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Modelling and mapping the Natural Forest Distribution of Italy and predicting changes for the year 2080 under IPCC SRES A2a scenario


The object of this work is to simulate potential changes of Italian natural forest distribution under climate change scenarios. Download full presentation.


In the period between 1950 and 2000, natural disturbance has caused several million cubic meters of forest damage each year (Schelhaas et al. 2003). An increase of forest damage can be foreseen directly and indirectly due to climate change projections (McCarthy et al. 2001). In the latest decades not only have damaged areas been reforested, but a trend of afforestation of agricultural land has been observed with a general increase in forested land (Kuusela 1996). European forest landscapes are changing shape and content. Next century afforestation and reforestation will be a crucial decision and policy-making topic in the context of landscape management towards European sustainability. Our study is linked to conservation policy making, approaching theoretical distributional modelling, ecological theory and applied landscape management. This is achieved by combining fine resolution predictors with homogeneous and dense field data by means of robust modelling techniques.
Within this applied spatial ecoogical modeling theme, we modelled actual and future Natural forest categories in Italy and estimated the shifts in vegetation in the period 2000–2080 under the IPCC SRES A2a scenario. The actual and future distribution of the 10 most dominant European Forest Categories (EEA, 2006) are simulated using the Random Forest classifier. Random Forest is an ensemble of model machine-learning techniques and relates to the natural forest formations in Italy and environmental predictor surface maps.
Environmental predictor variables have a resolution of 1km2 pixel and include soil factors (European Soil Database), bioclimatic factors (Worldclim database, Hijmans et al. 2005) and topographic factors (SRTM digital elevation model). See Input Data section.
According to the future climatic simulation we expect Mediterranean vegetation to gain suitability areas and temperate forest to decrease their extent. In mountainous areas vegetation belts are expected to shift towards upper altitude levels.


In our approach we model the current distribution of the natural forest in Italy according to environmental variables. In order to do so we are going to construct an input response / predictor table relating the distribution of the Natural forest formations in Italy to climatic, soil and geomorphologic factors. Successively we use the input response / predictor table and the machine-learning ensemble classifier Random Forest (Breiman, 2001) to create a forest type / environmental factors predictive model.
Once the model is trained for the current climate we are going project vegetation shift under future climate conditions.

Input Data

As input data we use the following datasets:

Computational Implementation

You can reproduce the case study and learn how to use open source tools following the command lines of each script step by step. Scripts are available online in a wiki format. This format may cause trouble when copying and pasting command syntax into the terminal. Be careful! Scripts are also available to download online and are stored on your local virtual machine. (see links below). If you choose to use the sripts we recommend using a text editor and to add your comments.

You have the choice of reproducing the case study by downloading scripts online or to open on your local machine. The modeling and mapping procedure includes the following steps and relative scripts:


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wiki/forestmod.txt · Last modified: 2020/07/17 06:39 (external edit)