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NARROW
GeoRef Subject
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all geography including DSDP/ODP Sites and Legs
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East Pacific Ocean Islands
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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Indian Ocean Islands
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Mascarene Islands
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Reunion
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Piton de la Fournaise (1)
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Oceania
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Polynesia
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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United States
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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Primary terms
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data processing (2)
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East Pacific Ocean Islands
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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education (1)
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Indian Ocean Islands
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Mascarene Islands
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Reunion
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Piton de la Fournaise (1)
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land subsidence (1)
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Oceania
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Polynesia
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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remote sensing (2)
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United States
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Hawaii
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Hawaii County Hawaii
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Hawaii Island
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Kilauea (1)
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volcanology (2)
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Abstract Accurate and fast delivery of information about recent lava flows is important for near-real-time monitoring of eruptions. Here, we have characterized the October 2010 lava flow at Piton de la Fournaise using various InSAR datasets. We first produced a map of the area covered by the lava flow (i.e. Area lava =0.71–0.75 km 2 ) using the coherence of two syn-eruptive interferograms. Then we analysed two post-eruptive InSAR datasets (i.e. monostatic and bistatic data). The monostatic database provided us simultaneously with the displacement rates, lava thickness, volume and volume flux. We found that the lava flow was subsiding and moving eastward at maximum rates of 13±0.3 and 4±0.2 cm a −1 , respectively. Also, it had a mean thickness of Z mean =5.85 m, Vol DRE =1.77±0.75×10 6 m 3 (1σ) and MOR=1.25±0.53 m 3 s −1 . The bistatic database provided us only with the thickness and volume information (i.e. Z mean =6.00 m, Vol DRE =1.83±0.65×10 6 m 3 and MOR=1.29±0.46 m 3 s −1 ). Finally, we used a thermal remote sensing technique to verify the InSAR-derived measurements. Results show that the monostatic and bistatic datasets were both well within the range for the DRE volume obtained from MODIS data (2.44–4.40×10 6 m 3 ). Supplementary material: Tables A1 and A2 give satellite images used in this study. Table A3 gives the parameters used for the calculation of the effusion rates. The figures give the data processing of the post-eruptive radar images. These are available at https://doi.org/10.6084/m9.figshare.c.2213563
Conclusion: recommendations and findings of the RED SEED working group
Abstract RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.