Congreso Forestal Español. Gestion del monte: servicios ambientales y bioeconomía. Available at: http://7cfe.congresoforestal.es/sites/default/files/actas/7CFE01-439.pdf.
   Google Scholar
• Carmona A, González ME, Nahuelhual L, Silva J. 2012. Spatio-temporal effects of human drivers on fire danger in Mediterranean Chile. Bosque (Valdivia). 33(3):31–32. https://doi.org/10.4067/S0717-92002012000300016
   Google Scholar
• Chaparro D, Piles M, Vall-Llossera M, Camps A. 2016. Surface moisture and temperature trends anticipate drought conditions linked to wildfire activity in the Iberian Peninsula. Eur J Remote Sens. 49(1):955–971. https://doi.org/10.5721/EuJRS20164950
   Google Scholar
• Chelli S, Maponi P, Campetella G, Monteverde P, Foglia M, Paris E, Lolis A, Panagopoulos T. 2015. Adaptation of the Canadian fire weather index to Mediterranean forests. Nat Hazards. 75(2):1795–1810. https://doi.org/10.1007/s11069-014-1397-8
   Google Scholar
• Chen F, Du Y, Niu S, Zhao J. 2015. Modeling forest lightning fire occurrence in the Daxinganling mountains of Northeastern China with MAXENT. Forests. 6(12):1422–1438. https://doi.org/10.3390/f6051422
   Google Scholar
• Chowdhury EH, Hassan QK. 2013. Use of remote sensing-derived variables in developing a forest fire danger forecasting system. Nat Hazards. 67(2):321–334. https://doi.org/10.1007/s11069-013-0564-7
   Google Scholar
• Chowdhury EH, Hassan QK. 2015. Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sens. 7(3):2431–2448. https://doi.org/10.3390/rs70302431
   Google Scholar
• Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez J, Martín S, Ibarra P, et al. 2010. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model. 221(1):46–58. https://doi.org/10.1016/j.ecolmodel.2008.11.017
   Google Scholar
• Chuvieco E, Aguado I, Jurdao S, Pettinari ML, Yebra M, Salas J, Hantson S, de la Riva J, Ibarra P, Rodrigues M, et al. 2014. Integrating geospatial information into fire risk assessment. Int J Wildland Fire. 23(5):606. https://doi.org/10.1071/WF12052
   Google Scholar
• Chuvieco E, Allgöwer B, Salas J. 2003. Integration of physical and human factors in fire danger assessment. In: Chuvieco E, editor. Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific Publishing; pp. 197–218. https://doi.org/10.1142/9789812791177_0007
   Google Scholar
• Costafreda-Aumedes S, Comas C, Vega-Garcia C. 2017. Human-caused fire occurrence modelling in perspective: a review. Int J Wildland Fire. 26(12):983. https://doi.org/10.1071/WF17026
   Google Scholar
• Costafreda-Aumedes S, Vega-Garcia C, Comas C. 2018. Improving fire season definition by optimized temporal modelling of daily human-caused ignitions. J Environ Manage. 217:90–99.
   Google Scholar
• Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 17(1):43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
   Google Scholar
• Food and Agriculture Organization (FAO). 2006. Fire management: voluntary guidelines. Principles and strategic actions. Fire Management Working Paper 17. Rome, Italy: FAO. Available at http://www.fao.org/docrep/pdf/009/j9255e/j9255e00.pdf
   Google Scholar
• Franklin J. 2010. Mapping species distributions. New York: Cambridge University Press.
   Google Scholar
• Garcia CV, Woodard PM, Titus SJ, Adamowicz WL, Lee BS. 1995. A logit model for predicting the daily occurrence of human caused forest-fires. Int J Wildland Fire. 5(2):101. https://doi.org/10.1071/WF9950101
   Google Scholar
• Guangmeng G, Mei Z. 2004. Using MODIS land surface temperature to evaluate forest fire risk of Northeast China. IEEE Geosci Remote Sens Lett. 1(2):98–100. https://doi.org/10.1109/LGRS.2004.826550
   Google Scholar
• Hanley JA, McNeil BJ. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1):29–36.
   Google Scholar
• Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DMJS. 2015. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6:7537. https://doi.org/10.1038/ncomms8537
   Google Scholar
• Koutsias N, Allgöwer B, Kalabokidis K, Mallinis G, Balatsos P, Goldammer J. 2016. Fire occurrence zoning from local to global scale in the European Mediterranean basin: implications for multi-scale fire management and policy. iForest. 9(2):195–204. https://doi.org/10.3832/ifor1513-008
   Google Scholar
• Leone V, Koutsias N, Martínez J, Vega-García C, Allgöwer B, Lovreglio R. 2003. The human factor in fire danger assessment. In: Chuvieco E, editor. Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific Publishing; pp. 143–196.
   Google Scholar
• Leone V, Lovreglio R, Martín MP, Martínez J, Vilar L. 2009. Human factors of fire occurrence in the Mediterranean. In: Chuvieco E, editor. Earth observation of wildland fires in Mediterranean ecosystems. Berlin: Springer Berlin Heidelberg; pp. 149–170. https://doi.org/10.1007/978-3-642-01754-4_11
   Google Scholar
• Maingi JK, Henry MC. 2007. Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. Int J Wildland Fire. 16(1):23. https://doi.org/10.1071/WF06007
   Google Scholar
• Martínez J, Chuvieco E, Martín MP. 2004. Estimating human risk factors in wildland fires in Spain using logistic regression. International symposium on fire economics, planning and policy: A global vision. April 9–22; Córdoba: University of Córdoba.
   Google Scholar
• Martínez J, Vega-Garcia C, Chuvieco E. 2009. Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage. 90(2):1241–1252. https://doi.org/10.1016/j.jenvman.2008.07.005
   Google Scholar
• Martínez de Aragón J, Riera P, Giergiczny M, Colinas C. 2011. Value of wild mushroom picking as an environmental service. Forest Pol Econ. 13(6):419–424. https://doi.org/10.1016/j.forpol.2011.05.003
   Google Scholar
• Martínez-Fernández J, Chuvieco E, Koutsias N. 2013. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Syst Sci. 13(2):311–327. https://doi.org/10.5194/nhess-13-311-2013
   Google Scholar
• McCune B, Grace JB, Urban DL. 2002. Analysis of ecological communities. Glenden Beach: MJM Software Design.
   Google Scholar
• Ministerio de Agricultura Pesca y Alimentación (MAPA). 2006. Análisis del Parque Nacional de Tractores Agrícolas. 2005–2006. Madrid (Spain): Secretaría General de Agricultura y Alimentación. Available at: https://www.mapa.gob.es/va/agricultura/publicaciones/parque_tractores_tcm39-57883.pdf
   Google Scholar
• Ministerio de Agricultura y Pesca Alimentación y Medio Ambiente (MAPAMA). 2012. Los Incendios Forestales en España. Decenio 2001–2010. Madrid (Spain): Área de Defensa contra lncendios Forestales (ADCIF) del Ministerio de Agricultura, Alimentación y Medio Ambiente. Available at: http://www.prodetur.es/prodetur/AlfrescoFileTransferServlet?action=download&ref=a318783c-223d-4540-86ed-3837e0841679
   Google Scholar
• Moreno MV, Malamud BD, Chuvieco EA. 2011. Wildfire frequency-area statistics in Spain. Procedia Environ Sci. 7:182–187. https://doi.org/10.1016/j.proenv.2011.07.032
   Google Scholar
• National Wildfire Coordinating Group (NWCG). 2018. Glossary of wildland fire terminology. Available at: https://www.nwcg.gov/glossary/a-z.
   Google Scholar
• Ordóñez C, Saavedra A, Rodríguez-Pérez JR, Castedo-Dorado F, Covián E. 2012. Using model-based geostatistics to predict lightning-caused wildfires. Environ Model Softw. 29(1):44–50. https://doi.org/10.1016/j.envsoft.2011.10.004
   Google Scholar
• Padilla M, Vega-García C. 2011. On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. Int J Wildland Fire. 20(1):46. https://doi.org/10.1071/WF09139
   Google Scholar
• Parisien M-A, Moritz MA. 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecol Monogr. 79(1):127–154. https://doi.org/10.1890/07-1289.1
   Google Scholar
• Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A. 2007. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J Biogeogr. 34:102–117.
   Google Scholar
• Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecol Model. 190(3–4):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
   Google Scholar
• Phillips SJ, Dudík M, Schapire RE. 2004. A maximum entropy approach to species distribution modeling. Twenty-First International Conference on Machine Learning - ICML ’04. ACM Press, New York, NY; p. 83. https://doi.org/10.1145/1015330.1015412
   Google Scholar
• Renard Q, Pélissier R, Ramesh BR, Kodandapani N. 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire. 21(4):368. https://doi.org/10.1071/WF10109
   Google Scholar
• Rodrigues M, de la Riva J. 2014a. An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environ Model Softw. 57:192–201. https://doi.org/10.1016/j.envsoft.2014.03.003
   Google Scholar
• Rodrigues M, de la Riva J. 2014b. Assessing the effect on fire risk modeling of the uncertainty in the location and cause of forest fires. In: Advances in forest fire research. Coimbra: Imprensa da Universidade de Coimbra; pp. 1061–1072. https://doi.org/10.14195/978-989-26-0884-6_116
   Google Scholar
• Rodrigues M, de la Riva J, Fotheringham S. 2014. Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl Geogr. 48:52–63. https://doi.org/10.1016/j.apgeog.2014.01.011
   Google Scholar
• Rodrigues M, Jiménez A, de la Riva J. 2016. Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain. Nat Hazards. 84(3):2049–2070. https://doi.org/10.1007/s11069-016-2533-4
   Google Scholar
• Salis M, Ager AA, Finney MA, Arca B, Spano D. 2014. Analyzing spatiotemporal changes in wildfire regime and exposure across a Mediterranean fire-prone area. Nat Hazards. 71(3):1389–1418. https://doi.org/10.1007/s11069-013-0951-0
   Google Scholar
• San Miguel-Ayanz J, Carlson JD, Alexander M, Tolhurst K, Morgan G, Sneeuwjagt R, Dudley M. 2003. Current methods to assess fire danger potential. In: Chuvieco E, editor. Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific.
   Google Scholar
• San-Miguel Ayanz J, Camia A. 2009. Forest fires at a glance: facts, figures and trends in the EU. In: Birot Y, editor. Living with wildfires: what science can tell us. A contribution to the science-policy dialogue. Joensuu, Finland: European Forest Institute; pp. 11–18.
   Google Scholar
• Sesnie SE, Gessler PE, Finegan B, Thessler S. 2008. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sens Environ. 112(5):2145–2159. https://doi.org/10.1016/j.rse.2007.08.025
   Google Scholar
• Syphard AD, Clarke KC, Franklin J. 2007. Simulating fire frequency and urban growth in southern California coastal shrublands. Landscape Ecol. 22(3):431–445. https://doi.org/10.1007/s10980-006-9025-y
   Google Scholar
• Tardío J, Pardo-de-Santayana M, Morales R. 2006. Ethnobotanical review of wild edible plants in Spain. Bot J Linn Soc. 152(1):27–71. https://doi.org/10.1111/j.1095-8339.2006.00549.x
   Google Scholar
• Vasconcelos MJP, Silva S, Tomé M, Alvim M, Pereira JMC. 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogramm Eng Remote Sens. 5:101–111.
   Google Scholar
• Vélez R. 2001. Fire situation in Spain. In: Goldammer JG, Mutch RW, Pugliese P, editors. Global forest fire assessment 1990-2001. Roma: FAO.
   Google Scholar
• Verdú F, Salas J, Vega-García C. 2012. A multivariate analysis of biophysical factors and forest fires in Spain, 1991–2005. Int J Wildland Fire. 21(5):498–509.
   Google Scholar
• Vilar L, Camia A, San-Miguel-Ayanz J, Martín MP. 2016. Modeling temporal changes in human-caused wildfires in Mediterranean Europe based on land use-land cover interfaces. Forest Ecol Manage. 378:68–78. https://doi.org/10.1016/j.foreco.2016.07.020
   Google Scholar
• Vilar L, Gómez I, Martínez-Vega J, Echavarría P, Riaño D, Martín MP. 2016. Multitemporal modelling of socio-economic wildfire drivers in central Spain between the 1980s and the 2000s: comparing generalized linear models to machine learning algorithms. PLoS One. 11(8):e0161344.
   Google Scholar
• Vilar del Hoyo L, Martín Isabel MP, Martínez Vega J. 2008. Empleo de técnicas de regresión logística para la obtención de modelos de riesgo humano de incendio forestal a escala regional. Boletín de la Asociación de Geógrafos Españoles, 47(2008): 5–29.
   Google Scholar
• Zhou, X-H, McClish DK, Obuchowski NA. 2009. Statistical methods in diagnostic medicine. Vol. 569. John Wiley & Sons. Hoboken, New Jersey.
   Google Scholar
• Zumbrunnen T, Pezzatti GB, Menéndez P, Bugmann H, Bürgi M, Conedera M. 2011. Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. Forest Ecol Manage. 261(12):2188–2199. https://doi.org/10.1016/j.foreco.2010.10.009
   Google Scholar