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The significance of mangroves and the associated risks they face have prompted government and the private sector to invest in projects aimed at conserving and restoring mangroves. Despite this interest, there is currently little information available on the effectiveness of these investments in mangrove conservation and restoration efforts. Therefore, this study aimed to use Sentinel-2 imagery with 10-m resolution through the Google Earth Engine to evaluate the effectiveness of these projects in mangrove areas in two regions: the Tahiry Honko project in Madagascar and the Abu Dhabi Blue Carbon Demonstration Project in the United Arab Emirates. The study compared the U-Net and SVM for mangrove classification. The U-Net model demonstrated superior performance, achieving an accuracy of 90%, with a Kappa coefficient value of 0.84. In contrast, the SVM had an overall accuracy of 86% and Kappa coefficient of 0.78. The analysis of changes in the mangrove area using U-Net model revealed a decline of 355 ha over four years in the Tahiry Honko project, while in the Abu Dhabi Project, the mangrove area increased by 5857 ha over 5 years. These findings can provide valuable information for policy-makers and management strategies.
Farzanmanesh et al. (Tue,) studied this question.