Optimizing fuel management operations


In Portugal, there is often a mismatch between the machinery used in forest operation and the specific field conditions – such as vegetation and stand characteristics, plot size, slope and other local characteristics – this project aims at increasing the efficiency and reducing the environmental impact of forest operations, by allocating the right equipment taking into account specific site conditions.  One of the factors contributing to this issue is the lack of easily accessible technical information on the suitability of different types of equipment under different conditions. This project aims to develop a technological solution to support and improve the decision making process of allocating the most suitable equipment to each site, according to its specific site conditions. The model will is based on Key Performance Indicator (KPIs) related to slope, stoniness, vegetation characteristics as well as different categories of equipment.

Forest services provider teams working on the vegetation management of the Right of Way of REN are being assessed to define performance data for the different equipment working under different site conditions. The results will support INESC-TEC to develop and adjust an optimization model. The objective is to develop a technological solution, with an integrated GIS platform, with the capacity of selecting an intervention area to which the productivity and some others KPIs – such as time and cost per unit area, greenhouse gas emissions – area estimated for a set of predetermined alternatives.

Main Activities

Research, Development & Innovation

Dissemination & Promotion

Contributes to PPS


– new mapping services – land cover map for 2022, bi-monthly land use and land cover change maps, maps of vegetation cover in fuel management strips;

– new artificial intelligence (AI) models: stock classes (eucalyptus and maritime pine), estimated age classes (eucalyptus and maritime pine), post-fire landscape evolution, occurrence of pests (eucalyptus and maritime pine);

– rural fire risk warning and forecasting platform.