Next Generation Fire Detection from Geostationary Satellites
| Autor: | Luke Wallace, Chathura H. Wickramasinghe, Simon Jones, Bryan Hally, Chermelle Engel, Karin Reinke |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Visible Infrared Imaging Radiometer Suite
010504 meteorology & atmospheric sciences Fire detection Anomaly (natural sciences) 0211 other engineering and technologies Frequency data 02 engineering and technology 01 natural sciences Temporal resolution Geostationary orbit Environmental science Satellite Moderate-resolution imaging spectroradiometer 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss.2018.8518812 |
| Popis: | The utility of Geostationary active fire detection and surveillance has recently been supplemented by two new algorithms developed by our group: the AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) and the Broad Area Training (BAT) method [1], [2]. Here we present results from a large area validation of these products to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. The AHI-FSA/BAT algorithms were developed and tested on a number of case study areas in Western Australia. Initial results demonstrate a high potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes) fire detections. However, the AHI-FSA and BAT products need to be validated over a large area to quantify the performance of the algorithms. This paper validates their performance in the Northern Territory of Australia (1.4 million km2) over a 10 day period by comparing AHI-FSA/BAT to well-established products from LEO satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses difficulties in validating high temporal resolution products with existing low temporal resolution LEO satellite products. Results indicate that the multi-resolution approach developed for AHI-FSA/BAT significantly improves fire detection. When compared to the MODIS thermal anomaly products, BAT omission error was only 2%. High temporal frequency data results in AHI-FSA/BAT detecting fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement. |
| Databáze: | OpenAIRE |
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