Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group

Pritchard, M. E.; Poland, Michael P.; Reath, K.; Andrews, B.; Bagnardi, M.; Biggs, J.; Carn, S.; Coppola, D.; Ebmeier, S.K.; Furtney, M.A.; Girona, Társilo; Griswold, J.; Lopez, T.; Lundgren, P.; Ogburn, S. E.; Pavolonis, M.; Rumpf, E.; Vaughan, R. G.; Wauthier, C.; Wessels, R.; Wright, R.; Anderson, K. R.; Bato, M.G.; Roman, A.

doi:10.3133/sir20225116

Cited by 9 publications

(12 citation statements)

References 63 publications

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“…Further work is necessary to pinpoint causes and potential solutions to increase data acquisition and public access in the potentially understudied regions and volcanoes. Current efforts include the development of global databases (e.g., Galetto et al., 2023; Ogburn et al., 2013), and international efforts to coordinate satellite observations at volcanoes (Pritchard et al., 2022). Further work is necessary to determine if there are eruptive scenarios that are systematically understudied and not well‐represented in the data sets here (e.g., eruptive products that are more difficult to measure could include those that extend into water, or small, short‐lived events).…”

Section: Discussionmentioning

confidence: 99%

“…This leads to delays in data acquisition, release, and processing. These issues can be addressed in part by an international effort to coordinate satellite assets, as recommended by the USGS Powell Center Volcano Remote Sensing Working Group (Pritchard et al., 2022). There are a few examples of open access work such as ArcticDEM, a product created from WorldView satellite data (Porter et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Temporal Resolution Needs for Volcano Topographic Change Data Sets Based on Past Eruptions (1980–2019)

Eiden,

Pritchard,

Lundgren

2023

Earth and Space Science

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Up‐to‐date topography data sets are essential for forecasting volcanic hazards and monitoring deformation. Digital elevation models are used to quantify eruption rates, used in flow modeling programs, and are necessary to accurately process interferometric synthetic aperture radar data for surface deformation. We can track topographic change at volcanoes through fieldwork, airborne instruments, and satellite data, with the last providing the greatest potential for global coverage. Despite this global coverage, we do not know the characteristics of topographic change at volcanoes over a given time interval. We define the specific acquisition needs for topography data using topographic change detected from recent eruptions. We review existing literature and compile a data set of eruptive products (121 lava flows, 99 domes and 163 pyroclastic density currents (PDCs)) from eruptions between 1980 and 2019. We find that different sensing capabilities are required for different use cases. A vertical accuracy of 1 m would detect 92% of all eruptive products including 100% of lava domes and lava flows, but only 78% of PDCs. A horizontal resolution of 13 × 13 m pixels is the minimum necessary to detect 90% of all eruptive products. Explosive eruptions (with PDC products) typically lasted less than 1 day and would need a temporal resolution of 1 day while a longer repeat interval is acceptable at effusive eruptions (lava domes and flows), which could last weeks to years. We find a lack of consistent data acquisition, with 45% of the 383 eruptive products reported not having published spatial dimensions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Resolution Needs for Volcano Topographic Change Data Sets Based on Past Eruptions (1980–2019)

Eiden,

Pritchard,

Lundgren

2023

Earth and Space Science

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“…We envision an opportunity whereby local monitoring infrastructure could be supported by remote methods for data continuity and adding layers of observational detail. The recent studies have proposed an international remote sensing strategy for this purpose (Pritchard et al, 2022). In eruptions that produce volcanic lightning, global lightning detection networks provide robust datasets with high temporal resolution and are especially powerful when combined with other remote methods like infrasound, seismic, and satellite.…”

Section: Discussionmentioning

confidence: 99%

Remote Characterization of the 12 January 2020 Eruption of Taal Volcano, Philippines, Using Seismo-Acoustic, Volcanic Lightning, and Satellite Observations

Perttu

Assink

Eaton

et al. 2023

Bulletin of the Seismological Society of America

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On 12 January 2020, an eruption began on the shores of the Main Crater Lake (MCL) of Taal Volcano—a caldera system on the southern end of Luzon Island in the Philippines. Taal, one of the most active volcanoes in the Philippines, is located 30 km south of Manila—a major metropolitan area with a population of 13.5 million people. Eruptive activity intensified throughout the day on 12 January, producing prolific volcanic lightning, ashfall, and a sustained plume that reached 16–17 km altitude. The chronology of events was well documented by the Philippine Institute of Volcanology and Seismology and the Tokyo Volcanic Ash Advisory Center. The wealth of data collected during the eruption provides a unique opportunity to investigate how the combination of different remote sensing methods may complement local observations and monitoring. Remote systems tend to provide lower resolution data but are also less likely to be compromised by the eruptive activity, thus providing continuous records of eruptive processes. Here, we present a postevent analysis of the 12 January activity, including data from long-range lightning, infrasound, and seismic arrays located at distances up to several thousands of kilometers from the volcano. By combining these datasets, we distinguish five phases of activity and infer a major shift in eruption behavior around 12:00 on 12 January (UTC). The remote observations suggest that the most of the water within the MCL (∼42 million m3) was vaporized and incorporated into the volcanic plume within the first 12 hr of the eruption.

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“…Therefore, monitoring changes in hotspot activity can provide key insights into a volcano's behavior by indicating the presence of thermal volcanic features and characterizing them over time. Due to the utility of these observations, thermal satellite data are used by volcano observatories as part of their daily monitoring operations (Dehn et al, 2000;Dehn et al, 2002;Harris et al, 2016;Harris et al, 2017;Cameron et al, 2018;Coombs et al, 2018;Coppola et al, 2020;Pritchard et al, 2022;Chevrel et al, 2023). Automating the detection and quantification of volcanic hotspots can provide near-real time information to volcano observatory scientists to inform decision-making and provide a mechanism to generate long time series of thermal activity for volcanoes around the world.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

Saunders-Shultz,

Lopez,

Dietterich

et al. 2024

Front. Earth Sci.

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An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

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Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group

Cited by 9 publications

References 63 publications

Spatial and Temporal Resolution Needs for Volcano Topographic Change Data Sets Based on Past Eruptions (1980–2019)

Spatial and Temporal Resolution Needs for Volcano Topographic Change Data Sets Based on Past Eruptions (1980–2019)

Remote Characterization of the 12 January 2020 Eruption of Taal Volcano, Philippines, Using Seismo-Acoustic, Volcanic Lightning, and Satellite Observations

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

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