Assessment of active fire detection algorithm for fire detection in natural resources using MODIS images (case study: Golestan national park)

Document Type : Complete scientific research article

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Abstract

Abstract:
Monitoring of fire in natural resource using satellite imagery is the most effective method to detect active fires. The main objective of this study was to explore the capability of global active fire detection algorithm for active fire detection using the MODIS images.
The study was performed in the Golestan national park located in the western Caspian forests. Totally 229 MODIS images acquired by Terra and Aqua satellites were processed for active fire detections. In order to investigate on capabilities of MODIS images in active fire detection, we evaluated the results of MODIS fire products with three reference fires occurred in the study area. The locations of the burned areas were surveyed by GPS. MODIS images from fire days as well as the previous and past days of fires were collected. The Aqua images were obtained from the L1B level, which was suitable for using in this algorithm whereas Terra images were obtained from PDS format and then preprocessed to the L1B.
After processing of images, detected fires were compared with reference fires. The results revealed that the fires in July 2011 (Yekbarmakh) and November 2010 (Cheshme-Sardar) were detected, while the relative small fire (Gharangi forest) in March 2010 was not detectable. Based on the principle of active fire detection algorithm the higher temperature of fires increase the probability of detection. It can be concluded that MODIS images provide a valuable source of information about fire activity. However contextual fire detection algorithm didn’t detect high percent of fires. complementary researches and investigation are needed to develop a regional fire detection algorithm for the Caspian forests of Iran. It is recommended that active fire detection algorithm to be applied for fire detection for the Caspian forests of Iran using MODIS images before complementary researches.

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