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.

Coastal flooding driven by sea-level rise and land subsidence poses severe risks to low-lying communities. This study evaluates the causes and impacts of coastal sinking in Sayung, Demak, Central Java, using multi-temporal Landsat imagery (1977, 2024), tidal gauge data, and GPS measurements. A set of spectral indices—Normalized Difference Vegetation Index (NDVI), Weighted Modified Normalized Difference Water Index (WMNDWI), Land Surface Water Index (LSWI), and Normalized Difference Built-up Index (NDBI)—were calculated and integrated as input features for a Random Forest machine learning model to detect and classify environmental changes. Results indicated an average land subsidence rate of approximately 6 cm/year ± 0.8 cm/year, validated against InSAR-based measurements, and a classification accuracy of 91% (RMSE of 0.8 cm/year). A substantial decline in vegetation indices was observed, reflecting the conversion of agricultural land into built-up areas and water bodies. Extensive flooding and shoreline retreat were documented, with high-risk zones concentrated along densely developed coastlines. These findings highlight the urgent need for integrated management strategies, including stricter groundwater regulation, continuous remote-sensing-based monitoring, and large-scale mangrove restoration, to safeguard ecological functions and enhance the socio-economic resilience of coastal communities in the face of accelerating climate change impacts.

Coastal biodiversity conservation is challenged by fragmented datasets and the limited integration of environmental conditions into marine spatial planning (MSP). This study develops an operationalized adaptive Marine Spatial Planning (MSP) to support biodiversity conservation by linking remote sensing data, IoT-based water quality measurements, and spatial optimization within Spatial Decision Support System (SDSS). The Tidung Islands are used as a case study, where benthic habitats are mapped from 3 m PlanetScope imagery. Water quality observations are processed into the Nemerow Pollution Index (NPI) and subsequently interpolated through an ensemble approach that combines inverse distance weighting, random forest, and gradient boosting. A key innovation of this study is the incorporation of the Nemerow Pollution Index (NPI) as a dynamic environmental cost layer within Marxan-based conservation prioritization. These data were incorporated alongside anthropogenic pressures to evaluate multiple conservation scenarios The ensemble interpolation demonstrated strong predictive performance (R²=0.76;MAE=0.0306), enabling reliable spatial representation of environmental conditions. The results show that integrating environmental quality into MSP significantly improves spatial efficiency, reduces fragmentation, and enhances ecological representation compared to conventional approaches based on static variables. Moderate conservation targets (≈30%) produced the most optimal solutions (~2,200 cost; ~11 km boundary), while more ambitious targets resulted in fragmented and inefficient spatial configurations. The proposed framework offers a transferable approach for data-limited coastal regions, contributing to the advancement of adaptive biodiversity conservation strategies.

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.