Vol. 5, No. 6, June 2024

E-ISSN: 2723-6692

P-ISSN: 2723-6595

http://jiss.publikasiindonesia.id/

Journal of Indonesian Social Sciences, Vol. 5, No. 6, June 2024 1410

Utilisation of Sentinel-2A Imagery for Estimation of Mangrove

Carbon Stock in Mamminasata Area, South Sulawesi

Munajat Nursaputra, Kurniawan, Daud Malamassam

Universitas Hasanuddin Makassar, Indonesia

Email: munajatnur[email protected]

Correspondence: munajatnursap[email protected]

KEYWORDS

ABSTRACT

Carbon Reserves; NDVI; Citra

Sentinel-2A; Mangrove Forest;

Mamminasata

Population growth and land conversion have led to the degradation of

mangrove forests on the southern coast of South Sulawesi, especially the

Mamminasata area. Reduced mangroves increase carbon dioxide in the

atmosphere. However, data on the potential carbon absorption of

mangroves is still lacking. To overcome this, remote sensing is used to

estimate carbon reserves. This reseach utilises Sentinel-2A imagery to

estimate mangrove carbon stocks in Mamminasata. The image processing

process includes radiometric correction, atmospheric correction, image

classification, and extraction of NDVI values. The NDVI value is used to

classify the density of mangroves into sparse, medium, and dense,

covering 1,244.75 hectares. Field data collection was carried out through

a survey of forest stand measurements. The results of NDVI

transformation show a value range of 0.2 to 0.8 for mangrove objects in

the Mamminasata area. The NDVI data on the analysed images were then

made into three density classes. The rare density class has a carbon value

of 3.56  21.16 Ton C/ha, the medium density class is between 21.17 

31.49 Ton C/ha, and the dense density class is between 31.50  39.18 Ton

C/ha. Regression analysis shows a strong correlation between NDVI and

carbon stock (R² = 0.7134). This study confirms the effectiveness of

remote sensing in environmental monitoring and mangrove conservation.

These findings support conservation efforts and sustainable management

policies by highlighting areas with high carbon sequestration potential.

Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

1. Introduction

Mangrove forests in Indonesia great importance, with the most extensive distribution in the

world. According to data from the National Institute of Aeronautics and Space (LAPAN) in 2016,

mangrove forests in Indonesia cover about 2,408,652.39 hectares, highlighting their critical

ecological value for the country. These mangroves play an important role in coastal protection,

biodiversity, and carbon sequestration, which is essential for climate change mitigation and

supporting sustainable ecosystems (A. Malik et al., 2016; Mukrimin et al., 2021). South Sulawesi is

one of Indonesia's provinces with a large mangrove ecosystem, with an area of about 9,335.18

hectares. This mangrove area is spread along the west, south, and east coasts of the province.

However, population growth and land conversion are significant threats to the sustainability of

these mangrove forests. In particular, the southern coast of South Sulawesi, including the

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Mamminasata metropolitan area (consisting of Makassar City, Maros Regency, Gowa Regency, and

Takalar Regency), faces a critical challenge from mangrove degradation (Indrayani et al., 2021;

Siddiq et al., 2020).

This forest degradation can adversely affect natural protection mechanisms and increase the

vulnerability of these areas to environmental and climate change. Data from LAPAN shows that

between 2016 and 2017, the area of mangrove forests on the southern coast of South Sulawesi

decreased by 119.67 hectares, from 874.58 hectares to 754.91 hectares. This alarming trend

highlights the urgent need for more intensive conservation efforts (Hu et al., 2020). Mangrove

forests are known to have a high carbon storage capacity, able to store up to four times more carbon

than other tropical forests. This makes its conservation important for global climate change

mitigation efforts. While important, data on the carbon sequestration potential of mangrove forests

is still limited, posing challenges to effective conservation and management strategies. Therefore,

efficient methods for estimating and monitoring carbon stocks in mangrove forests are essential for

sustainable ecosystem management and policy formulation (Pham et al., 2019).

Remote sensing has emerged as an effective technique for estimating carbon stocks, offering a

cost-effective and accurate method for monitoring ecosystems at scale. The Sentinel-2A satellite,

equipped with multispectral sensors, has been used to detect and analyse the condition of mangrove

forests more precisely and efficiently. Recent studies have demonstrated the usefulness of Sentinel-

2A imagery in mapping mangrove distributions and estimating carbon stocks, demonstrating its

potential in environmental monitoring (Sugara et al., 2022; Wang et al., 2018). The studies highlight

the effectiveness of remote sensing in providing detailed and accurate data on mangrove

ecosystems, which is critical for conservation efforts (Putra et al., 2022; Rahmadi et al., 2021). While

promising advances in remote sensing applications for mangrove monitoring, there are still

significant gaps in the precise estimation of carbon stocks in various mangrove ecosystems.

Research in Labuan Tereng, West Lombok Regency, using Sentinel-2A imagery, found that R.

mucronata dominated the area with an average carbon reserve of 122.1 tons per hectare, with a

total of 1,359.9 tons of carbon for the area. However, variations in the composition of local

mangrove species and their carbon storage capacity require further region-specific studies to

ensure accuracy (Hambali et al., 2023).

Research on the south coast of Sampang Regency using a hybrid model for carbon estimation

revealed that the total carbon stock of mangroves is 350,721.7 tons, with an average of 300.3 tons

per hectare. These findings highlight the need for a tailored remote sensing methodology to address

the diverse characteristics of mangrove forests in different regions. In addition, integrating various

remote sensing data sources can improve the accuracy of carbon stock estimates, but it is still

underexplored in many areas (Muhsoni, 2018). In addition, although the Sentinel-2 sensor has been

validated for its usefulness in mapping mangrove areas and species, more comprehensive studies

that integrate multispectral data with field measurement efforts are needed. This integration will

help refine the estimation models and ensure they are robust and applicable in a variety of

ecological environments. The limited availability of such integrative studies highlights the critical

research gaps seeks to address (Wang et al., 2018).

This study aims to estimate carbon stocks in mangrove forests throughout the Mamminasata

region, including Makassar City, Maros Regency, Gowa Regency, and Takalar Regency. The study

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leverages the advanced capabilities of the Sentinel-2A satellite to provide accurate and up-to-date

data on mangrove carbon reserves, which are essential for effective conservation and management

strategies. The application of Sentinel-2A multispectral imagery in the specific context of the

Mamminasata region is a novelty that has not been studied before. By integrating remote sensing

data with field measurements, this research will contribute to understanding the carbon

sequestration potential of mangrove forests in South Sulawesi and provide a scientific basis for

informed conservation policies and practices. In addition, the methodology used in this study can be

developed to estimate carbon stocks in other mangrove ecosystems that face similar environmental

threats.

2. Materials and Methods

Location and Time of Research

This research was carried out for 8 months, starting from March 2021 to November 2021. The

research is divided into two main stages, namely field activities and data analysis. Field activities

were carried out in the Mamminasata area, which covers the areas of Makassar City, Maros Regency,

Gowa Regency and Takalar Regency, as shown in Error! Reference source not found.. The data

analysis stage was carried out at the Forestry Planning and Information Systems Laboratory,

Hasanuddin University.

Figure 1 Map of the Research Location

Research Tools and Materials

The tools used for field data collection include the Global Positioning System (GPS) to

determine location coordinates, measuring tapes and rollmeters for plotting and measuring the

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diameter of the stands, as well as neat ropes and tally sheets to record data in the field. In addition,

ArcGIS software is used for data processing and spatial analysis and mapping. The main material

used in this study is the Sentinel-2A Level L1C Image Recorded in 2020.

Field Data Collection

Field data collection was carried out through measurements of mangrove forest stands on a

predetermined sample plot. The location of the sample plot was selected using the stratified

sampling method based on the density of mangroves obtained from the results of satellite image

processing. A sample plot measuring 10 m x 10 m was used to record vegetation at the tree level

(BSN, 2011). The stand variables measured in the field include the type of tree and the diameter of

the tree at chest height (DBH). The number of sample plots is determined using the Slovin formula

(Sevilla, 2007), taking into account the population area and fault tolerance limits.

Data Analysis

1. Interpretation of Mangrove Distribution and Density

Sentinel-2A image processing includes radiometric correction, atmospheric correction, image

classification, and extraction of vegetation index values using the Normalized Difference Vegetation

Index (NDVI). Radiometric correction is performed to correct pixel values due to atmospheric

disturbances, which are a major source of errors in satellite image processing (Kamal et al., 2020;

Wu et al., 2018). This process uses a radiometric calibration tool to eliminate distortions caused by

the position of the sun. Atmospheric correction is performed using a flash atmospheric correction

tool to eliminate distortions that affect the reflectance value in the image (Belov & Myasnikov, 2016;

Cetin et al., 2017).

After corrections, the next classification of mangrove vegetation was carried out. To clarify the

object to be classified, a combination of bands 4 (red), 3 (green), and 2 (blue) is used. These bands

were chosen because they have high sensitivity and high reflectance value to vegetation, making it

easier to identify mangrove cover (Ghorbanian et al., 2021; Purwanto & Asriningrum, 2019). After

that, image sharpening is carried out to help visually interpret the mangrove cover (Ulfarsson et al.,

2019). This identification involves the ability to interpret images through elements such as colour,

texture, size, shape, pattern, shadow, and association to recognise objects. The estimation of above-

surface biomass using the Sentinel-2A image will be based on the value of the NDVI vegetation index

and the results of biomass measurements in the field. NDVI values were obtained through the

transformation of the vegetation index which was regressed to the actual biomass values from the

field. The calculation of the vegetation index using NDVI involves the red band and the near infrared

band reflected by the vegetation. On Sentinel-2A satellite imagery, NDVI is calculated using bands 8

(near-infrared) and 4 (red) (Muhsoni et al., 2018; Rakuasa & Sihasale, 2023).

2. Estimated Carbon Stocks

The value of carbon reserves is obtained from the conversion of the value of the stand

biomass. Information on mangrove biomass content was obtained using an allometric formula

approach. This formula, which takes into account factors such as the diameter of the tree, the

density of the wood, and the height of the tree, is essential for accurate calculations (Adame et al.,

2017; Fatoyinbo et al., 2018; A. Malik et al., 2016; Sitoe et al., 2014; Trissanti et al., 2022). The

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allometric equations for several types of mangroves found in the Mamminasata Area are presented

in Error! Reference source not found..

Table 1 Allometric equations of several mangrove species

Spesies

Allometric equations

Source

Rhizophora apiculata

  󰇛󰇜



(Amira, 2008; Pambudi, 2011)

Rhizophora

mucronata

  󰇛󰇜



(Fromard et al., 1998)

Avicennia sp

  󰇛󰇜



(Komiyama et al., 2005)

Avicennia marina

  󰇛󰇜



(Dharmawan & Siregar, 2008)

Where: B is Biomass (kg), = BJ Wood (g/cm3), DBH is Chest Height Tree Diameter (cm).

The distribution of biomass values obtained at the research site was then converted into the

value of carbon reserves, which were multiplied by the value of the percentage of carbon content of

47% of the biomass. The creation of carbon stock maps is carried out based on the best models. The

best model was obtained from the relationship between the content of biomass above the surface

and the NDVI value of Citra Sentinel-2A, which was analysed using mathematical equations with

linear, exponential and logistic models. The best model is depicted with a coefficient of

determination (R2) value. This coefficient aims to measure how far the model can apply dependent

variable variations, which are described as very low (0-0.19), low (0.2-0.39), medium (0.4-0.59),

strong (0.6-0.79) and very strong (0.8-1.0) (Sugiyono, 2010). The carbon stock values were then

distributed using a raster calculator in the ArcGIS Software spatial analyst tool. Then, the carbon

reserve value in each mangrove density class is issued using zonal statistics as a table in the spatial

analysis tool ArcGIS Software, where the data entered is the mangrove forest density raster data and

the carbon stock raster data that was made previously.

3. Results and Discussions

Distribution and Density of Mangroves

The results of the interpretation show that the mangrove land cover in the Mamminasata area

is found in Makassar City, Takalar Regency, and Maros Regency, covering an area of about 1,244.75

hectares, as depicted in Error! Reference source not found.. This interpretation shows a

substantial distribution of mangrove forests in the three administrative regions. The validation

process was carried out through field checks at 20 points, resulting in an overall accuracy of 90%.

However, there are differences at two points, namely point 3 in Lakkang Village and point 19 in

Maccini Baji Village, where the initial interpretation identifies these points as mangrove land cover,

while the field check is a fish pond.

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Figure 2 Interpretation Map of Mangrove Distribution in the Mamminasata Area

The findings of this study are in line with previous research that shows the effectiveness of

Sentinel-2 imagery in mangrove forest mapping. For example, Hu et al. (2020) highlighted the

significant improvement in spatial and temporal resolution offered by Sentinel-2 compared to

Landsat imagery. Studies such as those conducted by Sugara et al. (2022) and Jia et al. (2019) telah

berhasil menggunakan Sentinel-2 untuk mendeteksi mangrove yang terendam, mapping mangrove

density, and estimating carbon stocks with reported accuracy exceeding 80%. Similarly, Aulia et al.

(2022) recorded up to 86.07% accuracy for mangrove density mapping using Sentinel-2. The

differences identified in Lakkang Village and Maccini Baji Village reflect the challenges noted by

previous researchers in distinguishing mangrove land cover from other land uses such as fish

ponds. This highlights the need to integrate multispectral data with field measurement efforts to

improve the accuracy of mangrove mapping. Chen et al. (2022) and Maung (2023) It also

emphasises the importance of combining remote sensing data with field validation to improve the

accuracy of ecosystem assessments.

The identified mangrove areas in the study area were analysed to determine the level of

mangrove density. Using the Normalization-Related Vegetation Index (NDVI), mangrove objects in

the Mamminasata region show NDVI values ranging from 0.2 to 0.8. The NDVI data was then

categorised into three density classes, namely -1  0.32 (rare), 0.33  0.42 (moderate), and 0.43  1

(dense) (Departemen Kehutanan, 2005), with the results presented in Error! Reference source

not found. and Error! Reference source not found.. The total area for sparse density is 58.17

hectares, medium density covers 57.46 hectares, and dense density covers 1,129.12 hectares.

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Table 2 Mangrove Forest Density in Mamminasata Area

Regency /

City

District

Village

Area per Class Density (Ha)

Total

(Ha)

Infrequently

Medium

Heavy

Makassar

Biring Kanaya

Untia

0,29

0,33

19,46

20,08

Panakkukang

Pampang

2,00

1,72

58,85

62,57

Panaikang

1,31

1,04

37,75

40,10

Tello Baru

0,01

0,00

0,52

0,53

Tallo

Lakkang

2,59

2,41

16,28

21,28

Tallo

0,21

0,13

3,30

3,64

Tamalanrea

Bira

0,13

0,19

15,11

15,43

Kapasa

0,83

1,08

10,29

12,19

Parang Loe

3,29

3,17

39,05

45,51

Tamalanrea Indah

1,55

1,54

41,59

44,68

Tamalanrea Jaya

0,00

0,01

0,00

0,01

Total Makassar

12,21

11,62

242,20

266,02

Maros

Bontoa

Ampekale

1,73

1,00

25,25

27,98

Bonto Bahari

0,71

0,52

6,77

8,00

Pajukukang

0,19

0,09

5,44

5,72

Lau

Marrannu

0,69

0,73

24,52

25,94

Maros Baru

Borimasunggu

1,03

1,16

29,28

31,47

Marusu

Nisombalia

1,39

1,24

50,02

52,65

Pabentengan

0,18

0,20

5,22

5,60

Total Maros

5,92

4,94

146,50

157,36

Takalar

Mangara

Bombang

Banggae

0,09

2,94

3,12

Laikang

0,45

0,31

3,46

4,22

Panyangkalang

0,16

0,13

3,57

3,86

Mappakasunggu

Maccinibaji

33,22

31,59

424,6

489,41

Mattirobaji

5,41

8,23

285,84

299,48

Takalar Kota

0,14

0,08

4,32

4,54

Pattallassang

Pallantikang

0,17

0,2

8,07

8,44

Polombangkeng

Selatan

Pa'bundukang

0,4

0,28

7,62

8,29

Total Takalar

40,04

40,91

740,42

821,36

Total Mamminasata Area

58,17

57,47

1.129,12

1.244,74

The use of NDVI for mangrove density mapping is in line with various studies that have shown

its effectiveness in estimating canopy cover, forest health, and mangrove structure (Lovelock et al.,

2017; Pamungkas, 2023; Ticman et al., 2021). NDVI is widely recognized for its ability to monitor

mangrove forest restoration, track changes in mangrove areas, and assess damage based on canopy

density (Anggraini, 2023; Faizal et al., 2023). In addition, NDVI has been used to classify mangrove

canopy density and estimate above-ground biomass within mangrove ecosystems, emphasising its

flexibility and reliability in remote sensing analysis (Rinjani, 2024).

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Figure 3 Map of Mangrove Density in Mamminasata Area

The findings in the Mamminasata region, which shows a dense density area of 1,129.12

hectares, are consistent with studies highlighting the extensive canopy cover of healthy mangrove

ecosystems. Classification of mangrove densities into sparse, moderate, and dense categories

facilitates targeted conservation efforts, as seen in other studies using NDVI to categorise canopy

density levels and implement effective management practices (Singgalen et al., 2021). This density

class is the basis for determining the number of sample plots for the calculation of carbon reserves

in the field, where 2 plots for sparse density, 2 plots for medium density, and 39 plots for dense

density are obtained. The application of Slovin's formula to determine the number of sample plots

based on density class further strengthens the systematic approach used in this study to ensure

accurate and representative sampling.

Estimated Carbon Stocks

The calculation of mangrove stand biomass in 43 sample plots in the Mamminasata area

showed an average mangrove biomass of 689.20 kg/plot. The lowest biomass was recorded at

235.23 kg/plot in plot 21, which is categorised as sparse density, while the highest is 963.96

kg/plot, categorized as dense density. The biomass values obtained at the study site are then

converted into carbon reserve information, which is usually expressed in carbon tons per hectare

(tonC/ha). The average carbon reserve in the Mamminasata area was found to be 34.46 tonsC/ha.

Furthermore, regression analysis was carried out to explore the relationship between NDVI values

and carbon stocks, with the results of the regression analysis presented in

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Table 3 Carbon Stock Estimation Model

Regresi

Model Persamaan

Linear

y = 84,963x + 15,012

0,6733

Eksponensial

y = 24,312e

1.594x

0,6144

Logaritmik

y = 43,564ln(x) + 90,225

0,7134

The logarithmic regression model shows the highest coefficient of determination (R²) of

0.7134, so it is selected as the best predictor model for estimating carbon stocks. It shows a strong

relationship between carbon stock values and NDVI, where 71.34% of the variation in carbon stocks

can be explained by NDVI values, with the remaining 28.66% due to other variables. These findings

are consistent with previous studies that used regression models to analyse the relationship

between NDVI and carbon stocks in various ecosystems. Malik et al., (2023) and Hidayah et al.,

(2022) has demonstrated the potential of NDVI as a predictor for carbon stock estimation, with the

determination coefficient ranging from 0.7 to 0.98, highlighting the significance of NDVI in affecting

above-ground biomass and carbon stocks.

The carbon stock in the study area is then calculated based on the density of mangrove forests

to evaluate the potential carbon stocks in each vegetation density class. This was done to see the

potential value of carbon stocks in each class of mangrove vegetation density. The carbon content is

divided into three classes, namely sparse, medium and dense density classes. The distribution of

carbon reserve values has been divided into three classes based on the classification of vegetation

density. The rare density class has a carbon value of 3.56  21.16 Tons C/ha, the medium density

class has a carbon value between 21.17  31.49 Tons C/ha, and the dense density class has a carbon

value between 31.50  39.18 Tons C/ha. More details of carbon distribution in the Mamminasata

area mangrove forest are presented in Error! Reference source not found..

Table 4 Carbon Distribution of Mangrove Forests in the Mamminasata Area

No.

Density

Type

comprehensive

(ha)

Lowest

Carbon

Stocks

(Ton C/ha)

Highest

Carbon

Reserves

(Ton C/ha)

Infrequently

58,17

3,56

21,16

Medium

57,46

21,17

31,49

Heavy

1.129,12

31,5

39,18

Total

1.244,75

56,23

91,83

The spatial distribution of mangrove carbon stocks using NDVI values as shown in Error!

Reference source not found., has actually been widely studied using remote sensing data and

spatial modeling techniques to map and estimate carbon stocks in mangrove ecosystems. These

studies highlight the spatial variability of carbon stocks in different areas of mangroves, providing

essential insights into the distribution patterns and carbon sequestration potential of mangrove

forests. For example, Pham et al., (2020) showed that the NDVI values obtained from satellite

imagery effectively assessed changes in carbon stocks and identified areas with high carbon density.

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The findings of this study, which classifies carbon stocks into sparse, medium, and dense classes, are

in line with previous research that emphasized the usefulness of NDVI in determining vegetation

density and appropriate carbon stocks (Aulia et al., 2022; I Gusti Agung Indah Mahasani et al., 2021;

Sugara et al., 2022). The categorization of carbon stock values in the Mamminasata region into

different density classes reflects the methodological approach used in similar studies, which further

validates the effectiveness of NDVI in mangrove carbon stock assessments.

Figure 4 Map of the Distribution of Mangrove Forest Carbon Reserves in the Mamminasata Area

The findings of this study have significant implications for the conservation and management

of mangrove ecosystems in the Mamminasata area. By accurately mapping the spatial distribution of

carbon stocks based on vegetation density, this study provides important data for targeted

conservation efforts. Identifying areas with high carbon density, which have greater carbon

sequestration potential, is essential to prioritising conservation actions and mitigating the impacts

of climate change. The integration of NDVI values in spatial analysis has proven to be very helpful in

mapping and evaluating mangrove carbon stocks, contributing to a better understanding of the role

of mangroves in carbon sequestration (Adni, 2024). The data generated from this study can inform

conservation policy decisions and strategies, ensuring sustainable management of mangrove forests

and their ecosystem services. Additionally, the study emphasises the importance of remote sensing

technology in environmental monitoring, offering a reliable and efficient method for assessing

ecosystem health and carbon stock dynamics over time.

4. Conclusion

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This study successfully utilised Sentinel-2A imagery and NDVI values to estimate and map the

carbon stock of mangrove forests in the Mamminasata area, South Sulawesi. The findings show that

mangroves in the region cover an area of 1,244.75 hectares, with carbon stock values ranging from

3.56 to 39.18 Tons C/ha in various density classes. Logarithmic regression models show the highest

accuracy in predicting carbon stocks, with an R² value of 0.7134, which shows a strong correlation

between NDVI values and estimated carbon stocks. These results confirm the effectiveness of

remote sensing technology, specifically the Sentinel-2A imagery, in providing accurate and reliable

data for environmental monitoring. The study contributes to an increased understanding of the role

of mangroves in carbon sequestration, emphasising its importance in climate change mitigation and

ecosystem conservation. By identifying areas with high carbon sequestration potential, the study

supports targeted conservation efforts and informs sustainable management practices. The

integration of NDVI in spatial analysis offers a powerful method for assessing and monitoring

mangrove ecosystems, providing valuable insights for policymakers and conservationists. Future

research should focus on refining this methodology and expanding its application to other regions,

ensuring a comprehensive and effective mangrove conservation strategy. The study highlights the

critical role of mangroves in carbon storage and the importance of advanced remote sensing

technology in environmental science.

5. References

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