conducted mangrove species mapping based on spectral reflectance using extremely high-resolution satellite data from WorldView 2. This study was carried out in the Karimunjawa and Kemujan Islands, located in the central Java province of Indonesia. To create a detailed inventory of mangrove forest biodiversity, particularly in Java, where the diversity of mangrove species is exceptionally high, the utilization of very high-resolution imagery for species mapping in this context was not only possible, but also essential. The study area was found to house approximately 44 species, including 25 true mangroves and 19 mangrove associates. To perform species mapping, these researchers employed the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms. Field data, such as mangrove species identification, coordinate locations of targeted mangrove species, and spectral reflectance measurements of mangrove species using a field spectrometer, were all collected. Dendrogram analysis utilizing the Ward linkage method was conducted to classify mangrove species based on their distances between clusters of spectral reflectance patterns. This classification process resulted in two, four, and five species groups for Levels 1 to 3, respectively, while individual species were identified under Level 4. The SID algorithm achieved the highest overall accuracy, while the SFF algorithm yielded the most accurate results for mapping individual species. As they expected, these findings indicated that as the number of classes to be mapped increased, the mapping accuracy consequently decreased. [PERSON] et al. conducted a spatiotemporal simulation of mangrove forests under different scenarios in Hainan Island, China. Their study utilized a total of 12 drivers, including the elevation, slope, enhanced vegetation index (EVI), EVI change trends, distance to major roads, distance to minor roads, distance to the sea, distance to rivers, distance to aquaculture ponds, distance to building land, distance to suitable land for mangroves, and a spatial autocorrelation factor, for the simulation. Various models, such as logistic regression, support vector regression, and the random forest model, were compared with one another in terms of their ability to capture the spatial characteristics of mangrove forests. Three development scenarios were established: a natural growth scenario (NGS), an economic development scenario (EDS), and a mangrove protection scenario (MPS). The CLUE-S model was employed to predict the spatiotemporal distribution of mangrove forests from the years 2022 to 2037 under these different scenarios. Subsequently, based on the prediction results, the future change trends of mangrove forests from 2017 to 2037 were analyzed. The simulation results of these different models demonstrated that AutoRF (random forest with spatial autocorrelation) performed the best in simulating the spatial characteristics. Importantly, specific factors, such as the enhanced vegetation index (EVI), various location indices, and the spatial autocorrelation factor were all found to significantly improve the accuracy of the mangrove simulations. The prediction results for Hainan Island indicated that under the NGS, the mangrove area would experience slow growth, while under the EDS, it would decrease significantly. Conversely, the mangrove area would increase significantly under the MPS. Based on these findings, the MPS was identified as the most suitable development direction for the future, as it offers a balanced approach that promotes economic development while ensuring mangrove preservation. [PERSON] et al. utilized Landsat dense time series data to monitor the mangrove forest cover and its phenology in Central Queensland, Australia. Although the exploitation of mangroves by coastal communities is strictly regulated in Australia, these forests are still being threatened with landscape modifications and hydrological alterations upstream.