SWAMP Dataset-Mangrove necromass biomass-West Papua-2019

S.D., Sasmito; Silanpää, M.; M.A., Hayes; Bachri, Samsul; M.F., Saragi-Sasmito; Sidik, Frida; Hanggara, B.; W.Y., Mofu; V.I., Rumbiak; V.I., Hendri; M.I., Rumbiak; Taberima, Sartji; Suhaemi, S.; J.D., Nugroho; T.F., Pattiasina; Widagti, Nuryani; Barakalla, N.; J.S., Rahajoe; Hartantri, Heru; Nikijuluw, Victor P.H.; R.N., Jowey; Heatunubun, C.; P.z., Ermgassen; T.A., Worthington; Howard, Jennifer; C.E., Lovelock; D.A., Friess; L.B., Hutley; Murdiyarso, Daniel

doi:10.17528/cifor/data.00194

Cited by 2 publications

(1 citation statement)

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“…Field data are fundamental for estimating mangrove AGB from remote sensing data. In this study, we obtained 510 plot measurements from previously published articles and free-access mangrove biomass databases, such the Sustainable Wetlands Adaptation and Mitigation Program (https://data.cifor.org/dataverse/swamp) [57,58]. Since these in situ plot measurements were collected from a variety of sources using different protocols, we used three filtering criteria to ensure their quality: (1) the plot has a georeferenced location, (2) the inventory was taken after 2000, and (3) the site was not surveyed using harvesting methods.…”

Section: Surface Measurements Of Mangrove Agbmentioning

confidence: 99%

Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

Zhang

et al. 2020

Remote Sensing

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Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

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Section: Surface Measurements Of Mangrove Agbmentioning

confidence: 99%

Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

Zhang

et al. 2020

Remote Sensing

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Mangrove blue carbon stocks and dynamics are controlled by hydrogeomorphic settings and land‐use change

Sasmito

Sillanpää

Hayes

et al. 2020

Global Change Biology

103

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Globally, carbon-rich mangrove forests are deforested and degraded due to land-use and land-cover change (LULCC). The impact of mangrove deforestation on carbon emissions has been reported on a global scale; however, uncertainty remains at subnational scales due to geographical variability and field data limitations. We present an assessment of blue carbon storage at five mangrove sites across West Papua Province, Indonesia, a region that supports 10% of the world's mangrove area. The Additional supporting information may be found online in the Supporting Information section. How to cite this article: Sasmito SD, Sillanpää M, Hayes MA, et al. Mangrove blue carbon stocks and dynamics are controlled by hydrogeomorphic settings and land-use change. Glob

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SWAMP Dataset-Mangrove necromass biomass-West Papua-2019

Cited by 2 publications

References 0 publications

Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

Mangrove blue carbon stocks and dynamics are controlled by hydrogeomorphic settings and land‐use change

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