Coastal lagoons in the United Arab Emirates serve as critical habitats for globally threatened marine megafauna

Mateos-Molina, Daniel; Bejarano, Ivonne; Pittman, Simon J.; Möller, Mona; Antonopoulou, Marina; Jabado, Rima W.

doi:10.1016/j.marpolbul.2024.116117

Cited by 2 publications

(1 citation statement)

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“…They distribute and diversify riverine sediments [1], reduce turbulence of tidal flow [2], regulate seasonal current water circulation [3], and enrich shelf waters with nutrients [4]. Worldwide, coastal lagoons are among the most productive and biodiverse systems, providing essential habitat for a wide variety of aquatic species and threatened marine fauna [5][6][7]. Coastal and brackish water lagoons that form continuities in the terrestrial and shelf ecosystems present transitional aquatic ecosystems located in the zones between land and sea.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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