Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

US Atlantic and Gulf Coast Annual Wetland Land Cover and Change Maps, 1985 to 2022

<h3>This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392</h3> Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2. Updates to version 2 were as follows: <ul> <li>Includes watersheds in Texas that were not included in Version 1.</li> <li>A color map (using ArcPro) was added for improved interpretation.</li> <li>A sub-pixel scale offset in the change type map, related to map projection errors, was corrected.</li> </ul> <h2>Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories</h2> Courtney A. Di Vittorio1, Melita Wiles2, Yasin W. Rabby2, Saeed Movahedi2, Jacob Louie1, Lily Hezrony1, Esteban Coyoy Cifuentes1, Wes Hinchman1, Alex Schluter1 1Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA. 2Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA. <h3>Abstract</h3> The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.

opencc-by-4.0Mar 2024View details →

A map selection of wigeon stopover sites (core areas) based on wetland expert knowledge

Stopover areas (core areas only) along the migration route of wigeons tracked with GPS transmitters were selected when they exhibited forests on more than 50% of their total surface or had less than 50% cover by water and/or wetland on the ESA’s global land cover map. We created a sample of 5,630 regions of interest (3,403 for training and 2,227 for validation), delineated with polygons assigned to land classes listed in the Table 1. We used archives of Google Earth, ESRI, and BING satellites for the photointerpretation of the land classes as described in Table 1. The classification was performed with a Sentinel-2 MultiSpectral Instrument, Level-2A image collection in Google Earth Engine (GEE) through the R-package Rgee to create a batch process applying the GEE Random forest classifier to each selected core home range. The cloudless (maximum 3%) images were selected within the period from 01/06/2021 to 30/09/2021. The optimal number of trees was estimated at 100 for an out of bag error of 14%. The overall accuracy on the validation sample was 82 %.

opencc-by-4.0Jul 2021View details →

Indicative distribution map for Ecosystem Functional Group F2.9 Geothermal pools and wetlands

This archive contains indicative distribution maps and profiles for F2.9 Geothermal pools and wetlands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (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).

opencc-by-4.0Jul 2021View details →

Indicative distribution map for Ecosystem Functional Group F3.2 Constructed lacustrine wetlands

This archive contains indicative distribution maps and profiles for F3.2 Constructed lacustrine wetlands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (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).

opencc-by-4.0Jul 2021View details →

Indicative distribution map for Ecosystem Functional Group TF1.2 Subtropical/temperate forested wetlands

This archive contains indicative distribution maps and profiles for TF1.2 Subtropical/temperate forested wetlands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (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).

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Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)

<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. International Journal of Applied Earth Observation and Geoinformation 133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format. The first dataset (Dataset_1.gpkg) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total:  4200). Fields description: <ul> <li>"ID": unique identifier</li> <li>"CLASS": EUNIS first level habitat type, classified as following: <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"DATE": Date associated with EUNIS habitat sample</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"TYPE": Either training ("train") or validation ("test") sample</li> </ul> The second dataset (Dataset_2.gpkg) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland). Fields description: <ul> <li>"ID": unique identifier</li> <li>"LON": Point longitude in decimal degrees</li> <li>"LAT": Point latitude in decimal degrees</li> <li>"REFERENCE": Change class reference</li> <li>"CCDC": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"PCCD": Change class obtained from the Post-Classification Change Detection approach</li> </ul> Supplementary layout files (Dataset_1.qml and Dataset_2.qml) support formatting of the points in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># EUNIS habitat</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution.  Habitat maps are given for the two approaches in years 1984 and 2022: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_HABITAT_1984.tif and CCDC_HABITAT_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_HABITAT_1984.tif and PCCD_HABITAT_2022.tif)</li> </ul> Supplementary layout files (CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml) support formatting of raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># Change detection during the 1984-2022 period</h3> Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2. Change detection maps are given for the two approaches: <ul> <li>CCDC: Continuous Change Detection and Classification (CCDC_CHANGE_1984_2022.tif)</li> <li>PCCD: Traditional post-classification approach (PCCD_CHANGE_1984_2022.tif)</li> </ul> Supplementary layer files (CCDC_CHANGE_1984_2022.qml and PCCD_CHANGE_1984_2022.qml) support formatting of the raster layers in QGIS software. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- <h3># GEE repository</h3> To get direct access to GEE scripts and assets, please follow those two links: https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin

opencc-by-4.0Mar 2024View details →

zenodo40/100

Peatland maps and wetland GHG emission factors

The data set includes maps of degraded (~46 Mha globally) and intact peatland (~375 Mha globally) for the year 2015. The spatial resolution is 0.5 degree. The data set also includes IPCC wetland GHG emission factors for degraded and rewetted peatlands. This dataset has been published originally as supplementary data set in  Humpenöder, F., Karstens, K., Lotze-Campen, H., Leifeld, J., Menichetti, L., Barthelmes, A., and Popp, A. (2020). Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. 15, 104093. DOI <a href="https://10.1088/1748-9326/abae2a">10.1088/1748-9326/abae2a</a>.

opencc-by-4.0May 2022View details →

zenodo40/100

A 5 m wetland suitability map in mainland France

Wetland suitability map with continuous values from 0 (low suitability) to 100 (high suitability) covering mainland France at 5 m spatial resolution. Dataset includes: <ul> <li>280 GeoTIFF raster files (MH_prob_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;</li> <li>1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.</li> </ul> A complete description of the method used to produce this map can be found in the following article https://doi.org/10.1016/j.heliyon.2023.e13482

opencc-by-4.0Sep 2023View details →

zenodo36/100

Mapping Russian Wetlands and Estimating Methane Fluxes

<h3>Mapping Russian Wetlands and Estimating Methane Fluxes</h3> 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. <h3>Data Overview</h3> 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: <ul> <li> Total Wetland Area: 173.96 million hectares of mapped wetlands, capturing diverse ecosystems, including bogs, fens, and swampy areas. </li> <li> Open Water Area: Lakes, rivers, and smaller water bodies within wetland zones were separately mapped, totaling 42.6 million hectares. </li> </ul> <h3>Emission Modeling and Ecosite Analysis</h3> 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. <h3>Methane Emission Estimates</h3> 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: <ol> <li> Ecosystem Area Calculation: Area estimates for each ecosite type were derived from field-based proportions applied to the classified wetland map. </li> <li> Emission Period: Calculated for each grid cell based on Bio10 data, varying continuously across climatic zones. </li> <li> 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. </li> </ol> 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 <ul> <li> Total Annual Methane Emissions: 11.39 MtCH₄ per year from all mapped wetland areas. </li> <li> Open Water Contributions: 2.54 MtCH₄ per year from open water bodies, including intra-wetland lakes and rivers. </li> </ul> <h3>Data Highlights</h3> <ul> <li> High-resolution wetland classification covering 173.96 million hectares across diverse wetland ecosystems. </li> <li> Detailed methane emission data derived from multi-year field measurements and validated against climatic data, providing spatially continuous methane flux estimates across Russia. </li> <li> 50x50 km² grid cell calculations, accounting for methane emission rates, emission periods, and ecosystem proportions for each cell. </li> </ul> 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

opencc-by-4.0Oct 2024View details →

opencc-by-4.0Nov 2023View details →

Time-series global 30 m wetland maps from 2000 to 2022

Basic informationA novel global 30 m wetland annual dataset with fine classification system is generated, covering the period of 2000-2022 and containing 8 wetland subcategories (permanent water, swamp, marsh, flooded flats, saline, mangrove forest, salt marshes, and tidal flats). Notes:The GWL_FCS30 annual maps are divided into 961 5°×5° geographical tiles, and each tile contains 23 bands which denotes the tidal flat maps in 2000, 2001, 2002,..., 2021, 2022.Usage Policy:If you plan to use our data in a scientific analysis paper, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.Citations:Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, <a href="https://doi.org/10.5194/essd-15-265-2023">https://doi.org/10.5194/essd-15-265-2023</a>, 2023

Distribution. Discontinuous and limited to wetland environments in SE Sudan, perhaps the Rift Valley of Ethiopia, W Kenya surrounding Lake Victoria (two locations, very rare), Uganda, Burundi, Rwanda, NW Tanzania, and NE DR Congo. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments in SE Sudan, perhaps the Rift Valley of Ethiopia, W Kenya surrounding Lake Victoria (two locations, very rare), Uganda, Burundi, Rwanda, NW Tanzania, and NE DR Congo. Maps and distributional information here are provisional pending future research.

Distribution. Discontinuous and limited to wetland environments in the Congo Basin N and W of the range of the Zambezi Sitatunga in S Benin (Porto Novo), S Nigeria, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Republic of the Congo, N DR Congo; also several isolated populations in W Africa (Senegal, Gambia & Guinea-Bissau), NE Nigeria and W Chad, and perhaps extreme S Ghana. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments in the Congo Basin N and W of the range of the Zambezi Sitatunga in S Benin (Porto Novo), S Nigeria, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Republic of the Congo, N DR Congo; also several isolated populations in W Africa (Senegal, Gambia & Guinea-Bissau), NE Nigeria and W Chad, and perhaps extreme S Ghana. Maps and distributional information here are provisional pending future research.

Distribution. Discontinuous and limited to wetland environments in two areas of S Sudan: small swamps in SW Sudan near the DR Congo border, where the type specimen was collected, and the Sudd Swamps (Bahr-el-Ghazal) of the upper White Nile. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments in two areas of S Sudan: small swamps in SW Sudan near the DR Congo border, where the type specimen was collected, and the Sudd Swamps (Bahr-el-Ghazal) of the upper White Nile. Maps and distributional information here are provisional pending future research.

Distribution. Discontinuous and limited to wetland environments in the Congo Basin N and W of the range of the Zambezi Sitatunga in S Benin (Porto Novo), S Nigeria, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Republic of the Congo, N DR Congo; also several isolated populations in W Africa (Senegal, Gambia & Guinea-Bissau), NE Nigeria and W Chad, and perhaps extreme S Ghana. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments in SE Sudan, perhaps the Rift Valley of Ethiopia, W Kenya surrounding Lake Victoria (two locations, very rare), Uganda, Burundi, Rwanda, NW Tanzania, and NE DR Congo. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments in two areas of S Sudan: small swamps in SW Sudan near the DR Congo border, where the type specimen was collected, and the Sudd Swamps (Bahr-el-Ghazal) of the upper White Nile. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments from S Republic of the Congo through C DR Congo and SW Tanzania, S to Zambia, Angola, and Botswana. Maps and distributional information here are provisional pending future research. in Bovidae

Distribution. Discontinuous and limited to wetland environments from S Republic of the Congo through C DR Congo and SW Tanzania, S to Zambia, Angola, and Botswana. Maps and distributional information here are provisional pending future research.