Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia

Abstract

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.

Spatio-temporal dynamics of mangrove extent in coastal Bekasi Regency, Indonesia

“…Based on these results, we assumed that all the mangrove maps produced could be used for mangrove monitoring analysis. Furthermore, in the literature, we found that the spectral and vegetation indices, namely the Modified Normalised Water Index (MNDWI) and Green Chlorophyll Vegetation Index (GCVI), were the best predictors for deriving the mangrove extent using the Random Forest algorithm over spatial and temporal analysis [18]. Overall accuracy: 90.50%; kappa coefficient: 0.8105.…”

“…Each year in the Sentinel collection process is from the first of January to the end of December for every year, except for 2023 which start from the first January to July 2023. The median reducer function helps choose the best pixel in the GEE algorithm, whether covered by clouds or shadows [18]. Finally, we calculated the spectral indices and combined them with the original bands from Sentinel for RF Classification input.…”

“…The RF algorithm has attracted increased interest owing to the good handling of noise and outliers, and the majority has higher accuracy, in addition to other classifiers [21]. Based on the recommendations of previous studies [17][18], we selected 100 trees (ntree = 100) with 5 predictors (100,5). In this process, we used several band ratios and vegetation indices for RF input, namely NDVI, NDMI, MNDWI, SR, Band Ratio, and GCVI.…”

Systematic review on the implementation of mangrove community-based restoration in Indonesia and beyond

“…Mangroves also present to support biodiversity habitat, especially as feeding, spawning and nursery ground of marine and other wildlife species [3]. Related to climate change mitigation, mangrove forest is important not only for adaptation to the impact e.g., protect coastal community from natural disaster, but also for mitigation as carbon stocks in mangrove is estimated to be 3 times higher than terrestrial forest ecosystem [4], [5], and [6].…”

Aboveground Biomass Estimation of Mangrove Ecosystem in the Anambas Islands Using Remote Sensing Data

“…Mangrove is one of the Earth's most crucial marine ecosystems, offering an extensive array of services to our environment. They play a pivotal role in delivering a diverse set of benefits spanning ecological, social and economic benefits [1][2], including coastal protection from surges and waves, water filtration, a food source and nursery for fish and crustaceans, as well as a source of wood fuel, and opportunities for nature-based recreation [3][4][5][6]. Mangroves, being the only woody halophytes [7], have a crucial role in carbon storage within the context of climate change [8][9].…”