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A SSESSING THE IMPACT OF FOREST FIRES IN MARITIME PINE STANDS Botequim, B 1., Borges, J. G. 1, Garcia-Gonzalo, J. 1, Marques S. 1, Oliveira, M.M. 2, Silva,

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Presentation on theme: "A SSESSING THE IMPACT OF FOREST FIRES IN MARITIME PINE STANDS Botequim, B 1., Borges, J. G. 1, Garcia-Gonzalo, J. 1, Marques S. 1, Oliveira, M.M. 2, Silva,"— Presentation transcript:

1 A SSESSING THE IMPACT OF FOREST FIRES IN MARITIME PINE STANDS Botequim, B 1., Borges, J. G. 1, Garcia-Gonzalo, J. 1, Marques S. 1, Oliveira, M.M. 2, Silva, A. 1, Soares, P. 1, Tomé M. 1 1 Centro de Estudos Florestais, Instituto Superior de Agronomia, Technical University of Lisbon, Portugal 2 Universidade de Évora, Portugal I. Objectives II. Materials and methods References: Tomé, M., Meyer, A., Ramos, T., Barreiro, S., Faias, S.P., Cortiçada, A., (2007). Relações hipsométricas e equações de diâmetro da copa desenvolvidas no âmbito do tratamento dos dados do Inventário Florestal Nacional Publicações GIMREF. RT 3/2007. Universidade Técnica de Lisboa. Instituto Superior de Agronomia. Centro de Estudos Florestais. Lisboa Acknowledgments: This work was supported by project PTDC\AGR-CFL/64146/2006 funded by the Portuguese Science Foundation. ab Figure 3a. Burnt plots: burnt stump (a); burnt Pinus pinaster stands (b) III. Results Regression parameters were estimated as R 2 =.23 and AIC = Significant covariates include fire severity, number of trees, dbh and height (Figure 5) Figure 5. Regression results Data acquisition encompassed both the measurement of biometric variables (e.g. height, diameter at breast height, burnt stump height, burnt canopy height, degree of stump destruction, fire damage) and the characterization of the plot (e.g. elevation, aspect, slope, presence of soil erosion, shrubs species (Figure 2). Figure 2. Field forms and terrain guide Figure 4. Estimating the diameter at breast height of burnt trees from stumps Statistical analysis aimed at explaining the relative importance of explanatory variables in the response variable, positive percentage of dead trees in a burned plot (p). Regression analysis is an adequate approach to address this objective. Several distributions were tested to check the best fit for p. In this poster we present results for the best fit where the predicted variable (y) was the arcsin transformation of the proportion of dead trees: arcsin(SQRT(p)) ~N(a2; b2). The approach was implemented using the software R version 2.9. (eq II) (eq I) Figure 1. Data acquisition in national forest inventory (NFI) plots. The map at left shows the NFI plots (≈12200). The top-right maps display the 2007 fire perimeters and the 78 burnt NFI plots. The bottom-right maps display the 2008 fire perimeters and the 66 burnt NFI plots In most cases, original NFI data did not correspond to the actual forest cover in the plots. Thus a reverse engineering approach had to be designed to re-built the forest before the fire. In the case of plots with standing burned trees, both pre-fire diameter at breast height and height were estimated using equations developed by Tomé et al, (equations I and II). In other cases, when at inventory date, the burnt trees had been harvested, we measured the stump diameter from burned trees and an equation was ajusted to predict the diameter at breast height (Figure 4). Figure 3b. Burnt plots: pine stand burnt with low severity (c); high severity damage in a pinus stand (d). The development of post-fire tree survival models (Pinus pinaster) that may be appropriate for forest planning purposes in Portugal. Two models are developed: a)a stand-level model to estimate the proportion of trees that will die as a consequence of a fire event and b)a tree-level model for the probability to survive a forest fire. Both models are based on easily measurable forest characteristics (e.g. height, diameter at breast height) and stand topographical parameters (altitude, slope and aspect). The models are instrumental for the forest manager to predict the damage impact of wildfires according to stand structure and species composition. d The Maritime Pine inventory data was collected in burnt plots located in fire perimeters larger than 5 ha corresponding to wildfires that occurred between 2006 and 2008 (Figure 1). c Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Coefficients and significance


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