Nonnegative matrix factorization-based environmental monitoring of marine mucilage

Çağatay Esi*, Alp Ertürk, Esra Erten

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Marine mucilage, also known as sea-snot, is an environmental disaster with ecological, economical, and public health-related impacts, which is occurring with increasing frequency and severity worldwide. One of the latest mucilage occurrences, the mucilage outbreak of Spring 2021 in the Sea of Marmara, lasted for well over three months and covered more than area of 1160 (Formula presented.), and was repeated in 2022, although to a smaller extent in terms of duration, coverage area, and severity. Remotely sensed images acquired by Earth observation sensors provide a convenient and promising alternative for environmental monitoring of marine mucilage, with respect to field and laboratory work which are costly, require expert knowledge, and which have to be conducted in a small time-window and have limited generalizability due to the under-sampling of a large medium with a highly dynamic character. This work presents an unmixing-based approach using nonnegative matrix factorization for marine mucilage monitoring from remotely sensed hyperspectral data. The proposed method enables to identify the spectral signature of mucilage, and its variants where applicable, and the precise detection and estimation of mucilage concentrations in affected regions. Additionally, the proposed approach facilitates monitoring the temporal variations of mucilage presence, distribution, and accumulation, between data acquired from the same region at different dates or times. This enables quantitative spatio-temporal mapping of distributions and the potential of assessing the severity of outbreaks, without the requirement of manually prepared ground truth maps or carefully selected thresholds. Real hyperspectral data acquired by the PRISMA mission are used to validate the proposed approach qualitatively and quantitatively. The paper also discusses future directions and recommendations to enhance the performance and applicability of the proposed approach for marine mucilage analysis, and environmental monitoring in general.

Original languageEnglish
Pages (from-to)3764-3788
Number of pages25
JournalInternational Journal of Remote Sensing
Volume45
Issue number11
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Environmental monitoring
  • hyperspectral
  • mucilage
  • nonnegative matrix factorization (NMF)
  • PRISMA
  • unmixing

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