@article {bnh-5136, title = {Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data}, journal = {International Journal of Digital Earth}, year = {2018}, month = {07/2018}, abstract = {

A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire{\textquoteright}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.

}, doi = {https://doi.org/10.1080/17538947.2018.1497099}, url = {https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1497099?journalCode=tjde20}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Andrew Skidmore} } @article {bnh-5137, title = {Estimating fire background temperature at a geostationary scale - an evaluation of contextual methods for AHI-8}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {08/2018}, chapter = {1368}, abstract = {

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel{\textquoteright}s 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 pixel{\textquoteright}s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65\% of a target pixel{\textquoteright}s 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{\texttimes}5pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4\%{\textendash}4.4\% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

}, doi = {https://doi.org/10.3390/rs10091368}, url = {https://www.mdpi.com/2072-4292/10/9/1368}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Chermelle Engel and Andrew Skidmore} } @article {bnh-5338, title = {Estimating Fire Background Temperature at a Geostationary Scale{\textemdash}An Evaluation of Contextual Methods for AHI-8}, journal = {Remote Sensing }, volume = {10}, year = {2018}, month = {08/2018}, chapter = {1368}, abstract = {

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel{\textquoteright}s 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 pixel{\textquoteright}s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65\% of a target pixel{\textquoteright}s 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{\texttimes}5pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4\%{\textendash}4.4\% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

}, keywords = {contextual methods, fire attribution, fire background temperature, geostationary sensors}, doi = {10.3390/rs10091368}, url = {https://www.mdpi.com/2072-4292/10/9/1368}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Simon Jones and Chermelle Engel and Andrew Skidmore} }