@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} } @article {bnh-5138, title = {Implementation of a new algorithm resulting in improvements in accuracy and resolution of SEVIRI hotspot products}, journal = {Remote Sensing Letters}, volume = {9}, year = {2018}, month = {07/2018}, abstract = {

Active wildfire detection, surveillance and mapping is an important application of satellite remote sensing. The Active Fire Monitoring (FIR) products, from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellites, provide rapid-fire detection data every 5 to 15 over the European and African continents. However, the real world application of this high temporal frequency data is hindered due to the product spatial resolution of 3\ \ 3, thus limiting the application in fire surveillance and mapping activities. This letter implements a modified version of the Advanced Himawari-8 Imager {\textendash} Fire Surveillance Algorithm (AHI-FSA) for SEVIRI with the aim of improving the spatial resolution of fire activity mapping. Initial results demonstrate the algorithm was able to improve the resolution of fire detection from 3\ \ 3\ km to 1\ \ 1\ km and simultaneously reduce the commission and omission errors by 25\% and 16\% respectively.

}, doi = {https://doi.org/10.1080/2150704X.2018.1484955}, url = {https://www.tandfonline.com/doi/abs/10.1080/2150704X.2018.1484955?journalCode=trsl20}, author = {Chathura Wickramasinghe and Luke Wallace and Karin Reinke and Simon Jones} } @article {bnh-5139, title = {Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products}, journal = {International Journal of Digital Earth}, year = {2018}, month = {09/2018}, abstract = {

The AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) is a recently developed algorithm designed to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. At present, the AHI-FSA algorithm has only been tested on a number of case study fires in Western Australia. Initial results demonstrate potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes), multi-resolution fire-line detections. This paper intercompares AHI-FSA across the Northern Territory of Australia (1.4 million km2) over a ten-day period with the well-established fire products from LEO (Low Earth Orbiting) satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses the difficulties and solutions when comparing high temporal frequency fire products with existing low temporal resolution LEO satellite products. The results indicate that the multi-resolution approach developed for AHI-FSA is successful in mapping fire activity at 500 m. When compared to the MODIS, daily AHI-FSA omission error was only 7\%. High temporal frequency data also results in AHI-FSA observing fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement.

}, doi = {https://doi.org/10.1080/17538947.2018.1527402}, url = {https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1527402?journalCode=tjde20}, author = {Chathura Wickramasinghe and Luke Wallace and Karin Reinke and Simon Jones} } @conference {bnh-3892, title = {Enhanced estimation of background temperature for fire detection using new geostationary sensors}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

Recent increases in the frequency and intensity of active fire has heightened the importance of remote sensing as a source of early warning information for fire incidents. The launches of new geostationary sensors, such as Himawari-8 over the Asia-Pacific, have vastly increased the information available with which to detect and attribute these incidents, with observations every 10 minutes possible over key parts of the electromagnetic spectrum (3.8 -- 4{\textmu}m). Remotely sensed fire products such as Sentinel Hotspots and the MODIS active fire product have focussed upon use of contextually derived background temperatures for isolating hotspots, dictated by the low temporal frequency of available images. This research proposes a new paradigm in fire detection, which utilises the increased temporal resolutions of geostationary sensor imagery to provide a baseline dataset for land surface temperature estimation based upon location and time of day. To achieve this, a multi-temporal diurnal characterisation of temperature is calculated for each pixel based upon a large area latitudinal transect. Hot spot anomalies are then identified based upon the deviation of the location{\textquoteright}s temperature from the expected diurnal cycle. Validation of the fire detection algorithm has focussed upon case study fires from the 2016/17 fire season, by way of inter-comparison with commonly used MODIS and VIIRS active fire products, and a burned area product. Results show increased capability for early fire detection using the new algorithm in comparison to traditional single image contextual algorithms employed for polar orbiting systems. Other advantages include notable resilience to sources of occlusion such as cloud and smoke. Further research will focus on the wider application of this method across the Australian continent and methods for countering more challenging detection conditions.

}, author = {Bryan Hally and Luke Wallace and Karin Reinke and Chathura Wickramasinghe and Simon Jones} } @article {bnh-3538, title = {Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8}, journal = {Remote Sensing}, volume = {8}, year = {2016}, month = {11/2016}, chapter = {932}, abstract = {

Satellite remote sensing is regularly used for wildfire detection, fire severity mapping and burnt area mapping. Applications in the surveillance of wildfire using geostationary-based sensors have been limited by low spatial resolutions. With the launch in 2015 of the AHI (Advanced Himawari Imaginer) sensor on board Himawari-8, ten-minute interval imagery is available covering an entire earth hemisphere across East Asia and Australasia. Existing active fire detection algorithms depend on middle infrared (MIR) and thermal infrared (TIR) channels to detect fire. Even though sub-pixel fire detection algorithms can detect much smaller fires, the location of the fire within the AHI 2 {\texttimes} 2 km (400 ha) MIR/TIR pixel is unknown. This limits the application of AHI as a wildfire surveillance and tracking sensor. A new multi-spatial resolution approach is presented in this paper that utilizes the available medium resolution channels in AHI. The proposed algorithm is able to map firelines at a 500 m resolution. This is achieved using near infrared (NIR) (1 km) and RED (500 m) data to detect burnt area and smoke within the flagged MIR (2 km) pixel. Initial results based on three case studies carried out in Western Australia shows that the algorithm was able to continuously track fires during the day at 500 m resolution. The results also demonstrate the utility for wildfire management activities.

}, doi = {http://dx.doi.org/10.3390/rs8110932}, url = {http://www.mdpi.com/2072-4292/8/11/932}, author = {Chathura Wickramasinghe and Simon Jones and Karin Reinke and Luke Wallace} }