Mangrove forests play a vital role in providing ecosystem services such as coastal protection and mitigating the impacts of climate change, necessitating mapping for assessment, monitoring, conservation, and management. Advances in remote sensing have enabled rapid and accurate mapping of these forests. This study aims to determine the best method for mapping Iran's mangrove forests (northern coasts of the Persian Gulf and the Gulf of Oman) by comparing the Mangrove Vegetation Index (MVI) and Random Forest (RF) classification using Landsat-9 and Sentinel-2 satellite data, as well as evaluating the accuracy of land cover products from the European Space Agency (ESA), the GLC_FCS30 land cover product, and the Global Mangrove Watch (GMW) product. The results show respective mangrove class accuracies of 95%, 84%, 91%, 86%, 83%, 80%, and 78% for MVI with Sentinel-2 data, MVI with Landsat-9 data, RF classification with Sentinel-2 data, RF classification with Landsat-9 data, ESA product, GLC_FCS30 product, and GMW product. The corresponding areas were 11,509 ha, 11,834.5 ha, 10,779.41 ha, 13,702.23 ha, 15,814 ha, 11,441.5 ha, and 11,117 ha, respectively. The findings indicate that Sentinel-2 data show higher potential than Landsat-9 data for mapping Iran's mangrove forests. Furthermore, the results demonstrate the higher accuracy of the generated maps compared to existing remote sensing products. These findings not only highlight the potential of modern remote sensing data for enhancing mangrove forest mapping but also pave the way for more precise and cost-effective monitoring strategies, which are crucial for conservation efforts in coastal ecosystems.
