JIT Journal of Innovation and Technology e-ISSN: 2721-8562.
p-ISSN: 2721-8570 Vol.
No.
April 2025.
Page: 12 Ae 20 Integrating Blue Carbon Estimation into Mangrove-Based Coastal Protection Infrastructure Planning: A Case Study in Dompak Island.
Tanjungpinang Dian Kharisma Dewi 1.
Sapta Nugraha 1, 1 Faculty of Engineering and Maritime Technology.
Universitas Maritim Raja Ali Haji.
Tanjungpinang, 29145.
Indonesia *Corresponding Author: diankharisma@umrah.
Article history Received: 10.
Revised: 22.
Accepted: 18.
DOI:10.
31629/jit.
Abstract Ecosystem-based coastal protection offers a strategic approach to mitigating the impacts of climate change, particularly in small island contexts.
This study integrates remote sensing and geospatial analysis to estimate blue carbon stocks in the mangrove ecosystems of Dompak Island.
Tanjungpinang, using Google Earth Engine (GEE) and Sentinel-2 satellite imagery.
Through the application of the Normalized Difference Vegetation Index (NDVI) and an empirical model for Above Ground Biomass (AGB), spatial carbon stock distributions were generated and classified into three functional zones: conservation, restoration, and critical protection.
Results reveal that areas with moderate vegetation exhibit the highest carbon stock .
tons/h.
, suggesting ongoing biomass accumulation.
This spatial analysis informs a zoning framework that supports ecosystem-based infrastructure planning.
The integration of carbon mapping with adaptive civil engineering strategies demonstrates a scalable model for climate-resilient coastal development in vulnerable regions.
Keywords: Blue carbon, mangroves.
Google Earth Engine, coastal protection, natural Introduction Coastal ecosystems in Tanjungpinang are are increasingly threatened by anthropogenic pressures, including trade expansion, urban development, and infrastructure growth.
Among the most affected habitats are mangrove forests, which face degradation due to illegal logging and land These exacerbate coastal erosion, increase flood risks, and compromise marine biodiversity, ultimately threatening the livelihoods of local communitiesAi particularly indigenous groups such as the Suku Laut in the Riau Archipelago.
Environmental decline hampers access to food and other livelihood sources, heightens vulnerability to natural disasters, and worsens socio-economic conditionsAiultimately hindering sustainable development in coastal areas.
Mangroves play a critical role in coastal Their complex root systems attenuate wave energy, reduce shoreline erosion, and support sediment stability.
Their dense root systems trap sediments, strengthen soil structures, and provide habitats for various marine species.
Moreover, mangrove forests are recognized for their substantial carbon sequestration potential, storing carbon both above ground in biomass and below ground in soils.
This makes them pivotal in global climate change A 2025 This open access article is distributed under a Creative Commons Attribution (CC-BY) license.
D K Dewi and S Nugraha.
Journal of Innovation and Technology.
April 2025.
Vol.
6 No.
DOI: DOI:10.
31629/jit.
mitigation strategies.
Despite their ecological and socio-economic value, ecological parameters like blue carbon stocks are often excluded from infrastructure planning.
They can store substantial amounts of carbon, both in their aboveground biomass and in the soil.
This carbon sequestration capability makes mangroves a vital component in climate change mitigation and greenhouse gas emission reduction strategies.
One of the key challenges in coastal protection efforts is the lack of integration of ecological data such as information on mangroves and other coastal ecosystems into civil engineering planning and The disconnect between civil engineering and environmental data can lead to poorly adapted developments that fail to protect coastal ecosystems or mitigate climate-related hazards.
Bridging this gap requires an integrated approach that combines ecological assessment, spatial analysis, and engineering design to foster resilient, nature-based coastal infrastructure.
Collaboration among scientists, engineers, and policymakers can lead to more holistic and sustainable coastal management Integrating ecological data into infrastructure design enhances coastal resilience to climate change impacts.
Estimating blue carbon defined as the amount of carbon stored in coastal ecosystems such as mangroves is essential for recognizing the ecological and economic value of these habitats.
Such data can inform coastal protection planning, conservation zoning, and the development of ecosystem-based strategies.
Incorporating blue carbon estimations into coastal protection plans enables more informed and sustainable decision-making.
This integration supports the development of policies that acknowledge the critical role of coastal ecosystems in climate mitigation and land protection.
Consequently, restoration efforts not only protect the environment but also deliver economic and social benefits to coastal communities.
Google Earth Engine (GEE) has emerged as a powerful tool for monitoring coastal vegetation dynamics, particularly in mangrove ecosystems.
Its ability to access and process satellite imagery efficiently enables robust spatiotemporal analyses of land cover changes.
For example, a study in Probolinggo utilized Sentinel-2A imagery and NDVI in GEE to monitor mangrove extent from 2019 to 2023, revealing significant fluctuations linked to human activity and environmental factors.
The Normalized Difference Vegetation Index (NDVI) is a widely used indicator for evaluating vegetation health and canopy density.
the context of mangroves.
NDVI helps identify areas experiencing significant degradation or growth.
study by Bagas .
employed MODIS data in GEE to visualize NDVI changes across Indonesia between 2018 and 2022, offering insights into national vegetation dynamics.
The integration of GEE and NDVI enables efficient detection of mangrove change.
Research in Bekasi Regency used Landsat 8 imagery and GEE to map a decade of deforestation trends.
This approach provides accurate data for coastal ecosystem conservation and rehabilitation planning.
The use of machine learning algorithms in GEE further enhances mangrove vegetation mapping Recent studies have shown that combining Sentinel-1 and Sentinel-2 imagery with the Random Forest algorithm in GEE produces highly accurate mangrove maps, supporting coastal resource management and conservation efforts.
Despite the many advantages of GEE and NDVI, challenges such as limited spatial resolution and the need for representative training data persist.
Nevertheless, advancements in technology and improved satellite data access continue to expand the opportunities for enhanced coastal ecosystem monitoring and Method This study was conducted along the coastline of Dompak Island.
Tanjungpinang, located in the Riau Archipelago Province.
Indonesia.
Dompak Island was selected as the study site due to its extensive mangrove forests, strategic position as the centre of government for the Riau Islands Province, and ongoing development pressures on coastal The research method employed a spatial analysis approach using the Google Earth Engine (GEE) platform.
The primary data source was Sentinel-2 multispectral imagery, which offers 10meter spatial resolution in visible and near-infrared bands suitable for vegetation analysis.
The study used imagery from the year 2023, selected based on minimal cloud cover to ensure optimal vegetation D K Dewi and S Nugraha.
Journal of Innovation and Technology.
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DOI: DOI:10.
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The workflow consisted of several key Preprocessing Satellite Imagery Restoration Zones: Moderate NDVI with potential for rehabilitation Critical Zones: Low NDVI and degraded vegetation requiring urgent Sentinel-2 Level-2A imagery was accessed via GEE.
Cloud masking was applied using the QA60 band and the s2cloudless function to eliminate cloud-covered pixels.
The results of this zoning provide guidance for planning ecosystem-based coastal infrastructure and targeted mangrove conservation.
The overall workflow of this research is illustrated in Figure 1, which outlines each step from literature review and data acquisition to spatial analysis and interpretation.
This comprehensive assessment of blue carbon estimation in mangrove ecosystems using remote sensing and geospatial techniques.
Mangrove Detection and NDVI Calculation Mangrove distribution was identified through the Normalized Difference Vegetation Index (NDVI), calculated using the red (Band .
and near-infrared (Band .
wavelengths as follows:
cAyaycIOeycIyay.
ycAyaycOya = .
cAyaycI ycIyaya AA.
Threshold values were determined based on visual interpretation and previous studies, with NDVI > 0.
4 indicating healthy and dense mangrove vegetation.
Literature Studies and Location Determinantion Sentinel-2 Imagery Acquisition using GEE Blue Carbon Estimation To estimate blue carbon stock.
NDVI values were converted to Above Ground Biomass (AGB) using an empirical linear model adapted from literature on tropical Pre-Processing Sentinel-2 Imagery using Script in GEE Mngrove Detection and NDVI Calculation Blue Carbon Estimation AGB=aUINDVI ba.
The AGB values .
n tons/h.
were then converted into carbon stock using a biomass-to-carbon conversion factor of Finally, the carbon stock values were converted into COCC equivalent (COCC.
using the IPCC standard factor:
Zoning for Coastal Protection and Restoration Recommendation and Further Research yaycC2 yce = ya y 3.
Figure 1.
Flowchart of the research methodology.
This allowed for spatial visualization of blue carbon potential across DompakAos coastal Zoning for Coastal Protection and Restoration Based on NDVI and carbon distribution maps, the coastal area was categorized into three ecological zones:
Results and Analysis 1 Mangrove Distribution Map and Estimated Carbon Stock per Hectare The spatial distribution of mangrove vegetation across Dompak Island was effectively mapped using Sentinel-2 imagery combined with Normalized Difference Vegetation Index (NDVI) thresholding This remote sensing approach enabled a clear classification of mangrove density into distinct Conservation Priority Zones: High NDVI and carbon stock D K Dewi and S Nugraha.
Journal of Innovation and Technology.
April 2025.
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DOI: DOI:10.
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categories: non-mangrove, sparse, moderate, and dense vegetation cover.
Figure 2 illustrates the NDVI-based mangrove density map, where various shades of green correspond to different levels of vegetation health and density.
The darker green areas represent dense mangrove coverage, while lighter shades indicate moderate to sparse Grey areas denote regions identified as non-mangrove.
This classification provides valuable insight into the spatial variability of mangrove ecosystems and their extent across the island.
The map also reveals key areas with degraded or less vegetated zones, offering essential baseline information for future conservation planning, restoration initiatives, and blue carbon stock estimation efforts.
Figure 2.
NDVI Mapping of Dompak Island Based on processed data, the total area covered by mangrove in the study location was estimated at 45 hectares, primarily concentrated along the coastal fringes of Dompak Island.
Using established NDVI-to-AGB AGB-to-carbon conversion models, the average carbon stock per hectare was estimated to be 35.
965 tons/ha, with values ranging from 0 to 45 tons/ha.
This corresponds to an average COCC equivalent (COCC.
of 81 tons/ha.
Compared to previous mangrove studies in Benoa Bay.
Bali, which reported carbon stocks ranging from 55Ae90 tons/ha.
Dompak's average reflects influenced by anthropogenic pressures and fragmented canopy cover.
Table 1 summarizes the spatial statistics of mangrove zones categorized by vegetation density and carbon stock level.
These results highlight key areas of carbon sequestration and can guide conservation and zoning efforts.
Table 1.
Summary of Carbon Estimation Results in Dompak Island NDVI Class Average Carbon Stock .
ons/h.
Average COCCe .
ons/h.
> 0.
6 (Dens.
3Ae0.
6 (Moderat.
< 0.
3 (Spars.
D K Dewi and S Nugraha.
Journal of Innovation and Technology.
April 2025.
Vol.
6 No.
DOI: DOI:10.
31629/jit.
Notably, the moderate NDVI class .
3Ae0.
exhibits the highest average carbon stock .
00 tons/h.
and COCCe equivalent .
40 tons/h.
, indicating that these zones play a particularly significant role in carbon storage despite not having the densest vegetation cover.
This may reflect optimal growing conditions or restoration zones with active biomass In contrast, areas with dense vegetation (NDVI > 0.
, though ecologically mature, show slightly lower average carbon values .
13 tons/h.
, which could be attributed to saturation in biomass accumulation or variation in species structure.
Meanwhile, sparse vegetation zones (NDVI < 0.
contribute the least to carbon stock .
50 tons/h.
, underlining their vulnerability and the need for targeted restoration efforts.
These distinctions emphasize the dynamic relationship between vegetation health, as indicated by NDVI, and ecosystem service provisioning, especially in terms of blue carbon potential.
2 Spatial Interpretation: Priority Areas for Protection/Restoration Integrating these insights with zoning enhances both ecological and engineering planning.
The resulting carbon stock map was visualized in GEE, revealing distinct spatial patterns across the study area.
Highdensity mangrove zones, typically characterized by carbon stock values exceeding 45 tons/ha, were observed in the Northeast coastal sections of Dompak Island.
These zones are considered highpriority for conservation, given their ecological integrity and contribution to carbon sequestration.
Conversely, fragmented mangrove patches or cleared areas with significantly lower carbon stock values below 20 tons/ha were identified as potential sites for ecological restoration.
To determine priority areas for mangrove protection and restoration, spatial analysis was conducted using the carbon stock estimation layer derived from Sentinel-2 imagery.
The entire workflow was performed using Google Earth Engine (GEE), enabling efficient processing of highresolution satellite data.
The resulting carbon stock map revealed that high-density mangrove zones, typi cally with stock values exceeding 40 tons/ha, were concentrated in the Notheast coastal segments of Dompak Island.
These areas are high-priority for Conversely, fragmented or cleared areas with carbon stock below 30 tons/ha were identified as restoration zones.
This classification aligns with studies by Alongi .
, which demonstrate that dense mangrove forests not only store carbon but also attenuate wave energy, stabilize shorelines, and enhance coastal protection.
Figure 3 displays the spatial distribution of mangrove carbon stock across Dompak Island, visualized using a color gradient ranging from light yellow .
ow carbon stoc.
to dark red .
igh carbon stoc.
, with values ranging from 0 to 70 tons per Figure 3.
The Carbon Stock Mapping in Dompak Island D K Dewi and S Nugraha.
Journal of Innovation and Technology.
April 2025.
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DOI: DOI:10.
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This visualization allows for a nuanced understanding of spatial variability in ecosystem service provisioning.
Based on these carbon stock the island can be zoned into three key management categories:
and remote areas with high temporal resolution.
The integration with cloud computing platforms like Google Earth Engine enables efficient data processing and repeatability for multi-temporal However, limitations still exist.
The medium spatial resolution .
may not detect narrow or sparse mangrove belts, especially in fragmented coastlines.
Moreover, the biomass estimation relies on empirical models that may not fully account for local species composition or age structure of mangroves.
Ground-truthing and integration with LiDAR or UAV data could enhance the accuracy of these estimates in future studies.
High-Priority Protection Zones Ae Represented by darker orange to red hues, these areas exhibit the highest levels of carbon stock .
ypically above 50 t/h.
and are likely composed of well-preserved, mature mangrove stands.
These zones should be prioritized for strict conservation measures to maintain their carbon sequestration function and ecological Restoration Potential Zones Ae Indicated by intermediate orange shades, these areas have moderate carbon stock values .
round 20Ae 50 t/h.
and may include degraded or recovering mangroves.
Targeted restoration or enrichment planting efforts in these zones could substantially enhance their carbon storage capacity and biodiversity value.
Low-Intervention or Monitoring Zones Ae Highlighted in yellow tones .
elow 20 t/h.
, these regions have minimal carbon stock and likely represent non-mangrove areas, sparsely vegetated land, or regions unsuitable for mangrove establishment.
While they may not require intensive intervention, ongoing monitoring is recommended to detect any potential landuse change or degradation.
4 Implications for Civil Engineering: Toward Green Infrastructure Design The findings of this study underscore the potential of integrating mangrove ecosystems into green infrastructure planning for coastal protection.
In civil engineering, this translates into hybrid solutions where mangrove conservation is combined with traditional structures like seawalls or breakwaters to reduce wave impact and erosion.
Spatial estimation of carbon stock in mangrove ecosystems serves a dual purpose: informing ecological management and guiding the integration of ecosystem-based approaches into sustainable coastal engineering design.
Based on the mapped results of NDVI and carbon stock, we propose a zoning strategy that aligns ecological conditions with appropriate civil engineering interventions.
This study proposes a zoning-based approach to integrate mangrove ecosystem data into coastal engineering.
Narayan.
found that mangrove belts can reduce annual flood damage by over $65 billion globally, emphasizing the economic value of natural This classification framework supports informed decision-making for coastal zone management by aligning ecological data with policy It offers a practical tool for local authorities and stakeholders to allocate resources efficiently, protect high-value ecosystems, and plan sustainable mangrove rehabilitation programs tailored to spatial Therefore, hybrid designs that combine mangroves with engineered structures such as bamboo wave breakers or adaptive seawalls are These zonesAiA (Conservatio.
B (Restoration/Hybri.
, and C (Critical Protectio.
Aiare aligned with NDVI and carbon stock data.
Recommended Zoning and Ecosystem-Based Civil Engineering Strategies:
3 Strengths and Limitations of Satellite-Based Approaches The use of satellite imagery such as Sentinel-2 offers significant advantages in mapping mangrove distribution and estimating carbon stock over large D K Dewi and S Nugraha.
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April 2025.
Vol.
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DOI: DOI:10.
31629/jit.
Zone A Ae Intensive Conservation Characteristics: NDVI > 0.
carbon stock > 60 tons/ha.
dense and healthy mangrove Recommendations:
Designated as a full conservation zone with strict protection measures.
No structural interventions are recommended to avoid disturbing the ecosystem balance.
Monitoring should be conducted regularly using remote sensing technology to track ecological Civil Engineering Approach:
Development is not recommended in this Efforts should focus on mapping, delineating protective buffer zones, and reinforcing policy-based conservation.
Zone B Ae Restoration and Hybrid Engineering controlled mangrove planting, thereby establishing a functional green buffer zone.
Civil Engineering Approach:
Employ adaptive engineering designs that can respond to long-term sea-level rise.
Favor the use of locally sourced and environmentally friendly materials, and include provisions for ecological enhancement within the structure.
By aligning engineering practices with ecological data, this zonation framework encourages a shift from purely structural approaches to natureinclusive supporting both climate resilience and sustainable development goals.
5 Zonation Mapping Based on the Case Study in Dompak Island Based on the spatial mapping results conducted in this study using satellite imagery and processing through Google Earth Engine (GEE), the coastal area of Dompak Island was classified into three main zones according to mangrove health indicators (NDVI) and estimated carbon stock per hectare.
Zone A Ae Intensive Conservation:
Coastal segment located in Northeast of Dompak Coastline shows NDVI values 6 and carbon stock exceeding 60 tons/ha.
This area is characterized by dense and healthy mangrove cover, making it a priority for full conservation without structural interventions.
Zone B Ae Restoration and Hybrid Engineering:
The North and Southeast Dompak Coastlin.
falls into the intermediate category with NDVI between 0.
3Ae0.
6 and moderate carbon stock .
Ae60 tons/h.
Mangrove stands are patchy and fragmented, indicating the need for ecological restoration combined with softengineering techniques.
Zone C Ae Critical Protection:
At South-Southwest-West of Dompak Coastline the mapping shows NDVI values 3 and carbon stock less than 30 tons/ha.
These areas are under high anthropogenic pressure or are actively eroding, requiring more robust civil Characteristics: NDVI between 0.
3Ae0.
carbon stock between 30Ae60 tons/ha.
fragmented or sparsely growing mangroves.
Recommendations:
Restoration efforts are recommended, ideally through participatory, community-based Nature-based solutions such as brushwood dams, bamboo wave breakers, or floating barriers can be introduced to reduce wave energy and promote sediment accumulation.
Civil Engineering Approach:
Apply eco-hydraulic design principles that work with natural processes to support mangrove regrowth.
This includes assessing local hydrodynamics and geotechnical conditions to optimize intervention layout and material selection.
Zone C Ae Critical Protection Characteristics: NDVI < 0.
carbon stock < 30 tons/ha.
exposed to severe erosion or heavy anthropogenic pressure .
, settlements, port Recommendations:
Where necessary, introduce hard coastal protection structures such as lightweight revetments or seawalls.
However, these structures should be designed to integrate D K Dewi and S Nugraha.
Journal of Innovation and Technology.
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Vol.
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DOI: DOI:10.
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engineering protection such as lightweight revetments or adaptive seawalls.
on the spatial variation in carbon stock as derived from Sentinel-2 imagery and Carbon Stock-based This zonation approach is designed to facilitate targeted management strategies by aligning ecological characteristics with specific intervention Figure 4 presents the zonation mapping of Dompak Island, which divides the island into four primary zonesAiZone A.
Zone B, and Zone C, based Zone B Zone A Zone C Zone B Figure 4.
The Zone Mapping in Dompak Island protection planning.
By utilizing Sentinel-2 imagery and NDVI in the Google Earth Engine (GEE) platform, we successfully mapped mangrove distribution and quantified carbon stock in Dompak IslandAos coastal zone.
The resulting data enabled the classification of coastal segments into three functional zones conservation, restoration, and critical protection each accompanied by tailored engineering strategies.
The integration of ecological indicators with civil engineering recommendations provides a replicable model for sustainable coastal infrastructure planning.
This approach bridges environmental conservation with climate-resilient development, especially in areas vulnerable to erosion, sea-level rise, and habitat degradation.
Moving forward, incorporating field validation, higher-resolution imagery, and hydrodynamic modelling is recommended to enhance accuracy and inform more robust engineering designs.
The findings of this study support the adoption of naturebased solutions and hybrid infrastructure as essential components of coastal resilience strategies.
This zonation provides a spatially explicit recommendation for ecosystem-based coastal protection planning and highlights how remote sensing data can guide targeted engineering The integration of ecological and engineering parameters supports a sustainable and site-specific approach to coastal infrastructure Although this study provides valuable insights through satellite-based estimation, it acknowledges the limitation of not incorporating field-based validation data.
The biomass and carbon stock models rely on generalized NDVIAeAGB correlations derived from literature, which may not fully capture local characteristics, or mangrove age structure.
Future studies are recommended to incorporate groundtruth measurements and local calibration to improve the accuracy and contextual relevance estimations.
Conclusion This study demonstrates the effectiveness of combining remote sensing and geospatial analysis in estimating blue carbon stock and informing coastal D K Dewi and S Nugraha.
Journal of Innovation and Technology.
April 2025.
Vol.
6 No.
DOI: DOI:10.
31629/jit.
References