An automated volcanic hot-spot detection algorithm based on FY-4A/AGRI infrared data
Abstract
Remote sensing technology provides an effective means of providing early warnings of volcanic eruptions and of monitoring volcanic conditions after eruptions, and such technology is usually the main source of information for remote volcanoes. FY-4A is the first of China's new-generation geostationary satellites and was launched on 11 December 2016 (Yang et al. 2017). FY-4A represents a technological advance in Chinese geostationary meteorological satellites from the first generation (Fengyun-2 series) to the second generation (Fengyun-4 series). A new algorithm (FYVOLC) for the automated detection of volcanic hot spots using data obtained from China's new-generation satellite FY-4A is developed and tested. FYVOLC improves the detection ability of the Volcanic Anomaly SofTware (VAST) algorithm by incorporating a Normalized Difference Brightness Temperature Index (NBTDI) as a detection criterion. In addition, FYVOLC introduces a mid-infrared brightness temperature criterion to identify volcanic hot spots by making calculations based on the image itself without artificially determining any parameters. To test the volcanic hot-spot detection performance of FYVOLC, FY-4A Advanced Geostationary Radiation Imager data were used for eruptions from four volcanoes: Mayon Volcano in the Philippines (26 January 2018), and Bromo (1-2 September 2018), Lawu (3-4 September 2018), and Soputan volcanoes (3-4 October 2018) in Indonesia. The results of the FYVOLC algorithm were compared with those of three existing volcanic hot-spot detection algorithms: a simplified contextual algorithm, the VAST algorithm, and the HOTSAT algorithm. It is shown that the simplified contextual algorithm and the VAST algorithm are prone to generating false positives, whereas the HOTSAT algorithm is prone to generating false negatives. The FYVOLC algorithm has the best detection accuracy using FY-4A data owing to the adopted NBTDI and image-based mid-infrared brightness temperature criterion. This study is the first to realize the automated detection of volcanic hot spots based on FY-4A satellite data. The results have significance for the continuing development of global volcanic early-warning systems and for the dynamic monitoring of volcanoes after eruptions.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.V31H0102Z
- Keywords:
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- 1030 Geochemical cycles;
- GEOCHEMISTRY;
- 1031 Subduction zone processes;
- GEOCHEMISTRY;
- 8414 Eruption mechanisms and flow emplacement;
- VOLCANOLOGY;
- 8485 Remote sensing of volcanoes;
- VOLCANOLOGY