Researchers from the UPM are developing a method to detect forest fires by using a new color index. The index is based on methods for the vegetation classification and has been adapted to detect the tonalities of flames and smoke.
By color treatment, researchers from the Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM) at Universidad Politécnica de Madrid (UPM) have achieved to detect forest fire as well as the smoke found during combustion, all this isolated from the rest of the scene. Due to the rapidity and precision of the detection, the use of this innovative system is focused on environmental surveillance systems using drones. This study has been recently released in Sensors Journal.
A research line developed at CITSEM is the study of surveillance systems based on the imaging processing for their application to different phenomenon that have impacts on the environment such as deforestation, fires or floods. In short, researchers propose these types of early detection systems in order to detect such events and prevent further environmental disasters.
In the case of deforestation, researchers suggest various algorithms that allow them to detect the fire and smoke generated during a forest fire as well as their fundamental characteristics (area, wind direction…). The algorithms have high accuracy in real time, and what it is more, they show low computational load that allows them to address the problem in real time and implement such algorithms in autonomous systems (drones) and perform continuous monitoring.
A relevant aspect of the developed algorithm, called Forest Fire Detection Index (FFDI), is its capacity to detect under any perspective, including the aerial. Besides, effective detections in initial combustion processes have been proved by using this algorithm as well as in other scenarios different from forest environments.
The developed method could be used in real-time in Unmanned Aerial Systems (drones) with the aim of monitoring a wider area than through fixed surveillance systems. Thus, it would result in more cost-effective outcomes than conventional systems implemented in helicopters or satellites. These drones could also reach inaccessible locations without jeopardizing people's safety.
The authors said: "we carried out diverse detection tests using commercial drones and the results confirm the utility, efficiency, versatility and low cost of the developed algorithm, becoming an efficient tool for surveillance and monitoring of such events."
Cite This Page: