26 research outputs found
Keeping an Eye on Lake Erie: Using Remote Sensing Imagery to Identify Characteristics of Harmful Algal Blooms
No full textTriggering of volcanic activity by large earthquakes
No full textStatistical 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
Remote Sensing of Cyanobacterial and Harmful Algal Blooms in Lake Okeechobee and Biscayne Bay, Florida
No full textCyanobacterial 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
No full texthttps://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
Triggering of volcanic degassing by large earthquakes
No full text© 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
Effective Harmful Algal Bloom Monitoring in Diverse Waters
No full texthttps://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
Subpicosecond vibrational relaxation of the S 1 states of azulene and guaiazulene in solution
No full textThe S-1 population decay times of azulene and 1,4-dimethyl-7-isopropylazulene (guaiazulene) in solution have been determined as a function of their initial vibrational energy content, E(vib)(i), using a pump-probe experiment with subpicosecond time resolution. The S-1 lifetime of azulene does not depend on E(vib)(i) for energies up to 1760 cm(-1), whereas the lifetime of the shorter-lived S-1 state of guaiazulene is independent of E(vib)(i) only for energies up to similar to 1000 cm(-1). At higher energies, the lifetime decreases with increasing E(vib)(i), and exhibits the same behavior in two structurally different solvents. It is suggested that internal conversion to the electronic ground state from a vibrationally unrelaxed S-1 state is responsible for the effects observed in guaiazulene, and that intramolecular vibrational redistribution occurs with a time constant of several hundred femtoseconds.PT: J; CR: AMIRAV A, 1984, J CHEM PHYS, V81, P4200 AVOURIS P, 1977, CHEM REV, V77, P793 ELSAESSER T, 1991, ANNU REV PHYS CHEM, V42, P83 ENGLMAN R, 1970, MOL PHYS, V18, P145 HERITAGE JP, 1976, CHEM PHYS LETT, V44, P76 HUPPERT D, 1977, ISRAEL J CHEM, V16, P277 IPPEN EP, 1975, APPL PHYS LETT, V27, P488 IPPEN EP, 1977, CHEM PHYS LETT, V46, P20 KULKARNI SK, 1988, J CHEM PHYS, V89, P4441 MATSUMOTO T, 1992, CHEM PHYS LETT, V191, P627 SCHWARZER D, 1991, BER BUNSEN PHYS CHEM, V95, P933 SHANK CV, 1978, CHEM PHYS LETT, V57, P433 SOBOLEWSKI AL, 1987, CHEM PHYS, V115, P469 TITTELBACHHELMR.D, 1993, CHEM PHYS LETT, V209, P464 TRAN P, 1994, J CHEM PHYS, V100, P4165 WAGNER BD, 1992, J PHYS CHEM-US, V96, P7904 WAGNER BD, 1993, J CHEM PHYS, V98, P301; NR: 17; TC: 5; J9: CAN J CHEM; PG: 4; GA: QQ128Source type: Electronic(1
An Integrated Sensor Network and Data Driven Approach to Satellite Remote Sensing of Dissolved Organic Matter
No full textAbstract Traditional remote sensing retrieval models for water quality have historically relied on limited, localized data sets due to the prohibitive costs of extensive field campaigns and logistical challenges of collecting match‐up data with satellite overpasses. As a result, these models often lack generalizability across seasons, tides, and sites. Furthermore, small field data sets limit the utility of modern machine learning techniques to advance remote sensing retrieval models. In situ optical sensors deployed in a sensor network to continuously monitor larger water bodies can drastically increase the number of measurements, providing the opportunity to develop new approaches for building robust remote sensing retrieval models by leveraging both remote sensing data and in situ networks as an integrated monitoring system. This study leverages a large “ground‐to‐space” sensor network that combines an in situ optical sensor network with satellite‐based remote sensing to overcome these limitations. Utilizing a large‐scale data set from the U.S. Geological Survey's Sacramento—San Joaquin River Delta monitoring network, of dissolved organic matter fluorescence measurements, and remote sensing data from the European Space Agency's Sentinel‐2A and ‐2B satellites, this study implemented a data driven approach for dissolved organic matter models. The data set, consisting of 982 samples collected between 2018 and 2021 was used to train and validate a random forest model (R2 = 0.76, RMSE = 6.1 Quinine Sulfate Equivalents), with demonstrated applicability across diverse site conditions, tidal stages, and seasons. This work provides a scalable solution to address critical challenges in water quality monitoring and offers a replicable framework for global water quality management
Applicability of Spectral Decomposition by Varimax-Rotated, Principal Component Analysis to the Surface Biology and Geology (SBG) VNIR Mission Concept
No full textCyanobacterial 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
Of Mechanism Design and Multiagent Planning
No full textMultiagent planning methods are concerned with planning by and for a group of agents. If the agents are selfinterested, they may be tempted to lie in order to obtain an outcome that is more rewarding for them. We therefore study the multiagent planning problem from a mechanism design perspective, showing how to incentivise agents to be truthful. We prove that the well-known truthful VCG mechanism is not always truthful in the context of optimal planning, and present a modification to fix this. Finally, we present some (domain-dependent) poly-time planning algorithms using this fix that maintain truthfulness in spite of their non-optimality.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc
