Triggering of volcanic activity by large earthquakes

Statistical analyses of temporal relationships between large earthquakes and volcanic eruptions suggest seismic waves may trigger eruptions even over great (\u3e1000 km) distances, although the causative mechanism is not well constrained. In this study the relationship between large earthquakes and subtle changes in volcanic activity was investigated in order to gain greater insight into the relationship between dynamic stresses propagated by surface waves and volcanic response. Daily measurements from the Ozone Monitoring Instrument (OMI), onboard the Aura satellite, provide constraints on volcanic sulfur-dioxide (SO2) emission rates as a measure of subtle changes in activity. Time series of SO2 emission rates were produced from OMI data for thirteen persistently active volcanoes from 1 October 2004 to 30 September 2010. In order to quantify the affect of earthquakes at teleseismic distances, we modeled surface-wave amplitudes from the source mechanisms of moment magnitude (Mw) ≥7 earthquakes, and calculated the Peak Dynamic Stress (PDS). We assessed the influence of earthquakes on volcanic activity in two ways: 1) by identifying increases in the SO2 time series data and looking for causative earthquakes and 2) by examining the average emission rate before and after each earthquake. In the first, the SO2 time series for each volcano was used to calculate a baseline threshold for comparison with post-earthquake emission. Next, we generated a catalog of responses based on sustained SO2 emission increases above this baseline. Delay times between each SO2 response and each prior earthquake were analyzed using both the actual earthquake catalog, and a randomly generated catalog of earthquakes. This process was repeated for each volcano. Despite varying multiple parameters, this analysis did not demonstrate a clear relationship between earthquake-generated PDS and SO2 emission. However, the second analysis, which was based on the occurrence of large earthquakes indicated a response at most volcanoes. Using the PDS calculations as a filtering criterion for the earthquake catalog, the SO2 mass for each volcano was analyzed in 28-day windows centered on the earthquake origin time. If the average SO2 mass after the earthquake was greater than an arbitrary percentage of pre-earthquake mass, we identified the volcano as having a response to the event. This window analysis provided insight on what type of volcanic activity is more susceptible to triggering by dynamic stress. The volcanoes with very open systems included in this study, Ambrym, Gaua, Villarrica, Erta Ale and, Turrialba, showed a clear response to dynamic stress while the volcanoes with more closed systems, Merapi, Semeru, Fuego, Pacaya, and Bagana, showed no response

Applicability of Spectral Decomposition by Varimax-Rotated, Principal Component Analysis to the Surface Biology and Geology (SBG) VNIR Mission Concept

Cyanobacterial and Harmful Algal Blooms (CyanoHABs) are a growing concern in coastal and inland waters. But, spectral interference from multiple constituents in optically complex waters can hamper application of remote sensing using traditional image processing methods. The Kent State University (KSU) spectral decomposition method can be applied to multispectral and hyperspectral remote sensing images (e.g. HICO and the NASA Glenn HSI2) to partition and identify signals related to cyanobacteria, algae, pigment degradation products and suspended sediment in each pixel. Fundamental to the use of remote sensing data is the ability to extract independent signals from correlated hyperspectral VNIR data cubes. The Kent State University varimax-rotated, principal component analysis method (VPCA) is important to integrate into the SBG VNIR mission concept because it provides greater specificity, a software-based SNR boost relative to hardware performance, and can assist with Cal/Val, Modeling and Applications. We present examples of the hyperspectral application of the KSU VPCA method with relevance to SBG. The information extracted by VPCA can be validated spectrally or spatially with laboratory and/or in situ sensors, which capture spatial or time series of information at discrete points within remote sensing images. Comparisons show hyperspectral sensors extract more components than multispectral ones, but more independent information can be extracted from multispectral sensors by VPCA than traditional band ratio approaches. The spectral decomposition method is capable of enhancing the signal to noise ratio (SNR) of the NASA Glenn, second-generation hyperspectral imager by a factor of 7x to 20x, with a spectral reproducibility of 3%. The spectral decomposition method, when compared against existing remote sensing monitoring methods exhibits both greater specificity and a lower detection limit. The method has been validated with multispectral images in Lake Erie to quantify the Microcystis CyanoHAB and from the Indian River Lagoon, Florida to quantify the Brown Tide resulting from A. lagunesnsis. Field operations in the Western Basin of Lake Erie were conducted using a bbe Fluoroprobe to collect vertical profiles and horizontal tows along a transect from the Toledo to the Detroit Lighthouse during coincident satellite overpasses. Extraction of pixel values from the MODIS Aqua sensor yields agreement between in situ field and lab-based measures of cyanobacterial, cryptophyte, diatoms and green algae, suspended sediment and pigment degradation products with R2>0.8

Remote Sensing of Cyanobacterial and Harmful Algal Blooms in Lake Okeechobee and Biscayne Bay, Florida

Cyanobacterial and Harmful Algal Blooms (CyanoHABs) have become a major topic of concern for homeowners and environmental groups in Florida, with blooms occurring in both Lake Okeechobee and Biscayne Bay in prior years. While Biscayne Bay and Lake Okeechobee are distinct water bodies, with different manifestations of the blooms, in both environments CyanoHABs can contain toxins that are harmful to humans and animals, can lead to fish and wildlife kills, as well as disrupt ecosystems. Furthermore, recreational and economic use of the waters of Biscayne Bay and Lake Okeechobee are negatively impacted by these blooms. Monitoring and assessment of the CyanoHABs in both water bodies is a vital aspect of understanding the drivers and impacts of CyanoHAB growth in Florida. Spectral decomposition of satellite remote sensing images of Lake Erie has been shown to be effective at discriminating between in-water constituents, both those related to CyanoHABs, and those that are non-HAB forming. Here we show that the KSU spectral decomposition method is also successful in identifying in-water constituents in Florida waters using images from the Sentinel 3A- Ocean and Land Color Instrument, acquired on 16 July 2017 and 28 July 2018. We identify the CyanoHAB signal in Lake Okeechobee on both days, as well as the sediment and algal signal in Biscayne Bay.</p

Using VPCA Spectral Decomposition to Analyze Optical Components Off the USVI With Sentinel – 3A/B OLCI

https://kent-islandora.s3.us-east-2.amazonaws.com/node/10097/10210-thumbnail.jpgThe oceans are a diverse soup of organic life and are major regulators of the earth\u27s many systems. Tracking ocean systems is necessary for the regulation of healthy habitats, maintaining clean recreational environments, and monitoring pollutants. Using satellite sensors, we have access to tons of real-time public data of the world\u27s surface, which can be used to do all these things. In this project, a statistical approach to remote sensing called varimax--rotated, principal component analysis (VPCA) was utilized to identify the suspended matter in the water. This approach takes the derivative of the reflectance spectra and unmixes it to give us a more accurate reading of materials that influence the reflected light. Mainly we are looking for any color-producing agents (CPAs) suspended in the water i.e., phytoplankton, detritus, and dinoflagellates or sediment or sediment minerals. By comparing the values to an existing spectral library, we can identify the components. In 2017, Hurricane Irma struck the US Virgin Islands leaving behind a wake of destruction. By comparing images before and after the hurricane, we can track how pigment distribution changed after the event. We observe that all the same components were identified between both dates, but that their distributions vary. Possible further applications to this project include creating seasonal time series to understand distributions year-round and validating our data with samples collected in the USVI from around these dates.</p

Effective Harmful Algal Bloom Monitoring in Diverse Waters

https://kent-islandora.s3.us-east-2.amazonaws.com/node/10085/10202-thumbnail.jpgThere are many approaches to detecting in-water constituents, like color producing agents, in the field of remote sensing. Previously, harmful algal bloom (HAB) monitoring practices via satellite imagery analysis have held a similar goal of identifying a single constituent associated with HAB’s, particularly chlorophyll. Recently, the Kent State University Spectral Decomposition Method has been developed to better distinguish multiple water constituents, such as phylum level Cyanobacteria, Chlorophyta, Bacillariophyta, and Ochrophyta, as well as constituents of HAB’s, color dissolved organic matter (CDOM), and sediment within large water bodies. Using this technique, we can more effectively monitor HAB’s by separating mixed water signals using a varimax-rotated principal component analysis to remotely detect in-water constituents including HAB-causing cyanobacteria. The KSU Spectral Decomposition Method has been successful using sensors such as the Malvern Panalytical Fieldspec HH2, the NASA Glenn second-generation hyperspectral imagery (HSI2), MODIS, Landsat 8 OLI, and Sentinel 3A/B OLCI. It is apparent that better monitoring practices make better management practices possible, and our goal is to provide a method that will trailblaze the path to better water management practices globally. Case studies in Guantanamo Bay, Cuba and Lake Okeechobee, Florida are presented to document the success of the KSU Spectral Decomposition Method.</p

Triggering of volcanic degassing by large earthquakes

© 2017 Geological Society of America. Statistical analysis of temporal relationships between large earthquakes (Mw ≥ 7) and volcanic eruptions suggests that seismic waves may trigger eruptions over great (\u3e 1000 km) distances from the epicenter, but a robust relationship between volcanic and teleseismic activity remains elusive. Here we investigate the relationship between dynamic stresses propagated by surface waves and a volcanic response, manifested by changes in sulfur dioxide (SO2) emissions measured by the spaceborne Ozone Monitoring Instrument (OMI). Surface wave amplitudes for a catalog of 69 earthquakes in A.D. 2004-2010 are modeled at 12 persistently degassing volcanoes detected by the OMI. The volcanic response is assessed by examining daily OMI SO2 measurements in 28 day windows centered on earthquakes meeting a variable peak dynamic stress threshold. A positive volcanic response is identified if the average post-earthquake SO2 mass was at least 20% larger than the pre-earthquake SO2 mass. We find two distinct volcanic responses, correlating strongly with eruption style. Open-vent, basaltic volcanoes exhibit a positive response to earthquake-generated dynamic stress (i.e., the earthquake triggers increased SO2 discharge), and andesitic volcanoes exhibit a negative response. We suggest that the former is consistent with disruption or mobilization of bubbles, or magma sloshing, in low-viscosity magmas, whereas the latter observation may reflect more dominant controls on degassing in viscous magmas or a post-earthquake reduction in permeability. Overall this analysis suggests that the potential effects of large earthquakes should be taken into account when interpreting trends in volcanic gas emissions

Intercomparison of Approaches to the Empirical Line Method for Vicarious Hyperspectral Reflectance Calibration

Analysis of visible remote sensing data research requires removing atmospheric effects by conversion from radiance to at-surface reflectance. This conversion can be achieved through theoretical radiative transfer models, which yield good results when well-constrained by field observations, although these measurements are often lacking. Additionally, radiative transfer models often perform poorly in marine or lacustrine settings or when complex air masses with variable aerosols are present. The empirical line method (ELM) measures reference targets of known reflectance in the scene. ELM methods require minimal environmental observations and are conceptually simple. However, calibration coefficients are unique to the image containing the reflectance reference. Here we compare the conversion of hyperspectral radiance observations obtained with the NASA Glenn Research Center Hyperspectral Imager to at-surface reflectance factor using two reflectance reference targets. The first target employs spherical convex mirrors, deployed on the water surface to reflect ambient direct solar and hemispherical sky irradiance to the sensor. We calculate the mirror gain using near concurrent at-sensor reflectance, integrated mirror radiance, and in situ water reflectance. The second target is the Lambertian-like blacktop surface at Maumee Bay State Park, Oregon, OH, where reflectance was measured concurrently by a downward looking, spectroradiometer on the ground, the aerial hyperspectral imager and an upward looking spectroradiometer on the aircraft. These methods allows us to produce an independently calibrated at-surface water reflectance spectrum, when atmospheric conditions are consistent. We compare the mirror and blacktop-corrected spectra to the in situ water reflectance, and find good agreement between methods. The blacktop method can be applied to all scenes, while the mirror calibration method, based on direct observation of the light illuminating the scene validates the results. The two methods are complementary and a powerful evaluation of the quality of atmospheric correction over extended areas. We decompose the resulting spectra using varimax-rotated, principal component analysis, yielding information about the underlying color producing agents that contribute to the observed reflectance factor scene, identifying several spectrally and spatially distinct mixtures of algae, cyanobacteria, illite, haematite, and goethite. These results have implications for future hyperspectral remote sensing missions, such as PACE, HyspIRI, and GeoCAPE