Comparison of Different Vegetation Indices for Assessing Mangrove Density Using Sentinel-2 Imagery

Muhsoni, Firman Farid; Sambah, Abu Bakar; Mahmudi, M.; Wiadnya, Dewa Gede Raka

doi:10.21660/2018.45.7177

Cited by 33 publications

(29 citation statements)

References 27 publications

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“…Recently, Kamal, Phinn, and Johansen [59] investigated the ability of different optical sensors such as WorldView-2, ALOS AVNIR-2, and Landsat TM for measuring and monitoring mangrove LAI, and showed that ALOS AVNIR-2 data yields the highest accuracy for the LAI estimation of mangrove ecosystems in Karimunjawa Island, Indonesia. More recently, Sentinel-2A data have been employed to retrieve and model mangrove biophysical parameters such as LAI, leaf chlorophyll, and tree density [66,84]. Mangrove canopy heights can also be estimated using the high-resolution stereo-photogrammetry of WorldView-1 data in the Mozambique [58,61].…”

Section: Relationships Between Biophysical Parameters Of Mangroves Anmentioning

confidence: 99%

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

et al. 2019

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The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

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Section: Relationships Between Biophysical Parameters Of Mangroves Anmentioning

confidence: 99%

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

et al. 2019

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“…Chlorophyll Index red-edge ( B7 B5 ) − 1 [32,151,152] Modified Simple Ratio red-edge [32,153,154] Modified Simple Ratio red-edge narrow [32] While less commonly used than other approaches, SMA has successfully assessed burn severity in a few studies [157][158][159]. Quintano et al [158] used a version of SMA called multiple endmember SMA (MESMA) to classify levels of burn severity in three Mediterranean sites located in Spain.…”

Section: Red-edge Spectral Indices Colum Equation Referencesmentioning

confidence: 99%

A Review of the Applications of Remote Sensing in Fire Ecology

2019

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Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.

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“…Green radiance (G) = ρG (14) Red radiance (R) = ρR (15) Near -infared radiance (NIR) = ρNIR (16) Green band Plus Red band = ρG + ρR (17) Green band Minus Red band = ρG -ρR (18)…”

Section: Difference Vegetation Index (Dvi) = ρNir -ρR (9)mentioning

confidence: 99%

Relationships of Vegetation Indices and Biomass of Mangrove Forest Plantation in Thailand

Suanpaga¹,

Suanpaga²

2019

IJESD

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The objective of this research was to analyzed the relationship of vegetation index and biomass of mangrove forest plantation in in Don Sak national reserved mangrove forest, Don Sak district, Surat Thani province. The methodology was conducted by analysis relationship between the five vegetation indices, and five reflectances calculated from LANDSAT 8 satellite imagery which has a resolution of 30 meters and the data from field biomass survey. The proportional stratified sampling system classified by the age class; tree aged between 1-19 years, 14 age classes, three sample plots per age class, plot size 15x15 meters. The diameter and height of all trees in sample plot were collected for biomass calculation by using allometric equation. The result of this study found that the general linear and quadratic relationship of green radiance (G) and biomass were significant (Biomass = -1,318.715*(G) + 129.191, R 2 = 0.702; Biomass = 462.134 -9,002.43(G) + 43,823.11(G 2 ) , R 2 = 0.786) Meanwhile, there was no relationship between other vegetative indexes and biomass. Index Terms-Biomass, LANDSAT 8, mangrove, vegetative index.

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Comparison of Different Vegetation Indices for Assessing Mangrove Density Using Sentinel-2 Imagery

Cited by 33 publications

References 27 publications

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

A Review of the Applications of Remote Sensing in Fire Ecology

Relationships of Vegetation Indices and Biomass of Mangrove Forest Plantation in Thailand

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