The impact of fragmentation on the GPP was found to depend on the type of land transformation along with patch characteristics, such as size, edge, and shape complexity. [PERSON] and [PERSON] conducted a study to evaluate the natural recovery of mangroves in the Wunbaik mangrove forests of Myanmar following human disturbance, specifically focusing on the abandoned shrimp ponds. These researchers utilized cloud-free Sentinel-2 images that were captured on 21 January 2020 and 23 December 2015, both during the dry seasons. They employed an artificial neural network (ANN) classification approach with a transfer learning method to classify these two dates' images. Additionally, a post-classification change analysis approach was employed to assess the changes that occurred. To identify the naturally recovering mangroves, three abandoned shrimp ponds were selected based on field investigations and their change detection results were then extracted. The proposed methodology achieved a high level of accuracy, with an overall accuracy of 95.98% and a Kappa coefficient of 0.92 for the 2020 classification. For the 2015 prediction, the transfer learning approach improved the model's performance, resulting in an overall accuracy of 97.20% along with a Kappa coefficient of 0.94. The analysis of the change detection results revealed a slight decrease of mangrove forests within the Wunbaik mangrove forests between the years 2015 and 2020. However, naturally recovering mangroves were identified in approximately 50% of each abandoned site within a relatively short abandonment period. These findings contribute to our understanding of the natural re generation process of mangroves following human disturbance, specifically in abandoned shrimp ponds. In the final paper of this Special Issue, [PERSON] et al. presented a comprehensive review paper on spectral indices for mangrove remote sensing. This study covered the period between 1996 and 2021, examining the range of spectral indices that have been developed and utilized in the context of mangrove remote sensing. This review revealed that spectral indices have been predominantly used for various aspects of mangrove analysis, excluding the identification of mangrove species. The aspects that were covered included the mangrove extent, their distribution, and above-ground parameters (such as the carbon density, biomass, canopy height, and leaf area index estimation), as well as changes in these aspects over time. Among the spectral indices, the NDVI emerged as the most widely applied, appearing in 82% of the reviewed studies, followed by the EVI, which was noted as being used in 28% of the studies examined. While the development and application of potential indices for characterizing mangrove cover have shown growth (with six indices having been published so far), the NDVI remains the most popular index for mangrove remote sensing. However, this review also highlights the limitations and gaps that are present within current studies. Considering the digital era, the authors suggested future directions for research, emphasizing the need to explore spectral index applications in connection with time series imagery and the fusion of optical sensors for more comprehensive and accurate mangrove studies. In summary, the utilization of cloud computing platforms for data analysis in mangrove research is growing. Researchers benefit from the flexibility, cost savings, enhanced security, scalability, and improved collaboration offered by cloud computing. There is a rising demand for mapping mangroves at higher resolutions, specifically at levels higher than 5 m. Compared to the past, supervised classification approaches are now more commonly used instead of unsupervised classification. The most popular application used in the remote sensing of mangroves is the mapping of mangrove and non-mangrove areas and conducting time series change analysis. Both optical and synthetic aperture radar data are currently being employed for mangrove mapping.