@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} } @mastersthesis {bnh-6715, title = {Developing a wildfire surveillance algorithm for geostationary satellites}, year = {2018}, month = {12/2018}, school = {RMIT University}, address = {Melbourne}, abstract = {

Wildfire surveillance is an important aspect of e↵ective wildfire management, requiring near continuous observations to detect and monitor fires. Geostationary satellites have the potential to meet this challenge, capturing full disk images every 10 to 30 minutes at ground sample distances down to 500 m for some sensors. However, the MIR (Middle Infrared) and TIR (Thermal Infrared) channels on geostationary satellite sensors have a coarse ground sample distance of 2-4 km. Currently, fire detection algorithms depend on these channels to detect thermal anomalies. The coarse spatial resolution in the MIR and TIR channels limits the application of geostationary satellite for wildfire surveillance. This thesis looks to fully exploit the potential of geostationary satellites for wildfire surveillance through a multi-spatial and multi-temporal approach.

The first research question in this thesis, develops and tests an algorithm to improve the wildfire surveillance capabilities of the geostationary satellites. The new algorithm utilises the MIR, NIR and visible channels, linking them to biophysical processes on the ground. The MIR channel is used to detect thermal anomalies, the NIR channel is used to detect changes in vegetation cover, and the visible channel detects smoke from the fire. By combining these detections, or observations, fire surveillance can be achieved at the highest ground sampling resolution available (typically in the visible wavelength channels). Initial algorithm development and testing were conducted on the Advanced Himawari Imager (AHI) sensor onboard the Himawari-8 satellite. The MIR, NIR and RED channels on AHI have 2 km, 1 km and 500 m ground sampling distances respectively, enabling the new algorithm to detect 2 km thermal anomalies and 500 m fire-line pixels. Fire-line pixels is a new product designed to\ detect the trailing edge of the fire.

Quantifiable methods for assessing algorithm performance in geostationary satellites are dicult to apply due to their high temporal resolution and lack of concurrent in-situ information. The second research question investigates methods for assessing the performance by considering the near continuous temporal sampling of geostationary satellites and the higher spatial ground sampling resolution a↵orded from LEO (Low Earth Orbiting) satellite observations. The study examines di↵erent evaluation methods and suggests a three-step process to provide the optimum performance evaluation for geostationary wildfire surveillance products, inter-compared with LEO satellite-based thermal anomaly detections.

Algorithm performance is further evaluated in research question three using the intercomparison method developed in research question 2 and applied to case study fires over Northern Australia. Subsequently, the algorithm is evaluated using an annual dataset (2016) comprising of nine study areas across Australia (totalling 360.000 km2) stratified by tree canopy cover. The algorithm reported an omission error of 27 \% at 2 km ground resolution when compared to VIIRS (Visible Infrared Imaging Radiometer Suite) hotspots over the nine study grids. In Northern Australia, the algorithm detected fires up to three hours before LEO observations due to the high temporal frequency of observations. Furthermore, in comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) hotspots, there was a 73 \% chance of detecting fire activity at the location of the MODIS hotspot, before the MODIS overpass. The algorithm also demonstrated a 40 \% detection probability for fires less than 14 ha over Northern Australian woodlands. The fire-line pixels with a ground sampling distance of 500 m demonstrated a 25 \% commission error when compared to VIIRS hotspots over the nine study grids. Over Northern Australia, this figure was 7 \% inter-compared to Landsat-8 burnt scars.

The fourth research question applied the developed algorithm to the SEVIRI (Spinning Enhanced Visible and Infrared Image) sensor onboard the European Meteosat Second Generation (MSG) satellite. SEVIRI has an operational fire product (FIR (Active Fire Monitoring)) which provides 3 km ground resolution hotspots using the MIR and TIR channels. The algorithm initially developed for AHI was modified to work with SEVIRI 3 km MIR channel and the High-Resolution Visible (HRV) channel (1 km). An inter-comparison\ of the modified algorithm with FIR products showed a 28 \% and 16 \% improvement in commission and omission errors respectively over a large case study fire in Portugal. The modified algorithm also improved the SEVIRI wildfire surveillance ground sampling resolution to 1 km taking advantage of the HRV channel.

The algorithm developed in this study demonstrates a novel approach to utilise geostationary satellites for wildfire surveillance with improved spatial resolution. Compared to the 2 km thermal anomaly hotspots derived through existing algorithms for AHI, the new algorithm provides 2 km thermal anomaly detections and 500 m fire-line pixels with performance comparable to that of medium resolution LEO satellites. Near-real time implementation of the algorithm has the potential to provide high temporal fire surveillance capabilities. The fire-line pixels from the algorithm could also be used to derive fire behaviour parameters such as heading and speed, providing an essential tool for wildfire surveillance in remote parts of Australia and other areas, where resources can only be deployed for a hand full of high-risk fires.

}, keywords = {AHI-FSA, HIMAWARI-8, inter-comparison, SEVIRI, wildfire surveillance}, url = {https://researchbank.rmit.edu.au/view/rmit:162721}, author = {Chathura Wickramasinghe} }