Wetland Dynamics Research Articles

Monitoring and Management of Asan Reservoir Wetland and Environs using High Resolution Satellite data

Asan Conservation Reserve (ACR) is one of 42 wetlands of International Importance under Ramsar convention and lies in the Central Asian-Indian Flyway and East Asian-Australian Flyway of migratory birds in Doon Valley in Western Himalaya. The valley-landscape is unique and complex with a mix of land use/land cover such as forests, rivers, urban as well agricultural landscape in the Himalayan Biodiversity Hotspot region with high seasonality, water flow and area fluctuations, vegetation physiognomy and phenology, tourism, etc. Even though the migratory and resident birds occupy wetland for most of their stay time, however, the role of the adjacent land use practices play a very important role in food supply chain. We used six-time high resolution time-series temporal (summer and winter) satellite images of LISS IV sensor for the years 2004, 2013 and 2016 to understand the wetland dynamics and its unique ecological significance in terms of conservation and management of ACR for avifauna, specially migratory birds and aquatic vegetation. The area analysis of wetland and its environs between 2004 to 2016 indicates decreasing trend of about 3% (0.14 km 2 ), 4% (0.65 km 2 ), 1% (0.18 km 2 ) and 15% (2.02 km 2 ) in wetland area, riverine scrub, mango orchards and agriculture land, respectively. An increasing trend has been observed by 4% (0.348 km 2 ), 1% (0.067 km 2 ), 3% (0.18 km 2 ), 4% (0.2 km 2 ) and 5% (0.35 km 2 ) in settlement area, siltation, total forest cover, water body and wet riverbed, respectively. However, dry riverbed has remained almost unchanged occupying about 17%. The water and vegetation ratio is nearly ideal in the ratio of 60:40, and if needed, desiltation may be carried after mid-March.

Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments.

Wetland spectral unmixing using multispectral satellite images

Wetlands are of great importance to the diversity of biota and ecology, thereby to humans. Monitoring such valuable areas is essential for sustainable development. When the sizes, geographic distribution, and total coverage of wetlands across the earth are taken into account, remote sensing shines out as the most economically and technically feasible method to realise the monitoring task. Concerning the utilisation of medium resolution satellite images as the input, the pixel-level approach falls short of understanding the wetland dynamics since vast amounts of pixels in such areas have mixed content. This study proposes a framework for determining the extent of wetlands and extracting their ground characteristics at the sub-pixel level. In the extent determination part, Tasselled Cap Water Index (TCWI) values are calculated on time series, and their variations throughout the year are modelled by fitting a double-sided sigmoid function. This information is coupled with Digital Terrain Model (DTM) thresholding to extract the final extent. A sub-pixel analysis is proposed for the latter part, which includes adopting a systematic approach using a three-element (soil, vegetation, water) scheme for establishing wetland ontology and implementing supervised spectral unmixing enhanced by band weight optimisation. Balıkdamı, one of the most impressive wetlands of Turkey, is chosen as the test area. Open-access optical satellite data acquired by the Sentinel-2 constellation are utilised as the primary data input. Since the abundance values of land cover classes in each Sentinel-2 pixel are estimated, reference abundance data with a 10 m ground sampling distance (GSD) are generated using four-band aerial images having a 30 cm GSD for the verification stage. A new method entitled ‘Abundance Confusion Matrix’ is introduced for comparison and detailed assessment of fractional land cover. Experimental results demonstrate that the extent determination is addressed with a precision of 99.21% and a miss rate of 5.75%. In addition, the abundance values of land cover classes are identified with an overall accuracy of 66.17% after the optimisation step. The proposed method proves to be a valuable tool for the detailed monitoring of wetlands.

Carbon Pool in Mexican Wetland Soils: Importance of the Environmental Service.

Mexican wetlands are not included in Earth system models around the world, despite being an important carbon store in the wetland soils in the tropics. In this review, five different types of wetlands were observed (marshes, swamps, flooded grasslands, flooded palms and mangroves) in which their carbon pool/carbon sequestrations in Mexican zones were studied. In addition, it was shown that swamps (forested freshwater wetlands) sequestered more carbon in the soil (86.17 ± 35.9 Kg C m−2) than other types of wetlands (p = 0.011); however, these ecosystems are not taken into consideration by the Mexican laws on protection compared with mangroves (34.1 ± 5.2 Kg C m−2). The carbon pool detected for mangrove was statistically similar (p > 0.05) to data of carbon observed in marshes (34.1 ± 5.2 Kg C m−2) and flooded grassland (28.57 ± 1.04 Kg C m−2) ecosystems. The value of carbon in flooded palms (8.0 ± 4.2 Kg C m−2) was lower compared to the other wetland types, but no significant differences were found compared with flooded grasslands (p = 0.99). Thus, the carbon deposits detected in the different wetland types should be taken into account by policy makers and agents of change when making laws for environmental protection, as systematic data on carbon dynamics in tropical wetlands is needed in order to allow their incorporation into global carbon budgets.

Parasitism of Enallagma civile Hagen in Selys, 1853 (Zygoptera: Coenagrionidae) by Arrenurus water mites

We compared the prevalence and intensity of Arrenurus sensu stricto water mite parasites on Enallagma civile Hagen in Selys, 1853 (Zygoptera: Coenagrionidae) from 10 freshwater wetlands (playas) in two different land-cover contexts in western Texas from 2006-2007. Vulnerability to parasitism may be a consequence of disturbance, so we predicted that the more natural form of regional land cover (grasslands) surrounding playas should be associated with a lower water mite load than more disturbed land cover (tilled croplands). Additionally, we examined Arrenurus occurrence and intensity of infection by host sex. Overall prevalence was 38.46% of 130 damselflies sampled having mites; this varied by land-cover type but with opposite trends between years. Overall average parasite load was ~11 water mites per infected host (range: 1-40 mites); intensity was significantly higher in hosts from cropland playas in 2006, but there was no difference by surrounding land cover in 2007. Although there were consistent trends in both years of more males being parasitized than females, the highly uneven distribution of parasites on hosts and differences in average mite load between years generated variability that obscured any statistically significant patterns. Thus, land-cover context surrounding playas, but not host sex, had an impact on parasite load in one of the two years of our study. Future work is needed to identify the mechanisms by which land cover may affect water mite-odonate host-parasite relationships as well as the role of the odonate assemblage as a whole in dispersal of parasites in a temporally dynamic wetland network.

Gridded maps of wetlands dynamics over mid-low latitudes for 1980–2020 based on TOPMODEL

Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a diagnostic model based on the TOPography-based hydrological MODEL (TOPMODEL), this study produced an ensemble of 28 gridded maps of monthly global/regional wetland extents (with more reliable estimates at mid-low latitudes) for 1980–2020 at 0.25° × 0.25° spatial resolution, calibrated with a combination of four observation-based wetland data and seven gridded soil moisture reanalysis datasets. The gridded dynamic maps of wetlands capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent satellite-based terrestrial water storage estimates over wetland areas. The long temporal coverage extending beyond the era of satellite datasets, the global coverage, and the opportunity to provide real-time updates from ongoing soil moisture data make these products helpful for various applications such as analyzing the wetland-related methane emission.

Sediment sorting and bedding dynamics of tidal flat wetlands: Modeling the signature of storms

Field studies suggest that storms can considerably affect the morphology, sedimentology and bedding structure of tidal flat wetlands because of their high-energy, while numerical modeling studies unravelling the underlying mechanisms remain rare. With a tidal flat of the Changjiang Delta, China as a reference site, this study explores the role of storms on sediment sorting and bedding behaviors using a biomorphodynamic model that couples hydrodynamics, sediment transport, marsh dynamics and morphological change. Model results indicate that storms can leave clear signatures on tidal flats in both horizontal and vertical sedimentary features, in agreement with field observations. The high bed shear stress induced by storms can initiate the motion of relatively coarse sediment which can be brought shoreward during the flood and settle on the upper tidal flats (or saltmarshes), altering the typically observed cross-shore “landward fining” phenomenon. The storm-entrained coarse sediment on the higher flat is usually hard to be brought offshore during calm weather, and hence leaving an evident sand-dominated layer (SDL) within neighboring mud-dominated layers (MDL). Storms can also destroy tidal rhythmites formed under calm weather. With the increase in storm intensity and duration, the thickness and the sand fraction of the SDL increase accordingly. The SDL thickness generated by a weaker storm during the spring tide is larger than that of a stronger storm during the neap tide. Storm chronology also plays an important role. In particular, the impact of two overlapping or temporally close storms is larger than the sum of the two individual storms. Overall, this study results in a more in-depth mechanistic understanding of storm impacts on tidal flat biomorphodynamics, providing a scientific facet to make sustainable management strategies for coastal wetlands.

Assessing degradation of lake wetlands in Bashang Plateau, China based on long-term time series Landsat images using wetland degradation index

Continuous monitoring of wetland dynamics and regional-scale assessment of wetland degradation are important to understand wetland ecosystem processes and to formulate restoration measures. The lake wetlands in Bashang Plateau play vital roles in providing essential resources and maintaining biodiversity in North China, while they were not fully investigated in previous studies. In addition, previous studies on wetland degradation assessment mostly relied on wetland area change every five or ten years. Continuous dynamics of wetlands was rarely considered. In this study, we developed a new wetland degradation index (WDI) to assess lake wetland degradation in Bashang Plateau based on time series Landsat imagery from 1985 to 2020 on Google Earth Engine platform. WDI integrates three indicators, namely lake shrinkage index (LSI), vegetation degradation index (VDI) and soil degradation index (SDI), which represents hydrodynamics of wetland inundation, vegetation, and soil conditions of wetlands, respectively. Different from previous research that utilized remotely sensed image to extract wetland every five or ten years, the LSI considers time-series continuous dynamics of lake water inundations and the transitions between water and non-water on a pixel-by-pixel basis. Our results showed that small lake wetlands with area less than 8 ha are dominant in Bashang Plateau. Both the number and the area of lake wetlands showed large interannual variations. The number (area) of lake wetlands reached 1715 (31,419 ha) in 1995–1996 and were only 493 (7,717 ha) in 2009–2010. The total area of the lake wetlands in 2019–2020 was only around 1/3 of that in 1995–1996. The LSI, VDI, SDI, as well as WDI demonstrate large spatial variations among the lake wetlands and within the lake wetlands. Wetlands in the western and central subregions experienced more severe degradation than those in the eastern subregion, and the edge of the wetlands normally showed more severe degradation. The resulted degradation grades are consistent with our field investigations. Among the 11 counties in Bashang Plateau, Zhangbei (ZB) county in the western subregion has the largest area with severe and very severe degradation grades. Degradation of lake wetlands was mainly associated with groundwater level declination which was induced by anthropogenic activities and economic development. The proposed WDI in this study can be widely applicable for regional-scale wetland degradation assessment using remote sensing techniques and thus further help decision making in lake wetland conservation and restoration.

Prairie wetlands as sources or sinks of nitrous oxide: Effects of land use and hydrology

National and global greenhouse gas (GHG) budgets are continually being refined as data become available. Primary sources of the potent GHG nitrous oxide (N2O) include agricultural soil management and burning of fossil fuels, but comprehensive N2O budgets also incorporate less prominent factors such as wetlands. Freshwater wetland GHG flux estimates, however, have high uncertainty, and wetlands have been identified as both sources and sinks. Here, we analyzed a regional database of >26,000 N2O chamber flux measurements sampled across >150 wetlands from the Prairie Pothole Region (PPR) in the Great Plains of North America. Our goal was to identify important land use and hydrologic drivers of N2O flux to help reduce uncertainty in N2O models, and to incorporate these drivers into an upscaled estimate of wetland N2O emissions from the U.S. portion of the PPR. Within individual wetlands, exposed soils with no standing water, such as along wetland edges, were hotspots that accounted for greater than 90% of wetland N2O emissions. In contrast wet (i.e., ponded) areas had minimal or negative N2O flux. N2O flux from wetlands nested within croplands (16.3–17.3 μg N2O m−2 hr−1) was, in some instances, nearly double that from wetlands within grasslands (9.2–14.4 μg N2O m−2 h−1). We estimated that seasonal N2O flux from PPR wetlands equated to roughly 0.2% (1.04 Tg CO2 equivalents) of the U.S. N2O budget (c. 2019). Overall, even though PPR wetlands are a small net source of N2O to the atmosphere, their emissions are negligible relative to agricultural soil management. Policy and management to restore wetland hydrology and surrounding uplands from cropland to grasslands can reduce landscape N2O fluxes. Future activities focused on wetland N2O flux would benefit from inclusion of adjacent land use and hydrologic factors, as well as from incorporation of temporally dynamic ponded wetland areas.

Spatiotemporal dynamics of dissolved organic carbon in a subtropical wetland and their implications for methane emissions

Wetlands represent about one-third of the global soil organic carbon storage and are the largest natural source of atmospheric methane, a powerful greenhouse gas. Understanding the factors that influence the carbon dynamics in wetlands, and their influence on methane emission, is therefore important. Dissolved organic carbon (DOC) is a major form of carbon export from wetlands and plays an essential role in the aquatic carbon cycling process. However, constraints on spatial and temporal dynamics of DOC in vertical peat layers and their relationship to wetland methane emissions are still rare. Here we investigated spatiotemporal changes in porewater DOC concentration and optical properties (SUVA254, E2/E3, E4/E6) in surface (0–10 cm) and deep (20–30 cm, 50–60 cm, 100–110 cm) peat layers in a subtropical wetland complex, central China, with the aim to explore DOC dynamics and their relationship to methane emissions from wetlands. DOC dynamics and environmental controls were investigated in the context of high-resolution environmental monitoring, including air temperature, rainfall, water table depth, water chemistry, and methane emissions. DOC of surface peat layers generally had higher concentrations, higher aromaticity, lower humification, and greater temporal variations compared to deeper peat layers. During summer floods and high water table conditions, DOC concentrations in the surface peat increased significantly, whilst DOC exports from rivers and lakes also increased synchronously. In contrast, DOC concentrations and spectral properties of deep peat layers remained constant across the sampling months and water table fluctuations. Methane emissions were positively correlated with air temperature and DOC concentration in the porewater of surface peat layers, and negatively correlated with water table depth. Our results suggest that DOC concentration in the porewater of surface peat layers may also have affected the flux of methane emission from wetlands.

Trade-off between tree planting and wetland conservation in China

Trade-offs between tree planting programs and wetland conservation are unclear. Here, we employ satellite-derived inundation data and a process-based land surface model (ORCHIDEE-Hillslope) to investigate the impacts of tree planting on wetland dynamics in China for 2000–2016 and the potential impacts of near-term tree planting activities for 2017–2035. We find that 160,000–190,000 km2 (25.3–25.6%) of historical tree planting over wetland grid cells has resulted in 1,300–1,500 km2 (0.3–0.4%) net wetland loss. Compared to moist southern regions, the dry northern and western regions show a much higher sensitivity of wetland reduction to tree planting. With most protected wetlands in China located in the drier northern and western basins, continuing tree planting scenarios are projected to lead to a > 10% wetland loss relative to 2000 across 4–8 out of 38 national wetland nature reserves. Our work shows how spatial optimization can help the balance of tree planting and wetland conservation targets.

Waterbird communities and wetland dynamics in the Mexican Highlands, 1951–2006

Waterbird communities and wetland dynamics in the Mexican Highlands, 1951–2006

Cooling Effects Revealed by Modeling of Wetlands and Land‐Atmosphere Interactions

AbstractWetlands are important ecosystems—they provide vital hydrological and ecological services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extents and hydrological processes is important for valuing wetlands' function. The purpose of this study is to dynamically represent the spatial extents and hydrological processes of wetlands and investigate their feedback to regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah‐MP land surface model with two major modifications: (a) modifying the subgrid saturation fraction for spatial wetland extent and (b) incorporating a dynamic wetland storage to simulate hydrological processes. This scheme was evaluated at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13‐year climate forcing produced by a high‐resolution convection‐permitting model. The differences between wetland and no‐wetland simulations are significant, with increasing latent heat and evapotranspiration while suppressing sensible heat and runoff in the wetland scheme. Finally, the dynamic wetland scheme was applied in the Weather Research and Forecasting (WRF) model. The wetlands scheme not only modifies the surface energy balance but also interacts with the lower atmosphere, shallowing the planetary boundary layer height and promoting cloud formation. A cooling effect of 1–3°C in summer temperature is evident where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for >10 days over the summer of 2006, when a long‐lasting heatwave occurred. This research has great implications for land surface/regional climate modeling and wetland conservation, especially in mitigating extreme heatwaves under climate change.

Integrated Fire Management as a Renewing Agent of Native Vegetation and Inhibitor of Invasive Plants in Vereda Habitats: Diagnosis by Remotely Piloted Aircraft Systems

The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of spatial resolution and the application of advanced digital processing techniques. In this study, we investigated a vereda (humid area) of a conservation unit in the state of Mato Grosso, Brazil. Object-Based Image Analysis (OBIA) was applied to images obtained by RPAS to distinguish the phytophysiognomies of plant strata from the vereda and to diagnose the recovery of native and invasive vegetation after prescribed burning. The study was carried out in the following five stages: biomass collection; quality analysis of the land cover; phytosociological survey; collection of control points using a GNSS receiver (type L1/L2); and the capture of aerial images with an RGB camera coupled to a DJI Phantom 4 Pro, which was performed through overflights in three different periods. Object–Based Image Analysis was subsequently performed using the Nearest Neighbor classifier combined with Feature Space Optimization, obtaining classifications with accuracy and Kappa indexes greater than 80% and 0.80, respectively. The results of image processing allowed us to infer that fire acted as a renewing agent for native vegetation and as an inhibiting agent for invasive vegetation. The classification analyses combined with the phytosociological analysis allowed us to infer that the vereda is in the process of maturation. Therefore, the study demonstrated the potential of data obtained by RPAS for the diagnosis and analysis of vegetation dynamics in small wetlands submitted to Integrated Fire Management (IFM).

Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities.

What drives patchiness in palmiet wetlands?

Wetland communities are shaped by high levels of stress, disturbance and competition. Using South African palmiet wetlands as a case study (Prionium serratum dominated valley-bottom wetlands), we explore whether autogenic or allogenic succession is the dominant process driving community dynamics in valley-bottom wetlands in drylands. Several wetland rehabilitation programmes in South Africa use the dominant wetland species palmiet (P. serratum) as a pioneer to facilitate recolonization. However research is needed on palmiet wetland dynamics and formation to guide these restoration efforts. We explore vegetation patterns by analyzing which environmental parameters drive dominance of palmiet, resulting in the characteristic patchiness of palmiet wetlands, and which plant functional traits account for this. In 20 plots from three palmiet wetlands distributed across the Cape Floristic Region of South Africa, key soil, groundwater and vegetation parameters, as well as community composition were measured. Twenty-two dominant species were selected and 13 functional traits measured. Soil pH and relative groundwater depth were the main environmental parameters driving community assembly in palmiet wetlands. Palmiet-dominated communities were characterized by greater stem diameter, leaf length–width ratio, leaf area and cellulose and lignin concentration compared to fynbos communities. These traits suggest adaptations to disturbances such as fires (thicker stems) and floods (long, thin leaves, flexible shoots and thicker stems). We propose three hypotheses of palmiet wetland development which shed light on palmiet wetland restoration and highlight gaps for future research.

Examining Human Disturbances and Inundation Dynamics in China's Marsh Wetlands by Using Time Series Remote Sensing Data

Examining Human Disturbances and Inundation Dynamics in China's Marsh Wetlands by Using Time Series Remote Sensing Data

Sedimentary Carbon and Nitrogen Stocks of Intertidal Seagrass Meadows in a Dynamic and Impacted Wetland: Effects of Coastal Infrastructure Constructions and Meadow Establishment Time

Sedimentary Carbon and Nitrogen Stocks of Intertidal Seagrass Meadows in a Dynamic and Impacted Wetland: Effects of Coastal Infrastructure Constructions and Meadow Establishment Time

A strong mitigation scenario maintains climate neutrality of northern peatlands

A strong mitigation scenario maintains climate neutrality of northern peatlands

Spatially Unraveling the Wetland Dynamics from Hydro-Dams, Local Anthropogenic Influences, and Climate Variability in Southeast Asia

Spatially Unraveling the Wetland Dynamics from Hydro-Dams, Local Anthropogenic Influences, and Climate Variability in Southeast Asia