Mapping Russian Wetlands and Estimating Methane Fluxes

Introduction

Wetlands are crucial in regulating the Earth’s climate, acting as both carbon sinks and significant methane sources. Russian wetlands represent one of the largest and most diverse wetland complexes globally, extending across biomes from Arctic tundra to boreal forests. Despite their importance, these wetlands remain underexplored, particularly in terms of their spatial distribution and greenhouse gas contributions. This dataset provides a detailed typological map of Russian wetlands and accompanying methane flux estimates, representing the most comprehensive methane emissions dataset for Russian wetlands to date. The maps and calculations were developed in Google Earth Engine (GEE) through a combination of multi-seasonal Landsat composites, PALSAR radar imagery, and extensive field-based validation data from peatland sites across Western Siberia.

Data Overview

Input Layers

The wetland mapping relied on seasonal Landsat composites (spring, summer, fall) and PALSAR radar data to capture the distinct structural and hydrological characteristics of each wetland type. Additional layers, such as GMTED topographic slope and Hansen’s TreeCover, were included to exclude non-wetland areas and to enhance the classification by distinguishing forested from non-forested wetlands.

Training Points

A comprehensive training site database was created, integrating field knowledge, high-resolution imagery, and georeferenced photos. Approximately 2,450 representative points were selected to capture 12 primary wetland types across Russia, with each point validated against high-resolution imagery to ensure accuracy. Points were collected to represent the wide-ranging wetland ecosystems in Russia, from open water and patterned bogs to swampy and forested fens, providing robust ground-truth data for training the classification model.

Random Forest Classifier

The random forest classifier was chosen for its capacity to handle large datasets and complex relationships among input layers. Optimized for Landsat and PALSAR inputs, the classifier used over 100 trees, each making independent predictions based on subsets of data, which were averaged to produce the final classification. This ensemble approach minimized overfitting, a crucial factor for the varied ecological regions across Russia.

Russian Wetlands Map

The final Russian Wetlands Map encompasses 12 wetland types, detailing their distribution and extent across the country:

• Total Wetland Area: 173.96 million hectares of mapped wetlands, capturing diverse ecosystems, including bogs, fens, and swampy areas.

• Open Water Area: Lakes, rivers, and smaller water bodies within wetland zones were separately mapped, totaling 42.6 million hectares.

Emission Modeling and Ecosite Analysis

Ecosite Proportions for Methane Emission Modeling

Each wetland type was further divided into ecosite units representing distinct, smaller areas with uniform hydrological and geochemical properties. This level of detail enabled precise methane emission estimates by capturing the variability within complex wetland ecosystems. For instance, ridges and hollows within patterned bogs exhibit unique methane emission dynamics due to differences in vegetation and water levels. Ecosite proportions for methane emission were calculated from 20-30 representative field sites per wetland type, capturing the typical area breakdown of each wetland type across Russia.

Methane Emission Period Calculation

To estimate seasonal methane emission periods across Russia’s climatic zones, the average summer temperature (Bio10) parameter from WorldClim data was used. Bio10 values reflect seasonal variation in emission potential, correlating with longer and warmer summers in southern regions versus shorter, cooler summers in the north. Using these data, an emission period was calculated for each 50 km x 50 km grid cell based on a regression model derived from Western Siberia data:
Emission Period (hours) = 303 * Bio10 – 675

This equation, which explained 98% of the variation in emission duration, provided a dynamic method for estimating emission periods across Russia’s diverse landscape.

Methane Emission Estimates

Calculation Approach

Methane emission estimates were derived from a multi-step approach that incorporated ecosystem-specific emission factors, ecosystem area, and the estimated emission period:

1. Ecosystem Area Calculation: Area estimates for each ecosite type were derived from field-based proportions applied to the classified wetland map.

2. Emission Period: Calculated for each grid cell based on Bio10 data, varying continuously across climatic zones.

3. Methane Flux Values: Based on quantiles from field measurements within three main zones (Tundra, Northern Taiga, and Southern Taiga) to account for natural variability in methane emissions.

Using this approach, methane emissions were calculated for each 50 km per 50 km grid cell, factoring in the unique emission characteristics of each wetland type and zone. This produced a spatially detailed estimate of methane fluxes, reflective of the temperature and vegetation gradients across Russia.

Resulting National Estimate

• Total Annual Methane Emissions: 11.39 MtCH₄ per year from all mapped wetland areas.

• Open Water Contributions: 2.54 MtCH₄ per year from open water bodies, including intra-wetland lakes and rivers.

Data Highlights

• High-resolution wetland classification covering 173.96 million hectares across diverse wetland ecosystems.

• Detailed methane emission data derived from multi-year field measurements and validated against climatic data, providing spatially continuous methane flux estimates across Russia.

• 50x50 km² grid cell calculations, accounting for methane emission rates, emission periods, and ecosystem proportions for each cell.

This dataset serves as an essential tool for environmental scientists, climate modelers, and conservationists, supporting further research into wetland carbon dynamics, climate mitigation strategies, and regional land-use planning. The high resolution data availbale at url: https://code.earthengine.google.com/d6a9d4045255fd84298777e56a38ae03

Compilation of data used to generate figures and tables in the manuscript. This dataset is associated with the following publication: Hernandez, C., L. Sharpe, C. Jackson, M. Harwell, and T. DeWitt. Connecting Stakeholder Priorities and Desired Environmental Attributes for Wetland Restoration Using Ecosystem Services and a Heat Map Analysis for Communications. Frontiers in Ecology and Evolution. Frontiers, Lausanne, SWITZERLAND, 12: 1290090, (2024).

Wetlands are lands where water saturation is the dominant factor. Wetlands occupy about 18% of Canada, and Canada has about 25% of the world’s wetlands. Wetlands foster the growth of hydrophytic vegetation and other biological activities such as the sustenance of large numbers of waterfowl, storage and release of large quantities of water, and the production of large amounts of energy in the form of peat. They offer food and shelter, slow down soil erosion, and contribute to the natural water purification process. Wetland conservation is important particularly in the human-dominated ecozones of southern Canada. The map shows the percentage of ecoregions covered by wetland.

This map of the terrestrial ecosystems of Myanmar was

developed using a supervised learning approach to classify earth observation data and other geospatial datasets into broad mappable units, which was then split into the ecosystem types defined by the IUCN Red List of Ecosystems of Myanmar project.The shapefile presents the hierarchical ecosystem typology for Myanmar, including attributes that indicate ecosystems grouped at the Biome (11 classes), Functional Ecotype (21 classes) and Ecosystem Type (64 classes) level of the Myanmar ecosystem typology.

For further information refer to the users guide, and the report:

Murray, N.J., Keith, D.A., Tizard, R., Duncan, A., Htut, W.T., Hlaing, N., Oo, A.H., Ya, K.Z., Grantham, H. (2020) Threatened Ecosystems of Myanmar. An IUCN Red List of Ecosystems Assessment. Version 1.0. Wildlife Conservation Society. ISBN: 978-0-9903852-5-7.

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

The National Wetland Map version 5 (NWM5) shows the distribution of inland wetland ecosystem types across South Africa and includes estuaries and the extent of some rivers. A confidence map was compiled to identify areas where wetland extent and hydrogeomorphic (HGM) units (which contributed to defining the inland wetland ecosystem types together with the regional setting) attained at a higher level of certainty compared to other areas. Higher levels of certainty are associated [code 5 in field Confidence_nr] with areas that have been visited in-field by a wetland specialist(s) over multiple seasons and cycles of the wetland hydroperiod, and are therefore more accurately represented in the dataset. Codes 4 to 1 indicate lower levels of confidence that the extent and HGM unit are represented well. If the Estuaries are used, please cite Van Niekerk et al., 2019. Technical Report of the Estuarine Ecosystems for the NBA 2018. For queries on the National Wetland Map 5 and associated Confidence Map datasets please contact the Principal Investigator [HvDeventer@csir.co.za] and cc the Freshwater@sanbi.org.za. For contributions and queries regarding future revisions of the National Wetland Map please contact Freshwater@sanbi.org.za. Updates will be incorporated into the National Wetland Map 6 which is under way.

Framing ecological restoration and monitoring goals from a human benefits perspective (i.e., ecosystem services) can help inform restoration planners, surrounding communities, and relevant stakeholders about the direct benefits they may obtain from a specific restoration project. We used a case study of tidal wetland restoration in the Tillamook River watershed in Oregon, USA, to demonstrate how to identify and integrate community stakeholders/beneficiaries and the environmental attributes they use to inform the design of and enhance environmental benefits from ecological restoration. Using the U.S. Environmental Protection Agency’s Final Ecosystem Goods and Services (FEGS) Scoping Tool, we quantify the types of ecosystem services of greatest common value to stakeholders/beneficiaries that lead to desired benefits that contribute to their well-being in the context of planned uses that can be incorporated into the restoration project. This case study identified priority stakeholders, beneficiaries, and environmental attributes of interest to inform restoration goal selection. This novel decision context application of the FEGS Scoping Tool also included an effort focused on how to communicate the connections between stakeholders, and the environmental attributes of greatest interest to them using heat maps.

This map displays wetland habitat predictions in 2080 under 104 cm (3.4 ft) when the underlying digital elevation map is not corrected for elevation bias due to dense vegetation. These data are used in the project described below. Tidal marshes support coastal food webs, improve water quality, and buffer against storm and wave damage. Sea level influences the structure and function of coastal marshes in ways that alter the services they provide. This project enhanced a tidal marsh model with new field data to better understand the impacts of sea-level rise on marshes in the San Francisco Bay-Delta Estuary, allowing coastal managers to evaluate wetland vulnerability and inform restoration. Results of this project provide managers in California and other regions with improved management tools, such as vegetation-corrected, digital elevation models (DEMs), and habitat predictions under sea-level rise.This application (https://storymaps.arcgis.com/stories/768622e923024ef19a211b5073af0e2b) highlights the outcomes and data products associated with our project, including the following:Improved estuary-wide data on vegetation, productivity, and decomposition responses of tidal marsh plant species under various elevation and salinity gradients.Refined marsh elevations using remotely sensed and on-the-ground GPS data, resulting in a high-resolution estuary-wide digital elevation model.Documented sediment deposition rates based on plant species composition, season, storms, and tidal elevation, improving parameters required for sea-level rise models.Projections of future habitat distributions from WARMER (Wetland Accretion Rate Model of Ecosystem Resilience) using the updated biological and physical processes parameters to assess marsh accretion.This project was led by Oregon State University and the U.S. Geological Survey and was funded through NOAA’s Effects of Sea Level Rise program. Additional details on the project and links to publications associated with this project are available here: https://www.usgs.gov/centers/werc/science/coastal-ecosystem-response-sea-level-rise?qt-science_center_objects=0#qt-science_center_objectshttps://coastalscience.noaa.gov/project/ecosystem-model-inputs-sea-level-rise-vulnerability-san-francisco-bay-estuary/

This archive contains indicative distribution maps and profiles for F2.9 Geothermal pools and wetlands, a ecosystem functional group (EFG, level 3) of the IUCN Global Ecosystem Typology (v2.0). Please refer to Keith et al. (2020) for details.

The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Framing ecological restoration and monitoring goals from a human benefits perspective (i.e., ecosystem services) can help inform restoration planners, surrounding communities, and relevant stakeholders about the direct benefits they may obtain from a specific restoration project. We used a case study of tidal wetland restoration in the Tillamook River watershed in Oregon, USA, to demonstrate how to identify and integrate community stakeholders/beneficiaries and the environmental attributes they use to inform the design of and enhance environmental benefits from ecological restoration. Using the U.S. Environmental Protection Agency’s Final Ecosystem Goods and Services (FEGS) Scoping Tool, we quantify the types of ecosystem services of greatest common value to stakeholders/beneficiaries that lead to desired benefits that contribute to their well-being in the context of planned uses that can be incorporated into the restoration project. This case study identified priority stakeholders, beneficiaries, and environmental attributes of interest to inform restoration goal selection. This novel decision context application of the FEGS Scoping Tool also included an effort focused on how to communicate the connections between stakeholders, and the environmental attributes of greatest interest to them using heat maps.

This archive contains indicative distribution maps and profiles for F3.2 Constructed lacustrine wetlands, a ecosystem functional group (EFG, level 3) of the IUCN Global Ecosystem Typology (v2.0). Please refer to Keith et al. (2020) for details.

The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

This archive contains indicative distribution maps and profiles for TF1.2 Subtropical/temperate forested wetlands, a ecosystem functional group (EFG, level 3) of the IUCN Global Ecosystem Typology (v2.0). Please refer to Keith et al. (2020) for details.

The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Le but de ce travail était de définir et de cartographier un ensemble de classes écohydrologiques répétitives sur les îles Calvert et Hécate à l'aide de données de télédétection et d'une technique de classification non supervisée. La carte qui en résulte fournit un nouvel outil pour caractériser l'étendue et les propriétés internes des différentes classes d'écosystèmes, pour stratifier les plans d'étude futurs et pour évaluer l'influence des caractéristiques du paysage terrestre sur les processus des bassins versants.

« Traditionnellement, l'inventaire forestier et la cartographie des écosystèmes à l'échelle locale et régionale reposent sur l'interprétation manuelle de photographies aériennes, sur la base de schémas de classification normalisés et pilotés par des experts. Ces approches actuelles fournissent les informations nécessaires à la gestion des écosystèmes forestiers mais limitent la résolution thématique et spatiale de la cartographie et sont rarement répétées. L'objectif de cette recherche était de démontrer l'utilité d'une technique quantitative non supervisée basée sur des données LiDAR (Light Detection And Ranging) et des images satellitaires multispectrales pour cartographier les écosystèmes à l'échelle locale sur un paysage hétérogène d'écosystèmes forestiers et non forestiers. Nous avons dérivé une gamme de mesures caractérisant le terrain et la végétation locaux à partir d'images LiDAR et RapidEye pour les îles Calvert et Hécate, en Colombie-Britannique. Ces paramètres ont été utilisés dans une analyse de grappes pour classer et caractériser quantitativement les unités écologiques de l'île. Au total, 18 grappes ont été dérivées. Les grappes ont été attribuées avec des statistiques sommaires quantitatives à partir des entrées de données de télédétection et contextualisées par comparaison avec des unités écologiques délimitées dans une méthode de cartographie traditionnelle dirigée par des experts à l'aide de photographies aériennes. Les 18 groupes décrivent des écosystèmes allant des zones arbustives ouvertes aux forêts denses et productives et comprennent une zone riveraine et de nombreux écosystèmes plus humides et humides. Les grappes fournissent des informations détaillées et spatialement explicites pour caractériser le paysage en tant que mosaïque d'unités définies par la topographie et la structure de la végétation. Cette étude démontre que l'utilisation de divers types de données de télédétection dans une classification quantitative peut fournir aux scientifiques et aux gestionnaires des informations multivariées uniques à celles qui résultent des méthodes traditionnelles de cartographie des écosystèmes basées sur des experts. » - Résumé de Thompson et al. 2016.

Une explication complète des méthodes est disponible dans Thompson et al. 2016. Régionalisation basée sur les données de l'écosystème forestier et non forestier de la côte de la Colombie-Britannique à l'aide d'images LiDAR et RapidEye. Le manuscrit est disponible ici : Thompson et al. 2016

Un petit nombre de vides de données dans la couverture LiDAR de 2012 étaient présents et ont été exclus de l'analyse. Bien que les vides aient depuis été comblés par de nouvelles données LiDAR acquises en 2014, les nouvelles données n'ont pas été incluses dans l'analyse de Thompson et al. D'autres « lacunes » dans la couverture spatiale de la carte finale sont le résultat de l'exclusion des zones non végétalisées (conformément à l'indice de végétation par différence normalisée (NDVI) et à l'Atlas provincial des eaux douces (FWA) : http://geobc.gov.bc.ca/base-mapping/atlas/fwa/index.html). Outre les petits plans d'eau, ces zones non végétalisées comprennent quelques petites zones à haute altitude qui étaient recouvertes de neige au moment de l'acquisition de l'image RapidEye.

DOI : http://dx.doi.org/10.21966/1.135248

This dataset (WETLAND_BROAD_POLY) is a composite of several individual wetland mapping projects, collectively referred to as “Wetlands - Broad Scale". The methodology used, map accuracy, and credits vary by project; these project details are outlined in Table 5 (see below). The spatial extent of individual projects are delineated in a separate feature class entitled, "WETLAND_BROAD_EXTENTS_POLY". The "Project Name" attribute field, common to both feature classes, denotes the specific project each feature is associated with. This dataset is intended to be used as a broad scale planning and management tool to identify potential distribution and abundance of wetlands. Wetlands were mapped to wetland class (shallow water, marsh, swamp, fen, and bog), following the Canadian Wetland Classification System using a predictive model. This dataset is intended to support land management and regional land use planning processes. Local scale (10k) manual wetland mapping, and additional physical assessments (i.e. ground inspections) may be required to undertake habitat enhancement, environmental assessment, reclamation planning, or environmental mitigation over small to moderate areas.Map development:The "Wetlands - Broad Scale" dataset was developed using a random forest machine learning model to predict wetland classes. Various satellite imagery sources and landscape variables derived from a digital elevation model (DEM) were used as primary inputs to predict wetlands. The source dataset has a resolution of 10 x 10 m. Training and validation data are a mix of ground plots (site visit and ecosystem plots), aerial survey plots, and interpreted polygons. Each predictive wetland map within the composite has met the minimum criteria of a map accuracy greater than or equal to 70 % and a Kappa coefficient greater than 0.60. The map (or producer's) accuracy measures the percentage of wetland features that are correctly classified to one of the five wetland classes. The Kappa coefficient statistic is used to measure the extent to which the model has correctly predicted, given the set of validation data. A value of 0 indicates predicted values are entirely random. A value of 1 indicates a perfect model. As a general rule, Kappa coefficients less than 0.60 indicate a poorly performing model, values of 0.61 to 0.80 indicate substantial agreement between predicted and validation data, and values of 0.81 to 1.00 indicate almost perfect agreement.The size of the smallest wetland that can be reliably mapped, the Target Mapping Unit (TMU), was not established for this dataset at the time of publication. Wetland classes smaller than a TMU of 2.0 hectares in this dataset should be used with caution. Wetlands below the TMU have a higher potential to be associated with classification error. The reported map accuracy is adequate for the intended purpose, and assumes that training data has adequately captured variation in landscape and vegetation structure between and within wetland classes. Feature attributes:Each polygon feature is associated with a combination of feature attributes grouped by value number, as shown in Table 1: ecological realm, wetland group, and wetland class. Table 1. Feature attributes grouped by value numberValueEcological RealmWetland GroupWetland Class0Other1FreshwaterWater (non-wetland)2WetlandMineralShallow Water3WetlandMineralMarsh4WetlandMineralSwamp5WetlandPeatlandFen6WetlandPeatlandBogThe ecological realm is a broadly defined ecosystem with common water source and character, described in Table 2.Table 2. Ecological realm breakdown into broad ecosystem categoriesEcological RealmDescriptionFreshwaterInland aquatic ecosystemsWetlandEcosystems dominated by plants adapted to saturated soils and periodically or permanently anaerobic soil conditionsThe wetland group is defined by the accumulation (or lack) of organic matter or peat, as described in Table 3. Table 3. Wetland groupsWetland GroupDescriptionPeatlandOrganic wetland classes that have more than 40 cm of organic matter (peat) accumulation (Warner and Rubec 1997) on which Organic soils or Organic Cryosols develop. Organic wetlands are characterized by poorly to moderately decomposed peat, mostly comprised of peat mosses, brown mosses, and/or sedges, but can also include woody remains of shrubs, or other plants. Mapped fen and bog wetland classes may have as little as 30 cm of peat accumulation, in the Yukon Wetland Classification System to be recognized as a peatland.MineralMineral wetlands occur in areas where an excess of water collects on the surface or within the rooting zone of plants for a significant portion of the growing season and which, for geomorphic, hydrologic, biotic, edaphic (factors related to soil), or climatic reasons, accumulate little to no organic matter or peat (typically less than 40 cm). Gleysol or Gleysolic Cryosol soils, or peaty phases of these soils, are characteristic of these wetlands (Warner and Rubec 1997). Swamps may have more than 30 cm of peat accumulation; however mineral swamps and peatland swamps are not distinguished from each other at the group level in this map.Table 4 describes five wetland classes and one non-wetland class that apply to this dataset. The five wetland classes that are recognized based on broadly similar site conditions along dominant environmental gradients as reflected in physiognomy (the life form, structure, and stature of vegetation) and species with similar adaptations. Non-wetland water systems are also mapped at this level.Table 4. Wetland class breakdown.Wetland ClassDescriptionShallow WaterShallow water wetlands have standing or flowing water above the surface and less than 2 m deep in mid-summer. Vegetation is dominated by submerged or floating aquatic plants, algae, and aquatic mosses.MarshMarshes are mineral wetlands characterized by shallow surface water, which fluctuates dynamically daily, seasonally, or annually. The water table may be below, at, or above the ground surface at a given time. They are dominated by aquatic macrophytes largely rushes, reeds, grasses, sedges, and sometimes herbs.SwampA swamp is a treed or tall or medium shrub dominated wetland that is influenced by minerotrophic groundwater. A swamp occurs on either mineral or organic soils.FenFens are nutrient medium peatlands where minerotrophic groundwater is within the rooting zone. Stands can be treed, shrubby, or sedge dominated. Brown mosses usually dominate the moss layer. BogBogs are nutrient poor peatlands where the rooting zone occurs above the mineral-enriched groundwater. Stands can be treed, shrubby, or moss dominated, where the moss layer is comprised mostly of peat moss.Water (non-wetland)This is a land cover class in the freshwater realm. It represents lacustrine (lake) and riverine (moving water) systems that are not wetlands. Table 5: Unique project details for each mapping area within the "Wetlands - Broad Scale" dataset.Project NameInformationDescriptionBeaver RiverLast UpdateOctober 2019Project AreaThe Beaver River Watershed wetland map is located in east central Yukon and has a total area of 6,146 km2. The wetland map consists of the Beaver River watershed, including the Rackla and East Rackla rivers, and a portion of the Keno Ladue watershed.MethodsSentinel-1, Sentinel-2 and landscape variables derived from the ArcticDEM, version 3, were used as primary inputs to predict wetlands. Sentinel imagery was from 2018. Training and validation data was comprised of 250 ground plots (site visit and ecosystem plots), 264 aerial survey plots, and 1,621 interpreted polygons. Polygons were interpreted from a combination of SPOT-6, Pleiades-1, ESRI World Imagery, and Sentinel-2 imagery. Training polygons reflected the extent and variability of nine land cover classes within the planning area and are in proportion to the aerial extent of land cover class (including wetland classes). Interpreted polygons were used to train the model. The model was validated using point location of aerial field calls and ground plots. The ratio of training to validation data was 3:1. In this dataset, the smallest mapped wetland, or minimum mapping unit (MMU), is 2 pixels or 200 square metres. The pixel resolution of the wetland map is 10 m, however all single pixels were merged into to their neighbouring pixel value.AccuracyThe final classification map accuracy was 81 % with a Kappa of 0.77 across all wetland and land cover classes. Isolating specifically the wetland classes, the map accuracy was 78 % (Kappa 0.69). The resulting map accuracy meets the project goal of greater than 70 % accuracy for a predictive map produced at a survey level intensity 4 to 5 as per the ELC guidelines for mapping. CreditsPreliminary wetland classification and final predictive map was completed by the Ecosystem and Landscape Classification (ELC) Program, Fish and Wildlife (F&W) Branch, Department of Environment, Government of Yukon, the Government of Yukon with input from Palmer Environmental Consulting Group, Drosera Ecological Consulting, and CryoGeographic Consulting. Training and assessment data was collected by Drosera Ecological Consulting, Lori Schroeder Consulting, CryoGeographic Consulting, and F&W staff. Classification of wetlands was completed by CryoGeographic Consulting with input from Drosera Ecological Consulting and ELC program staff. PeelLast UpdateMarch 2022Project AreaThe Peel Watershed wetland map is located in northern Yukon and has a total area of 67,366 km2. The watershed is drained by six major tributaries—the Snake, Wind, Bonnet Plume, Hart, Ogilvie, and Blackstone. MethodsSentinel-1, Sentinel-2, ALOS PALSAR (HH and HV polarizations), and landscape variables derived from the ArcticDEM, version 3, were used as primary inputs to predict wetlands. Sentinel imagery was from 2018. Segmented objects were used to assign a wetland class* and can be considered the minimum map unit (MMU) (as opposed to a single pixel). Segments were

This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production. The maps include current dominant wetland type, restoration category, potential removal of atmospheric greenhouse gases in units of mass carbon dioxide with estimates of uncertainty.

These are future tidal marsh model results from 2080 under 104 cm (3.4 ft) of sea level rise created using the WARMER model (Wetland Accretion Rate Model of Ecosystem Resilience). These data support the project and products described below.Tidal marshes support coastal food webs, improve water quality, and buffer against storm and wave damage. Sea level influences the structure and function of coastal marshes in ways that alter the services they provide. This project enhanced a tidal marsh model with new field data to better understand the impacts of sea-level rise on marshes in the San Francisco Bay-Delta Estuary, allowing coastal managers to evaluate wetland vulnerability and inform restoration. Results of this project provide managers in California and other regions with improved management tools, such as vegetation-corrected, digital elevation models (DEMs), and habitat predictions under sea-level rise.This application (https://storymaps.arcgis.com/stories/768622e923024ef19a211b5073af0e2b) highlights the outcomes and data products associated with our project, including the following:Improved estuary-wide data on vegetation, productivity, and decomposition responses of tidal marsh plant species under various elevation and salinity gradients.Refined marsh elevations using remotely sensed and on-the-ground GPS data, resulting in a high-resolution estuary-wide digital elevation model.Documented sediment deposition rates based on plant species composition, season, storms, and tidal elevation, improving parameters required for sea-level rise models.Projections of future habitat distributions from WARMER (Wetland Accretion Rate Model of Ecosystem Resilience) using the updated biological and physical processes parameters to assess marsh accretion.This project was led by Oregon State University and the U.S. Geological Survey and was funded through NOAA’s Effects of Sea Level Rise program. Additional details on the project and links to publications associated with this project are available here: https://www.usgs.gov/centers/werc/science/coastal-ecosystem-response-sea-level-rise?qt-science_center_objects=0#qt-science_center_objectshttps://coastalscience.noaa.gov/project/ecosystem-model-inputs-sea-level-rise-vulnerability-san-francisco-bay-estuary/

Coastwide vegetation surveys have been conducted multiple times over the past 50 years (e.g., Chabreck and Linscombe 1968, 1978, 1988, 1997, 2001, and 2013) by the Louisiana Department of Wildlife and Fisheries (LDWF) in support of coastal management activities. The last survey was conducted in 2013 and was funded by the Louisiana Coastal Protection and Restoration Authority (CPRA) and the U.S. Geological Survey (USGS) as a part of the Coastal Wetlands Planning, Protection, and Restoration Act (CWPPRA) monitoring program. These surveys provide important data that have been utilized by federal, state, and local resource managers. The surveys provide information on the condition of Louisiana’s coastal marshes by mapping plant species composition and vegetation change through time. During the summer of 2021, the U.S. Geological Survey, Louisiana State University, and the Louisiana Department of Wildlife and Fisheries jointly completed a helicopter survey to collect data on 2021 vegetation types using the same field methodology at previously sampled data points. Plant species were identified and their abundance classified at each point. Based on species composition and abundance, each marsh sampling station was assigned a marsh type: fresh, intermediate, brackish, or saline marsh. The field point data were interpolated to classify marsh vegetation into polygons and map the distribution of vegetation types.
We then used the 2021 polygons with additional remote sensing data to create the final raster dataset. We used the polygon marsh type zones (available in this data release), as well as National Land Cover Database (NLCD; https://www.usgs.gov/centers/eros/science/national-land-cover-database) and NOAA Coastal Change Analysis Program (CCAP; https://coast.noaa.gov/digitalcoast/data/ccapregional.html) datasets to create a composite raster dataset. The composite raster was created to provide more detail, particularly with regard to “Other”, “Swamp”, and “Water” categories, than is available in the polygon dataset. The overall boundary of the raster product was extended beyond past surveys to better inform swamp, water, and other boundaries across the coast. A majority of NLCD and CCAP classification during a 2010-2019 period was used, rather than creating a raster classification specific to 2021, as there was a desire to use published datasets. Users are cautioned that the raster dataset is generalized but more specific than the polygon dataset.
This data release includes 3 datasets: the point field data collected by the helicopter survey team, the polygon data developed from the point data, and the raster data developed from the polygon data plus additional remote sensing data as described above.

This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.

This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data. All 30 x 30-meter C-CAP pixels were extracted that are coded as estuarine emergent, scrub/shrub, or forested in either 2006 or 2010. A soil database for model fitting and validation was compiled from 49 different studies with spatially explicit empirical depth profile data and associated metadata, totaling 1,959 soil cores from 18 of the 22 coastal states. Reported estimates of carbon stocks were derived with modeling approaches that included (1) applying a single average carbon stock value from the compiled soil core data, (2) applying models fit using the empirical data and applied spatially using soil, vegetation and salinity maps, (3) relying on independently generated soil carbon maps from The United States Department of Agriculture (USDA)'s Soil Survey Geographic Database (SSURGO), and the NWI that intersected with mapped tidal wetlands, and (4) using a version of SSURGO bias-corrected for bulk density. Comparisons of uncertainty, precision, and accuracy among these four approaches are also provided.

1) Geographic distribution map of water bodies and marsh wetlands in the Tibetan Plateau region, with map codes indicating: 1 for water bodies, 2 for marshes; 2) Data sources: Multiple temporal Landsat TM, ETM+, OLI, SRTM DEM, etc., processed using machine learning techniques; 3) Data quality: 30-meter spatial resolution; 4) Data applications: Revealing the distribution, boundaries, and spatiotemporal dynamics of high-altitude wetland ecosystems in the Tibetan Plateau, providing a geographic reference for field surveys and experiments on wetland ecosystems, and facilitating the management, development, and protection of high-altitude wetlands.