SMART AND RECONFIGURABLE MULTI-SENSOR
The 1st objective is to pursue research and develop a low-cost multi-sensor node using an energy-efficient sensor-to-digital interface (SDI) and a dedicated power management unit (PMU). It will include advanced imaging processing of the fire event, while keeping periodic communications with the Decision Support System (DSS).
Although, temperature and humidity can be predicted accurately by weather forecast, wind direction and speed is critical for a DSS. Having real-time sensing will be mandatory to predict fire propagation, especially for the case of the rapid acceleration that occurs with extreme fires, as they become larger.
LCLU MAPPING AND SATELLITE IMAGE PROCESSING
The 2nd objective is to produce updated raster and object-based LCLU maps before and during the fire season to be used by the NRT fire spread simulator and to provide updated satellite images with enhanced resolution to be integrated into the DSS.
NRT FIRE SPREAD SIMULATOR
The 3rd objective is to compile the input data and quantify the uncertainty associated with key variables and perform near real-time (NRT) fire spread predictions (FSP).
We will investigate how NRT FSP can support better fire suppression decisions, by providing fire managers simple maps of the probability of burning and expected intensity of active wildfires.
VISUAL AND INTERACTIVE DSS
The main goal of project foRESTER is to incorporate the outputs from the multi-sensor nodes, enhanced LCLU maps and SR satellite images and support the NRT fire spread modelling, into a visual and interactive DSS, which will be the interface between end-users and the system.
It will research the data fusion process, early warning methods, and the best techniques for supporting the decision process during fire suppression and post-analysis.