Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The random forest (RF) method was used to classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced AGC per species. Moreover, the model was built with linear regression equations for one variable x, and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests, because it has been proven to be able to increase the accuracy value of previous studies using optical images.

Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.

In the face of rising the seas, relying solely on seawalls and other reinforced coastlines is unsustainable due to the high costs associated with their construction and upkeep and unforeseen effects on habitats. Restoring and conserving coastal habitats can be more affordable, long-term solutions for protecting the coast. Yet, basic knowledge about where, how, and for whom habitats decrease the risk of coastal disasters is typically lacking from decision-makers. This study investigates how climate change may affect coastal areas and how natural coastal habitats may help protect them. We studied two small tropical islands, Bintan and Seribu Islands, Indonesia. The research applied the InVEST Coastal Vulnerability model to calculate the hazard index. To assess the effect on the vulnerable population, a grid system for the coastal population was created and overlaid with the hazard index. The comprehensive analysis indicates that if the coastal habitat is lost in the future, Bintan and Seribu Islands will face a severe threat from rising sea levels, with 96% and 63% of the inhabitants being highly exposed to climate hazards. Furthermore, the model shows that preserving natural coastal habitats by 2040 could help protect approximately 104 and 17 km of coastline areas in Bintan and Seribu Islands, respectively. Ecosystems offer safeguards to certain thinly inhabited, distant coastal villages, certain of those with a large, vulnerable population. Thus, this study highlights that natural coastal habitat is essential in climate change adaptation. Enhancing and safeguarding these natural habitats is crucial for mitigating climate-related hazards and ensuring community resilience.

Mangrove ecosystems provide critical ecological services, including supporting coastal fisheries, protecting shorelines, and functioning as significant carbon sinks within the global carbon cycle. These ecosystems play a substantial role in climate change mitigation, as they are capable of sequestering up to three times more carbon dioxide (CO₂) than terrestrial forests. Given increasing anthropogenic pressures and environmental change, continuous monitoring of mangrove health is essential to sustain these ecological functions and ecosystem services. Remote sensing technology offers a cost-effective and efficient approach for large-scale assessment, spatial analysis, and long-term monitoring of mangrove conditions. This study utilized cloud-free Landsat-8 imagery from 2013 to 2020 to evaluate mangrove health in Balikpapan Bay using the Mangrove Health Index (MHI), which classifies conditions into three categories: excellent, moderate, and poor. Carbon storage was estimated using a model integrating vegetation indices with Above-Ground Carbon (AGC). The results revealed a fluctuating yet overall positive trend in mangrove health, with the “excellent” category increasing by 1.023% (174.78 ha), while the “poor” category decreased by 1.098% (187.67 ha) over the study period. AGC values exhibited a comparable pattern, reaching a peak of 375.54 Mg.ha-1 in 2018 (mean: 125.85 Mg.ha-1). A strong positive correlation (r = 0.994) was observed between estimated mangrove health and AGC values, indicating that healthier mangrove ecosystems possess greater carbon sequestration potential. These findings highlight the importance of sustained monitoring and adaptive management strategies to support mangrove conservation and strengthen their contribution to climate change mitigation at regional and landscape management and policy scales.