study compared the performance of the classification algorithms for the interpretation of the Landsat 7 ETM+ and Landsat-8 OLI data. Their results indicated that the decision tree algorithm with the parameter combination of NDMI, NDSI, and DEM effective for the classification of Landsat-7 ETM+. The random forest classification algorithm outperformed the decision tree algorithm in mapping mangrove forests, as all the parameters of the random forest model exhibited a higher producer accuracy. [PERSON] et al. conducted a change analysis in three provinces on the northern coast of Vietnam, namely Thai Binh, Nam Dinh, and Hai Phong, using multi-temporal Landsat imagery. This study aimed to monitor the dynamic nature of mangrove forests in this region, focusing on the time period between 1990 and 2022. These researchers employed the Google Earth Engine (GEE) cloud computing platform. The overall accuracy obtained in the year 2022 was 91.98%, while the Kappa coefficient was 0.84. Their results also revealed fluctuations in the mangrove area over time, with periods of decline and elevation. However, since 2005, mangrove forests have exhibited a continuous increase, mainly due to the implementation of restoration programs and policies by the Vietnamese government and local authorities. Notably, this study demonstrated the potential of Landsat time series imagery, pixel-based algorithms, and the GEE platform for the long-term monitoring of mangrove forests. [PERSON] et al. conducted a multiscale diagnosis of mangrove status in a data-poor context using very high spatial resolution (VHSR) satellite images in the Pichavaram mangrove forest, located in Tamil Nadu, India. Pichavaram mangrove forests face threats from cyclones as well as reduced freshwater flow from upstream sources. This study analyzed the changes in the mangrove area spanning from the years 2003 to 2019 at a spatial resolution of 4 m. Specifically, they employed Quickbird (QBD) images in the years of 2003 and 2005 (that were captured shortly after the Indian Ocean tsunami which significantly impacted the region on 26 December 2004), along with GeoEye-1 (GEO) images that were obtained in 2011 and 2016, and a Worldview-3 (WV3) image from 2019. They classified the mangrove and non-mangrove areas using supervised classification available in ERDAS Imagine processing software. Accuracy assessment was performed for each year, achieving an overall accuracy of 85% or higher. Post-classification change analysis was conducted for both natural and planted mangroves between 2003 and 2019, with their results revealing that the mangrove forest area experienced a 28.0% increase (201.2 hectares) from 2003 to 2019. The expansion of these mangrove areas predominantly resulted from the conversion of non-mangrove areas into mangrove habitats. These researchers concluded that despite the limited availability of ground-truth data, VHSR data proved to be valuable in providing a multiscale diagnosis of this ecosystem's condition. [PERSON] et al. emphasized the work performed by the Global Mangrove Watch (GMW) in updating the extent of mangrove forests from the year 2010. These researchers highlighted that the latest version of the GMW, namely GMW v2.5, represents a significant improvement over the previous version. They identified 204 regions that were either inaccurately mapped or completely missing from the previous maps, and these areas were updated accordingly. The primary objective of this endeavor was to enhance the existing map through the incorporation of new information. To achieve this, they employed the XGBoost binary classification algorithm for their classification process. As a result of this revision, an additional 2660 square kilometers of mangroves were added, leading to a revised global mangrove extent equivalalating to approximately 140,260 square kilometers for the year 2010. However, it is important to note that this study did not examine for any potential forest loss in other areas, which thereby presents a significant limitation of this research. [PERSON] et al.