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Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
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- Michigan Tech Research Institute (5)
- Lake Erie (3)
- Cyanobacteria (2)
- Harmful algal blooms (2)
- Remote sensing (2)
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- Archaeology (1)
- Big data (1)
- Classification (1)
- Cyanobacterial harmful algal blooms (1)
- Department of Social Sciences (1)
- Drylands (1)
- FluoroProbe (1)
- Harmful algal bloom (1)
- Hazards (1)
- Historical GIS (1)
- Hyperspectral (1)
- Hyperspectral remote sensingNASA Glenn HSI2Spectral decompositionUnsupervised classification (1)
- LiDAR (1)
- MESMA (1)
- PACE (1)
- Postindustrial (1)
- Real time data products (1)
- SAM (1)
- Satellite (1)
- Time series (1)
- Urban (1)
- Vertical structure (1)
Articles 1 - 6 of 6
Full-Text Articles in Social and Behavioral Sciences
Regional Scale Dryland Vegetation Classification With An Integrated Lidar-Hyperspectral Approach, Hamid Dashti, Andrew Poley, Nancy Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Et. Al.
Regional Scale Dryland Vegetation Classification With An Integrated Lidar-Hyperspectral Approach, Hamid Dashti, Andrew Poley, Nancy Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Et. Al.
Michigan Tech Publications
The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral …
Understanding Cumulative Hazards In A Rustbelt City: Integrating Gis, Archaeology, And Spatial History, Daniel Trepal, Don Lafreniere
Understanding Cumulative Hazards In A Rustbelt City: Integrating Gis, Archaeology, And Spatial History, Daniel Trepal, Don Lafreniere
Michigan Tech Publications
We combine the Historical Spatial Data Infrastructure (HSDI) concept developed within spatial history with elements of archaeological predictive modeling to demonstrate a novel GIS-based landscape model for identifying the persistence of historically-generated industrial hazards in postindustrial cities. This historical big data approach draws on over a century of both historical and modern spatial big data to project the presence of specific persistent historical hazards across a city. This research improves on previous attempts to understand the origins and persistence of historical pollution hazards, and our final model augments traditional archaeological approaches to site prospection and analysis. This study also demonstrates …
Evaluating Visible Derivative Spectroscopy By Varimax-Rotated, Principal Component Analysis Of Aerial Hyperspectral Images From The Western Basin Of Lake Erie, Joseph D. Ortiz, Dulci M. Avouris, Stephan J. Schiller, Jeffrey C. Luvall, John D. Lekki, Roger P. Tokars, Robert C. Anderson, Robert Shuchman, Michael Sayers, Richard Becker
Evaluating Visible Derivative Spectroscopy By Varimax-Rotated, Principal Component Analysis Of Aerial Hyperspectral Images From The Western Basin Of Lake Erie, Joseph D. Ortiz, Dulci M. Avouris, Stephan J. Schiller, Jeffrey C. Luvall, John D. Lekki, Roger P. Tokars, Robert C. Anderson, Robert Shuchman, Michael Sayers, Richard Becker
Michigan Tech Publications
The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center's, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, …
Real Time Habs Mapping Using Nasa Glenn Hyperspectral Imager, Reid W. Sawtell, Robert Anderson, Roger Tokars, John D. Lekki, Robert Shuchman, Karl Bosse, Michael Sayers
Real Time Habs Mapping Using Nasa Glenn Hyperspectral Imager, Reid W. Sawtell, Robert Anderson, Roger Tokars, John D. Lekki, Robert Shuchman, Karl Bosse, Michael Sayers
Michigan Tech Publications
The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument …
Satellite Monitoring Of Harmful Algal Blooms In The Western Basin Of Lake Erie: A 20-Year Time-Series, Michael Sayers, Amanda Grimm, Robert Shuchman, Karl Bosse, Gary L. Fahnenstiel, Steven A. Ruberg, George A. Leshkevich
Satellite Monitoring Of Harmful Algal Blooms In The Western Basin Of Lake Erie: A 20-Year Time-Series, Michael Sayers, Amanda Grimm, Robert Shuchman, Karl Bosse, Gary L. Fahnenstiel, Steven A. Ruberg, George A. Leshkevich
Michigan Tech Publications
Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (>18 mg/m3) combined with water temperature >20 °C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the …
Spatial-Temporal Variability Of In Situ Cyanobacteria Vertical Structure In Western Lake Erie: Implications For Remote Sensing Observations, Karl Bosse, Michael Sayers, Robert Shuchman, Gary L. Fahnenstiel, Steven A. Ruberg, David L. Fanslow, Dack G. Stuart, Thomas H. Johengen, Ashley M. Burtner
Spatial-Temporal Variability Of In Situ Cyanobacteria Vertical Structure In Western Lake Erie: Implications For Remote Sensing Observations, Karl Bosse, Michael Sayers, Robert Shuchman, Gary L. Fahnenstiel, Steven A. Ruberg, David L. Fanslow, Dack G. Stuart, Thomas H. Johengen, Ashley M. Burtner
Michigan Tech Publications
Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability …