Estimating Fire Background Temperature at a Geostationary Scale-An Evaluation of Contextual Methods for AHI-8

Hally, B, Wallace, L, Reinke, K, Jones, S, Engel, C and Skidmore, A 2018, 'Estimating Fire Background Temperature at a Geostationary Scale-An Evaluation of Contextual Methods for AHI-8', Remote Sensing, vol. 10, no. 9, pp. 1368-30.


Document type: Journal Article
Collection: Journal Articles

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Title Estimating Fire Background Temperature at a Geostationary Scale-An Evaluation of Contextual Methods for AHI-8
Author(s) Hally, B
Wallace, L
Reinke, K
Jones, S
Engel, C
Skidmore, A
Year 2018
Journal name Remote Sensing
Volume number 10
Issue number 9
Start page 1368
End page 30
Total pages -1337
Publisher M D P I AG
Abstract An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixels background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixels surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixels total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5×5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI
Subject Forestry Fire Management
Photogrammetry and Remote Sensing
Keyword(s) Fire attribution
Fire background temperature
Contextual methods
Geostationary sensors
DOI - identifier 10.3390/rs10091368
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 2072-4292
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