The 1997 eruption of Okmok Volcano, Alaska: a synthesis of remotely sensed imagery

Patrick, Matthew R.; Dehn, J.; Papp, K.R.; Lü, Zhiming; Dean, Kenneth; Moxey, L.; Izbekov, Pavel; Guritz, R.

doi:10.1016/s0377-0273(03)00180-x

Cited by 37 publications

(39 citation statements)

References 33 publications

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“…These volumes can be used in magma supply models and time-series analyses of volcanic activity to further the understanding of eruption mechanisms (Burt et al 1994). At volcanoes situated in remote or politically unstable locations where groundbased monitoring and fieldwork are hazardous or impossible, airborne and spaceborne remote sensing techniques are particularly useful (Abrams et al 1991;Harris et al 1999;Carn and Oppenheimer 2000;Patrick et al 2003). For Nyamuragira volcano in the Democratic Republic of the Congo (D.R.…”

Section: Lava Flow Mappingmentioning

confidence: 99%

Mapping lava flows from Nyamuragira volcano (1967–2011) with satellite data and automated classification methods

Head¹,

Maclean²,

Carn³

2013

Geomatics, Natural Hazards and Risk

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The volume, location and extent of historical lava flows are important when assessing volcanic hazards, as well as the productivity or longevity of a volcanic system. We use a Landsat/Hyperion/ALI dataset and automated classification methods to map lava flows at Nyamuragira volcano in the Democratic Republic of the Congo. The humid tropical climate of Nyamuragira is advantageous because its lava flows are emplaced onto heavily forested flanks, resulting in strong contrast between lava and vegetation, which contributes to efficient flow mapping. With increasing age, there is an increase in Landsat band-4 reflectance, suggesting lava flow revegetation with time. This results in a distinct spectral contrast to delineate overlapping flows emplaced *5 years apart. Areal extents of the flows are combined with published lava flow thicknesses to derive volumes. The Landsat/ Hyperion/ALI dataset is advantageous for mapping future flows quickly and inexpensively, particularly for volcano observatories where resources are limited.

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Section: Lava Flow Mappingmentioning

confidence: 99%

Mapping lava flows from Nyamuragira volcano (1967–2011) with satellite data and automated classification methods

Head¹,

Maclean²,

Carn³

2013

Geomatics, Natural Hazards and Risk

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“…4. Use of satellite optical or thermal data Patrick et al 2003), laser altimeter (Mazzarini et al 2005) and/or interferometric radar data (Rowland 1996;Rowland et al 1999Rowland et al , 2003Zebker et al 1996) to obtain flow area and thickness, and hence volume by integrating thickness over the area (e.g. .…”

Section: Volume-based Measurementsmentioning

confidence: 99%

Lava effusion rate definition and measurement: a review

2007

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Measurement of effusion rate is a primary objective for studies that model lava flow and magma system dynamics, as well as for monitoring efforts during on-going eruptions. However, its exact definition remains a source of confusion, and problems occur when comparing volume flux values that are averaged over different time periods or spatial scales, or measured using different approaches. Thus our aims are to: (1) define effusion rate terminology; and (2) assess the various measurement methods and their results. We first distinguish between instantaneous effusion rate, and time-averaged discharge rate. Eruption rate is next defined as the total volume of lava emplaced since the beginning of the eruption divided by the time since the eruption began. The ultimate extension of this is mean output rate, this being the final volume of erupted lava divided by total eruption duration. Whether these values are total values, i.e. the flux feeding all flow units across the entire flow field, or local, i.e. the flux feeding a single active unit within a flow field across which many units are active, also needs to be specified. No approach is without its problems, and all can have large error (up to ∼50%). However, good agreement between diverse approaches shows that reliable estimates can be made if each approach is applied carefully and takes into account the caveats we detail here. There are three important factors to consider and state when measuring, giving or using an effusion rate. First, the time-period over which the value was averaged; second, whether the measurement applies to the entire active flow field, or a single lava flow within that field; and third, the measurement technique and its accompanying assumptions.

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“…The 1997 and 2008 eruptions are extremely well-documented thanks, in part, to the advent of space-based observations-especially the Synthetic Aperture Radar (SAR) interferometry (InSAR) technique. The 1997 eruption, which began in early February 1997 and ended in late April 1997, was preceded by several years of inflationary deformation amounting to tens of centimeters, accompanied by over a meter of subsidence, and followed by tens of cm of re-inflation [1,6,[12][13][14][15]. Based on these InSAR-derived displacement patterns, Lu et al [16] modeled the deformation as due to a point source or spherical magma reservoir located beneath the center of the caldera at a depth of 2.6-3.2 km below sea level [17].…”

Section: Introductionmentioning

confidence: 99%

“…Deformation at the volcano has been thoroughly examined with InSAR and GPS, while field and remote sensing investigations have characterized both historic and prehistoric eruptive activities e.g., [1,[9][10][11][12][13][14][15][16][18][19][20][28][29][30][31]. Although the general geometry of deformation is relatively constant over time, suggesting a persistent source, deformation rates varied greatly in time, which suggests changes in magma supply of the volcano's shallow reservoir system [1,11,20].…”

Section: Introductionmentioning

confidence: 99%

Post-Eruptive Inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014

Lü

Poland

et al. 2015

Remote Sensing

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Okmok, a~10-km wide caldera that occupies most of the northeastern end of Umnak Island, is one of the most active volcanoes in the Aleutian arc. The most recent eruption at Okmok during July-August 2008 was by far its largest and most explosive since at least the early 19th century. We investigate post-eruptive magma supply and storage at the volcano during 2008-2014 by analyzing all available synthetic aperture radar (SAR) images of Okmok acquired during that time period using the multi-temporal InSAR technique. Data from the C-band Envisat and X-band TerraSAR-X satellites indicate that Okmok started inflating very soon after the end of 2008 eruption at a time-variable rate of 48-130 mm/y, consistent with GPS measurements. The "model-assisted" phase unwrapping method is applied to improve the phase unwrapping operation for long temporal baseline pairs. The InSAR time-series is used as input for deformation source modeling, which suggests magma accumulating at variable rates in a shallow storage zone at~3.9 km below sea level beneath the summit caldera, consistent with previous studies. The modeled volume accumulation in the six years following the 2008 eruption is~75% of the 1997 eruption volume and~25% of the 2008 eruption volume.

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The 1997 eruption of Okmok Volcano, Alaska: a synthesis of remotely sensed imagery

Cited by 37 publications

References 33 publications

Mapping lava flows from Nyamuragira volcano (1967–2011) with satellite data and automated classification methods

Mapping lava flows from Nyamuragira volcano (1967–2011) with satellite data and automated classification methods

Lava effusion rate definition and measurement: a review

Post-Eruptive Inflation of Okmok Volcano, Alaska, from InSAR, 2008–2014

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