A. A. Md. Ananda Putra Suardana

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.

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.

Lemuru fishing activity in the Bali Strait is the most dominant fishery sector in that waters. One of the environmental factors that affect lemuru is Sea Surface Temperature (SST) spread seasonally. One Remote Sensing technology that can be used in determining the value of the distribution of SST is Moderate Resolution Imaging Spectroradiometer (MODIS). This study aimed to explore the influence of SST on the production of lemuru fishing (Sardinella lemuru) in the waters of the Bali Strait each seasonally. The method used in this research is descriptive qualitative by look influence between SST seasonally to the production of lemuru fishing at Bali Strait. This study used correlation and regression polynomial equation. The results showed influence of SST seasonally to the production of lemuru fishing in west season amounted to 54.86% (proportional), in east season by 43.88% (inversely), in the transitional seasons I amounted to 37.05% (proportional), and on the intermediate season by 30.64% (proportional). The weak impact of SST on the production of lemuru fishing in the waters of the Bali Strait in every season caused by state of the SST is relatively constant, while the production of fishing lemuru in annually increasing.