A spatially explicit integrative model for estimating the risk of wildfire impacts in New-Caledonia

References

• Jaffre T, Morat P, Veillon JM, et al. Changements dans la végétation de la Nouvelle-Calédonie au cours du tertiaire: la végétation et la flore des roches ultrabasiques. Bulletin du Muséum national d’Histoire naturelle. Section B. 1988;9:365–391.
   Google Scholar
• Morat P, Jaffré T, Tronchet F, et al. Le référentiel taxonomique Florical et les caractéristiques de la flore vasculaire indigène de la Nouvelle-Calédonie. Adansonia. 2012;34:179–221. Available from: http://www.documentation.ird.fr/hor/fdi:010058851
   Google Scholar
• Stevenson J. A late-Holocene record of human impact from the southwest coast of New Caledonia. Holocene. 2004;14:888–898. DOI:10.1191/0959-683604hl755rp
   Google Scholar
• Stott P. Combustion in tropical biomass fires: a critical review. Prog Phys Geogr. 2000;24:355–377. DOI:10.1177/030913330002400303
   Google Scholar
• Ibanez T, Hély C, Gaucherel C. Sharp transitions in microclimatic conditions between savanna and forest in New Caledonia: Insights into the vulnerability of forest edges to fire. Austral Ecol. 2013;38:680–687. DOI:10.1111/aec.12015/abstract
   Google Scholar
• Hely C, INC. INC: Incendies et biodiversité des écosystèmes en Nouvelle-Calédonie. Projet ANR-07-BDIV-08. Programme BDIV 2007. 2012. Available from: oeil.nc/cdrn/index.php/files/bibliographie/9690
   Google Scholar
• Gomez C, Mangeas M, Curt T, et al. Wildfire risk for main vegetation units in a biodiversity hotspot: modeling approach in new caledonia, south pacific. Ecol Evol. 2015;5:377–390. DOI:10.1002/ece3.1317
   Google Scholar
• Finney MA. An overview of flammap fire modeling capabilities. Conference Proceedings. Fire Station, Portland, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station; 2006. p. 213–220. Available from: https://www.treesearch.fs.fed.us/pubs/25948
   Google Scholar
• Myers N, Mittermeier RA, Mittermeier CG, et al. Biodiversity hotspots for conservation priorities. Nature. 2000;403:853–858. Available from: http://www.nature.com/nature/journal/v403/n6772/full/403853a0.html
   Google Scholar
• Mittermeier RA, Turner WR, Larsen FW, et al. Global biodiversity conservation: the critical role of hotspots. Biodiver Hotspots. 2011;1:3–22.
   Google Scholar
• Giglio L, Loboda T, Roy DP, et al. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens Environ. 2009;113:408–420. Available from: http://www.sciencedirect.com/science/article/pii/S0034425708003180
   Google Scholar
• Bernier PY, Gauthier S, Jean PO, et al. Mapping local effects of forest properties on fire risk across canada. Forests. 2016;7. Available from: http://www.mdpi.com/1999-4907/7/8/157
   Google Scholar
• Wagner CEV. Development and structure of the canadian forest fireweather index system. Can For Serv Forestry Tech Rep. 1987;35:1–49.
   Google Scholar
• Didelez V. Causality: models, reasoning and inference. Judea Pearl: Cambridge University Press, New York (NY); 2000. No. of pages: 384. Price: $39.95$. ISBN 0-521-77362-8, Statistics Med. 2002;21:2292--2293. DOI:10.1002/sim.1006/abstract
   Google Scholar
• Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997;29:131–163. Available from: http://link.springer.com/article/10.1023/A:1007465528199
   Google Scholar
• Darwiche A. Modeling and reasoning with Bayesian networks. Cambridge University Press; 2009. Available from: https://books.google.com/books?id=HsGrXdZMXg4C
   Google Scholar
• Aggarwal, Charu C. Managing and mining uncertain data. Boston (MA): Springer; 2009. p. 36. chap. 1.
   Google Scholar
• Chen R, Herskovits EH. A Bayesian network classifier with inverse tree structure for voxelwise magnetic resonance image analysis. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD ’05. New York, NY, USA: ACM; 2005. p. 4–12. DOI:10.1145/1081870.1081875
   Google Scholar
• Nelder JA, Wedderburn RWM. Generalized linear models. J R Stat Soc Ser A (General). 1972;135:370–384. Available from: http://www.jstor.org/stable/2344614
   Google Scholar
• Rokach L. Data mining with decision trees: theory and applications. World Scientific; 2008. Available from: https://books.google.it/books?id=GlKIIR78OxkC
   Google Scholar
• Vapnik VN. The nature of statistical learning theory. New York (NY): Springer-Verlag; 1995.
   Google Scholar
• Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinf. 2006;7:91–98.
   Google Scholar
• Morris MD. Factorial sampling plans for preliminary computational experiments. Technometrics. 1991;33:161–174. Available from: http://www.jstor.org/stable/1269043
   Google Scholar
• Campolongo F, Cariboni J, Saltelli A. An effective screening design for sensitivity analysis of large models. Environ Model Softw. 2007;22:1509–1518. DOI:10.1016/j.envsoft.2006.10.004
   Google Scholar
• Finney MA. Fire growth using minimum travel time methods. Can J Forest Res. 2002;32:1420–1424. DOI:10.1139/x02-068
   Google Scholar
• Finney M. FARSITE: fire area simulator – model devel opment and evaluation. USDA For. 1998;47:1–52.
   Google Scholar
• Finney MA. The challenge of quantitative risk analysis for wildland fire. Forest Ecol Manag. 2005;211:97–108.
   Google Scholar
• Whelan R. The Ecology of fire. Cambridge University Press; 1995. Available from: https://books.google.com/books?id=AK2zCMbXJrQC
   Google Scholar
• Dlamini WM. A bayesian belief network analysis of factors influencing wildfire occurrence in swaziland. Environ Model Software. 2010;25:199–208. Available from: http://www.sciencedirect.com/science/article/pii/S1364815209002035
   Google Scholar
• Archibald S, Roy DP, Van Wilgen BW, et al. What limits fire? an examination of drivers of burnt area in southern africa. Global Change Biol. 2009;15:613–630. DOI:10.1111/j.1365-2486.2008.01754.x
   Google Scholar
• Dlamini WM. Application of bayesian networks for fire risk mapping using gis and remote sensing data. GeoJournal. 2011;76:283–296. Available from: http://www.jstor.org/stable/41148456
   Google Scholar
• Jahdi R, Salis M, Darvishsefat AA, et al. Evaluating fire modelling systems in recent wildfires of the golestan national park, iran. Forestry Int J Forest Res. 2016;89:136–149. DOI:10.1093/forestry/cpv045
   Google Scholar
• Moron V, Barbero R, Mangeas M, et al. Prediction of september-december fire in new caledonia (southwestern pacific) using july nino-4 sea surface temperature index. J Appl Meteorol Climatol. 2013;52:623–633.
   Google Scholar
• Dumas P, Toussaint M, Herrenschmidt J, et al. Le risque de feux de brousse sur la grande terre de nouvelle-calédonie: l’homme responsable, mais pas coupable. Revue Géographique de l’Est. 2013;53. Available from: http://rge.revues.org/4598
   Google Scholar
• Estes BL, Knapp EE, Skinner CN, et al. Factors influencing fire severity under moderate burning conditions in the klamath mountains, northern california. USA, Ecosphere. 2017;8:e01794–n/a. DOI:10.1002/ecs2.1794
   Google Scholar
• Holden ZA, Morgan P, Evans JS. A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area. Forest Ecol Manage. 2009;258:2399–2406. Available from: http://www.sciencedirect.com/science/article/pii/S0378112709005799
   Google Scholar