Fluxes of methane and distribution of sulfate as influenced by coastal salt-marsh soil ecosystem of Northern Germany

The study was conducted in Schleswig-Holstein at the Wadden sea coast of Northern Germany to evaluate the possible factors controlling methane (CH4) and sulfate (SO4) dynamics along a toposequence of daily to seasonally ?ooded coastal salt marsh soils. The soil at the top end of the salt marsh (with a height of 1.8 m above sea level: a.s.l. and a dense vegetation cover) was salic silty to clayic Typic Sulfaquent, while the soil at the bottom end (with some salt bushes and a 1.4 m a.s.l.) was sandy to silty Haplic Sulfaquent. The mean (depth: 0-100 cm) values of pH were around 7, and of redox potentials in the Typic Sulfaquent were ranged from -162 to +104 mV during all the seasons. The annual average emissions of CH4 were almost 10 fold higher (0.3 g m-2 a-1) in Haplic Sulfaquent than that (0.03 g m-2 a-1) of the Typic Sulfaquent. In all the pro?les, the concentrations of CH4 were very low and varied signi?cantly (p?0.05) with the seasons and soil depths. The concentrations of CH4 showed no dependence to temperature. The SO4 contents were observed maximum in the Typic Sulfaquent followed by Haplic Sulfaquent during all the seasons. There is no noticeable correlation was obtained between the SO4 and CH4 concentrations. Moreover, even CH4 was determined at depths where the SO4 concentration in the soil solution was around 1200 mg SO4 L-1.Bangladesh J. Sci. Ind. Res. 52(3), 177-186, 2017

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Influence of salt marsh ecosystem on the concentration and emission of CO2 from the Wadden sea coast soil of northern Germany

Bangladesh Journal of Scientific Research ◽

10.3329/bjsr.v29i2.32326 ◽

2017 ◽

Vol 29 (2) ◽

pp. 101-109

Author(s):

Md Harunor Rashid Khan

Keyword(s):

Salt Marsh ◽

Vegetation Cover ◽

Chemical Properties ◽

Mean Annual Temperature ◽

Redox Potentials ◽

Surface Soils ◽

Coastal Salt Marsh ◽

Average Maximum ◽

Soil Temperatures ◽

The Mean

A field study was conducted to evaluate the possible factors controlling the fluxes of carbon dioxide along a toposequence of daily to seasonally flooded coastal salt marsh soils. The soil at the top end of the salt marsh (with a height of 1.8 m above sea level (a.s.l.) and a dense vegetation cover) was salic silty to clayic (Typic Sulfaquent), while the soil at the bottom end (with some salt bushes and a 1.4 m a.s.l.) was sandy to silty (Haplic Sulfaquent). The mean (depth: 0 - 100 cm) values of pH were around 7, and of redox potentials (Eh) in the Typic Sulfaquent ranged from -162 to + 104 mV during all the seasons. The average net-emission of CO2 (-14.0 g m-2 a-1) above the vegetation cover was negative for the Haplic Sulfaquent but highly positive for Typic Sulfaquent round the year (857 g m-2 a-1). The average maximum concentrations of CO2 were detected within the surface soils 20 to 40 cm in both the profiles. In the surface soils of 0 to 20 cm the concentrations of CO2 measured were relatively low though the values were about 5 to 20 times higher than that of the atmospheric (0.35 g/v) concentration. For the average of two Haplic Sulfaquents, the soil temperatures were almost 2°C higher than that of the Typic Sulfaquent and it was also 2.5°C higher than the mean annual temperature (9.5°C) of the soils. The current results show that the CO2 fluxes seasonally varied significantly and for certain periods of the year the coastal salt marsh soils can act either as a sink or source for atmospheric CO2 depending on the physical and chemical properties of the soils.Bangladesh J. Sci. Res. 29(2): 101-109, December-2016

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Source and Sink Strength of Carbon Dioxide, Methane and Distribution of Sulfate in Salt-marsh Soils at the Wadden Sea Coast of Northern Germany

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v11i2.55051 ◽

2010 ◽

Vol 11 (2) ◽

Author(s):

MD Khan ◽

H Rashid ◽

HP Blume ◽

T Adachi ◽

U Pfisterer ◽

...

Keyword(s):

Carbon Dioxide ◽

Salt Marsh ◽

Wadden Sea ◽

Sink Strength ◽

Northern Germany ◽

Source And Sink ◽

Sea Coast

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Effects of dispersant used for oil spill remediation on nitrogen cycling in Louisiana coastal salt marsh soil

Chemosphere ◽

10.1016/j.chemosphere.2014.07.050 ◽

2015 ◽

Vol 119 ◽

pp. 562-567 ◽

Cited By ~ 19

Author(s):

Jason P. Pietroski ◽

John R. White ◽

Ronald D. DeLaune

Keyword(s):

Salt Marsh ◽

Nitrogen Cycling ◽

Oil Spill ◽

Marsh Soil ◽

Coastal Salt Marsh ◽

Salt Marsh Soil ◽

Oil Spill Remediation

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Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Sensors ◽

10.3390/s21134408 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4408

Author(s):

Iman Salehi Hikouei ◽

S. Sonny Kim ◽

Deepak R. Mishra

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Salt Marsh ◽

Bulk Density ◽

Remote Sensing Data ◽

Soil Bulk Density ◽

Soil Samples ◽

Marsh Soil ◽

Landsat 7 ◽

Salt Marsh Soil

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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