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A platform for research: civil engineering, architecture and urbanism
Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection
https://orcid.org/0000-0002-4201-2304
https://orcid.org/0000-0002-4264-6307
AbstractWe present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1‐km grid, using seven ocean color features from the MODIS‐Aqua satellite platform (2002–2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth‐dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K‐nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;00:1–15. © 2024 SETAC
KEY POINTS We developed a novel machine learning‐based method for detecting red tide (Karenia brevis) blooms off the Florida Gulf Coast at a 1‐km spatial resolution. We present a quantitative comparison of state‐of‐the‐art satellite methods for detecting K. brevis. The use of both satellite data as well as recent in situ samples produces better model prediction than either individually.
Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection
https://orcid.org/0000-0002-4201-2304
https://orcid.org/0000-0002-4264-6307
AbstractWe present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1‐km grid, using seven ocean color features from the MODIS‐Aqua satellite platform (2002–2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth‐dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K‐nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;00:1–15. © 2024 SETAC
KEY POINTS We developed a novel machine learning‐based method for detecting red tide (Karenia brevis) blooms off the Florida Gulf Coast at a 1‐km spatial resolution. We present a quantitative comparison of state‐of‐the‐art satellite methods for detecting K. brevis. The use of both satellite data as well as recent in situ samples produces better model prediction than either individually.
Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection
https://orcid.org/0000-0002-4201-2304
https://orcid.org/0000-0002-4264-6307
AbstractWe present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1‐km grid, using seven ocean color features from the MODIS‐Aqua satellite platform (2002–2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth‐dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K‐nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;00:1–15. © 2024 SETAC
KEY POINTS We developed a novel machine learning‐based method for detecting red tide (Karenia brevis) blooms off the Florida Gulf Coast at a 1‐km spatial resolution. We present a quantitative comparison of state‐of‐the‐art satellite methods for detecting K. brevis. The use of both satellite data as well as recent in situ samples produces better model prediction than either individually.
Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection
Integr Envir Assess & Manag
Fick, Ronald (author) / Medina, Miles (author) / Angelini, Christine (author) / Kaplan, David (author) / Gader, Paul (author) / He, Wenchong (author) / Jiang, Zhe (author) / Zheng, Guangming (author)
2024-03-01
Article (Journal)
Electronic Resource
English
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