Landscape pattern changes and its drivers inferred from salt marsh plant variations in the coastal wetlands of the Liao River Estuary, China

Chen, Xu; Zhang, Mingliang; Zhang, Wanchang

doi:10.1016/j.ecolind.2022.109719

Cited by 17 publications

(6 citation statements)

References 51 publications

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“…Most of these papers (80%) monitored LAI over time (e.g., [119]), whereas about half of the papers (60%) compared different methods to minimize errors and retrieved this parameter from several remote data (e.g., [120]).…”

Section: Leaf Area Indexmentioning

confidence: 99%

“…PP (1) LU/LC change -Yes [61] --PP (1) LU/LC change MODIS (0.5-1 km) Yes [389] -- PP (1) Macroalgae MODIS (0.5-1 km) Yes --[204] PP (1) Mangrove ecosystems MODIS (0.5-1 km) Yes --[470] PP (1) Primary production -Yes [69] [289] -PP (1) Primary production Landsat (15-30 m) No [139] --PP (1) Primary production MODIS (0.5-1 km) No [138,139] -- (1) This parameter was mapped and monitored together with other parameters. Salt marshes (1) Aboveground biomass ASD Portable (2) No -[140] -Salt marshes (1) Aboveground biomass Sentinel-2 (10-20-60 m) No -[140] -Salt marshes (1) Coastal salt marshes Sentinel-1 (~10 m) No [141] - [388] Salt marshes (1) Coastal salt marshes Sentinel-2 (10-20-60 m) No [141] --Salt marshes (1) Habitat Landsat (15-30 m) No - [119,422] -Salt marshes (1) Habitat Sentinel-2 (10-20-60 m) No --[427,428] Salt marshes (1) Habitat UAV-Lidar No -[142] -Salt marshes (1) Habitat UAV-MSI No -[142] -Salt marshes (1) Leaf area index Sentinel-2 (10-20-60 m) No -[469] - (1) This parameter was mapped and monitored together with other parameters; (2) hyperspectral data. SLA (1) Climate change -Yes --[147] SLA (1) Climate change NOAA Yes --[396,397] SLA (1) Coastline change -Yes [150,269] [499,500] - SLA (1) Coastal vulnerability assessment -Yes [273] --SLA (1) Coastal vulnerability assessment ENVISAT Yes --…”

Section: Parameter Phenomena Remote Data or Dataset Available Product...mentioning

confidence: 99%

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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Section: Leaf Area Indexmentioning

confidence: 99%

Section: Parameter Phenomena Remote Data or Dataset Available Product...mentioning

confidence: 99%

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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“…The estimation of wetland biomass [20,21] and carbon storage [22][23][24] has gradually become a research hotspot. A grasp of wetland landscape patterns and the laws that determine its change is critical for the ecological evaluation of the YRD's regional wetland, as well as its restoration and comprehensive management [25]. Recently, studies on the wetland landscape in the YRD which combined remote sensing (RS) and geographic information systems (GIS) have been conducted [26][27][28], aiming to reveal the laws that determine the spatio-temporal changes in the wetland landscape in the YRD.…”

Section: Introductionmentioning

confidence: 99%

The Changes in Dominant Driving Factors in the Evolution Process of Wetland in the Yellow River Delta during 2015–2022

Wei

Guo

et al. 2023

Remote Sensing

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Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km2, including an increase of 222.63 km2 in natural wetlands and 87.27 km2 in artificial wetlands. In the conversion between wetland types, 218.73 km2 of artificial wetlands were converted into natural wetlands, and 75.18 km2 of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon’s diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management.

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“…The saline water bodies of estuaries are diluted by inflowing freshwater, and studies have shown that water quality, water temperature, biogeochemical processes, and species abundances in estuaries are sensitive to changes in freshwater input (Bartsch et al, 2014;Zamparas and Zacharias, 2014;Priya et al, 2016;Onabule et al, 2020;Chin et al, 2022). Furthermore, freshwater inputs from rivers determine the estuarine salinity gradients which are critical to the growth and development of many salt-marsh plants in estuarine tidal flats (Sheldon et al, 2017;Chen et al, 2022). These influences are especially critical in shallow bays with weak connections to the ocean, or in estuaries affected or transformed by manmade structures and human activities.…”

Section: Introductionmentioning

confidence: 99%

Field survey and analysis of water flux and salinity gradients considering the effects of sea ice coverage and rubber dam: a case study of the Liao River Estuary, China

Guo

Yang

et al. 2023

Front. Mar. Sci.

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Predicting net river fluxes is important to promote good water quality, maritime transport, and water exchange in estuaries. However, few studies have observed and evaluated net water fluxes to estuaries under complex conditions. This study used advanced survey techniques to obtain high-frequency monitoring data of cross-sectional current velocity, water level, and salinity in the Liao River Estuary (LRE) from 2017 to 2020. The net water flux into the sea was computed based on field data and the impacts of the rubber dam and sea ice cover on water flux and salinity processes were analyzed in the study region. In the Liao River Station (LRS), the fluctuations of water level and discharge were not obvious in winter due to the sea ice cover. There were significant seasonal and inter-annual changes in water fluxes due to variability in river discharge and tidal oscillations. The results also showed that the net water flux into the sea from the LRS was positive in wet season, and greater during ebb tides than flood tides. The net water fluxes in the normal and dry seasons were mostly negative due to the influence of tides, indicating that the annual runoff from the Liao River fluctuated greatly throughout the year. The water flux in the LRS was more suitable for representing water flux into the sea than the Liujianfang Hydrometric Station (LHS) in the LRE. The impacts of the rubber dam and Panshan Sluice on water fluxes to the sea were both significant. Lower salinity in the study area coincided mostly with height water fluxes to the sea and periods when the rubber dam was raised. This study results provide us new insights to measure the water flux into sea under the condition of ice cover in the tidal reach of estuary and the method can be used for water flux observation for other estuaries.

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Landscape pattern changes and its drivers inferred from salt marsh plant variations in the coastal wetlands of the Liao River Estuary, China

Cited by 17 publications

References 51 publications

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

The Changes in Dominant Driving Factors in the Evolution Process of Wetland in the Yellow River Delta during 2015–2022

Field survey and analysis of water flux and salinity gradients considering the effects of sea ice coverage and rubber dam: a case study of the Liao River Estuary, China

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