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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., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In 2011, the US Environmental Protection Agency and its partners conducted the first National Wetland Condition Assessment at the continental-scale of the conterminous United States. A probability design for site selection was used to allow an unbiased assessment of wetland condition. We developed a vegetation multimetric index (VMMI) as a parsimonious biological indicator of ecological condition applicable to diverse wetland types at national and regional scales. Vegetation data (species presence and cover) were collected from 1138 sites that represented seven broad estuarine intertidal and inland wetland types. Using field collected data and plant species trait information, we developed 405 candidate metrics with potential for distinguishing least disturbed (reference) from most disturbed sites. Thirty-five of the metrics passed range, repeatability, and responsiveness screens and were considered as potential component metrics for the VMMI. A permutation approach was used to calculate thousands of randomly constructed potential national-scale VMMIs with 4, 6, 8, or 10 metrics. The best performing VMMI was identified based on limited redundancy among constituent metrics, sensitivity, repeatability, and precision. This final VMMI had four broadly applicable metrics (floristic quality index, relative importance of native species, richness of disturbance-tolerant species, and relative cover of native monocots). VMMI values and weights from the survey design for probability sites (n = 967) were used to estimate wetland area in good, fair, and poor condition, nationally and for each of 10 ecoregion by wetland type reporting groups. Strengths and limitations of the national VMMI for describing ecological condition are highlighted.

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Vernal pools are vulnerable to loss through development and agricultural and forestry practices owing to their isolation from open water bodies and their small size. Some vernal pool-dependent species are already listed in New England as Endangered, Threatened, or Species of Special Concern. Vernal pool creation is becoming more common in compensatory mitigation as open water ponds, in general, may be easier to create than wooded wetlands. However, research on vernal pool creation is limited. A recent National Research Council study (2001) cites vernal pools as “challenging to recreate.” We reviewed documentation on 15 vernal pool creation projects in New England that were required by federal regulatory action. Our purpose was to determine whether vernal pool creation for compensatory mitigation in New England replaced key vernal pool functions by assessing project goals and documentation (including mitigation plans, pool design criteria, monitoring protocols, and performance standards). Our results indicate that creation attempts often fail to replicate lost pool functions. Pool design specifications are often based on conjecture rather than on reference wetlands or created pools that function successfully. Project monitoring lacks consistency and reliability, and record keeping by regulatory agencies is inadequate. Strengthening of protection of isolated wetlands in general, and standardization across all aspects of vernal pool creation, is needed to ensure success and to promote conservation of the long-term landscape functions of vernal pools. [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. [ABSTRACT FROM AUTHOR]

South America is the continent of wetlands, which represents more than 20% of its surface. Since the ecological integrity of wetlands strongly depends on their water sources and dynamics, it is fundamental to understand their hydrology. Large wetlands are usually located in inaccessible areas where remote sensing results are a fundamental tool for wetland monitoring, providing information over a broad range of spatial and temporal scales. Radar spaceborne sensors provide an excellent all-weather tool for monitoring and recent investigations have also shown that Interferometric SAR (InSAR) can be very valuable for wetland monitoring in addition to its regular uses in DEM generation and surface deformation analyses. The availability of new SAR satellites such as the Sentinel 1 and SAOCOM missions, with short revisit times and open data policies, repeat-pass interferometry can now provide long time-series of coherence for wetland monitoring. In this article, we used a SOM neural network to cluster a yearly series of Sentinel 1 B coherence images from the coastal plain of Samborombón Bay, Argentina, into temporal coherence patterns. The timing and coherence values of these patterns were interpreted in terms of landcover, vegetation phenology, water sources, and waterlogged condition. Although the SOM patterns did not show a one to one relationship with landcover types nor with the main water sources, their spatial distribution and temporal signature of coherence gave information on wetlands with different water dynamics. A key outcome of our study was that temporal patterns of coherence could be used to assess the impacts of land-use practices on wetland functioning, which deserves further exploration. The spatial distribution of the temporal coherence patterns can be used as a hypothesis for wetland hydropatterns, a key to hydrological functional type wetland classification. This approach can help us gain a better understanding of complex wetlands and foster their sustainable management, particularly combined with in situ fieldwork and other remote sensing sources, particularly repeat pass L-band coherence, which can give a better indication of soil wetness. • Temporal patterns of interferometric coherence from Sentinel 1B images VV-band C were clustered by means of a Kohonen Self Organized Map (SOM). • The spatial distribution of the SOM patterns and their temporal coherence signature gave information on wetlands with different water dynamics. • SOM patterns did not show a direct relationship with landcover types or water sources. • Glasswort salt marshland showed very high coherence values during the austral winter, when high tides are usually lower. • Differences in areas with similar topographic position and landcover could be related to anthropogenic disturbances. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]