Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

Darmawan, Soni; Sari, Dewi Kania; Takeuchi, Wataru; Wikantika, Ketut; Hernawati, Rika

doi:10.1117/1.jrs.13.044519

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…However, the AGB of PMF was relatively low (91.49 t ha -1 ) compared to the other natural mangrove forests in Thailand and other tropical sites (Table 3). Darmawan et al (2019) reported that AGB of primary mangrove forest in Chumphon Province, Thailand, dominated by R. apiculata, was 231.40 t ha -1 , which was significantly higher than that of PMF in our study. In addition, the AGB of PMF was lower than that of primary mangrove forests in the tropics such as in Indonesia (252.56 t ha -1 ) (Darmawan et al 2019), Sulaman Lake Forest Reserve, Malaysia (134.59 t ha -1 ) (Suhaili et al 2020) and the Sundarbans mangrove forest, Bangladesh (153.70 t ha -1 ) (Kamruzzaman et al 2018).…”

Section: Vegetation Biomasscontrasting

confidence: 54%

“…Darmawan et al (2019) reported that AGB of primary mangrove forest in Chumphon Province, Thailand, dominated by R. apiculata, was 231.40 t ha -1 , which was significantly higher than that of PMF in our study. In addition, the AGB of PMF was lower than that of primary mangrove forests in the tropics such as in Indonesia (252.56 t ha -1 ) (Darmawan et al 2019), Sulaman Lake Forest Reserve, Malaysia (134.59 t ha -1 ) (Suhaili et al 2020) and the Sundarbans mangrove forest, Bangladesh (153.70 t ha -1 ) (Kamruzzaman et al 2018). The belowground biomass (BGB) of MF3 and MF14 increased drastically from 12.58 t ha -1 to 74.76 t ha -1 after rehabilitation.…”

Section: Vegetation Biomasscontrasting

confidence: 54%

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Vegetation structure and carbon stock of restored mangrove on abandoned shrimp pond in the International Mangrove Botanical Garden Rama IX, Thailand

WONGPROM,

MANEEANAKEKUL,

TARA

et al. 2023

Biodiversitas

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Abstract. Wongprom J, Maneeanakekul S, Tara A, Chandaeng W, Duangnamon D, Rueangket A, Wechakit D, Wanthongchai P, Maknual C. 2023. Vegetation structure and carbon stock of restored mangrove on abandoned shrimp pond in the International Mangrove Botanical Garden Rama IX, Thailand. Biodiversitas 24: 5821-5830. The conversion of mangrove forest into shrimp aquaculture has severely impacted biodiversity and increased carbon emissions. Restoration program is thus urgently needed for such mangrove forests. In this study, we investigated the vegetation structure and composition, and biomass and carbon stock of restored mangrove on abandoned shrimp pond. Observation plots at restoration sites with age of 3-year-old (MF3) and 14-year-old (MF14) were established in the International Mangrove Botanical Garden Rama IX, Chanthaburi Province, Thailand and at abandoned shrimp pond (ASP) and protected mangrove forest (PMF) as reference sites. The diameters and heights of mangrove species were measured and identified in order to determine the vegetation structure, composition, biomass, and carbon stock. Soil samples from four soil depths, i.e. 0-15, 15-30, 30-50, and 50-100 cm, were collected to quantify the soil carbon stock. The results showed the good growth of Rhizophora stand planted at the restored sites with high vegetation biomass and carbon stock. This species also promoted species diversity, suggesting that Rhizophora spp. are suitable species for the restoration of abandoned shrimp pond. The vegetation biomass and carbon stock of MF14 (230.99 t ha-1 and 102.54 t C ha-1, respectively) was higher than that of PMF (133.84 t ha-1 and 64.94 t C ha-1, respectively). Higher vegetation and soil carbon stocks of the restored mangrove forest was positively influenced by the Rhizophora stand. However, the ecosystem carbon pool of PMF was higher (366.38 t C ha-1). This result indicates that the conservation of mangrove forest is significant to maintain the forest diversity, ecosystem function, and reducing carbon emissions, while mangrove forest restoration is essential to improve the vegetation structure and increase the soil and vegetation carbon stocks.

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Section: Vegetation Biomasscontrasting

confidence: 54%

Section: Vegetation Biomasscontrasting

confidence: 54%

Vegetation structure and carbon stock of restored mangrove on abandoned shrimp pond in the International Mangrove Botanical Garden Rama IX, Thailand

WONGPROM,

MANEEANAKEKUL,

TARA

et al. 2023

Biodiversitas

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“…Over the past 10 years, various attempts have been made to obtain mangrove AGB estimations using simple linear regression [79] and multi-linear regression [18,19,80]; these attempts resulted in low performance with R 2 values ranging from 0.43 to 0.65. In recent years, ML algorithms such as Gaussian process regression, multi-layer perception neural networks, SVR, and RFR techniques have been employed to retrieve mangrove AGB, as reported in a number of published case studies [8,[20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

et al. 2020

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This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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“…The most widely used method is to extract pixel values of Visible-Infra Red (V-IR) bands (EM wave lenght 600 nm-950 nm) from medium resolution satellite images such as Landsat ETM 7, Landsat 8 OLI, ALOS, or the most recent Sentinel 2A/2B [7][8][9]. However, detection via the above mentioned passive satellites remote sensing is frequently limited by weather and thus cannot penetrate cloud cover.…”

Section: Introductionmentioning

confidence: 99%

Mapping mangrove above ground carbon stock of benoa bay bali using sentinel-1 satellite imagery

Hidayah,

Utama,

As-Syakur

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Mangrove forest ecosystems distributed in tropical countries play an important role in carbon sequestration. The presence of mangrove forests is estimated to be capable of reducing CO2 levels caused by air pollution. The process of photosynthesis absorbs CO2 gas from the atmosphere and stores it as organic matter in the form of plant biomass. The amount of carbon stock stored in mangrove forests can be estimated using a regression model based on satellite imagery pixel values and above ground biomass (AGB) measurements. This study used the backscattering value of SAR Sentinel 1 images and field AGB measurements to map carbon stocks in the mangrove forest ecosystem of Benoa Bay, Bali. The processed backscattering value is the result of VV and VH polarization in the SAR Sentinel 1 image acquired in 2022. Meanwhile, AGB is calculated using the allometric equation based on the mangrove’s species found at the study site. The biomass at the study location was estimated to be in the range of 64.18 Mg/Ha to 175.24 Mg/Ha, with an average of 115.34 ± 25.33 Mg/Ha. Therefore, the carbon stock of Benoa Bay can be estimated to have values in the range of 30.16 MgC/Ha to 82.36 MgC/Ha, with an average of 54.21 ± 11.09 MgC/Ha. In terms of estimating AGB biomass based on SAR pixel values, the VH polarization produces a better model (R2 = 0.635) than the VV polarization.

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Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

Cited by 17 publications

References 34 publications

Vegetation structure and carbon stock of restored mangrove on abandoned shrimp pond in the International Mangrove Botanical Garden Rama IX, Thailand

Vegetation structure and carbon stock of restored mangrove on abandoned shrimp pond in the International Mangrove Botanical Garden Rama IX, Thailand

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

Mapping mangrove above ground carbon stock of benoa bay bali using sentinel-1 satellite imagery

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