Carbon Stock Estimation of Mangrove Vegetation Using Remote Sensing in Perancak Estuary, Jembrana District, Bali

Hastuti, Amandangi Wahyuning; Suniada, Komang Iwan; Islamy, Fikrul

doi:10.30536/j.ijreses.2017.v14.a2841

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Mangroves that receive circulatory water input will grow better because they are supplied with sufficient water, accelerating vegetation growth. That carbon stocks in natural mangrove vegetation were higher than mangrove vegetation replanted in former ponds [21]. Not only that, but it also relates to the backscattering value generated by HV polarization.…”

Section: Resultsmentioning

confidence: 84%

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Mahasani

Osawa

Adnyana

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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Mangrove forests in tropics coastlines area play an essential role in carbon fixation and carbon storage. Mangrove forests in coastal areas are very effective and efficient in reducing the concentration of carbon dioxide (CO2) in the atmosphere because mangroves can absorb CO2 through photosynthesis by diffusion through stomata and then store carbon in the form of biomass. With the lack of efforts to manage mangrove forests, it needs to be developed so that forest functions can be utilized sustainably. This paper describes examining the use of remote sensing data, particularly dual-polarization ALOS-2 PALSAR-2 data, with the primary objective to estimate the carbon stock of mangrove forests in Benoa Bay, Bali. The carbon stock was estimated by analyzing HV Polarization, Above Ground Biomass (AGB), and ground biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the organic carbon value of 0.47. The potential carbon stock in the mangrove Benoa Bay area is 209,027.28 ton C to absorb carbon dioxide (CO2) of 767,130.11 ton CO2 Sequestration same with 3.87 X 1011 bottles in 2015 and 204.422,59 ton C to absorb carbon dioxide (CO2) of 750.230,93 ton CO2 Sequestration same with 3.79 x 1011 bottles in 2020.

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Section: Resultsmentioning

confidence: 84%

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Mahasani

Osawa

Adnyana

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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“…The vegetation index that showed the strongest correlation with field biomass values will be used for biomass modelling. Carbon stock modelling will be derived from biomass modelling using the following equation: (Hastuti et al, 2017) 𝐸𝐶 𝑏 = 𝐵 × %𝐶 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 (4)…”

Section: Carbon Stock Modellingmentioning

confidence: 99%

Spatial and temporal study of estimating carbon stocks distribution of mangrove forest in coastal area of Teluknaga, Tangerang

Yumnaristya,

Indra,

Supriatna

et al. 2023

EAM

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Coastal mangrove forests play a crucial role in balancing carbon emissions in the atmosphere as they are a significant carbon store. Previous studies have shown that mangroves can absorb carbon four times more efficiently than terrestrial tropical forests. Unfortunately, the massive development and land use changes in Teluknaga District's coastal areas threaten these ecosystems' existence. To address this concern, efforts are being made to increase conservation, including estimating carbon stock. The aim of this study is to analyze the spatial distribution of biomass and carbon stock of mangrove forests in Teluknaga between 2016-2022 based on vegetation indices such as ARVI, EVI, and SAVI. Sentinel-2 was calculated into ARVI, EVI, and SAVI vegetation indices to model biomass. Statistical correlation analysis was also used to determine the best vegetation index to model biomass in the coastal area of Teluknaga District. This study found that the ARVI vegetation index had the best correlation (R = 0.60) for modeling biomass, with an RMSE value of 36.67 kg/pixel. Most mangrove forests in the coastal area of Teluknaga District showed an increase in biomass and carbon stock between 2016-2022, with significant growth in Muara and Lemo villages' mangrove forests, which is in line with an increase in the area and density of mangrove forests.

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“…Vegetation indices (VIs) are used to determine the plant density from remote sensing images. In this research, correlation and regression analysis was used to exploit the association between the values of the NDVI and the carbon information from the existing assessment to estimate the mangroves' aboveground carbon (Hastuti et al, 2017). NDVI is one of the most corresponding vegetation indices for a related purpose (Bindu et al, 2020).…”

Section: Vegetation Indexmentioning

confidence: 99%

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

Sugara

Lukman²,

Rudiastuti³

et al. 2022

Geos. Ind.

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The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground carbon estimation. The results showed that mangroves mapping could derive overall accuracy of 89.09%, where the high-density class existed in 135.12 Ha of total area. It was also discovered that Sentinel-2 imagery could estimate mangroves carbon stock up to 61%. The carbon stock estimation based on the imagery has a value of 16.3992 – 115.134 t C/ha, while that of field survey data ranges from 19.69 to 326.06 t C/ha. These results showed that Sentinel-2B spectral data is functional and has a good chance of being able to predict carbon stock. Keywords : Carbon; mangroves; NDVI; remote sensing; sentinel-2B Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Carbon Stock Estimation of Mangrove Vegetation Using Remote Sensing in Perancak Estuary, Jembrana District, Bali

Cited by 11 publications

References 29 publications

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Spatial and temporal study of estimating carbon stocks distribution of mangrove forest in coastal area of Teluknaga, Tangerang

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

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