An improved algorithm for small and low-intensity fire detection in the temperate deciduous forests using MODIS data: a preliminary study in the Caspian Forests of Northern Iran

Farrokh Pourshakouri (), Ali A. Darvishsefat (), Farhad Samadzadegan (), Pedram Attarod () and Saeid Amini ()
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Farrokh Pourshakouri: Iranian Space Agency
Ali A. Darvishsefat: University of Tehran
Farhad Samadzadegan: University of Tehran
Pedram Attarod: University of Tehran
Saeid Amini: University of Isfahan

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 2, No 49, 2529-2547

Abstract: Abstract The past five decades have witnessed satellite remote sensing become one of the most efficient tools for fire detection and estimating total burned areas. Not all algorithms are appropriate for detecting high-temperature events on a global scale. It is difficult for traditional fire detection algorithms to capture small and low-intensity fires with significant accuracy. For this, we propose an improved fire detection algorithm by considering channel 4 and 11 µm of MODIS data with two different thresholds for hot and cold seasons. Moreover, false alarm rejections caused by the edges of clouds are designed for this algorithm. The validation against 231 reference fires showed good performance of our algorithm over the northern forests of Iran. This algorithm detected 72 fires, while the MODIS fire product, a widely used source for fire detection, detected only 26 fires. The results indicate an outperformance of 19.91%. The results show that our algorithm for the fire detection method overperforms the traditional methods and can be particularly useful for fire detection in the northern forests of Iran and can be applied in similar forests worldwide.

Keywords: Active fire detection; MODIS; Small and low-intensity fires; Spatiotemporal analysis; Temperate broadleaf deciduous forests (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05777-y

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