This paper uses the dataset present in the UCI machine learning repository which consists of physical factors and climatic conditions of the Montesinho park situated in Portugal. Data mining and machine learning techniques can provide an efficient prevention approach where data associated with forests can be used for predicting the eventuality of forest fires. By the time the authorities get to know about it, the fires would have already caused a lot of damage. Forest fires are dealt by collecting the satellite images of forest and if there is any emergency caused by the fires then the authorities are notified to mitigate its effects. The effects of forest fires have a lasting impact on the environment as it lead to deforestation and global warming, which is also one of its major cause of occurrence. A near real-time forest fire monitoring operation for the ASEAN region using both low and high resolution satellite imagery is described.įorest fires have become one of the most frequently occurring disasters in recent years. The advantages and limitations of each satellite sensor are elaborated. In this paper, the principles of fire and burn scar detection by satellite sensors are discussed. The burn scar maps, together with land cover and vegetation maps (also derivable from satellite imagery), are useful in ascertaining the types of land affected by fires during a fire event for effective fire fighting and fire management, and for damage assessment. Burn scar maps at various scales can be produced from satellite imagery. High-resolution imagery is used to zoom in onto the hot spot areas to accurately locate the individual fires and to determine the types of land cover affected by fires. Hot spots detected in low-resolution satellite imagery provide information on the general locations, spatial distributions and temporal evolution of fires. During a fire event, active fires can be detected by detecting the heat, light and smoke plumes emitted from the fires. Satellite remote sensing offers a useful tool for forest fire detection, monitoring, management and damage assessment.

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