Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.
Machine learning
Neural networks
Remote sensing
Journal
Integrated environmental assessment and management
ISSN: 1551-3793
Titre abrégé: Integr Environ Assess Manag
Pays: United States
ID NLM: 101234521
Informations de publication
Date de publication:
01 Mar 2024
01 Mar 2024
Historique:
revised:
01
02
2024
received:
16
11
2023
accepted:
01
02
2024
medline:
1
3
2024
pubmed:
1
3
2024
entrez:
1
3
2024
Statut:
aheadofprint
Résumé
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;00:1-15. © 2024 SETAC.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Florida Department of Environmental Protection
ID : LPR0017
Organisme : NSF Career Award
ID : 1652628
Informations de copyright
© 2024 SETAC.
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