Monitoring intertidal ecosystems: Assessing spatio–temporal variability with Sentinel-2 and Landsat 8
Intertidal zones are home to critical ecosystems that provide a wide range of ecological, social and economic benefits, but are increasingly vulnerable to climate change and anthropogenic pressures. This study aims to develop a robust methodology for mapping and analysing these areas using satellite...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003231 |
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| Summary: | Intertidal zones are home to critical ecosystems that provide a wide range of ecological, social and economic benefits, but are increasingly vulnerable to climate change and anthropogenic pressures. This study aims to develop a robust methodology for mapping and analysing these areas using satellite imagery, focusing on the creation of a new spectral index specifically designed for zoning marsh ecosystems. The methodology involves selecting optimal satellite data, correcting for solar reflectance, identifying intertidal pixels using the Normalised Difference Water Index (NDWI) and classifying these zones into categories such as seagrass beds, mudflats, low marsh and high marsh. By comparing the effectiveness of Sentinel-2 and Landsat 8 datasets, the research addresses common challenges in land cover mapping of intertidal environments — such as cloud cover, reflectance variability and tidal influences. The Bay of Cádiz (south-west Spain), with its extensive intertidal areas characterised by diverse habitats such as mudflats, marshes and seagrass beds, serves as an ideal case study for understanding coastal dynamics driven by tidal cycles. The results highlight the usefulness of the proposed spectral index in assessing changes in intertidal habitats over time, achieving classification accuracies of up to 93.6%, and supporting long-term monitoring efforts that are crucial for coastal conservation strategies. By refining intertidal mapping techniques and improving the detection of specific land cover classes, this research addresses existing methodological gaps and provides valuable insights for local coastal management. In future work, the methodology could be adapted to other intertidal systems and integrated with additional data sources to simulate future scenarios under sea level rise or extreme events. These improvements will help guide effective, data-driven strategies for conserving intertidal ecosystems in the face of accelerating environmental change. |
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| ISSN: | 1569-8432 |