Assessing wildfire occurrence probability in Pinus pinaster Ait. stands in Portugal
Abstract
Maritime pine (Pinus pinaster Ait.) is an important conifer from the western Mediterranean Basin extending over 22%of the forest area in Portugal. In the last three decades nearly 4% of Maritime pine area has been burned by wildfires. Yetno wildfire occurrence probability models are available and forest and fire management planning activities are thus carriedout mostly independently of each other. This paper presents research to address this gap. Specifically, it presents a modelto assess wildfire occurrence probability in regular and pure Maritime pine stands in Portugal. Emphasis was in developinga model based on easily available inventory data so that it might be useful to forest managers. For that purpose, data fromthe last two Portuguese National Forest Inventories (NFI) and data from wildfire perimeters in the years from 1998 to 2004and from 2006 to 2007 were used. A binary logistic regression model was build using biometric data from the NFI. Biometricdata included indicators that might be changed by operations prescribed in forest planning. Results showed that the probabilityof wildfire occurrence in a stand increases in stand located at steeper slopes and with high shrubs load while it decreaseswith precipitation and with stand basal area. These results are instrumental for assessing the impact of forestmanagement options on wildfire probability thus helping forest managers to reduce the risk of wildfires.
Downloads
References
Atlas do Ambiente [on line]. Available in: http://sniamb.apambiente.pt/webatlas/ [April 5th 2011].
Botequim B., Borges P., Carreiras J., Oliveira M. M. O., Borges J., 2009 Development of a shrub growth model in understory conditions (preliminary model), Technical Report — 7, FORCHANGE, Instituto Superior de Agronomia, Lisboa.
Brown J.K., 1971. A planar intersect method for sampling fuel volume and surface area. Forest Sciences 17(1) : 96-102.
Burnham K. P., Anderson D. R., 2003. Model selection and multi model in ference: A practical information-theoretic approach. New York: Springer.
Careay H., Shumann, M., 2003. Modifying Wildfire Behaviour — The Effectiveness of Fuel Treatments: The Status of Our Knowledge, National Community Forestry Center, Southwest Region Working Paper, 31 pp.
Carreiras J. M. B., Pereira J. M. C., 2006. An inductive fire risk map for Portugal. V International Conference on Forest Fire Research, Portugal.
Castro F.X., Tudela A., Sebastià M.T., 2003, Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain), Agricultural and Forest Meteorology 116 : 49-59. http://dx.doi.org/10.1016/S0168-1923(02)00248-4
Catry F, X., Rego F. C., Bacao F., Moreira F., 2009. Modelling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire 2009, 18, 921-931. http://dx.doi.org/10.1071/WF07123
Catry F. X., Rego F. C., Moreira, F., Bacao, F., 2008. Characterizing and modelling the spatial patterns of wildfire ignitions in Portugal: fire initiation and resulting burned area. In: Eds: de la Heras, J., Brebbia, C. A., Viegas, D., and Leone, V., Modelling, Monitoring and Management of Forest Fires. WIT Transactions on Ecology and the Environment, vol 199: 213-221. DOI: 10.2495/FIVA080221.
Ceccato P., Gobron N., Flasse S., Pinty B., Tarantola S., 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 Theoretical approach, Remote Sensing of Environment 82:188-197. http://dx.doi.org/10.1016/S0034-4257(02)00037-8
Chuvieco E., Aguado I., Yebra M., Nieto H., Salas J., Martín M. P., Vilar L., Martínez J., Martín S., Ibarra P., Riva J., Baeza J., Rodríguez F., Molina J. R., Herrera M. A., Zamora R., 2010, Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecological Modelling 221: 46-58 . http://dx.doi.org/10.1016/j.ecolmodel.2008.11.017
Cumming S.G. 2001. Forest type and wildfire in the Alberta boreal mixedwood: what do fires burn? Ecological Applications 11: 97-110. http://dx.doi.org/10.1890/1051-0761(2001)011[0097:FTAWIT]2.0.CO;2
DGRF., 2006. Resultados do IFN 2005/2006, Lisboa, 70 pp. [In Portuguese].
Durão R. M., Pereira M. J., Branquinho C., Soares A., 2010. Assessing spatial uncertainty of the Portuguese fire risk through direct sequential simulation, Ecological Modelling 221 : 27-33. http://dx.doi.org/10.1016/j.ecolmodel.2009.09.004
Falcão A., 1997. Dunas. — A growth model for the National Forest of Leiria, in Empirical and process-based models for forest tree and stand growth simulation, 20-26 September, Oeiras, Portugal.
Fernandes P. M., Rigolot E., 2007. The fire ecology and management of maritime pine (Pinus pinaster Ait.), Forest Ecology and Management 241: 1–13. http://dx.doi.org/10.1016/j.foreco.2007.01.010
Fernandes P., Luz, A., Loureiro, C., 2010. Changes in wildfire severity from maritime pine woodland to contiguous forest types in the mountain of northwestern Portugal, Forest Ecology and Management 260: 883-892. http://dx.doi.org/10.1016/j.foreco.2010.06.008
Finney M. A., 2005. The challenge of quantitative risk analysis for wildland fire, Forest Ecology and Management 211: 97-108. http://dx.doi.org/10.1016/j.foreco.2005.02.010
Garcia-Gonzalo J., Pukkala T., Borges J.G., 2011. Integrating fire risk in stand management scheduling. An application to Maritime pine stands in Portugal. Annals of Operational Research. DOI: 10.1007/s10479-011-0908-1. http://dx.doi.org/10.1007/s10479-011-0908-1
González J.R., Palia M., Pukkala T. 2006. A fire probability model for forest stands in Catalonia (north-east Spain). Annals of Forest Science 63: 1-8.
González J. R, Pukkala T., 2007. Characterization of forest fires in Catalonia (northeast Spain). European Journal of Forest Research. 126(3):421-429. http://dx.doi.org/10.1007/s10342-006-0164-0
González-Olabarria J. R., Palahí M., Trasobares A., 2008. Optimising the management of Pinus nigra Arn. Stands under endogenous risk of fire in Catalonia. Investigación Agraria: Sistemas y Recursos Forestales 2008 17(1), 10-17.
González-Olabarria J. R., Pukkala T., 2011. Integrating fire risk considerations in landscape-level forest planning, Forest Ecology and Management, 261(2): 278-287. http://dx.doi.org/10.1016/j.foreco.2010.10.017
Hanewinkel M., Peltola H., Soares P., Gonzalez-Olabarria J.R., 2010. Advanced models for the risk of storm and fire to European forests and their integration into simulation and decision support tools. Forest Systems. 19 (SI), 30-47.
Hardy C.C., 2005. Wildland fire hazard and risk: Problems, definitions, and context. Forest Ecology and Management 211:73–82. http://dx.doi.org/10.1016/j.foreco.2005.01.029
Hosmer D.W., Lemeshow S., 2000. Applied Logistic Regression, Second Edition, Wiley Series in Probability and Mathematical Statistics, New York, 307 pp. http://dx.doi.org/10.1002/0471722146
Jactel H., Nicoll B.C., Branco M., González-Olabarria J.R., Grodzki W., Långström B., Moreira F., Netherer S., 2009. The influences of forest stand management on biotic and abiotic risks of damage. Annals of Forest Science 66 (2009) 701. http://dx.doi.org/10.1051/forest/2009054
Jalkanen A, Mattila U., 2000 Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data. Forest Ecology and Management 135: 315-330. http://dx.doi.org/10.1016/S0378-1127(00)00289-9
Kleinbaum D.G., 1994. Logistic regression: a self- learning text. Stat Methods Med Res.1996; 5: 103-104.
Kozak A., Kozak R., 2003. Does cross validation provide additional information in the evaluation of regression models? Canadian Journal of Forest Research 33(6): 976-987 . http://dx.doi.org/10.1139/x03-022
Lohmander P, Helles F., 1987 Windthrow probability as a function of stand characteristics and shelter, Scandinavian Journal Forest Research. 2 227-238. http://dx.doi.org/10.1080/02827588709382460
Marques S., Borges J.G., Garcia-Gonzalo J., Moreira F., Carreiras J.M.B., Oliveira M.M., Cantarinha A., Botequim B. and Pereira J. M. C., 2011. Characterization of wildfires in Portugal. European Journal of Forest Research, 130(5): 775-784 DOI 10.1007/s10342-010-0470-4. http://dx.doi.org/10.1007/s10342-010-0470-4
Mercer D.E, Haigth R.G, Prestemon J.P., 2008. Analyzing trade-offs between fuels management, suppression, and damages from wildfire. In T.P. Holes et al. (eds). The Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species. Forestry Sciences 79 (IV): 247-272.
Monserud R., Serba H., 1999. Modeling individual tree mortality for Austrian tree species, Forest Ecology and Management 113 (2/3):109-123. http://dx.doi.org/10.1016/S0378-1127(98)00419-8
Moreira F., Vaz P., Catry F. X., Silva J.S., 2009. Regional variations in wildfire susceptibility of land-cover types in Portugal: implications for landscape management to minimize fire hazard, International Journal of Wildland Fire 18: 563-574. http://dx.doi.org/10.1071/WF07098
Neter J., and Maynes E.S. 1970. On the appropriateness of the correlation coefficient with a 0,1 dependent variable. J. Am. Stat. Assoc. 65: 501-509.
NIR, 2009. Portuguese National Inventory Report on Greenhouse Gases,1990-2007 submitted under the United Nations Framework Convention on Climate Change and the Kyoto Protocol, Portuguese Environmental Agency, Amadora. [In Portuguese].
Nunes C.S., Vasconcelos J., Pereira J.M.C., Dasgupta N., Alldredge R.J. & Rego F.C., 2005. Land cover type and fire in Portugal: do fires burn land cover selectively?, Landscape Ecology, 20, 661-673. http://dx.doi.org/10.1007/s10980-005-0070-8
Pasalodos-Tato M., Pukkala T. & Rojo Alboreca A., 2010. Optimal management of Pinus pinaster in Galicia (northwestern Spain) under endogenous risk of fire. International Journal of Wildland Fire. In press. http://dx.doi.org/10.1071/WF08150
Pereira M. G., Trigo R. M., Camara C. C. Pereira J. M. C., Leite S. M., 2005. Synoptic patterns associated with large summer forest fires in Portugal, Agricultural and Forest Meteorology 129: 11-25. http://dx.doi.org/10.1016/j.agrformet.2004.12.007
Pereira J.M.C., Santos M.T.N., 2003. Áreas queimadas e risco de incêndio em Portugal, Direcção Geral das Florestas, Lisboa, 65 pp.
Pereira J.M.C., Carreiras J.M.B., Silva J.M.N., Vasconcelos M.J., 2006. Alguns conceitos básicos sobre fogos rurais em Portugal, in: Eds: Pereira J.S., Pereira J.M.C., Rego F.C., Silva J.M.N., Silva T.P., Incêndios Florestais em Portugal, ISAPress, Lisboa, 133:161. [In Portuguese].
Peterson D.L., Johnson M.C., Agee J.K., Jain T.B., Mckenzie D., and Reinhard E.D., 2005. Forest structure and fire hazard in dry forests of the Western United States. PNWGTR- 628, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, Oregon, 30 pp.
Preisler H.K., Brillinger D.R., Burgan R.E., Benoit J.W., 2004. Probability based models for estimation of wildfire risk, International Journal of Wildland Fire 13: 133. http://dx.doi.org/10.1071/WF02061
Reed W.J., 1994. Estimating the historic probability os standreplacement fire using the age-class distribuction of undisturbed forest. Forest Science 40 (1) : 104-119.
Rothermel R.C., 1972. A mathematical model for predicting fire spread in wildland fuels. USDA For. Serv. Res. Pap. INT-115 Internt. For. and Range Exp. Stn. Ogden, Utah.
Rothermel R.C. and Philpot C.W., 1983. How to predict the spread and intensity of forest and range fires. GTR-INT-143, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 161 pp.
Ryan, K.C, Reinhardt, E.D. 1988. Predicting postfire mortality of seven western conifers. Canadian Journal of Forest Research 18: 1291-1297. http://dx.doi.org/10.1139/x88-199
SAS Institute Inc. 2004. User’s guide, SAS Institute Inc., Cary, NC.
Schmidt K.M.; Menakis J.P.; Hardy C.C.; Hann W.J., Bunnell D.L. 2002. Development of coarse-scale spatial data for wildland fire and fuel management. Gen. Tech. Rep. RMRS-GTR-87. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 41 pp.
Shapiro J.H., 1999. “Bounds on the area under the ROC curve”, J. Opt. Soc. Am. A 16, 53-57. http://dx.doi.org/10.1364/JOSAA.16.000053
Silva J.S., Moreira F., Vaz P., Catry F., Godinho-Ferreira P., 2009. Assessing the relative fire proneness of different forest types in Portugal, Plant Biosystems, 143(3): 597-608. http://dx.doi.org/10.1080/11263500903233250
Tomé M., Oliveira T. And Soares P., 2006. O modelo GLOBULUS 3.0.Dados e equações Relatórios Técnico — Científicos do GIMREF, nº2/2006, Dep. Engenharia Florestal, Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Lisboa. [In Portuguese].
Vasconcelos M.J.P., Silva S., Tomé M., Alvim M., Pereira J.M.C., 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrametric Engineering & Remote Sensing 67 (1): 73-81.
Velez R., 1990. Mediterranean forest fires: A regional perspective, Unasylva 162 10-12.
Wang M., Borders B., Zhao D., 2007. Parameter Estimation of Base-Age Invariant Site Index Models: Which Data Structure to Use? Forest Science 53(5):541-551.
Wittenberg L., Malkinson D. 2009. Spatio-Temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscapes, European Journal of Forest Research 128:297-304. http://dx.doi.org/10.1007/s10342-009-0265-7
© CSIC. Manuscripts published are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
Forest Systems is an Open Access Journal. All articles are distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the license CC-by must be expressly stated in this way when necessary.
