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| In the years between 1980 and 1991, a large area surrounding Mount St Helens was known to be deforming, resulting in a total of 1-3 cm of uplift. This uplift was measured by trilateration surveys conducted in 1982 and 1991 and was interpreted to be the result of post-eruptive recharge of a deep magma chamber beneath Helens. Interestingly, through the time of the eruption, no further deformation was measured through a synthesis of trilateration data, collected on a broad scale, and campaign GPS surveys conducted in 2000 and 2003, at 17 stations within 5km of the crater. However, if the total displacements from 2000-2003 were small, less than 2 cm, they may have been below the survey’s level of detection. Additional measurements made by a continuous GPS station located at the Johnston Ridge Observatory, approximately 9km away from the crater, did not indicate any surface deformation. | In the years between 1980 and 1991, a large area surrounding Mount St Helens was known to be deforming, resulting in a total of 1-3 cm of uplift. This uplift was measured by trilateration surveys conducted in 1982 and 1991 and was interpreted to be the result of post-eruptive recharge of a deep magma chamber beneath Helens. Interestingly, through the time of the eruption, no further deformation was measured through a synthesis of trilateration data, collected on a broad scale, and campaign GPS surveys conducted in 2000 and 2003, at 17 stations within 5km of the crater. However, if the total displacements from 2000-2003 were small, less than 2 cm, they may have been below the survey’s level of detection. Additional measurements made by a continuous GPS station located at the Johnston Ridge Observatory, approximately 9km away from the crater, did not indicate any surface deformation. | ||
| + | {{:mark:poland_and_lu.jpg}} | ||
| Because of the wide spatial and temporal spacing, and to a lesser extent, the levels of noise, in data collected by trilateration and GPS, no conclusions could be made about any potential surface deformation that may have occurred locally on the edifice or within the crater of Helens. The ability to produce spatially continuous maps of surface displacements gives InSAR the ability to resolve the question of whether localized deformation may have occurred at Mt St Helens prior to its 2004 eruption. A study conducted by Poland and Lu in 2008 attempted to image both pre and post eruptive deformation at Mount St Helens using interferogram stacking. Because of decorrelation caused by the presence of snow and dense vegetation, even stacks of interferograms were unable to obtain signal within the crater or on the edifice prior to the eruption (Fig ##). While the results prior to the 2004 eruption were inconclusive, post eruptive results successfully imaged subsidence around and on parts of the edifice. | Because of the wide spatial and temporal spacing, and to a lesser extent, the levels of noise, in data collected by trilateration and GPS, no conclusions could be made about any potential surface deformation that may have occurred locally on the edifice or within the crater of Helens. The ability to produce spatially continuous maps of surface displacements gives InSAR the ability to resolve the question of whether localized deformation may have occurred at Mt St Helens prior to its 2004 eruption. A study conducted by Poland and Lu in 2008 attempted to image both pre and post eruptive deformation at Mount St Helens using interferogram stacking. Because of decorrelation caused by the presence of snow and dense vegetation, even stacks of interferograms were unable to obtain signal within the crater or on the edifice prior to the eruption (Fig ##). While the results prior to the 2004 eruption were inconclusive, post eruptive results successfully imaged subsidence around and on parts of the edifice. | ||
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| In this study, the Stanford Method for Persistent Scatterers developed by Hooper et al. (2012) was applied to a set of eight SAR scenes collected by the ERS-2 satellite covering Mount St Helens over the pre-eruptive period, from 1996 to 2002 (Figs ##, ##). Many SAR scenes from track 156, frame 2673 were available, but only those from the summer and fall months were chosen in order to minimize the effects of snow. During processing, the number of scenes was further reduced to eight after interferogram pairs having poor perpendicular baselines and high decorrelation were eliminated. Additional SAR datasets from different track –frame combinations exists over Mount St Helens, but are more limited in the time they span, and their number of scenes. | In this study, the Stanford Method for Persistent Scatterers developed by Hooper et al. (2012) was applied to a set of eight SAR scenes collected by the ERS-2 satellite covering Mount St Helens over the pre-eruptive period, from 1996 to 2002 (Figs ##, ##). Many SAR scenes from track 156, frame 2673 were available, but only those from the summer and fall months were chosen in order to minimize the effects of snow. During processing, the number of scenes was further reduced to eight after interferogram pairs having poor perpendicular baselines and high decorrelation were eliminated. Additional SAR datasets from different track –frame combinations exists over Mount St Helens, but are more limited in the time they span, and their number of scenes. | ||
| Results | Results | ||
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| + | {{:mark:big_map.jpg}} {{:mark:table.jpg}} | ||
| StaMPS processing was run successfully on the pre-eruptive ERS-2 data, yielding a decent density of stable pixels both on the edifice and within the crater. Refined interferograms were created alongside maps of average velocity over the timespan of 1996-2002. An example interferogram and average velocity map overlain on Google Earth imagery are shown below (Figs ##, ##). | StaMPS processing was run successfully on the pre-eruptive ERS-2 data, yielding a decent density of stable pixels both on the edifice and within the crater. Refined interferograms were created alongside maps of average velocity over the timespan of 1996-2002. An example interferogram and average velocity map overlain on Google Earth imagery are shown below (Figs ##, ##). | ||
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| + | {{:mark:ex_int.jpg}} {{:mark:stamps.jpg}} | ||
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| **Discussion and Conclusions of StaMPS Processing** | **Discussion and Conclusions of StaMPS Processing** | ||
| Considering the map of average velocities, it can be seen that pixels on the edifice and in the crater are being selected as stable. The phases of the pixels selected over Mount St Helens are spatially correlated to a good degree, indicating that reliable and low noise phase information can in fact be pulled from areas which were decorrelated in previous studies. However, there is still much uncertainty about what physical features on the edifice the persistent scatterers correspond to. While it may appear that there is a distinct signal of uplift just off-center of the volcano, there is good reason to believe that the presented results are heavily influenced by atmospheric effects. In several of the interferograms created through StaMPS processing, a strong correlation between phase and elevation was present (Figure ##), indicating influence from atmospheric changes. | Considering the map of average velocities, it can be seen that pixels on the edifice and in the crater are being selected as stable. The phases of the pixels selected over Mount St Helens are spatially correlated to a good degree, indicating that reliable and low noise phase information can in fact be pulled from areas which were decorrelated in previous studies. However, there is still much uncertainty about what physical features on the edifice the persistent scatterers correspond to. While it may appear that there is a distinct signal of uplift just off-center of the volcano, there is good reason to believe that the presented results are heavily influenced by atmospheric effects. In several of the interferograms created through StaMPS processing, a strong correlation between phase and elevation was present (Figure ##), indicating influence from atmospheric changes. | ||
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| + | {{:mark:ph_v_elev.jpg}} | ||
| In generating the velocity map shown in Figure ##, a tool within StaMPS was used to try and estimate the atmospheric contribution to phase. This tool takes advantage of the fact that the atmospheric contribution to Interferometric phase, is often correlated with terrain elevation. Plots of phase versus elevation are displayed for each interferogram, and the user decides whether and how to fit a line to the data (Figure ##). The linear fit to the data is used to create an atmospheric phase mask which is subtracted from the interferogram after unwrapping phase. In some interferograms, however, the relationship between phase and elevation is less clear (Figure ##), and deciding how or whether to fit a line at all can be subjective, difficult, and substantially impact the final results. | In generating the velocity map shown in Figure ##, a tool within StaMPS was used to try and estimate the atmospheric contribution to phase. This tool takes advantage of the fact that the atmospheric contribution to Interferometric phase, is often correlated with terrain elevation. Plots of phase versus elevation are displayed for each interferogram, and the user decides whether and how to fit a line to the data (Figure ##). The linear fit to the data is used to create an atmospheric phase mask which is subtracted from the interferogram after unwrapping phase. In some interferograms, however, the relationship between phase and elevation is less clear (Figure ##), and deciding how or whether to fit a line at all can be subjective, difficult, and substantially impact the final results. | ||
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| **Dataset Description** | **Dataset Description** | ||
| - | The remote sensing atmospheric data used in this study comes from the Moderate Resolution Imaging Spectroradiometer instrument carried by NASA’s Terra and Aqua satellites. A set of 13 MODIS acquisitions spread over the year 2013 were selected to maximize the data coverage and resolution over Mount St Helens. Profiles of various atmospheric properties, including pressure, temperature, and water vapor mixing ratio are collected at roughly five kilometer spacing in a grid-like fashion. Vertical profiles are sampled at between ten and twenty pressure levels within the atmosphere. The altitude of each pressure level is estimated for each climate data point. Example profiles are shown in figure ##. The Digital Elevation Model (DEM) used is from the NASA’s Shuttle Radar Topography Mission (SRTM). | + | The remote sensing atmospheric data used in this study comes from the Moderate Resolution Imaging Spectroradiometer instrument carried by NASA’s Terra and Aqua satellites. A set of 13 MODIS acquisitions spread over the year 2013 were selected to maximize the data coverage and resolution over Mount St Helens. Profiles of various atmospheric properties, including pressure, temperature, and water vapor mixing ratio are collected at roughly five kilometer spacing in a grid-like fashion. Vertical profiles are sampled at between ten and twenty pressure levels within the atmosphere. |
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| + | {{:mark:profs.jpg}} | ||
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| + | The altitude of each pressure level is estimated for each climate data point. Example profiles are shown in figure ##. The Digital Elevation Model (DEM) used is from the NASA’s Shuttle Radar Topography Mission (SRTM). | ||
| **Methods** | **Methods** | ||
| - | Maps of phase lag for each MODIS acquisition time can be calculated from altitude profiles of pressure, temperature, and water partial pressure (calculated from pressure and water vapor mixing ratio). To calculate phase lag, first a profile of refractivity (N) with respect to height is calculated using Equation ##. The resulting refractivity profiles are then interpolated to the spacing of the DEM using a distance weighted spatial average. Finally, equation ## is applied to the refractivity profiles, integrating from the DEM height up to an arbitrarily high point, above which there is little atmospheric contribution to phase lag (Jung et al. 2014). An example map of phase lag over Mount St Helens is shown in Figure ##. | + | Maps of phase lag for each MODIS acquisition time can be calculated from altitude profiles of pressure, temperature, and water partial pressure (calculated from pressure and water vapor mixing ratio). To calculate phase lag, first a profile of refractivity (N) with respect to height is calculated using Equation ##. |
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| + | {{:mark:eqns.jpg}} | ||
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| + | The resulting refractivity profiles are then interpolated to the spacing of the DEM using a distance weighted spatial average. Finally, equation ## is applied to the refractivity profiles, integrating from the DEM height up to an arbitrarily high point, above which there is little atmospheric contribution to phase lag (Jung et al. 2014). An example map of phase lag over Mount St Helens is shown in Figure ##. | ||
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| + | {{:mark:lag.jpg}} | ||
| Atmospheric Phase Screens (APS) depict the difference in phase lag from one time to another and represent the atmospheric component that would be seen in an interferogram. It is important to note that the magnitude of the phase screen at each pixel is relative like phase in interferograms, and that atmospheric phase screens can be simply calculated by subtracting one phase lag scene from another. In this study, the APS calculated from the MODIS data are treated as interferograms containing no deformation or other source of error. A close approximation to the StaMPS processing chain, is applied to APS calculated from the 13 MODIS scenes spanning 2013 to investigate the algorithm’s effectiveness at mitigating atmospheric effects over Mount St Helens. | Atmospheric Phase Screens (APS) depict the difference in phase lag from one time to another and represent the atmospheric component that would be seen in an interferogram. It is important to note that the magnitude of the phase screen at each pixel is relative like phase in interferograms, and that atmospheric phase screens can be simply calculated by subtracting one phase lag scene from another. In this study, the APS calculated from the MODIS data are treated as interferograms containing no deformation or other source of error. A close approximation to the StaMPS processing chain, is applied to APS calculated from the 13 MODIS scenes spanning 2013 to investigate the algorithm’s effectiveness at mitigating atmospheric effects over Mount St Helens. | ||
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| **Results** | **Results** | ||
| - | Maps of atmospheric phase lag like the one shown in Figure ## are all tightly correlated with topography due to its control of the lower bound of integration in Equation ## and their magnitudes which are on the order of two meters can vary amounts up 20 cm. This effect can be clearly seen in an example of APS (Figure ##), where differences in delay of up to 12cm can exist across a scene, arising from differential changes in the water vapor content of the air. Figure ## shows the map of average apparent velocities that would result from the application of a StaMPS-like algorithm to a series of 12 APS made from 13 maps of atmospheric delay. Differences in velocity on the order of 2 cm/yr are seen over short length scales (~5km), smaller than the StaMPS scene over St Helens (Pictured). | + | Maps of atmospheric phase lag like the one shown in Figure ## are all tightly correlated with topography due to its control of the lower bound of integration in Equation ## and their magnitudes which are on the order of two meters can vary amounts up 20 cm. |
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| + | {{:mark:aps.jpg}} | ||
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| + | This effect can be clearly seen in an example of APS (Figure ##), where differences in delay of up to 12cm can exist across a scene, arising from differential changes in the water vapor content of the air. Figure ## shows the map of average apparent velocities that would result from the application of a StaMPS-like algorithm to a series of 12 APS made from 13 maps of atmospheric delay. Differences in velocity on the order of 2 cm/yr are seen over short length scales (~5km), smaller than the StaMPS scene over St Helens (Pictured). | ||
| **Discussion and Conclusions** | **Discussion and Conclusions** | ||
