Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data

Hally, B, Wallace, L, Reinke, K, Jones, S and Skidmore, A 2018, 'Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data', International Journal of Digital Earth, pp. 1-16.


Document type: Journal Article
Collection: Journal Articles

Title Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data
Author(s) Hally, B
Wallace, L
Reinke, K
Jones, S
Skidmore, A
Year 2018
Journal name International Journal of Digital Earth
Start page 1
End page 16
Total pages 16
Publisher Taylor & Francis
Abstract A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire's absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4 μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75% of cases. Potential improvements in detection time of up to 6 h over LEO products are also demonstrated.
Subject Photogrammetry and Remote Sensing
Forestry Fire Management
Keyword(s) advanced himawari imager
broad area training
diurnal variation
Fire detection
geostationary sensors
DOI - identifier 10.1080/17538947.2018.1497099
Copyright notice © 2018 Informa UK, trading as Taylor & Francis Group
ISSN 1753-8947
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