Processes controlling the seasonal and spatial variations in sulfate profiles in the pore water of the sediments surrounding Qi'ao Island, Pearl River Estuary, Southern China

Wu, Zucheng; Zhou, Huaiyang; Ren, Dezhang; Gao, Hang; Li, Jiangtao

doi:10.1016/j.csr.2015.02.001

Cited by 15 publications

(4 citation statements)

References 59 publications

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“…The average annual temperature is 22.4 • C, the annual average sunshine hours is 1907.4 h, and the average annual precipitation is between 1700 mm and 2200 mm [11,50]. The salinity of surface seawater ranges from 0.22 to 32.32 with an annual average of 18.22 [11,51]. The tidal pattern is an irregular semidiurnal tide.…”

Section: Study Areamentioning

confidence: 99%

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Zhu

Liu

et al. 2017

Remote Sensing

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Abstract:To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

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Section: Study Areamentioning

confidence: 99%

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Zhu

Liu

et al. 2017

Remote Sensing

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“…However, these crater-shaped submarine depressions did not become widely known. Canada, the Gulf of Mexico, the Black Sea, the East China Sea and the South China Sea [2][3][4][5][6][7][8][9]. Due to complexities and differences in the marine environments and submarine fluid activities, pockmarks show different sizes, morphological characteristics, distribution locations and water depths.…”

Section: Introductionmentioning

confidence: 99%

“…This can also lead to abnormal chloride concentrations and oxygen and hydrogen isotope values, and these anomalous values can be used to differentiate various sedimentary environments [20][21][22][23]. A sharp linear reduction in pore water sulfate concentrations and a relative shallow sulfate-methane transitional zone (SMTZ) reflect a high intensity of methane flux through the underlying sedimentary layers and suggest an anoxic sedimentary environment [8,24,25]. Thus, sulfate and halide concentrations, a high intensity of methane flux, authigenic carbonate minerals and other geochemical indicators are significant factors in studies on early diagenesis and the sedimentary environment [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…Small pockmarks are mostly distributed along underwater slopes and plains of estuaries and coasts at water depths of 5-100 m. In these areas, the terrigenous input is abundant, and structural activity is common, which favors organic matter decomposition and gas accumulation in the sediments and the formation of pockmarks of different sizes on the seafloor[58][59][60]. As seen fromFigure 6andFigure 7, the pockmarks in the estuary area (Figure 5, green asterisk)[8] feature shallow water depths, small scales and shallow SMTZs. The pockmarks in the submarine slope area (Figure 5, pink asterisk)[10] feature greater water depths, larger scales, and deep to nonexistentSMTZs.…”

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confidence: 99%

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Differences in the Sulfate-Methane Transitional Zone in Coastal Pockmarks in Various Sedimentary Environments

Cao¹,

Cai²,

Qi³

et al. 2020

Preprint

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Different types of pockmarks, including single pockmarks, circular pockmarks, elongated pockmarks, chain-type pockmarks and compound pockmarks, were identified in coastal areas around Fujian, China. The sediments associated with pockmarks were mainly silty clay to clay, with a small quantity of silt with fine sand. The sulfate content in the pore water in the sedimentary layers associated with pockmarks decreased with depth from the surface, whereas the free methane content increased with depth. The interaction between sulfate and methane is well known, but differences in the sulfate-methane transitional zone (SMTZ) were observed in different areas with different hydrologic characteristics. The sedimentary SMTZ of the offshore Zhe-min mud wedge was shallow, at 50–70 cm below the seafloor. The sedimentary SMTZ was moderately deep (90–115 cm) in the central bay area and deep (180–200 cm) in the sandy area offshore. This variability in SMTZ depth reflects different amounts of free methane gas in the underlying formations, with a shallower SMTZ indicating a higher free methane content. The free methane had δ13C values of -26.47&permil; to -8.20&permil; and a biogenic-hybrid genetic type. The flux of sedimentary gas from the pockmark surfaces, calculated according to Fick’s formula, was 2.89 to 18.85 l/m2·a. The shape, size and scale of the pockmarks are directly related to the substrate type and the gas production of the underlying strata and thus vary with the sedimentary environment and development stage. Therefore, different types of pockmarks, in various phases of development, are associated with different sedimentary and dynamical conditions. A single circular pockmark forms by strong methane flux. As the intensity of methane flux weakens, the pockmark becomes elongated in the direction of the water flow because of long-term erosion induced by regular hydrodynamic forces. Finally, under weak intensity of methane flux and the influence of complex hydrodynamic conditions, pockmarks merge to form large-scale, compound pockmarks.

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The Complex Interactions Between Sediment Geochemistry, Methylmercury Production, and Bioaccumulation in Intertidal Estuarine Ecosystems: A Focused Review

Bradford

Mallory

O’Driscoll

2022

Bull Environ Contam Toxicol

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Processes controlling the seasonal and spatial variations in sulfate profiles in the pore water of the sediments surrounding Qi'ao Island, Pearl River Estuary, Southern China

Cited by 15 publications

References 59 publications

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Differences in the Sulfate-Methane Transitional Zone in Coastal Pockmarks in Various Sedimentary Environments

The Complex Interactions Between Sediment Geochemistry, Methylmercury Production, and Bioaccumulation in Intertidal Estuarine Ecosystems: A Focused Review

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