Wildland fires are considered one of the major natural risks affecting almost every country in the world. The impacts of these fires are huge in term of environmental, economic, and social losses. Experts estimate that with the climate change and global warming, we will witness an increase in the frequency and size of fires in the next years. In this paper, we will present the advances in the use of multiple spectrum computer vision to process, analyze and understand wildland fires behavior. We will introduce different multispectral technologies used in image capture, the techniques developed to detect and extract the fires from the images, and how multispectral fusion is used in the context of wildland fires. We will show our recent results using multiple multimodal stereovision systems where different modalities are combined to extract important fires characteristics in threedimensional space. Finally, we will discuss the use of UAVs to monitor fires at a larger scale.
In fire research and forest firefighting, there is a need of robust metrological systems able to estimate the geometrical characteristics of outdoor spreading fires. In recent years, we assist to an increased interest in wildfire research to develop non destructive techniques based on computer vision. This paper presents a new approach for the estimation of fire geometrical characteristics using near infrared stereovision. Spreading fire information like position, rate of spread, height and surface, are estimated from the computed 3D fire points. The proposed system permits to track fire spreading on a ground area of 5mx10m. Keywords: near infrared, stereovision, spreading fire, geometrical characteristics