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<p>Mapping lava flows from a range of conditions and environments is critical for understanding flow behaviour and hazard assessment, but ground-based observations can be challenging, especially in remote regions. Here, we use 39 SAR backscatter images from the COSMO-SkyMed satellite to measure the extent and surface properties of lava flows from the 2017 to 2019 eruption of Erta ‘Ale volcano, Ethiopia. Using pairs of SAR backscatter images, we produce change difference images and map the flow outlines. We observe both increases and decreases in backscatter, which we attribute to differences in surface roughness. These manual-derived flow maps produced detailed outlines that are consistent with results obtained from other datasets, such as coherence. We then apply a sequential analysis technique, CUSUM, to corrected backscatter timeseries and find that it can automatically identify flows with an accuracy of 88%. Automated detection provides an objective and repeatable alternative to manual mapping. We find that at least four pre-eruption acquisitions are required as a baseline to achieve a robust classification. Our results demonstrate that SAR backscatter timeseries analysis can be used to characterise lava flow emplacement and surface evolution in remote volcanic environments.</p>

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Analysis and automatic detection of lava flows using SAR backscatter applied to the 2017 eruption of Erta ‘Ale Volcano, Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月8日

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  • Jemima Gosling,
  • Edna Warsame Dualeh,
  • Juliet Biggs

摘要

Mapping lava flows from a range of conditions and environments is critical for understanding flow behaviour and hazard assessment, but ground-based observations can be challenging, especially in remote regions. Here, we use 39 SAR backscatter images from the COSMO-SkyMed satellite to measure the extent and surface properties of lava flows from the 2017 to 2019 eruption of Erta ‘Ale volcano, Ethiopia. Using pairs of SAR backscatter images, we produce change difference images and map the flow outlines. We observe both increases and decreases in backscatter, which we attribute to differences in surface roughness. These manual-derived flow maps produced detailed outlines that are consistent with results obtained from other datasets, such as coherence. We then apply a sequential analysis technique, CUSUM, to corrected backscatter timeseries and find that it can automatically identify flows with an accuracy of 88%. Automated detection provides an objective and repeatable alternative to manual mapping. We find that at least four pre-eruption acquisitions are required as a baseline to achieve a robust classification. Our results demonstrate that SAR backscatter timeseries analysis can be used to characterise lava flow emplacement and surface evolution in remote volcanic environments.