Monitoring intertidal ecosystems: Assessing spatio–temporal variability with Sentinel-2 and Landsat 8
Metadatos
Mostrar el registro completo del ítemAutor
Zarzuelo Romero, Carmen; Lopez-Ruiz, Alejandro; Bermúdez, María; Ortega-Sánchez, Miguel; Caballero, IsabelEditorial
Elsevier
Materia
Intertidal zone Remote sensing Spectral index
Fecha
2025-08Referencia bibliográfica
Zarzuelo, C., López-Ruiz, A., Bermúdez, M., Ortega-Sánchez, M., & Caballero, I. (2025). Monitoring intertidal ecosystems: Assessing spatio–temporal variability with Sentinel-2 and Landsat 8. International Journal of Applied Earth Observation and Geoinformation: ITC Journal, 142(104676), 104676. https://doi.org/10.1016/j.jag.2025.104676
Patrocinador
MICIU/AEI/10.13039/501100011033 - European Union Next Generation EU/PRTR (CNS2023-143630); MICIU/AEI/10.13039/501100011033 - European Union NextGenerationEU/PRTR (PID2021-125895OA-I00; PLEC2022-009362); MICIU/AEI/10.13039/501100011033 - European Union’s Horizon Europe (Grant Agreement No. 101060874)Resumen
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.





