Document Type

Article

Publication Date

5-8-2025

Abstract

Mangrove forests are vital for ecosystem services and coastal management but face stressors from anthropogenic activities and climate change. This study estimates the extent and afforestation potential of mangroves along the Egyptian Red Sea coast from 1984 to 2022 using NDVI derived from Landsat-5 and Sentinel-2 data in Google Earth Engine. Aboveground biomass (AGB), belowground biomass (BGB), carbon (C) stock, and CO2 sequestration potential were evaluated using Sentinel-2 and elevation data. Future afforestation suitability (2020–2050) under SSP2-4.5 and SSP5-8.5 scenarios was assessed with the MaxEnt model. Mangrove area increased from 0.95 km2 in 1984 to 1.46 km2 in 2022, with Landsat-5 classification accuracy between 89 and 94 % and Sentinel-2 at 92–96 %. Mean AGB and BGB were 63.48 Mg/ha and 18.41 Mg/ha, while mean C stock and CO2 sequestration were 40.94 Mg/ha and 150.25 Mg/ha, respectively, below global averages. Under SSP2-4.5, 18.60 km2 and 21.40 km2 were identified as highly and moderately suitable for afforestation, decreasing to 12.34 km2 and 12.50 km2 under SSP5-8.5. Under the SSP2-4.5 scenario, high-suitability areas for mangroves would be predominantly located along the northern coast, with fewer areas identified along the southern coast. Under the SSP5-8.5 scenario, high-suitability areas are expected to shift and concentrate only in the northern regions. Significant predictors under SSP2-4.5 included sea water velocity, bathymetry, and air temperature, while SSP5-8.5 highlighted bathymetry, sea surface temperature, and salinity. This study provides insights into mangrove dynamics, biomass potential, and afforestation opportunities, offering critical guidance for conservation and restoration strategies.

Comments

This article was originally published in Environmental and Sustainability Indicators, volume 26, in 2025. https://doi.org/10.1016/j.indic.2025.100705

Peer Reviewed

1

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The authors

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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