Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.

Autor: Fick R; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA., Medina M; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA.; ECCO Scientific, LLC, St. Petersburg, Florida, USA., Angelini C; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA., Kaplan D; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA., Gader P; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA., He W; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA.; Computer & Information Science & Engineering, University of Florida, Gainesville, Florida, USA., Jiang Z; Center for Coastal Solutions, University of Florida, Gainesville, Florida, USA.; Computer & Information Science & Engineering, University of Florida, Gainesville, Florida, USA., Zheng G; NOAA/NESDIS Center for Satellite Applications and Research, College Park, Maryland, USA.; Cooperative Institute for Satellite Earth System Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA.
Jazyk: angličtina
Zdroj: Integrated environmental assessment and management [Integr Environ Assess Manag] 2024 Sep; Vol. 20 (5), pp. 1432-1446. Date of Electronic Publication: 2024 Mar 01.
DOI: 10.1002/ieam.4908
Abstrakt: We 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;20:1432-1446. © 2024 SETAC.
(© 2024 SETAC.)
Databáze: MEDLINE
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