Search

Climate change in northern latitudes is increasing the vulnerability of peatlands and the riparian transition zones between peatlands and upland forests (referred to as ecotones) to greater frequency of wildland fires. We examined early post‐fire vegetation regeneration following the 2011 Utikuma complex fire (central Alberta, Canada). This study examined 779 peatlands and adjacent ecotones, covering an area of ~182 km2. Based on the known regional fire history, peatlands that burned in 2011 were stratified into either long return interval (LRI) fire regimes of >80 years (i.e., no recorded prior fire history) or short fire return interval (SRI) of 55 years (i.e., within the boundary of a documented severe fire in 1956). Data from six multitemporal airborne lidar surveys were used to quantify trajectories of vegetation change for 8 years prior to and 8 years following the 2011 fire. To date, no studies have quantified the impacts of post‐fire regeneration following short versus long return interval fires across this broad range of peatlands with variable environmental and post‐fire successional trajectories. We found that SRI peatlands demonstrated more rapid vascular and shrub growth rates, especially in peatland centers, than LRI peatlands. Bogs and fens burned in 1956, and with little vascular vegetation (classified as "open peatlands") prior to the 2011 fire, experienced the greatest changes. These peatlands tended to transition to vascular/shrub forms following the SRI fire, while open LRI peatlands were not significantly different from pre‐fire conditions. The results of this study suggest the emergence of a positive feedback, where areas experiencing SRI fires in southern boreal peatlands are expected to transition to forested vegetation forms. Along fen edges and within bog centers, SRI fires are expected to reduce local peatland groundwater moisture‐holding capacity and promote favorable conditions for increased fire frequency and severity in the future. [ABSTRACT FROM AUTHOR]

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.

Wetlands play an important role in conserving biodiversity, the hydrosphere's equilibrium, and the maintenance of daily livelihood; therefore, the trophic process hastens the succession procedure in them, resulting in structural changes in the landscape. The study aimed to monitor and investigate the impact of the trophic procedure on landscape structural changes in Anzali Wetland, specifically domains related to vegetation canopy and water bodies, over 24 years. The TSI (trophic state index) of the Anzali Wetland, a vital habitat in the south of the Caspian Sea, was estimated by using the Carlson TSI for 1994, 2002, 2014, and 2018. Based on satellite data for these years, the structural landscape changes were also measured using metrics such as the number of patches (NumP), class area (CA), mean patch size (MPS), and mean shape index (MSI) of the measured patch using in Patch Analyst. The Spearman rank correlation coefficient was then used to calculate the correlation between the two variables of trophic index modifications and landscape metrics. Results showed that the TSI of the wetland touched 59.51 in 1994 and then reached 65.10 in 2018. Its water body area, which was 5283.90 ha in 1994, decreased to 4183.92 ha in 2018, indicating the greatest decrease in the area from 2002 to 2018. In addition, the maximum area of vegetation canopy in 2018 was 11696.31 ha. The trophic exhibited a positive correlation of 0.8 with the area of the vegetation canopy and a positive correlation of 0.4 with the NumP of the vegetation canopy. It also had an inverse correlation of −0.4 with the area and NumP of the water body. Based on the study findings, changes in the trophic level of Anzali Wetland can be regarded as a direct factor influencing the vegetation canopy and water body. [ABSTRACT FROM AUTHOR]

Quantifying habitat quality is dependent on measuring a site's relative contribution to population growth rate. This is challenging for studies of waterbirds, whose high mobility can decouple demographic rates from local habitat conditions and make sustained monitoring of individuals near‐impossible. To overcome these challenges, biologists have used many direct and indirect proxies of waterbird habitat quality. However, consensus on what methods are most appropriate for a given scenario is lacking. We undertook a structured literature review of the methods used to quantify waterbird habitat quality, and provide a synthesis of the context‐dependent strengths and limitations of those methods. Our search of the Web of Science and Scopus databases returned a sample of 666 studies, upon which our review was based. The reviewed studies assessed habitat quality by either measuring habitat attributes (e.g., food abundance, water quality, vegetation structure), or measuring attributes of the waterbirds themselves (e.g., demographic parameters, body condition, behavior, distribution). Measuring habitat attributes, although they are only indirectly related to demographic rates, has the advantage of being unaffected by waterbird behavioral stochasticity. Conversely, waterbird‐derived measures (e.g., body condition, peck rates) may be more directly related to demographic rates than habitat variables, but may be subject to greater stochastic variation (e.g., behavioral change due to presence of conspecifics). Therefore, caution is needed to ensure that the measured variable does influence waterbird demographic rates. This assumption was usually based on ecological theory rather than empirical evidence. Our review highlighted that there is no single best, universally applicable method to quantify waterbird habitat quality. Individual project specifics (e.g., time frame, spatial scale, funding) will influence the choice of variables measured. Where possible, practitioners should measure variables most directly related to demographic rates. Generally, measuring multiple variables yields a better chance of accurately capturing the relationship between habitat characteristics and demographic rates. [ABSTRACT FROM AUTHOR]

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO's usability and reusability. [ABSTRACT FROM AUTHOR]

The wetland cover is defined as the spatially homogenous region of a wetland attributed to the underlying biophysical conditions such as vegetation, turbidity, hydric soil, and the amount of water. Here, we present a novel method to derive the wetland-cover types (WCTs) combining three commonly used multispectral indices, NDVI, MNDWI, and NDTI, in three large Ramsar wetlands located in different geomorphic and climatic settings across India. These wetlands include the Kaabar Tal, a floodplain wetland in east Ganga Plains, Chilika Lagoon, a coastal wetland in eastern India, and Nal Sarovar in semi-arid western India. The novelty of our approach is that the derived WCTs are stable in space and time, and therefore, a given WCT across different wetlands or within different zones of a large wetland will imply similar underlying biophysical attributes. The WCTs can therefore provide a novel tool for monitoring and change detection of wetland cover types. We have automated the proposed WCT algorithm using the Google Earth Engine (GEE) environment and by developing ArcGIS tools. The method can be implemented on any wetland and using any multispectral imagery dataset with visible and NIR bands. The proposed methodology is simple yet robust and easy to implement and, therefore, holds significant importance in wetland monitoring and management. [ABSTRACT FROM AUTHOR]

Populations of amphibians that breed in isolated, ephemeral wetlands may be particularly sensitive to breeding and recruitment rates, which can be influenced by dynamic and difficult-to-predict extrinsic factors. The gopher frog Rana capito is a declining species currently proposed for listing under the U.S. Endangered Species Act, as well as one of many pond-breeding amphibians of conservation concern in the southeastern United States. To represent gopher frog breeding dynamics, we applied an occupancy modeling framework that integrated multiple data sets collected across the species' range to 1) estimate the influence of climate, habitat, and other factors on wetland-specific seasonal breeding probabilities; and 2) use those estimates to characterize seasonal, annual, and regional breeding patterns over a 10-y period. Breeding probability at a wetland was positively influenced by seasonal precipitation (Standardized Precipitation Index) and negatively influenced by fish presence. We found some evidence that the amount of suitable habitat surrounding a wetland was positively correlated with breeding probability during drought conditions. The percentage of sampled wetlands (N = 192) predicted to have breeding varied seasonally, annually, and regionally across the study. Within-year temporal patterns of breeding differed across the range: in most locations north of Florida, peaks of breeding occurred in winter and spring months; whereas breeding was more dispersed throughout the year in Florida. Peaks of breeding across the 10-y period often occurred during or in the season following high rainfall events (e.g., hurricanes). These results have direct applications for site-level management that aims to increase successful breeding opportunities of gopher frogs and other associated pond-breeding amphibians, including monitoring protocol and intensity, removal of fish, and improving terrestrial habitat conditions surrounding wetlands (e.g., via tree or shrub removal and prescribed fire). The results also have implications for better-informed management through the closer alignment of breeding activity monitoring with predicted seasonal peaks. Furthermore, estimates of breeding frequency can be incorporated into population viability analyses to inform forthcoming assessments of extinction risk and designation of the species' conservation status by the U.S. Fish and Wildlife Service. [ABSTRACT FROM AUTHOR]

The dynamic process of the wetland can reflect its impact on the environment, and finding a balance point supporting harmonious coexistence between man and nature has become an issue of increasing concern. On the basis of previous studies that have focused on local coastal wetlands, the temporal and spatial changes and driving forces of wetlands in the Yancheng coastal area from 1991 to 2021 were analyzed over a larger area. According to the study findings: (1) The results of the study of the Yancheng coastal wetland with a larger scope differed significantly from findings resulting from a study of coastal wetland only. This difference was mainly reflected in the relatively stable situation of wetland ecology as a whole, while the changes in local surface features were more significant. (2) Natural wetlands were transformed into artificial wetlands and non-wetland types, and artificial wetlands were transformed into non-wetland types; additionally, reverse transformations and internal transformations of surface features also took place. For instance, the saltpan was transformed into mudflats (86.26 km2), and some mudflats into herbaceous vegetation (193.47 km2). (3) When analyzing the impact intensity of human activities on the Yancheng wetland, it was found that this factor has experienced a process of first rising and then falling. The index was 0.650, 0.653, 0.664, 0.661, and 0.641 in 1991, 2000, 2008, 2016, and 2021, respectively. (4) Lastly, an analysis of factors driving wetland change revealed that human factors were the most critical reasons for wetland landscape change. Our work can play a reference and inspiration role in the monitoring and protection of similar coastal wetlands. [ABSTRACT FROM AUTHOR]

We assessed hydro-physicochemical (HP) settings and soil color attributes including redoximorphic features (RMFs) at four forested wetlands in Northern Virginia, USA, to identify whether four simply measurable HP attributes—inundation/saturation frequency, bulk density, soil moisture, and percent sand—can provide an explanatory framework for characterizing and classifying soil color attributes related to hydric soil field indicators. Study plots (n = 16) were grouped by site for initial characterizations and comparisons of HP (n = 4) and color attributes (n = 11); each attribute was additionally characterized and compared between three HP-based clusters formulated through k-means clustering analysis. Whereas only one HP attribute (inundation/saturation frequency) significantly differed between sites, all HP attributes but percent sand differed between HP-based clusters (p < 0.05), with PCA Dimensions 1 and 2 explaining over 80% of variability in plot HP attributes. Moreover, more sets of color attributes were significantly different when plots were grouped by HP-based cluster (n = 5: frequency of concentrations, non-matrix color count, hue, chroma, and depth to concentrations) compared to by site (n = 3: value, frequency of depleted matrices, depth to depletions) (p < 0.10). Simply measurable HP attributes are thus closely associated with certain soil RMF and color characteristics beyond site identity, potentially serving as a suite of measurements that can be adopted to assess and monitor redoximorphic features indicative of wetland soils. [ABSTRACT FROM AUTHOR]

Wetlands in estuary deltas functionally protect biodiversity, store water, and regulate ecological balance. However, wetland monitoring accuracy is low when using only synthetic aperture radar (SAR) images or optical images. This study proposes a novel method for extracting ground objects in a wetland using principal component analysis (PCA) and random forest (RF) classification, which combines the features of fully polarimetric SAR images and optical images. Firstly, polarization decomposition features and texture features were extracted based on polarimetric SAR data, and spectral features were extracted based on optical data. Secondly, the optical image was registered to SAR image. Then PCA was performed on the nine polarimetric features of the SAR images and the four spectral features of the optical images to obtain the first two principal components of each. After combining these components, a RF classification algorithm was used to extract the objects. The objects in the Yellow River Delta wetland were successfully extracted using our proposed method with Gaofen-3 fully polarimetric SAR data and Sentinel-2A optical data acquired in November 2018. The overall accuracy of the proposed method was 86.18%, and the Kappa coefficient was 0.84. This was an improvement of 18.96% and 0.22, respectively, over the GF-3 polarimetric features classification, and 11.02% and 0.13, respectively, over the Sentinel-2A spectral features classification. Compared with the results of the support vector machine, maximum likelihood, and minimum distance classification algorithms, the overall accuracy of the RF classification based on joint features was 2.03, 5.69, and 23.36% higher, respectively, and the Kappa coefficient was 0.03, 0.07, and 0.27 higher, respectively. Therefore, this novel method can increase the accuracy of the extraction of objects in a wetland, providing a reliable technical means for wetland monitoring.

Wetlands are among the most productive and essential ecosystems on earth, but they are also highly sensitive and vulnerable to climate change and human disturbance. One of the current scientific challenges is to integrate high-resolution remote sensing data of wetlands with wildlife movements, a task we achieve here for dynamic waterbird movements. We demonstrate that the White-naped cranes Antigone vipio wintering at Poyang Lake wetlands, southeast of China, mainly used the habitats created by the dramatic hydrological variations, i.e. seasonal water level fluctuation. Our data suggest that White-naped cranes tend to follow the water level recession process, keeping close to the boundary of water patches at most of the time. We also highlight the benefits of interdisciplinary approaches to gain a better understanding of wetland ecosystem complexity.

Unmanned aerial vehicles (UAVs) provide high‐spatial‐resolution imagery and allow the collection of data in locations or periods of time where field‐based data collection is challenging or impossible, such as in wetlands and floodplains. Computational deep learning techniques are transforming the way in which remotely sensed imagery and data can be used and are having an increasing role in remote sensing. Here, we describe a method using UAV and machine learning technique convolutional neural networks (CNNs) to estimate the cover of wetland features Phragmites australis reeds, leaf litter, water, bareground, and other vegetation in a large inland floodplain wetland in Western New South Wales (NSW), Australia. We firstly describe the process we took to train, validate, and test the model. We describe the model's performance by calculating a range of performance indicators and provide density maps and results from individual sites. The model had an overall accuracy of 0.947 and recognized and estimated Phragmites australis reeds to a very high accuracy (>98%). Here, we show an effective, accurate, and reproducible way to estimate the cover of Phragmites australis reeds and other wetland features using UAV and CNNs in a semi‐arid wetland. [ABSTRACT FROM AUTHOR]

The main biofuel produced in Brazil is ethanol representing 16% of energy production in the national energy matrix, that is why sugarcane is one of the most important crops for Brazilian agriculture. The land conversion associated with the advancement of agriculture is often carried out inappropriately advancing over areas of high environmental sensitivity, such as natural small wetlands. The level of detail required to monitor subtle changes in their dynamics and landscape makes very high-resolution images (+ 10 cm/pixel resolution) acquired by unmanned aerial vehicles (UAVs) an excellent data. The objective of this research was to use UAV orthomosaics and digital elevation models to carry out seasonal monitoring, simulate the water flow in the areas of hydric-contribution and inland flooding, and validate the flooding simulations. The studied wetland located in the State of São Paulo (Paulista Peripheral Depression), showed a seasonal surface water storage capacity (28,067 m³) and a loss of approximately 12.27% of its total area between October 2019 and February 2020. The flooding simulations were validated with data observed by the imagery (variation of ± 3.27%.), being possible to be reapplied in several small ecosystems.

Shoreline wetlands along Lake Ontario are valuable, multi-functional resources that have historically provided large numbers of important ecosystem goods and services. However, alterations to the lake's natural hydrologic regime have impacted traditional meadow marsh in the wetlands, resulting in competition and colonization by dense and aggressive Typha angustifolia and Typha x glauca (Cattails). The shift to a Typha-dominated landscape resulted in an array of negative impacts, including increased Typha density, substantial decreases in plant species richness and diversity, and altered habitat and changes in associated ecosystem services. Successful long-term adaptive management of these wetland resources requires timely and accurate monitoring. Historically, wetland landscapes have been surveyed and mapped using field-based surveys and/or photointerpretation. However, given their resource- and cost-intensive nature, these methods are often prohibitively time- and labor-consuming or geographically limited. Other remote sensing applications can provide more rapid and efficient assessments when evaluating wetland change trajectories or analyzing direct and indirect impacts across larger spatial and temporal scales. The primary goal of this study was to develop and describe methodology using U.S. Army Corps of Engineers National Coastal Mapping Program hyperspectral imagery, light detection and ranging data, and high-spatial resolution true-color imagery to provide updated wetland classifications for Lake Ontario coastal wetlands. This study used existing field-collected vegetation survey data (Great Lakes Coastal Wetland Monitoring Program), ancillary imagery, and existing classification information as training data for a supervised classification approach. These data were used along with a generalized wetland schema (classes based on physical and biological gradients: elevation, Typha, meadow marsh, mixed emergent, upland vegetation) to generate wetland classification data with Kappa values near 0.85. Ultimately, these data and methods provide helpful knowledge elements that will allow for more efficient inventorying and monitoring of Great Lake resources, forecasting of resource condition and stability, and adaptive management strategies. [ABSTRACT FROM AUTHOR]

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.

Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China – derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013 – are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.

Various forms of competition for water and amplified agricultural practices, as well as urban development in South Africa, have modified and destroyed natural wetlands and its biodiversity benefits. To conserve and protect wetlands resources, it is important to file and monitor wetlands and their accompanied land features. Spatial science such as remote sensing has been used with various advantages for assessing wetlands dynamic especially for large areas. Four satellite images for 1987, 1997, 2007 (Landsat 5 Thematic Mapper) and 2017 (Landsat 8 Operational Land Imager) were used in this study for mapping wetland dynamics in the study area. The result revealed that the natural landscapes in the area have experienced changes in the last three decades. Dense vegetation, sparse vegetation and water body have increased with about 14% (5976.495 km2), 23% (10,349.631km2) and 1% (324.621) respectively between 1987 and 2017. While wetland features (marshland and quag) in the same period experienced drastic decrease with an area coverage of about 16,651.07 km2 (38%). This study revealed that the shift in the vegetation and water body extents have contributed detrimentally to the drastic declined in the Isimangaliso Wetland Park in recent years. Consequently, this development might have negative effects on the wetland ecosystem and biodiversity and the grave state of the wetland in the study area requires an urgent need for protection of the dregs wetland benefits. [ABSTRACT FROM AUTHOR]

Due to extreme similarity of wetland spectra, a significant uncertainty lies in the accuracy of the traditional pixel-based classification, which is a bottleneck in the extraction of wetland information. Object-based image analysis (OBIA) has brought opportunity for fine monitoring of wetland information. However, previous studies on monitoring wetlands have focused mainly on exploratory experiments involving high-resolution images for small areas. The application of OBIA in various medium-resolution images for large areas needs further verification. Here, Landsat and China's HJ-CCD images, a new OBIA mixed binary decision tree, tasseled cap transformation, and field samples were used to refine monitoring of changes in wetlands in Hubei Province. The results showed that while the overall accuracy and Kappa coefficient of the extracted wetland information for 2000 were, respectively, 88.98% and 0.87, the overall accuracy and Kappa coefficient of the detected change were 94.75% and 89.41. This indicated that OBIA performed well with medium-resolution HJ-CCD and Landsat images in monitoring changes in wetlands at provincial scale. The area of wetlands in Hubei increased by 171.03 km2 during 2000–2010. Lakes and reservoirs/ponds increased the most in the province, with respective contributions to the total wetland area of 40.21% (108.97 km2) and 59.17% (160.37 km2). At administrative unit scale, Shiyan Prefecture (157.53 km2) and Fangxian County (317.33 km2) contributed the most to the increase of wetland area. The main reason for the increase in wetland area in Hubei in 2000–2010 was the implementation of major ecological projects during that decade. [ABSTRACT FROM AUTHOR]

Parvati Kunda, a small, alpine wetland located near the village of Gatlang in Rasuwa, Nepal, is a major source of drinking water for the village, possesses spiritual significance, and is a reservoir of local biodiversity. This study presents the first scientifically conducted biodiversity survey of the wetland. Here, biodiversity data (wetland plants, birds, mammals, aquatic insects), basic water chemistry (nutrients, pH, dissolved oxygen, conductivity), and basic bacterial tests (total coliform, Escherichia coli, Giardia, Salmonella, Shigella) for the Parvati Kunda wetland is presented from November 2016 and February and May 2017. Parvati Kunda, two of three alternate village water sources, and several village taps were found to be contaminated with E. coli bacteria. Within and around the wetland, 25 species of wetland plants, nine tree species, 10 macroinvertebrate taxa, 37 bird species, and at least six mammal species were documented. Acorus calamus was the dominant wetland plant and the rapid proliferation of this species over the past twenty years has been reported by community members. Future studies that further document and monitor wetland biodiversity are necessary. This study provides a valuable baseline for future research in this culturally and ecologically important wetland. [ABSTRACT FROM AUTHOR]

Wetlands are vital habitats which play a critical role in global biodiversity. Their functioning is intrinsically linked with their hydrological regime and so the monitoring of water level data is critical to their management and conservation. However, monitoring the hydrology of wetlands can be challenging. Their large number and wide distribution can make them impractical to monitor using traditional field instrumentation. In this context, this paper presents a novel approach for monitoring water levels in gauged and ungauged seasonally flooded wetlands using Sentinel-1 Synthetic Aperture Radar (SAR) data. The methodology, which was fully automated, used multitemporal sequences of SAR imagery to reconstruct hydrometric data at 44 seasonally flooded wetland sites across the Republic of Ireland. The procedure downloaded, processed and checked the suitability of SAR images for water classification. Suitable images were classified and filtered to remove common sources of misclassification, and the flood area in each image was cross-referenced against a predefined stage-area relationship (derived from a digital terrain model) to determine water level. The methodology was calibrated using observed field data and performance evaluated in two ways: 1) generic calibration whereby the process was optimised collectively to achieve the best accuracy for ungauged sites, and 2) site-specific calibration whereby the process was optimised for each site individually to achieve the best possible accuracy for each gauged site. The generic calibration yielded an average Nash-Sutcliffe Efficiency of 0.80 for both calibration and validation datasets while the site-specific optimisation produced results of 0.87 for both calibration and validation datasets. Results show the methodology was capable of accurately reproducing water levels in easonal wetlands for floods as small as 3 Ha. This demonstrates its potential as a viable tool for the remote monitoring of ecologically significant wetlands, and a means of providing the observational data necessary for their long-term sustainable management. • Automated approach for monitoring wetlands using Sentinel-1 data. • Multitemporal sequences of SAR imagery used to reconstruct water level data. • Hydrometric data produced for 44 wetland sites across the Republic of Ireland. [ABSTRACT FROM AUTHOR]

Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C- and L- bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors.

Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water-veg = 89.9%, veg-water 1 = 88.8%, veg-water 2 = 87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy. [ABSTRACT FROM AUTHOR]

In regions of the world where the climate is expected to become drier, meeting environmental water needs for wetlands and other dependent ecosystems will become increasingly challenging. Ecological models can play an important role, by quantifying system responses to reduced water availability and predicting likely ecological impacts. Anticipating these changes can inform both conservation and monitoring effort. We used water-plant functional group models to predict the effects of a declining water table for two wetland types reliant on the surface expression of groundwater but of contrasting basin morphology. Our interest was in quantifying the relative sensitivity of these wetland types to different amounts of groundwater decline. For the shallower, grass-sedge wetland, terrestrial plant probabilities increased markedly for declines between 0.25 and 0.5 m, but amphibious and submerged functional groups changed predictably, or not at all. However, mean inundated area reduced by over 70% for a 0.5 m groundwater decline, suggesting loss of area posed the greatest risk in this wetland type. In the deeper, steep-sided interdunal wetland, inundated area changed little, but models suggest clear transitions in plant functional group composition. Sedge-group probabilities increased sharply for declines between 0.25 and 0.5 m, while declines between 0.5 and 1.0 m predicted the loss of submerged species. As might be anticipated, the risks associated with groundwater level decline depend on basin morphology. However, by quantifying probable ways in which this will manifest in different wetland types, model predictions improve our ability to recognise and manage change. [ABSTRACT FROM AUTHOR]

Wetland is an important land and natural resources with many functions. It is closely related to the survival, reproduction and development of human beings, as well as one of the most important living environments of human beings. Hainan Province, which is located in the northern edge of the tropics with the tropical monsoon climate and covers a variety of wetland types. In this paper, in order to investigate the change of wetland distribution and the variations of area in study region, the remote sensing data of GF-1 and GF-2 from 2009 to 2015 were used. The method used in this study was automatic information extraction and human-computer interaction. The wetland types in study area mainly was divided into three level-1 classes, including coastal wetland, river wetland and lake wetland, and was also divided into eight level-2 classes at the same time. The results showed that the total area of wetland increased 9.13 km2 in study area from 2009 to 2015, in which the area of constructed wetland increased 6.29 km2, the natural wetland increased only 2.83 km2. The area of natural wetland has not changed much, but its proportion has been reduced. This reflected that the wetland in the research area has been more artificially intervened since 2009, which caused the increasing of the area of constructed wetland. As the wetland resources can coordinate the sustainable benefit of the society, the protection of natural wetland should be strengthened and valued. [ABSTRACT FROM AUTHOR]

Wetland plants are subject to a range of physical stresses (e.g. inundation, salinity) that affect their productivity or health, which in turn may translate into wetland soils that vary in resistance to physical perturbations in the coastal setting. A primary goal of this study was to test a newly developed instrument designed to measure in-situ resistance to shear failure (soil strength) of marsh soils. The Wetland Soil Strength Tester (WSST) was used at 11 marsh types in coastal Louisiana, where soil bulk density ranged from organic to mineral (0.02-1.24 g cm−3). Based on analyses of live and dead components of both above and belowground biomass, live belowground biomass explained the most variation in marsh soil strength among the vegetation types. The WSST was capable of detecting in-situ live root biomass differences for 8 of 11 marsh types, where only the young deltaic marsh types were not significant. For all the sample plots (n = 227), an increase of 10-Nm soil strength corresponded to an increase of 200 g m−2 of live belowground biomass (R2 = 0.35, p < 0.0001). WSST measurements, combined with other monitoring data, may help in the assessment of wetland condition. [ABSTRACT FROM AUTHOR]

Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China – derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013 – are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time. [ABSTRACT FROM AUTHOR]

This editorial describes a 25-paper special issue that began with a symposium at EcoSummit 2016 in Montpellier France in August/September 2016 with a focus on ecological engineering principles applied to the development of large-scale sustainable landscapes and catchments. The papers are divided into the following categories: watershed management (3 papers); wetland creation and restoration (5 papers); monitoring wetlands (3 papers); lake management (2 papers); restoration of wetland and terrestrial landscapes (6 papers); public health (2 papers); and ecotechnological solutions (4 papers). Some of these case studies are purposeful application of ecological engineering principles; others happened or are happening naturally through self-design. Several of the presentations focus on ecological engineering that involves scales of thousands of hectares or even larger catchments to solve ecological and environmental problems. Case studies are presented by scientists and engineering from North America, Europe, and South America. [ABSTRACT FROM AUTHOR]

Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 – 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands.

The use of East African freshwater wetlands for agriculture has increased in recent decades, raising concerns about potential impacts on wetlands and the long-term sustainability of such land use trends. WET-health is an indicator-based rapid wetland assessment approach developed in South Africa. It allows determining the conditions of wetlands in four assessment modules (hydrology, geomorphology, vegetation, and water quality) by observing the degree of deviation of a wetland from its anticipated natural reference state. We tested the transferability of the WET-health concept for East African inland valley swamps and floodplain wetlands based on 114 assessment units at four study sites. Due to large wetland areas and different environmental settings in East Africa, we modified the original approach using a random selection of assessment units and an assessment scheme based on disturbance types (Appendices A and B). Estimated WET-health impact scores were matched with biophysical and socioeconomic variables using a generalized linear mixed model. Land use included largely undisturbed wetland units occurring side by side with seasonally cropped or grazed units, and drained, permanently cultivated units. A strong differentiation of impact scores between the four assessment modules was apparent with highest scores for vegetation and lowest scores for geomorphology. Vegetation and water quality responded most sensitively to land use changes. The magnitude of wetland disturbance is predominantly determined by management factors such as land use intensity, soil tillage, drainage intensity, and the application of agrochemicals and influences vegetation attributes and the provision of ecosystem services. The proposed modification of WET-health enables users to assess large wetland areas during relatively short periods of time. While further studies will be required, WET-health appears to be a promising concept to be applied to wetlands in East Africa and possibly beyond. [ABSTRACT FROM AUTHOR]

Thirlmere Lakes is a group of five freshwater wetlands in the southwest fringe of Sydney, Australia, that is subject to cyclic wetting and drying. The lakes are surrounded by activities that have led to increasing pressure on the local surface and groundwater supply including farming and mining. The mine has been operating for more than 30 years, and in recent times, there has been speculation that the surface subsidence and underground pumping may have some impact on surface water and groundwater hydrology. A study was undertaken using satellite imagery to examine the relation between water area changes and rainfall variability. The study utilised Landsat time-series data during the period 1982-2014 to calculate changes in the lake water area (LA), through the normalised difference water index (NDWI) threshold. High classification accuracy was achieved using NDWI against high-resolution data that are available for the years 2008 (88.4 %), 2010 (92.8 %), and 2013 (96.9 %). The LA measurement was correlated against 11 historic observations that occurred in 2009, 2010, and 2011 during drier wetland conditions. Correlation analysis of the LA with the residual rainfall mass spread across the past 30 years has found that rainfall variability is a major dominant factor associated with the wetland changes. The underground mining operations, if verified by independent investigations, probably play a minor or negligible contributor to variations in total wetland area during the study period. This study has demonstrated that remote sensing is a technique that can be used to augment limited historic data. [ABSTRACT FROM AUTHOR]

A database of global wetland validation samples (GWVS) is the foundation for wetland mapping on a global scale. In this work, a database of GWVS was created based on 25 'wetland-related' keyword searches of a total of 3,506 full-text documents downloaded from the Web of Science. Eight hundred and three samples from a total of 68 countries and 141 protected areas were recorded by the GWVS, including samples of marine/coastal wetlands, inland wetlands and human-made wetlands, at ratios of 53 %, 41 % and 6 %, respectively. The results exhibit spatial distribution among Terrestrial Ecoregions of the World, the World Database on Protected Areas and the Database of Global Administrative Areas. Within most of the biomes, protected areas and countries examined, the very low concentration of samples requires more attention in the future. The greatest concentration of samples within a single biome is found in the tropical and subtropical moist broadleaf forest biome, accounting for 27 % of the total samples, while no sample is found in the biome of tropical and subtropical coniferous woodland. Greater efforts are expected to be made to record samples in Oceania, Central Europe, Northern Europe, Northern Africa, Central Africa, Central America, the Caribbean, and midwestern South America. Our data show that it is feasible to map global wetlands using Landsat TM/ETM+ at 30-m resolution. The continued improvement of the GWVS sharing platform should be reinforced in the future, making a strong contribution to global wetland mapping and monitoring. [ABSTRACT FROM AUTHOR]

Wetlands provide ecosystem goods and services vitally important to humans. Land managers and policymakers working to conserve wetlands require regularly updated information on the statuses of wetlands across the landscape. However, wetlands are challenging to map remotely with high accuracy and consistency. We investigated the use of multitemporal polarimetric synthetic aperture radar (SAR) data acquired with Canada's Radarsat-2 system to track within-season changes in wetland vegetation and surface water. We speculated, a priori, how temporal and morphological traits of different types of wetland vegetation should respond over a growing season with respect to four energy-scattering mechanisms. We used ground-based monitoring data and other ancillary information to assess the limits and consistency of the SAR data for tracking seasonal changes in wetlands. We found the traits of different types of vertical emergent wetland vegetation were detected well with the SAR data and corresponded with our anticipated backscatter responses. We also found using data from Landsat's optical/infrared sensors in conjunction with SAR data helped remove confusion of wetland features with upland grasslands. These results suggest SAR data can provide useful monitoring information on the statuses of wetlands over time. [ABSTRACT FROM AUTHOR]

Reflectance data from multiseasonal SPOT-5 imagery was combined with monthly measures of water levels collected in the Rhône river delta (Camargue) in 2005 and 2006. Classification tree and regression models using monthly values of 17 multispectral indices and 4 bands, as well as their seasonal variations, were used for predicting the presence and levels of water, independently of vegetation type and density in shallow marshes. Accuracy of the classification model was estimated by cross-validation and by calculating the percentage of correctly classified pixels on the resulting maps using an independent sampling. Goodness-of-fit of the regression model was assessed by calculating the coefficient of correlation between predicted and observed values. Predictive accuracy of both models was estimated by calculating NRMSE for the independent validation sample. Regression model robustness was also tested using Scheffé post-hoc analyses on the residuals. Biophysical parameters of Camargue marsh vegetation were used to interpret misclassifications and model deficiency. Both models were composed of a single variable consisting of a multispectral index using the mid-infrared band. The resulting classification tree provided a cross-validation accuracy of 76% and a map validation accuracy of 83%. With an R=0.5, the regression model predicted water level with a 6-cm precision up to 20cm of water depth. For both approaches, the predictive power of model was most affected by close canopy. This study highlights the usefulness of data mining for long-term monitoring of wetland hydrology based on multispectral indices using the mid-infrared band. [ABSTRACT FROM AUTHOR]

Bird monitoring is frequent in wetlands; however, in the absence of information on other variables, trends in bird numbers are difficult to interpret. In this article we describe a methodology for bird's habitat area assessment based on remote sensing. We calibrated the methodology to study the changes in the habitat area of Cygnus melancoryphus, the black-necked swan, at the Carlos Anwandter Sanctuary, a wetland located in Valdivia, Southern Chile. Swan habitat area was estimated by means of the Normalized Difference Vegetation Index (NDVI) based on Landsat images and calibrated through spectral photography by means of a portable Tetracam ADC Camera. Results show that calibrated NDVI values from Landsat images can be used to estimate habitat area but not to separate individual species of vegetation. We also show that the joint analysis of habitat area and swan count can indeed be used to separate some of the scales of variability of bird counts: those with 'local-influence' associated with changes in habitat area from those of larger scales not related with habitat area. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this work was to study the effect of plant presence (Phragmites australis) and inoculant origin on wetland mesocosm start-up dynamics. Eight mesocosms were studied based on a duplicated 22 factorial design tracking bacterial community and hydrological changes during an 8 month start-up period. The mesocosms were characterized in terms of their hydrological character based on evapotranspiration (ET), porosity, and a dispersion coefficient. The microbiological regime was characterized using a microbial activity measure and community-level physiological profiling (CLPP) employing BIOLOG™ ECO plates. CLPP-related indices such as substrate richness, substrate diversity, over-all community profile, and community divergence are also presented. It was found that mesocosm porosities decreased over time as a result of media-related biofilm development. This biofilm development also contributed to a substantial increase in the dispersion coefficient in the mesocosms over the start-up period. Dispersion coefficients in planted systems reached values of ∼50–55cm2/min whereas in the unplanted systems values of ∼30–35cm2/min were observed. Bacterial community divergence in the mesocosms was quantified using a Euclidean-based divergence metric. All mesocosms showed a sharp increase in community divergence until day 75, at which point a steady state was reached. The interstitial communities were also characterized in terms of similarity based on the experimental design treatments. Four stages of mesocosm development were identified that can be described by an initial community state based on the origins of the initial inoculum [days 0–6]; a dynamic period where adjustments and shifts in the bacterial community occurred in all mesocosms [days 7–26]; a period where all interstitial CLPPs were quite similar [days 27–73]; and finally a shift towards unplanted and planted mesocosm CLPP groupings [days 74–232]. [Copyright &y& Elsevier]

Abstract: Ecologists and conservationists need accurate and replicable tools for monitoring wetland conditions in order to develop and implement adaptive management strategies efficiently. The Rhone Delta (Camargue) in southern France encloses 9200ha of fragmented reed marshes actively managed for reed harvesting, waterfowl hunting or cattle grazing, and holding significant numbers of vulnerable European birds. We used multi-season SPOT-5 data in conjunction with ground survey to assess the predictive power of satellite imagery in modelling indicators of reed structure (height, diameter, density and cover of green/dry stems) relevant to ecosystem management and bird ecology. All indicators could be predicted accurately with a combination of bands (SWIR, NIR) and indices (SAVI, OSAVI, NDWI, DVI, DVW, MSI) issued from scenes of March, June, July, September or December and subtraction between these. All models were robust when validated with an independent set of satellite and field data. The high spatial resolution of SPOT-5 scenes (pixel of 10×10m) permits the monitoring of detailed attributes characterizing the reed ecosystem across a large spatial extent, providing a scientifically-based, replicable tool for managers, stakeholders and decision-makers to follow wetland conditions in the short and long-term. Combined with models on the ecological requirements of vulnerable bird species, these tools can provide maps of potential species ranges at spatial extents that are relevant to ecosystem functioning and bird populations. [Copyright &y& Elsevier]

Abstract: The invasion of the exotic common reed (Phragmites australis) is increasingly displacing local native species from northeastern coastal estuaries. This study evaluates the accuracy of a remote sensing technique to map the distribution of common reed, monitor the rate of invasion and determine areas of natural resistance to invasion. The current invasion footprint of Phragmites in the Hackensack Meadowlands District in Northern New Jersey was determined using high spectral and spatial resolution hyperspectral imagery. A tethered balloon-based imaging device with limited coverage area was used to assess the accuracy of the hyperspectral imagery classification. The accuracy assessment based on true color balloon images revealed that the hyperspectral classification technique from images covering hundreds of hectares was 90% accurate in separating the dominant common reed-invaded areas from the native vegetation. Furthermore, linear spectral un-mixing techniques for sub-pixel classification revealed that for mixed areas where Phragmites covered 75% or more of a pixel, the classification was correct 96% of the time. The accuracy dropped to 52% for pixels that contained 25% or less of Phragmites cover, and was only 4% for pixels where invasive and native species cover was the same (50–50%). [Copyright &y& Elsevier]