This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms.
The aim of PLURIFOR is to help with the development of regional and transnational risk management plans for forest area susceptible to biotic and abiotic hazards.
The project aimed to understand the nature of the wind damage risk to European forest, how this risk might change, the social, environmental and economic impacts of that change and the role policy
In this issue: From clouds to crowds - forest monitoring for everyone Storms - an increasing threat to Europe's forests Forest values and compliance with forestry legislation in Ghana
European forests are increasingly exposed to various disturbances which can be of abiotic or biotic nature. When being of more ‘catastrophic’ nature they can strongly disrupt targeted forest
European forests are affected by different disturbances. They range from pests and insect damages to megafires and transnational storm events and can have profound impacts on forest ecosystem services
