The main goal is to provide fire managers with useful and sound information to improve fire suppression strategy and decisions.

To accomplish this, foRESTER proposes a fast, reliable and informative Decision Support System (DSS) based on advanced computational intelligence and visualization techniques, that integrates innovative technologies from multi-sensor systems, cutting edge satellite image processing, and near real-time (NRT) fire spread predictions (FSP).

The project aims to provide a low-cost framework based on a WSN and additional information system tools to produce a pilot demonstrator of the effectiveness of DSS for supporting decision-making in fire suppression context. It will be built on past experience from MacFIRE, together with new layers of information, providing a more informative DSS for the fire managers.

Moreover, we will extend the pilot case study to the Médio-Tejo region and demonstrate it a scalable platform for the entire national territory. The proposed approach will include: a) using smart image classification approaches for analysis of Sentinel-2 imagery of forest areas; b) applying a data fusion algorithm to leverage the temporal information contained in the Sentinel- 2 image time series to derive maps of the dynamics of biomass accumulation; c) create a monitoring system capable of fusing (integrating) and processing satellite imagery and other spatial data, ground data (inventories) and cartographic data providing an intelligent decision support system.

Due to the inherent complexity of the system, we gathered a multidisciplinary team from different areas of expertise:
1) CTS-UNINOVA @ NOVA-FCT -hardware design, satellite image processing and multicriteria decision;
2) IT @ IST -image processing;
3) DGT - cartography and IPSen tinel platform;
4) MagIC @ NOVA-IMS - land cover land use cl assification;
5) CEF @ ISA - fire spread modelling; 6) CESAM @ UA - weather forecast.