A research line developed at CITSEM is the application of image-processing surveillance systems for phenomena with impacts on the environment, including deforestation, fires and 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 they have 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 new algorithm, called the Forest Fire Detection Index (FFDI), is its capacity to detect fire from any perspective, including aerial. Additionally, its effectiveness has been proven by using the algorithm in non-forest environments.
The method could be used in real-time in unmanned aerial systems with the aim of monitoring a wider area than through fixed surveillance systems. This could 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.
Welcome to drone day on the Adafruit blog. Every Monday we deliver the latest news, products and more from the Unmanned Aerial Vehicles (UAV), quadcopter and drone communities. Drones can be used for video & photography (dronies), civil applications, policing, farming, firefighting, military and non-military security work, such as surveillance of pipelines. Previous posts can be found via the #drone tag and our drone / UAV categories.
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