Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

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Assessing the Resilience of Coastal Wetlands to Extreme Hydrologic Events Using Vegetation Indices: A Review

Remote Sensing ◽

10.3390/rs10091390 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1390 ◽

Cited By ~ 3

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Remote Sensing ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Assessment Tool ◽

Anthropogenic Impacts ◽

Vegetation Indices ◽

Heavy Precipitation ◽

Life Forms ◽

Climatic Fluctuations ◽

Wetland Ecosystems

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales.

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Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

Espacio Tiempo y Forma Serie VI Geografía ◽

10.5944/etfvi.11.2018.20187 ◽

2018 ◽

pp. 195

Author(s):

Pedro Perez Cutillas ◽

Gonzalo G. Barberá ◽

Carmelo Conesa García

Keyword(s):

Remote Sensing ◽

Environmental Variables ◽

Input Data ◽

Vegetation Index ◽

Mineralogical Composition ◽

Soil Erodibility ◽

Estimation Algorithms ◽

K Factor ◽

Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

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Alteration and Remediation of Coastal Wetland Ecosystems in the Danube Delta: A Remote-Sensing Approach

Coastal Wetlands: Alteration and Remediation - Coastal Research Library ◽

10.1007/978-3-319-56179-0_17 ◽

2017 ◽

pp. 513-553

Author(s):

Simona Niculescu ◽

Cédric Lardeux ◽

Jenica Hanganu

Keyword(s):

Remote Sensing ◽

Coastal Wetland ◽

Danube Delta ◽

Wetland Ecosystems

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Remote Sensing ◽

10.3390/rs12244114 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4114

Author(s):

Shaobo Sun ◽

Yonggen Zhang ◽

Zhaoliang Song ◽

Baozhang Chen ◽

Yangjian Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Open Access ◽

Spatial Resolution ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Regional Scale ◽

Google Earth ◽

Human Welfare ◽

Bohai Rim ◽

Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

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Effects of Different Vegetation Zones on CH4and N2O Emissions in Coastal Wetlands: A Model Case Study

The Scientific World JOURNAL ◽

10.1155/2014/412183 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7

Author(s):

Yuhong Liu ◽

Lixin Wang ◽

Shumei Bao ◽

Huamin Liu ◽

Junbao Yu ◽

...

Keyword(s):

Coastal Wetlands ◽

Coastal Wetland ◽

Global Climate ◽

N2o Emissions ◽

Organic Carbon Content ◽

Dndc Model ◽

Model Case ◽

Wetland Ecosystems ◽

Factors Affecting ◽

Vegetation Zones

The coastal wetland ecosystems are important in the global carbon and nitrogen cycle and global climate change. For higher fragility of coastal wetlands induced by human activities, the roles of coastal wetland ecosystems in CH4and N2O emissions are becoming more important. This study used a DNDC model to simulate current and future CH4and N2O emissions of coastal wetlands in four sites along the latitude in China. The simulation results showed that different vegetation zones, including bare beach,Spartinabeach, andPhragmitesbeach, produced different emissions of CH4and N2O in the same latitude region. Correlation analysis indicated that vegetation types, water level, temperature, and soil organic carbon content are the main factors affecting emissions of CH4and N2O in coastal wetlands.

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The Case for a Critical Zone Science Approach to Research on Estuarine and Coastal Wetlands in the Anthropocene

Estuaries and Coasts ◽

10.1007/s12237-020-00851-9 ◽

2020 ◽

Author(s):

Min Liu ◽

Lijun Hou ◽

Yi Yang ◽

Limin Zhou ◽

Michael E. Meadows

Keyword(s):

Coastal Wetlands ◽

Coastal Wetland ◽

Critical Zone ◽

Global Network ◽

Wetland Ecosystems ◽

Ecological Service ◽

Integrated Research ◽

Strong Focus ◽

Trajectories Of Change ◽

Recent Attention

AbstractAs the focus of land-sea interactions, estuarine and coastal ecosystems perform numerous vital ecological service functions, although they are highly vulnerable to various kinds of disturbance, both directly and indirectly related to human activity, that have attracted much recent attention. Critical zone science (CZS) has emerged as a valuable conceptual framework that focuses on quantitative interactions between diverse components of the environment and is able to integrate anthropogenic disturbance with a view to predicting future trajectories of change. However, coastal and estuarine environments appear to have been overlooked in CZS and are notably under-represented, indeed not explicitly represented at all, in the global network of critical zone observatories (CZOs). Even in the wider network of environmental observatories globally, estuarine and coastal wetland ecosystems are only very rarely an explicit focus. Further strengthening of integrated research in coastal and estuarine environments is required, more especially given the threats these ecosystems face due to growing population at the coast and against the background of climate change and sea level rise. The establishment of one or more CZOs, or their functional equivalents, with a strong focus on estuarine and coastal wetlands, should be urgently attended to.

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Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

10.21203/rs.3.rs-691637/v1 ◽

2021 ◽

Author(s):

chao wang ◽

Chuanyan Zhao ◽

Kaiming Li ◽

Shouzhang Peng ◽

Ying Wang

Keyword(s):

Remote Sensing ◽

Linear Regression ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Total Nitrogen ◽

Vegetation Index ◽

Multiple Linear Regression Model ◽

Western China ◽

Linear Regression Models ◽

Soil Total Nitrogen

Abstract Soil organic carbon and soil total nitrogen stocks are important indicators for evaluating soil health and stability. Accurately predicting the spatial distribution of soil organic carbon and total nitrogen stocks is an important basis for mitigating global warming, ensuring regional food security, and maintaining the sustainable development of ecologically fragile areas. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out soil organic carbon and soil total nitrogen stocks estimation experiments in the Qilian Mountains in western China. A multiple linear regression model and a stepwise multiple linear regression model were used to estimate soil organic carbon and soil total nitrogen stocks. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results indicated that these two linear regression models showed good performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen stocks. In topsoil, remote sensing technology could be used to predict the soil properties in layers; however, as the soil depth increased, the performance of the linear regression models gradually decreased.

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Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data

Remote Sensing ◽

10.3390/rs13214321 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4321

Author(s):

Shaobo Sun ◽

Yafei Wang ◽

Zhaoliang Song ◽

Chu Chen ◽

Yonggen Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Aboveground Biomass ◽

Coastal Wetlands ◽

Carbon Stock ◽

Coastal Wetland ◽

Data Sets ◽

Special Role ◽

Climate Data ◽

Bohai Rim

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

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Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image

Remote Sensing ◽

10.3390/rs11161936 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1936 ◽

Cited By ~ 2

Author(s):

Abebe Mohammed Ali ◽

Roshanak Darvishzadeh ◽

Kasra Rafiezadeh Shahi ◽

Andrew Skidmore

Keyword(s):

Remote Sensing ◽

Dry Matter ◽

Vegetation Index ◽

Coastal Wetland ◽

Vegetation Indices ◽

Matter Content ◽

Process Models ◽

Dry Matter Content ◽

Leaf Dry Matter Content ◽

Sentinel 2

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

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Monitoring Hydroperiod and Hydropatterns of coastal wetland systems in Mexico using Landsat time series

10.5194/egusphere-egu2020-12991 ◽

2020 ◽

Author(s):

Bart (A.J.) Wickel ◽

Rene Colditz ◽

Rainer Ressl ◽

John Kucharski ◽

Sergio Salinas-Rodríguez

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Vegetation Index ◽

Coastal Wetland ◽

Full Range ◽

Water Bodies ◽

Bird Conservation ◽

Optical Remote Sensing ◽

Data Set ◽

The Pacific

The main objective of this study was the evaluation of remote sensing methods that allow for extraction of metrics that link riparian flow regimes to hydro-periods (duration) and -patterns (extent) of wetland systems known to be of critical importance to migratory water fowl and shorebirds along the Pacific Flyway in Mexico. In this study we emphasized the use of freely available and easily accessible optical remote-sensing data and their processing using free and open-source tools.&#160;Through application of a set of common and well documented water and vegetation indices on the full Landsat 5 and Landsat 7 record spanning two decades, we created a data set that captures episodic, intra-annual and inter-annual variability in inundation for two contrasting wetland systems. For this study we focussed on the Marismas Nacionales wetland system along the Pacific coast and the Alvarado Lagoon system on the Gulf coast. A comparison of indices designed to extract vegetation and water characteristics from Landsat data (NDVI, EVI, NDWI, Tasseled Cap and MNDWI) led us to conclude that the Modified Normalized Difference Water Index (MNDWI) was most effective for identifying inundated areas while the Normalized Difference Vegetation Index (NDVI) worked best for identifying differences in vegetated areas. Our study also established that the high sensitivity to thresholds requires site specific optimization.For the study we developed metrics to represent the hydro-pattern and hydro-periodicity of waterbodies in the study areas. The first method provides a metric for the intra-annual and inter-annual permanence of water bodies, while the second method quantifies recurrence of seasonal inundation. The Marismas Nacionales revealed a surprisingly strong and direct relationship between inundated area and gauge meassured discharge of the Rio San Pedro Mezquital. Annual and multi annual hydropatterns in this system are very strong and predictable, and primarily driven by large scale inundation of the delta of this river as it enters Marismas Nacionales. The relationship between discharge and inundated area was so string that the inundated area (up to several hundreds of sqare kilometers during peaks) remained correlated throught the full range of the hydrograph. For this system recurrent inundation patterns and their timing metrics were linked to specific ecosystem types and used to inform a bird conservation planning effort.At the Laguna de Alvarado a very different dynamic was observed, where large scale inundation was less frequent, permanent water bodies were much more persistent in space, and the correlation between inundated area and discharge was much weaker. In this region persistent cloud cover was an issue and SAR based approached may be the only way to monitor inundation dynamics more consistently. Earlier studies by WIckel et al for other systems using PALSAR data for wetland systems in Colombia revealed other technical shortcomings of these kinds of data. A study by Colditz et al for wetland systems in Mexico revealed a strong potential of MODIS derived MNDWI data as well. We propose that future efforts explore the possibilties of aplications of combined (optical and SAR) products.

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