Modeling post fire regeneration of planted and natural vegetation in planted Pine-forests in Israel
Gil Sapir, Yohay Carmel
Many wild fires occur every summer in Israel. Restoration processes are long, up to decades, and need economic and professional efforts. The restoration process can be done in two main approaches: self-restoration and active restoration. Vegetation restoration in Israel is important for preventing landscape degradation and reducing recreation possibilities. Forest managers need to decide which approach to take while planning the restoration in each area. Predictive models for post fire vegetation restoration after fire are essential tools for decision making and planning. Currently, quantitative tools for vegetation restoration processes after fire for analyzing, predicting and planning post fire vegetation restoration do not exist.
Vegetation dynamics after fire is investigated mainly for 3-5 years after fire. The Mediterranean vegetation type which is most investigated is the natural Pinus halepensis forest in Israel, Spain, France, Italy, Greece and North Africa.
There are no models for predicting Mediterranean vegetation development after fire because of the difficulties to predict its behavior.
A model capable of predicting vegetation formations 15-20 years after fire is a scientific challenge. It may also aid in decision making and serve as a planning tool for vegetation restoration after fire.
I will develop a model of post-fire vegetation regeneration that may predict the nature of vegetation formation (either pine forest, Mediterranean Maquis, shrubs or herbaceous vegetation) expected in the burnt area 15-20 years after the fire. This model will be based on the assumption that arboreal vegetation in the burnt area two years after the fire has the same composition as in the same area 15 years later, with differences in proportion cover and height only. I will use 3 different approaches for model building, and compare the performance of the respective models: (1) linear, deterministic approach: multivariate linear regression with vegetation cover two years after fire as the dependent variable, and pre-fire parameters of vegetation and habitat as explanatory variables. (2) Linear stochastic approach: fuzzy sets of membership functions will describe vegetation cover two years after fire. (3) Stochastic, non linear approach: multinomial logistic regression will use the same explanatory variables as in the first approach to predict the probability of each one of the four vegetation states mentioned above.
Preliminary results: A multivariate regression based on field data revealed that pre-fire vegetation cover parameters were significant predictors of the vegetation two years after the fire.