@article {bnh-8342, title = {Active fire detection using the Himawari-8 satellite - final project report}, number = {728}, year = {2022}, month = {05/2022}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Satellite sensors are an important source of observations of fire activity (or hotspots). Next generation geostationary satellites (Himawari-8, GeoKompsat 2A, GOES-16/17) provide earth observations very frequently (~every 10-minutes).\  This near-real time data provides opportunities for new and improved fire detection algorithms. Early fire detection algorithms that take advantage of such high frequency observations, and that are primed for Australian landscapes, are developed under this project.\ \ 

The performance of new fire detection data\  forms part of the development phase.\  How well do they perform? What are their limitations? What are their advantages for observing fire under different fire scenarios and in different landscapes?\  One aspect of evaluation is how does the algorithm, and implementation of the algorithm as a processing chain, perform under operational circumstances.\  To this end, an end-user trial was hosted by NSW RFS for the near-real time implementation of the new Himawari-8 hotspot algorithm (March 2019-March 2021) and expanded to include Victoria over the 2019-2020 (black summer) bushfire season.\ \ 

Results from the project developed algorithms compare extremely favourably with existing polar-orbiting fire detections and other Himawari-based approaches. This has led to wide interest and project outputs being adopted by end-users.

}, issn = {728}, author = {Simon Jones and Karin Reinke and Chermelle Engel} } @article {bnh-8129, title = {Kangaroo Island Black Summer fire reconstruction}, number = {685}, year = {2021}, month = {07/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In the 2019-20 summer, wildfire affected an area of around 200,000 hectares on Kangaroo Island, South Australia, in what has become known as the Black Summer, with significant ongoing social, economic and environmental impacts.

The Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 provides infrared imagery at 2km spatial resolution at nadir in 10- minute intervals. This allows wildfires to be detected and monitored in quasi-real time using the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm (Engel, Jones and Reinke, 2020), developed in partnership between the Royal Melbourne Institute of Technology (RMIT) and the Bushfire and Natural Hazard CRC. This report outlines the methods used to verify hotspots detected by the BRIGHT algorithm and reconstruct the Black Summer fires using spatio-temporal clustering.

}, keywords = {black summer, Fire, fire impacts, kangaroo island, reconstructions, remote sensing, Satellite}, issn = {685}, author = {Simon Ramsey and Karin Reinke and Nur Trihantoro and Simon Jones and Chermelle Engel} } @article {bnh-6827, title = {Active fire detection using the Himawari-8 satellite - annual report 2018-19}, number = {563}, year = {2020}, month = {04/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This project is a critical part of the BNHCRC{\textquoteright}s value to the broader Australian government. The government run Sentinel Hostpots application is used by all levels of government, private sector, researchers and the public. This project will source for the Sentinel Hotspots application. Our vision is to create a world leading approach to monitor fire activity. To achieve this, we propose the use of remote sensing technologies for active fire detection and monitoring.

This project is a critical part of the BNHCRC{\textquoteright}s value to Geoscience Australia and the broader\ Australian government. The Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public {\textendash} this system would not be trusted by those parties without sound validation. This project will continue to assist the Australian government in developing and validating the capability of the Himawari-8 data source. Further, the project will assist in the ongoing improvement of vital bushfire information acquired through state-of-the-art remote sensing technology as needed by fire and emergency management now, and into the future.

The aim this project is to next generation, remote sensing satellite information to enhance Australia{\textquoteright}s operational capabilities and information systems for bushfire monitoring across a range of spatial scales and landscapes. Ultimately the outcomes of this research will enable measures of active fires in terms of areal extent and magnitude, which in turn will have the potential to inform decisions about bushfire response, fuel hazard management and ecosystem sensitivity to fire; during fire events and post-fire rehabilitation efforts.

The following sections describe the background, research approaches, key milestons, a utilisation study and outputs of our Himawari-8 active fire detection research.

}, keywords = {Active fire detection, HIMAWARI-8, Satellite}, issn = {563}, author = {Simon Jones and Karin Reinke and Chermelle Engel} } @article {bnh-7505, title = {Active fire detection using the Himawari-8 satellite - annual report 2019-2020}, number = {628}, year = {2020}, month = {11/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

This project is a critical part of the BNHCRC{\textquoteright}s value to the broader Australian government. The government run Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public.\  This project will assist the Australian government to develop and validate Himawari-8 as a data source for the Sentinel Hotspots application.\  Our vision is to create a world leading approach to monitor fire activity.\  To achieve this, we propose the use of remote sensing technologies for active fire detection and monitoring.

This project is a critical part of the BNHCRC{\textquoteright}s value to Geoscience Australia and the broader Australian government. The Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public {\textendash} this system would not be trusted by those parties without sound validation. This project will continue to assist the Australian government in developing and validating the capability of the Himawari-8 data source. Further, the project will assist in the ongoing improvement of vital bushfire information acquired through state-of-the-art remote sensing technology as needed by fire and emergency management now, and into the future.

The aim this project is to next generation, remote sensing satellite information to enhance Australia{\textquoteright}s operational capabilities and information systems for bushfire monitoring across a range of spatial scales and landscapes. Ultimately the outcomes of this research will enable measures of active fires in terms of areal extent and magnitude, which in turn will have the potential to inform decisions about bushfire response, fuel hazard management and ecosystem sensitivity to fire during fire events and post-fire rehabilitation efforts.

The following sections describe the background, research approaches, key milestones, utilisation study and outputs of our Himawari-8 active fire detection research.

}, keywords = {fire detection, HIMAWARI-8, Satellite}, issn = {628}, author = {Simon Jones and Karin Reinke and Chermelle Engel} } @article {bnh-7826, title = {A Seasonal-Window Ensemble-Based Thresholding Technique Used to Detect Active Fires in Geostationary Remotely Sensed Data}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, year = {2020}, abstract = {

This article introduces a new algorithm to detect active fires in geostationary remotely sensed data. The algorithm calculated dynamic statistical multispectral thresholds based on, and sensitive to, biogeographical region, subseason, and time-of-day. The spectral characteristics of nonfire and noncloud mid-infrared values were found to vary with biogeographical region, subseason, and time-of-day. These differences were exploited to define a new seasonal Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) multivariate adaptive threshold geostationary satellite fire anomaly algorithm. The algorithm was demonstrated on 12 months of daytime data acquired from the geostationary satellite system, the Advanced Himawari Imager (on Himawari-8) over Australia (7.69 million km{\texttwosuperior}). The resulting hotspots were compared with those from the existing Moderate Resolution Imaging Spectrometer (MODIS) polar-orbiting fire-hotspot algorithm. The intercomparison showed that BRIGHT wildfire hotspots, detected using Himawari-8 and Interim Biogeographic Regionalisation of Australia (IBRA) data, were also detected by MODIS polar-orbiting fire hotspots 88\% of the time. While MODIS hotspots were detected by BRIGHT hotspots only 39\% of the time; the majority of the undetected MODIS hotspots had low radiative power. BRIGHT provides a new method for the remote sensing of active fires providing reliable observations at spatial and temporal scales useful for fire managers.

}, keywords = {Advanced Baseline Imager, Advanced Himawari Imager, algorithm development, clear-sky, cloud detection, fire anomaly, fire detection, geostationary, HIMAWARI-8}, doi = {10.1109/TGRS.2020.3018455}, url = {https://ieeexplore.ieee.org/document/9186808/keywords$\#$keywords}, author = {Chermelle Engel and Simon Jones and Karin Reinke} } @article {bnh-5423, title = {Active fires: Early fire detection and mapping using HIMAWARI-8 Annual Report 2017-2018}, number = {458}, year = {2019}, month = {03/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This project is a critical part of the BNH CRC{\textquoteright}s value to Geoscience Australia and the broader Australian government. The Sentinel Hotspots application is used by all levels of government, private sector, researchers and the public {\textendash} this system would not be trusted by those parties without sound validation. This project will continue to assist the Australian government in developing and validating the capability of the Himawari-8 data source to Sentinel Hotspots program. Further, the project will assist in the ongoing improvement of vital bushfire information acquired through state-of-the-art remote sensing technology as needed by fire and emergency management now and in the future.

}, keywords = {Fire, HIMAWARI-8, mapping}, author = {Simon Jones and Karin Reinke and Chermelle Engel} } @conference {bnh-6510, title = {Detecting active fires from space using Himawari-8: a report from the regional New South Wales trial }, booktitle = {AFAC19 powered by INTERSCHUTZ - Bushfire and Natural Hazards CRC Research Forum}, year = {2019}, month = {12/2019}, publisher = {Australian Institute for Disaster Resilience}, organization = {Australian Institute for Disaster Resilience}, address = {Melbourne}, abstract = {

Continuous monitoring fires over Australia using Himawari-8 geostationary satellite data (available every 10 minutes) has the potential to change lives.

Active-fire hotspots are routinely available from polar-orbiting satellites such as MODIS and VIIRS (Giglio et al. 2003; Giglio et al. 2016; Schroder et al. 2014) over Australia. Active-fire hotspots from those systems are only available a few times a day, with the specific times dictates by the satellite orbits themselves. With satellite orbits not necessarily concurring with time of maximum fire activity. In late 2015 though, the Japanese Meteorological Agency launched the Himawari-8 geostationary satellite, with full-disk observations (including Australia) available every 10 minutes (Bessho et al. 2016). These frequent observations have the potential to support continuous real-time satellite monitoring of active fires over Australia.

Download the full non-peer reviewed research proceedings\ from the Bushfire and Natural Hazards CRC Research Forumhere.

}, keywords = {fire management, fires, monitoring, risk management, Satellite}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Chermelle Engel and Stuart Matthews and Simon Jones and Karin Reinke} } @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} } @conference {bnh-4777, title = {Performance of fire detection algorithms using himawari-8}, booktitle = {AFAC18}, year = {2018}, month = {09/2018}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Perth}, abstract = {

Accuracy is an important aspect of fire hotspot detection.\  Errors in the H8-AHI WFABBA fire hotspot detection can lead to a loss of trust in a fire hotspot detection product.\  Compared to MODIS and VIIRS polar-orbiting satellites hotspot detections, the WF-ABBA hotspot detection product over Australia 0400 UTC had minimum commission error rates of 31\% (Winter), 35\% (Summer), 48\% (Spring) and 54\% Autumn over 1 Dec 2015 {\textendash} 30 Nov 2016.\  The WF-ABBA algorithm was originally developed for America and was not tuned specifically for the Australian continent.\  Here we create a new Himawari fire-hotspot algorithm tuned dynamically for 419 Australian bioregions.\  The new algorithm 0400 UTC had minimum commission error rates, in comparison to MODIS/VIIR hotspot detections, of 6\% (Summer), 8\% (Spring), 20\% (Winter) and 41\% (Autumn) over 1 Dec 2015 {\textendash} 30 Nov 2016.\  These commission rates are a considerable improvement over the currently available (WF-ABBA) fire hotspot product.

}, author = {Chermelle Engel and Simon Jones and Karin Reinke} }