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The review article presents an analysis of the literature on environmental literacy in sustainable development. By utilizing techniques to examine multiple documents published between 2013 and 2023, including citation analysis, co-authorship analysis, subject area analysis, and keyword analysis, this study aims to provide valuable information and insights into the research landscape surrounding environmental literacy and its contribution to promoting sustainable development. A systematic search was conducted to gather several scientific articles, conference papers, and publications from the Scopus database from 2013 to 2023. The findings of this analysis shed light on authors, influential institutions, and active research groups that contributed to the study of environmental literacy and sustainable development. This comprehensive review offers an understanding of the state of research in this field while identifying areas for further exploration and research gaps. The insights gained from this study can be highly beneficial for researchers, policymakers, and practitioners seeking to advance knowledge and take action toward promoting literacy's role in sustainable development. This analysis is a foundation for advancing our understanding of literacy's significance while emphasizing its vital role in sustainable development efforts.

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. [ABSTRACT FROM AUTHOR]