Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

Ortiz, Joseph D.; Avouris, Dulcinea; Schiller, Stephen; Luvall, Jeffrey C.; Lekki, John; Tokars, Roger; Anderson, Robert C.; Shuchman, Robert A.; Sayers, Michael J.; Becker, R. H.

doi:10.1016/j.jglr.2019.03.005

Cited by 22 publications

(25 citation statements)

References 32 publications

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“…Moreover, given the state of sewage treatment with OSDS in the IRL, we predict that components associated with brown tide algae will be concentrated in the Banana River region, building upon the results of Barile (2018). The results from this study were part of a larger 2-year-long research project on the IRL designed to demonstrate the applicability of the method following prior studies conducted in the nutrient-polluted waters of Lake Erie (Ali et al, 2014Ortiz et al, 2017Ortiz et al, , 2019 and the meso-to oligotrophic waters of the U.S. Virgin Islands (Schlaerth, 2018).…”

Section: Introductionmentioning

confidence: 72%

“…The VPCA method addresses the mixed pixel problem arising from the presence of multiple constituents in optically complex water by partitioning their signals into independent, uncorrelated components (Ortiz et al, 2019). In this way, the method separates signatures of bottom reflection, suspended sediment, and pigment degradation products from various types of algal and cyanobacterial pigments because different processes or targets yield distinct spectral responses (Table S2).…”

Section: /2019gh000238mentioning

confidence: 99%

“…To isolate the signal associated with the brown tide bloom, we conducted separate VPCA on the ASD Fieldspec® HH2 field data and the Sentinel-3A OLCI derivative spectra datasets obtained from each of the 12 images. VPCA reduces the dimensionality of multivariate datasets by decomposing the integrated spectral signatures into orthogonal axes that each account for a portion of the total signal variability (Ortiz et al, 2019). In this way, it removes redundant information from the image.…”

Section: /2019gh000238mentioning

confidence: 99%

“…For satellite images, the component scores were displayed as distribution maps, which represent the fractional variance associated with each component at each pixel, while the component scores of the field spectra represent single location values at the field sampling site. These orthogonal VPCA component loadings and scores for the multispectral satellite imagery were calculated using code written for ENVI/IDL Ortiz et al, 2017Ortiz et al, , 2019. The hyperspectral spectroradiometer data was processed in SPSS using the same methodology.…”

Section: /2019gh000238mentioning

confidence: 99%

“…While phycocyanin is assumed to be indicative of cyanobacteria in inland waters, it is also present in Cryptophytes and Rhodophytes (Dring, 1982). The increasing number of bands on high-band count multispectral and hyperspectral sensors particularly when used in optically complex environments result in the inclusion of significant amounts of redundant information because all bands in the visible spectrum are highly correlated (Ortiz et al, 2013(Ortiz et al, , 2017(Ortiz et al, , 2019. New methods of analysis designed to work with redundant information and to capitalize on extraction of spectral shape information are needed.…”

Section: Introductionmentioning

confidence: 99%

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Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers

et al. 2020

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Frequent Aureoumbra lagunensis blooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early-warning system for harmful algal blooms, we apply varimax-rotated principal component analysis (VPCA) to 12 images spanning~1.5 years. The method partitions visible-NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel-3A Ocean and Land Colour Instrument (OLCI) and a field-based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated with A. lagunensis in both spectral datasets indicate high correlations to Ochrophyta cell counts (R 2 ≥ 0.92, p < 0.001). Recombining components exhibiting a red-edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel-3A OLCI-based VPCA spectral identification and indicate A. lagunensis was highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem. Plain Language Summary Toxic or nuisance blooms of microscopic plankton are causing environmental, economic, and public health problems in Indian River Lagoon, Florida, and other coastal waters. Monitoring from boats can be expensive compared to remote sensing methods, but the remote sensing signal must be validated. Here we present results that document that the brown tide that develops in the Indian River Lagoon can be identified with very little error using different types of supporting data sets. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.

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Section: Introductionmentioning

confidence: 72%

Section: /2019gh000238mentioning

confidence: 99%

Section: /2019gh000238mentioning

confidence: 99%

Section: /2019gh000238mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers

et al. 2020

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Field-validated detection of Aureoumbra lagunensis brown tide blooms in the Indian River Lagoon, Florida using Sentinel-3A/B OLCI and ground-based hyperspectral spectroradiometers

Judice

Widder

Falls

et al. 2019

Preprint

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Frequent Aureoumbra lagunensis blooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early-warning system for harmful algal blooms, we apply varimax-rotated principal component analysis (VPCA) to 12 images spanning~1.5 years. The method partitions visible-NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel-3A Ocean and Land Colour Instrument (OLCI) and a field-based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated with A. lagunensis in both spectral datasets indicate high correlations to Ochrophyta cell counts (R 2 ≥ 0.92, p < 0.001). Recombining components exhibiting a red-edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel-3A OLCI-based VPCA spectral identification and indicate A. lagunensis was highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.Plain Language Summary Toxic or nuisance blooms of microscopic plankton are causing environmental, economic, and public health problems in Indian River Lagoon, Florida, and other coastal waters. Monitoring from boats can be expensive compared to remote sensing methods, but the remote sensing signal must be validated. Here we present results that document that the brown tide that develops in the Indian River Lagoon can be identified with very little error using different types of supporting data sets. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.

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Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data

Avouris

Ortiz

2019

Journal of Great Lakes Research

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Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

Cited by 22 publications

References 32 publications

Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers

Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers

Field-validated detection of Aureoumbra lagunensis brown tide blooms in the Indian River Lagoon, Florida using Sentinel-3A/B OLCI and ground-based hyperspectral spectroradiometers

Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data

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