Wetland Classification Research Articles

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.

The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine

Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more training data to the classification process and training Random Forest (RF) models on the Google Earth Engine (GEE) platform within the boundaries of ecozones rather than provinces. A considerable effort has been devoted to data collection, preparation, standardization of datasets for each ecozone. The data cleaning reveals a data gap in several Northern ecozones. Accordingly, high-resolution optical data, from Worldview-2 and Pleiades, were acquired to delineate wetland training data based on visual interpretation in those regions. By using this well-distributed training data, this second generation wetland inventory map represents an improvement of 7% compared to the first generation map. Accuracy varied from 76% to 91% in different ecozones depending on available resources. Furthermore, the results of RF variable importance, which was carried out for each ecozone, demonstrate that and NDVI extracted from Sentinel-1 and Sentinel-2 data, respectively, were the most important features for wetland mapping.

Wetland Distribution Trends in Central Asia

Anthropogenic activities and climate change contribute to the deterioration of wetlands worldwide with Central Asia (CA) being among the regions which are most severely affected. This study examined how the distribution of wetlands in CA has changed in the last two decades. Emphasis was put on inland wetlands protected as International Bird and Biodiversity Areas. Time series of maps of wetlands (i.e. reed beds) were created for the years 2000, 2005, 2010, 2015 and 2018. A supervised classification approach was applied using NDVI of MODIS satellite images with 1000m resolution for the respective years. Ground control points were acquired through fieldtrips to the lower Chu River, upper Ili River and Ili River Delta in Kazakhstan and the Amu Darya River Delta and the Lower Amu Darya State Biosphere Reserve in Uzbekistan. The applied method is not applicable for the classification of wetlands in northern Kazakhstan. The vegetation there is too alike to the wetland vegetation in terms of values and seasonality of NDVI. For the remaining part of the study area, the applied method delivers satisfying results. However, it is difficult to determine a general trend for most wetland areas since there is a large variability between years. This study marks a first attempt at examining wetlands in CA at a regional scale. The results provide a baseline for further, more detailed research through either fieldtrips or by using higher resolution remote sensing data.

Mapping hurricane damage: A comparative analysis of satellite monitoring methods

Wetlands are the second-most valuable natural resource on Earth but have declined by approximately 70 % since 1900. Restoration and conservation efforts have succeeded in some areas through establishment of refuges where anthropogenic impacts are minimized. However, these areas are still prone to wetland damage caused by natural disasters. Severe storms such as Hurricane Irma, which made landfall as a Category 3 hurricane in southwest Florida (USA) on September 11, 2017, can cause the destruction of mangroves and other wetland habitat. Multispectral images from commercial satellites provide a means to assess the extent of the damage to different wetland habitat types with high spatial resolution (2 m pixels or finer) over large areas. Using such images presents a number of challenges, including deriving consistent and accurate classification of wetland and non-wetland vegetation. Machine learning methods have demonstrated high-accuracy mapping capabilities on small spatial scales, but require a large amount of robust training data. Meanwhile, ambitious efforts to map larger areas at finer resolutions may use hundreds of thousands of images, and therefore encounter Big-Data processing challenges. Large-scale efforts face the dilemma of adopting traditional mapping methods that may lend themselves to Big Data analytics but may result in accuracies that are inferior to new methods, or move to machine learning methods, which require robust training data. Given these considerations, we describe a version of the traditional Decision Tree (DT) approach and compare two common machine learning methods to derive land cover classes using a WorldView-2 image collected on November 12, 2018 to include one growing season after Hurricane Irma affected this area. Specifically, we compared the Support Vector Machine [SVM] and Neural Network [NN] methods, trained and validated with separate ground-truth datasets collected during a robust field campaign. Overall accuracies were only marginally different (85 % NN vs 83 % each DT and SVM), but healthy mangroves were more accurately identified with the DT (91 % vs 88 % NN and 86 % SVM), and degraded mangroves were more accurately identified with NN (62 % vs 57 % NN and 38 % DT). These results, combined with their respective training requirements, have implications for the direction with which large-scale high-resolution mapping of coastal habitats proceeds.

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.

The Saltpan Microbiome is Structured by Sediment Depth and Minimally Influenced by Variable Hydration.

Saltpans are a class of ephemeral wetland characterized by alternating periods of inundation, rising salinity, and desiccation. We obtained soil cores from a saltpan on the Mississippi Gulf coast in both the inundated and desiccated state. The microbiomes of surface and 30 cm deep sediment were determined using Illumina sequencing of the V4 region of the 16S rRNA gene. Bacterial and archaeal community composition differed significantly between sediment depths but did not differ between inundated and desiccated states. Well-represented taxa included marine microorganisms as well as multiple halophiles, both observed in greater proportions in surface sediment. Functional inference of metagenomic data showed that saltpan sediments in the inundated state had greater potential for microbial activity and that several energetic and degradation pathways were more prevalent in saltpan sediment than in nearby tidal marsh sediment. Microbial communities within saltpan sediments differed in composition from those in adjacent freshwater and brackish marshes. These findings indicate that the bacterial and archaeal microbiomes of saltpans are highly stratified by sediment depth and are only minimally influenced by changes in hydration. The surface sediment community is likely isolated from the shallow subsurface community by compaction, with the microbial community dominated by marine and terrestrial halophiles.

Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery

Wetlands are among the most valuable natural resources, being highly beneficial to both the environment and humans. Therefore, it is very important to map and monitor wetlands. Although various remote sensing datasets, including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR) imagery, have been widely applied to classify wetlands, it is still required to discuss the advantages/limitations of each of these datasets and suggest the best remote sensing methodology for wetland mapping. Thus, the Terra Nova National Park, located in Newfoundland, Canada, was initially selected as the study area to develop a supervised classification method along with object-based image analysis. To this end, different remote sensing-based scenarios were investigated using individual optical, SAR, and LiDAR datasets, as well as their various combinations. In addition, for achieving the highest accuracy, the effects of segmentation scales and the tuning parameters of the random forest (RF) classifier were examined. The results showed that a combination of optical, SAR, and LiDAR images with the segmentation scale of 150, the RF depth of 20, and the RF minimum sample number of 5 provided the highest classification accuracy with the overall accuracy of 87.2%. Moreover, based on the results, approximately 21% and 79% of the study area are covered by wetlands and nonwetlands, respectively. The proposed methodology shows an optimum scenario for future wetland classification tasks and can assist stakeholders in the effective management of wetlands and establishment of necessary policies.

Arboreal macrolichen community composition and habitat associations in boreal forested wetlands of Newfoundland, Canada

Forested wetlands are ecologically and economically important, but many are poorly understood. A robust inventory of species is important for sound management in these ecosystems, particularly ones that include cryptogams such as arboreal lichens, which are rich and abundant in forested wetlands. On the island of Newfoundland, Canada, little is known about what lichens are found in forested wetlands, how lichen communities interact with different forested wetlands, or whether there are lichens unique to forested wetlands. Therefore, we investigated the potential for macrolichens to act as indicators of forested bog, fen and swamp wetland classes in four regions. We counted macrolichen thalli, by species, on the lower bole of black spruce (Picea mariana) trees within plots from each forested wetland class in each region. We also collected data on habitat characteristics in each wetland: soil pH, canopy closure, and ground and shrub cover, all of which differed significantly among forested wetland classes. Macrolichen communities differed among regions and forested wetland classes but the greatest differences were among regions. We also attempted to identify reliable macrolichen indicator species for forested wetland classes and regions but were unsuccessful. A lichen of conservation concern, Erioderma pedicellatum (Hue) P.M.Jørg., was detected in some of our forested wetland sites, highlighting the importance of proper management of these unique habitats.

Hydrological connectivity: One of the driving factors of plant communities in the Yellow River Delta

Abstract Coastal wetlands play an important role in regional and even continental sustainable development because of their valuable ecosystem services. The coupling mechanism among hydrology, edaphic factors, and vegetation is the key to coastal wetland management. The current study conducted in the Yellow River Delta used an improved-comprehensive hydrological connectivity structure index, Topographic Over Field Capacity Index (TOFCI), based on both soil water conditions (SWC) and topography to highlight the role of hydrological connectivity on intertidal flat (IF), tidal marsh (TM), semi-artificial pond (AP) and riverside intermittent flooded (RS) wetland classes among other environmental variables. The results showed plant community distributions and structures among wetland classes differed from each other significantly, and hydrological connectivity structure is one of the driving factors. The results provided three pieces of evidence for this inference: 1) according to PCA results, the contribution of TOFCI is the highest among all environmental variables; 2) TOFCI values show significant difference among different plant communities; 3) TOFCI is also significantly correlated to species composition and distribution according to the CCA and RDA results. TOFCI values are positively linearly correlated to the plant coverage and biomass in intertidal flat wetlands according to linear regression analysis. Furthermore, salinity, soil total nitrogen and soil total phosphorus also influence plant community distribution and species composition. Finally, simple parameters for soil water conditions or topographic characteristics do not show significant explanatory power to hydrology-vegetation interaction. The results of the current study provide a new perspective for coastal wetland conservation and restoration.

Carbon Accumulation in Freshwater Marsh Soils: a Synthesis for Temperate North America

Freshwater marshes are prevalent in North America, yet their soil carbon stocks remain poorly quantified. To better understand these stocks, and rates of carbon accumulation over short and long timescales, we synthesize available data on soil properties and rates of carbon accumulation in freshwater marshes in the temperate region of North America. Our findings suggest that freshwater marshes are not defined consistently, and that wetland classification schemes may undervalue presumed carbon stocks in freshwater marsh soils. Rates of carbon accumulation in freshwater marshes are often measured over recent time scales (last 50–100 years); these short-term rates are on average (± SD) 155 ± 74 g C m−2 yr−1 in temperate North America. Long-term rates of carbon accumulation (measured over centuries and millennia) are on average 51 ± 38 g C m−2 yr−1 yet infrequently measured. Our synthesis of rates of carbon accumulation and vertical accretion, bulk densities, and organic carbon contents suggests that freshwater marshes are accumulating carbon at comparable rates to salt marshes over short timescales and temperate peatlands over longer timescales. These timescales need to be clearly defined in order to improve estimates of the capacity for freshwater marshes to be net carbon sinks presently and in the future.

Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets

As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May–September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Картографирование таежных болот Западной Сибири на основе дистанционной информации

Investigations of wetlands are important for understanding the climate processes because wetlands are the source of methane and the tank of CO2. However, high fine-scale heterogeneity of wetlands complicates estimating carbon balance using point observations. To estimate methane emission, we mapped wetlands and water body areas in taiga of Western Siberia using supervised classification of Landsat images. For these purposes, we used our own dataset of field observation on 28 test sites as well as the high-resolution images. A wetland classification scheme included 6 main wetland types that are widely spread in taiga zone. Overall accuracy of 79% was calculated using 1082 polygons of 10 × 10 pixel size. Wetland area was estimated at 54.6 Mha or 11% of the global wetland areas. The average area of wetlands reached 29% of the zone area. Patterned bogs dominated in taiga (31% of the wetland area) followed by patterned bogs with lakes (18%) and forested bogs (18%). Fens (16%), water bodies (11%) and palsa complexes covers the rest of the area. We expect considerable growth of the regional methane flux as a result of significant changes of wetland areas comparing to the previous estimates. New map might be useful for model and global wetland dataset validation.

Depressional wetland classification and ecosystem service predictive models for the Integrative Landscape Modeling partnership

Depressional wetlands in the Great Plains are experiencing watershed alterations that impact the provisioning of important ecosystem services, which are unmonitored on a large scale (Smith et al. 2011). The Natural Resources Conservation Service’s (NRCS) Conservation Effects Assessment Project (CEAP) determines the effects of conservation programs on ecosystem service provisioning across the nation (Tomer et al. 2014). CEAP–Wetlands has focused on the effects of conservation programs and land use on ecosystem service provisioning in areas dominated by different wetland types. The High Plains region (HPR) primarily contains playa wetlands. The dominant land uses are native grassland, cropland, and conservation program land (Brinson and Eckels 2011). Predictive models were developed from CEAP–Wetlands data and can be used to estimate services by identifying playa wetlands and measuring variables required to populate the models (Duriancik et al. 2008). We developed a sampling manual that can be used to remotely classify playa function and ecosystem service provisioning. The manual and models can be used to monitor ecosystem services and inform USDA conservation decisions. Playas are depressional recharge wetlands that exist within western portions of Texas, Oklahoma, and Kansas, western and central Nebraska, the eastern plains of Colorado and New Mexico, and southeast Wyoming (Smith 2003) (figure 1). Playas exist within closed watersheds and receive water only through precipitation and overland flow (Bolen et al. 1989). Playa hydrology is similar among basins, allowing application of CEAP–Wetlands models to estimate service provisioning (Smith et al. 2015). Playas provide ecosystem services including habitat provisioning, floodwater storage, and carbon (C) sequestration (Smith et al. 2011). When cultivation occurs in …

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.

Classificação dos serviços ecossistêmicos prestados pelas áreas úmidas na zona estuarina do Rio Piancó-Piranhas-Açu (Nordeste, Brasil)

As áreas úmidas são ecossistemas importantes tanto para a sociedade como para a biodiversidade, apresentam-se em zonas de transição entre os ambientes terrestres e aquáticos, responsáveis por oferecer serviços para o bem estar humano e biológico, resultantes do seu capital natural. O presente trabalho teve como objetivo avaliar os serviços ecossistêmicos prestados pelas áreas úmidas no complexo estuarino do Rio Piancó-Piranhas-Açu (RN). Foi realizado o mapeamento e classificação das áreas úmidas, assim como a identificação dos serviços ecossistêmicos prestados na área de estudo. A partir desta perspectiva integrada da dinâmica entre o uso humano e seus reflexos nos processos ecológicos dos ecossistemas analisados (ex. manguezal, lagoas, lagunas, salinas solares, etc.), tem-se um melhor entendimento do desempenho destes processos, os quais propiciam a manutenção ou maior disponibilidade de serviços ecossistêmicos considerados chave para a gestão costeira.
 Palavras chaves: Capital Natural, Litoral Semiárido do Brasil, SIG, Classificação CICES.

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications.

The relative importance of wetland area versus habitat heterogeneity for promoting species richness and abundance of wetland birds in the Prairie Pothole Region, USA

AbstractRecent work has suggested that a tradeoff exists between habitat area and habitat heterogeneity, with a moderate amount of heterogeneity supporting greatest species richness. Support for this unimodal relationship has been mixed and has differed among habitats and taxa. We examined the relationship between habitat heterogeneity and species richness after accounting for habitat area in glacially formed wetlands in the Prairie Pothole Region in the United States at both local and landscape scales. We tested for area–habitat heterogeneity tradeoffs in wetland bird species richness, the richness of groups of similar species, and in species’ abundances. We then identified the habitat relationships for individual species and the relative importance of wetland area vs. habitat heterogeneity and other wetland characteristics. We found that habitat area was the primary driver of species richness and abundance. Additional variation in richness and abundance could be explained by habitat heterogeneity or other wetland and landscape characteristics. Overall avian species richness responded unimodally to habitat heterogeneity, suggesting an area–heterogeneity tradeoff. Group richness and abundance metrics showed either unimodal or linear relationships with habitat heterogeneity. Habitat heterogeneity indices at local and landscape scales were important for some, but not all, species and avian groups. Both abundance of individual species and species richness of most avian groups were higher on publicly owned wetlands than on privately owned wetlands, on restored wetlands than natural wetlands, and on permanent wetlands than on wetlands of other classes. However, we found that all wetlands examined, regardless of ownership, restoration status, and wetland class, supported wetland-obligate birds. Thus, protection of all wetland types contributes to species conservation. Our results support conventional wisdom that protection of large wetlands is a priority but also indicate that maintaining habitat heterogeneity will enhance biodiversity and support higher populations of individual species.

Interannual Variability of Global Wetlands in Response to El Niño Southern Oscillations (ENSO) and Land-Use

Wetlands are complex and diverse ecosystems, substantially contributing to natural capital. Projected variations in natural and anthropogenic scenarios are likely to alter wetland dynamics by substantially impacting the hydrological and ecosystem functions. This study focuses on monitoring the probable impacts of land use change and El Nino Southern Oscillation (ENSO) variations on wetlands dynamics by analysing the magnitude and extent of global wetland inundation trends during the study period. A microwave dataset was used to study the trends and interannual variability of surface inundation extent of global lakes, rivers, reservoirs and wetland classes. Between 1995 and 2015, the average rate of increase in surface inundation extent of global waterbodies and wetlands of 7029.6 km2 per year occurred with the average inundation of 2.9 million km2. Three periods of significant inter–annual variability 1998–2004, 2005–2009, and 2010–2015 have been observed during 21 years. The maximum contribution of 3.06 million km2 inundation has been recorded during the strong El Nino year of 2010. Swamps flooded forests and coastal wetlands has shown the most significant increase in surface inundation extent. Our results showed a positive lag correlation between Nino 3.4 SST anomalies and surface inundation of different GLWD classes. Moreover, lakes, rivers, reservoirs and wetlands have revealed varying responses to different anthropogenic drivers like cropland, natural vegetation and urban land. Cropland, natural vegetation and barren land buffered region has presented strong negative connection with coastal inundation extent. Whereas swamps demonstrated strongly positive correlation with urban-land and coastal with shrub/grassland land cover type. Through this study the extent of impact projected by climatic oscillations and anthropogenic drivers to water bodies and wetlands can be analysed for well–informed conflict management and decision–making practices for minimizing the human driven impact on natural water systems.

Development of Land-Use/Land-Cover Maps Using Landsat-8 and MODIS Data, and Their Integration for Hydro-Ecological Applications

The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen’s kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.