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

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

Remote Sensing ◽

10.3390/rs13204106 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4106

Author(s):

Shuai Wang ◽

Mingyi Zhou ◽

Qianlai Zhuang ◽

Liping Guo

Keyword(s):

Remote Sensing ◽

Organic Carbon ◽

Coastal Wetlands ◽

Environmental Variables ◽

Northeast China ◽

Vegetation Index ◽

Coastal Wetland ◽

Wetland Ecosystems ◽

Flat Terrain ◽

Soc Density

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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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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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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Моделювання просторового варіювання філогенетичного різноманіття рослинного покриву за допомогою даних дистанційного зондування Землі

Ukrainian Journal of Ecology ◽

10.15421/2017_19 ◽

2017 ◽

Vol 7 (2) ◽

pp. 37-54

Author(s):

O. V. Zhukov ◽

D. S. Ganzha ◽

Y. Y. Dubinina

Keyword(s):

Remote Sensing ◽

Phylogenetic Trees ◽

Vegetation Index ◽

Nature Reserve ◽

Anthropogenic Impacts ◽

Linear Regression Analysis ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Multiple Linear Regression Analysis ◽

Plant Phylogeny

The features of the plant community phylogenetic organization of the Dnipro arena within the nature reserve "Dnieper-Orelsky» and the regularities of its spatial variation involving remote sensing of the earth's surface data have been stated. Materials have been collected in the period of 2012-2016 within a nature reserve. Research polygon is within the first terrace (arena) of the river Dnieper valley. The sandy steppe, meadow, forest and marsh communities in the river Protoch floodplain and beam Orlova, as well as artificial pine plantations have been found as being present within research polygon. The vegetation description has been carried out on sites 10×10 m (100 m2). Total 94 descriptions of the geobotanical sample have been made. Data on plant phylogeny have been obtained by Phylomatic service. Phylogenetic diversity of the communities has been assessed by indices Feith, Simpson and Shannon. Phylogenetic analysis has been performed by means of a double principal coordinate analysis (DPCo). Earth remote sensing data in the public domain have been obtained from EarthExplorer. Vegetation index have been calculated by images from the Sentinel satellites. Digital elevation model has been constructed with the Shuttle Radar Topography Mission (SRTM) data. At the points in space where the geobotanical samples were collected, the value of spatial predictors has been extracted (vegetation indices and geomorphological indicators derivated from DEM).A multiple linear regression analysis has been conducted between the values of the axes obtained by DPCoA and environment predictors. The kernel-based machine regression has been used for modeling spatial patterns of dependent variables. The vegetation cover has been found to be represented by 189 species within the investigated polygon. Abundance Phylogenetic Deviation (APD) for the investigated metacommunity has been evaluated to –0.53 which is statistically significantly different from random alternatives (p = 0.001).APD negative value indicates that phylogenetic organization of the investigated metacommunity is overdispersed. Permutation procedure have allowed to establish that the DPCoA-axes eigenvalues obtained from the real phylogenetic tree was significantly higher than their own number for the random phylogenetic trees for the first seven axes. This indicates that the first seven axes are useful for additional information on metacommunity ordination structure. The axes 1, 2, 3 and 6 largely have been found to be labeled by vegetation index. This means that decryption of satellite images may be interpreted in terms of recent phylogenetic features of vegetation. Axis 4 and 7 have marked by geomorphological predictors. Axis 5 to some extent independent of the predictors considered as a reflection of digression-demutation vegetation caused by anthropogenic impacts.

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Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review

Remote Sensing of Land ◽

10.21523/gcj1.19030202 ◽

2020 ◽

Vol 3 (2) ◽

pp. 58-73

Author(s):

Vijay Bhagat ◽

Ajaykumar Kada ◽

Suresh Kumar

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Satellite Remote Sensing ◽

Vegetation Indices ◽

Unmanned Aerial System ◽

Sustainable Land Management ◽

Efficient Tool ◽

Site Specific ◽

The Earth ◽

Interesting Area

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

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Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽

10.3390/land10020223 ◽

2021 ◽

Vol 10 (2) ◽

pp. 223

Author(s):

Rubaiya Binte Mostafiz ◽

Ryozo Noguchi ◽

Tofael Ahamed

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Agricultural Land ◽

Expert Knowledge ◽

Vegetation Indices ◽

Land Suitability ◽

Assessment System ◽

Yield Estimation ◽

Suitability Assessment ◽

Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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