World Water Bodies provides a detailed basemap layer for the lakes, seas, oceans, large rivers, and dry salt flats of the world.

World Water Bodies represents the open water rivers, lakes, dry salt flats, seas, and oceans of the world.For complete hydrographic coverage, use this dataset in conjunction with the World Linear Water dataset.

Percent of each freshwater ecoregion’s area covered with lakes and man-made reservoirs.

We calculated the percentage of the ecoregion that is covered by lakes and reservoirs using lake and reservoir polygons from the Global Lakes and Wetlands Database (GLWD) (Lehner and Döll 2004). This database represents the best available source for lakes and wetlands on a global scale (1:1 to 1:3 million resolution). The GLWD contains shoreline polygons of the 3,067 largest lakes (surface area greater than or equal to 50 km2) and 654 largest reservoirs (storage capacity greater than or equal to 0.5 km3) worldwide, as well as shoreline polygons of approximately 250,000 smaller lakes, reservoirs, and rivers (surface area greater than or equal to 0.1 km2). For our calculations, only lake and reservoir polygons were used. It was not possible to separate natural lake polygons from reservoirs.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Lehner, B., and P. Döll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 1–22.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

World Lakes represents the major lakes and inland seas of the world.

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Lake Eyre Basin (LEB) is an internally-draining basin that takes up almost one sixth of Australia's land mass in the arid and semi-arid interior. It is unique in being one of the only unregulated dryland river systems in the world and having the most variable flows in the world (Puckridge et al. 1998). The first assessment of the health of LEB rivers found them to be in near-natural condition (LEBSAP 2008). The LEB contains wetlands of national and international importance for supporting Australia's waterbird populations (Reid et al. 2010) and nationally threatened and endemic species are found in the SA LEB (Morton et al. 2010). The ecology is driven by the flow regime and cycles from 'boom' periods following large floods through to 'bust' periods with little to no flow (Bunn et al. 2006).

Description

The aims of the LEBRM aquatic ecosystem mapping and classification (AEMC) project were to provide up-to-date mapping and classification of aquatic ecosystems in the SA LEB.

The specific project objectives were to:

* Improve the spatial mapping of aquatic ecosystems

* Build on the work undertaken for the WAD project (Denny & Berens 2014)

* Align with the Interim Australian National Aquatic Ecosystems (ANAE) classification framework (AETG 2012a)

* Identify where aquatic ecosystems are dependent on groundwater (both subsurface and surface expression).

* Consistency in describing aquatic ecosystems

* Grouping aquatic ecosystems with common attribute values into types.

Applications of the classification of aquatic ecosystems include:

* Linking different types to conceptual and other models of ecosystem function (e.g. Imgraben and McNeil 2013)

* Understanding the drivers of aquatic ecosystems to enable assessments of vulnerability and risk

* Inform identification and description of High Ecological Value Aquatic Ecosystems (AETG 2012b)

* Identification of priority areas for data collection by including confidence rankings and 'unknown' categories for each attribute

* Mapping of specific attributes of aquatic ecosystems (e.g. salinity, persistence)

Purpose

The overarching goal of the LEBRM project was to collate a baseline of scientific knowledge around the hydrology and ecology of aquatic ecosystems in the LEB, thus providing an advanced and up-to-date knowledge platform that can support the detailed modelling, impact and risk analysis needs of LEB bioregional assessments. The LEBRM project background, purpose, approach and links to the bioregional assessment are described in more detail in DEWNR (2014). For more information about LEBRM and other water knowledge projects see DEWNR (2014).

Dataset History

Attribution was firstly undertaken at the whole of study-region scale where national, statewide or regional data (including the WAD) were available. For the priority study catchments and major large lakes, local-scale attribution was undertaken to improve the first level attribution.

Whilst the LEBRM AEMC represents a considerable advancement in mapping and classifying aquatic ecosystems in the study region, particularly the priority catchments, significant knowledge gaps still exist. Over a third of all polygons had insufficient data to enable them to be assigned to the broad types identified for the LEBRM project (see Section 2.3.1) and low confidence values applied to many hydrological attributes for the majority of sites. Within the priority catchments, the mapping of floodplains is considered preliminary. Outside of the priority catchments, the lack of differentiation of floodplains from lacustrine system types limited the automated classification of some other attributes, particularly hydrological connectivity. Further, the LEBRM AEMC focused on classifying the priority western catchments; however there is significantly more information available in reports and project datasets that could be used to classify the aquatic ecosystems of the major eastern catchments, the Georgina-Diamantina and Cooper.

Two related issues that could not be resolved through the LEBRM AEMC were the occurrence of overlapping polygons and multiple polygons representing a single aquatic ecosystem. These issues arise from the different methods employed to map aquatic ecosystems in the data sources accessed by the WAD, the WAD importing multiple datasets for the same features, and the LEBRM AEMC adopting a new source of floodplain mapping which overlapped with some prior floodplain mapping.

Data Capture Method Existing data consolidation ; 'Heads-up' digitising, Remote sensing unsupervised classifications ;

Data Capture Scale 1:50 000 ; 1:100 000 ;

Completeness Complete. The spatial data is subject to amendment as and when more data become available.

Positional Accuracy The accuracy is to the scale of mapping (50 metres at 1:50,000 and 300 metres at 1:100,000 mapping scale). This spatial data is to be used at the regional, subregional and catchment level.

As the LEB project area covered a, extensive area, and as existing data was taken from a variety of existing projects they inevitability have a variety of capture scales and accuracies. Accuracies should be considered relative.

Attribute Accuracy 70%

Consistency ESRI ARC/INFO GIS software was used to do topological consistency checks to detect flaws in the spatial data structure, this check ensured that all classified polygons are closed, nodes are formed at the intersection of lines and that there is only one label in each polygon.

Dataset Citation

"SA Department of Environment, Water and Natural Resources" (2014) Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification. Bioregional Assessment Source Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/9be10819-0e71-4d8d-aae5-f179012b6906.

This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10? N to 25? S, 30? to 85? W). The data are in ASCII GRID format.

The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values.

More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf.

LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

We produced annual big lake maps contains lakes larger than 10 km2 on the TP from 1991 to 2018, by using the Landsat satellite imagery and cloud computing platform (Google Earth Engine).

This archive contains indicative distribution maps and profiles for F2.1 Large permanent freshwater lakes, 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).

The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature. Over ten years, the European Space Agency operated the largest Earth observation satellite built to date, ENVISAT. Its instruments provided optical and thermal observations, and allowed for retrieval accuracies not previously achieved by spaceborne remote sensing. Using these observations, we created a comprehensive database consisting of water quality parameters for more than 300 lakes in the whole World. The Diversity II inland water database is designed to meet the requirements of the aquatic biodiversity community, but represents beyond this purpose the first globally consistent and reproducible knowledge basis of its kind.We processed various water quality parameters and adopted lake surface water temperature from the ESA ARC Lake database. Monthly, yearly and a 9-year aggregated geophysical maps are provided. The potential of these products is demonstrated in the context of several case studies, wherein local experts assess the information content against the background of issues such as eutrophication and hypoxia, floating vegetation proliferation and the occurrence of cyanobacteria blooms. Qualitative links are established between those phenomena and biodiversity trends.File format is geotif. Minimal granularity is one geotif per month in zip archives per year. All 11 zip archives (2002-2012) of one lake are combined with the lake's annual and 9-year averaged zip archives in one *.7z archive. The table lists metadata and basic data of the 343 lakes with links to the *.7z archives. File size is between 100 kB and 20 GB.Legend for GLWD Use1, 2 and 3: c flood control, f fish breeding, h hydropower, i irrigation, n navigation, r recreation, s water supply, x others Supplement to: Odermatt, Daniel; Danne, Olaf; Philipson, Petra; Brockmann, Carsten (2018): Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes. Earth System Science Data, 10(3), 1527-1549

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

LTER - Long-Term Ecological Research Program/North Temperate Lakes (NTL)

LTER/NTL012 [Summary adapted from the LTER Core Data Set Catalog]:

A network of 11 monitoring wells is used to characterize regional groundwater chemistry in the Trout Lake area. A concentrated network of 30 wells along a groundwater flow path between two of the lakes permitted a better resolution of groundwater flow paths, velocities, and chemistry than is found in most studies. Chemical parameters measured include dissolved oxygen, pH, total alkalinity, calcium, magnesium, sodium, potassium, chloride, sulphate, iron, and dissolved reactive silica. An associated data set (Groundwater Flow in the Area Surrounding the North Temperate Lakes Primary Study Lakes, NTL011) contains monthly well water level measurements. Chemical data are available at a quarterly sampling frequency for some years. Analysis of the major chemical elements shows that some groundwater exhibits substantial seasonal variance. Chemical data are incorporated into a computerized chemical reaction model (PHREEQE) to evaluate minerals that control water composition, and critical solid/liquid reactions that occur along flow paths. Groundwater discharge into lakes can have a significant effect on lake chemistry.

Special Comments: Digitization of data is in progress. These data are tied with data set NTL011 on well water levels.

The North Temperate Lakes (NTL) site is affiliated with the Center for Limnology at the University of Wisconsin, Madison. The NTL site focuses its research on north temperate lake ecosystems in the Northern Highland Lakes District of Wisconsin. The Northern Highland Lakes District, has an area of approximately 10,000 sq. km. and has one of the highest concentrations of lakes in the world. Lakes range in size from 0.1 to 1,500 ha, in depth from 1 to 33 m, and in fertility from oligotrophic to eutrophic. Other representative limnological conditions include: rainwater dominated, groundwater dominated and drainage lakes; dystrophic lakes; lakes with varved sediments, winterkill lakes, temporary and permanent forest ponds, and reservoirs. Lakes are influenced by strong seasonality and are usually ice covered from late November to late April. Lakes within the Northern Highland exhibit near-natural water quality conditions. Nearly 80% of the land area and two-thirds of the lake frontage are protected.

Information about LTER is also available at 'http://lternet.edu/'

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Urmia Lake, the second largest hyper-saline lake in the world, has experienced a significant drop in water level during the last decade. This study was designed to examine the water quality of Urmia Lake and to characterize the spatial heterogeneity and temporal changes of the physico-chemical parameters between October 2009 and July 2010. Two spatial interpolation methods, Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), were used and compared with each other to derive the spatial distribution of ionic constituents as well as TDS and density along the lake. Results showed that the main dominant cations and anions in Urmia Lake were Na+, Mg++, K+, Ca++, Cl- , SO4--, and HCO3-, respectively. Although water quality of the lake is homogeneous with depth, it differs between the northern and southern parts. Water quality also varies seasonally, determined by river inflows and the lake bathymetry. Moreover, with the present salinity level, salt precipitation is likely in Urmia Lake and is becoming one of the principal factors determining the distribution of solutes within the lake. This study shows that the combined use of temporal and spatial water quality data improves our understanding of complex, large aquatic systems like Urmia Lake.

Global Hydrographic Data (GGHYDRO) was compiled in the mid-1980s (Cogley, 1988) as a tool for helping to describe the world's land surfaces to general circulation climate models. While more accurate or more highly resolved descriptions of the contents of some of the fields are now available, GGHYDRO Release 2.3 retains its original advantages of moderate size, internal consistency and useful content. It has been corrected periodically as errors have been detected.

GGHYDRO Release 2.3 is organized into 21 fields containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1 degree longitude by 1 degree latitude. Coverage includes the following:

Dry land (percent) (LAND) Perennial freshwater lakes (percent) (FLAK) Swamp, marsh and other wetlands (percent) (SWMP) Salt lakes (percent) (SLAK) Salt water of the ocean (percent) (OCEA) Intermittent water bodies (percent) (ILAK) Glacier ice, including shelf ice but excluding pack ice (percent) (GLAC) Sand dunes (percent) (DUNE) Saltmarsh (percent) (SMRS) Salt flats (percent) (SFLT) Land + Swamp + Sand dunes + Saltmarsh (percent) (DSRF) Saltwater, marine or terrestrial (percent) (SLTW) Perennial rivers (counts) (FRIV) Intermittent rivers (counts) (IRIV) Surface runoff of water (mm/a) (RNOF) Estimated root-mean-square error of RNOF (%) (RNER) Runoff of ice (mm/a) (RICE) Land mask (MS05) Major drainage basins (BAS1) Smaller drainage basins (BAS2) Main features of the cryosphere (CRYO)

The Global Hydrographic data set is available for download from Trent University at [http://www.trentu.ca/academic/geography/glaciology/glglgghy.htm]. Documentation, FORTRAN read utilities, and references are also available from this site.

More details about the content of GGHYDRO are provided by Cogley (2003), who also describes the format of the data and the compression scheme used to reduce file sizes. A more modern compression scheme is in development.

References

Cogley, J. G. 2003. GGHYDRO - Global Hydrographic Data, Release 2.3, Trent Technical Note 2003-1, Department of Geography, Trent University, Peterborough, Ontario, Canada.

Cogley, J. G. 1998. GGHYDRO - Global Hydrographic Data, Release 2.2, Trent Climate Note 98-1, Department of Geography, Trent University, Peterborough, Ontario, Canada.

As one of the wetland systems in the northern plain of China, Baiyangdian plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of the wetland ecosystem in Baiyangdian basin 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 network distribution map and water system distribution map of Baiyangdian basin wetland ecosystem in 2017 were drawn by artificial visual interpretation and machine automatic classification It provides the basis for the study of the connectivity (including hydrological connectivity and landscape connectivity). The boundary of Baiyangdian basin in this data set is 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 provided by ESA on September 20, 2017. This data set uses the second, third, fourth and eighth bands of 10 meter resolution in the image, carries out radiation calibration, mosaic, mosaic and other preprocessing operations in SNAP and ArcGIS 10.2 software, and carries out supervised classification in ENVI 5.3 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, mapping the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; obtaining river network of Baiyangdian basin by visual interpretation based on Google Earth high resolution satellite image (resolution 0.23m). The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (. SHP); Baiyangdian basin river network data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of the study area and the river 3 km buffer) (. tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). 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, 866 km in length, distributed in Northeast southwest direction, mostly at the junction of forest land and cultivated land; and 337 km2 river channels in plain area, 1574 km in length. Baiyangdian basin is divided into eight types of land use / cover: 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 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 woodland 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 high 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 swamp, 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 swamp 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. Ri...

During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted.

Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).

Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.

Natural Earth Vector includes features corresponding to the following:

Cultural Vector Data Thremes:

• Countries: matched boundary lines and polygons with names attributes for countries and sovereign states. Includes dependencies (French Polynesia), map units (U.S. Pacific Island Territories) and sub-national map subunits (Corsica versus mainland Metropolitan France).
• Disputed areas and breakaway regions - From Kashmir to the Elemi Triangle, Northern Cyprus to Western Sahara.
• First order admin (provinces, departments, states, etc.): internal boundaries and polygons for all but a few tiny island nations. Includes names attributes and some statistical groupings of the same for smaller countries.
• Populated places: point symbols with name attributes. Includes capitals, major cities and towns, plus significant smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining rankings.
• Urban polygons: derived from 2002-2003 MODIS satellite data.
• Parks and protected areas: US National Park Service units.
• Pacific nation groupings: boxes for keeping these far-flung islands tidy.
• Water boundary indicators: partial selection of key 200-mile nautical limits, plus some disputed, treaty, and median lines.

Physical Vector Data Themes:

• Coastline: ocean coastline, including major islands. Coastline is matched to land and water polygons.
• Land: Land polygons including major islands
• Ocean: Ocean polygon split into contiguous pieces.
• Minor Islands: additional small ocean islands ranked to two levels of relative importance.
• Reefs: major coral reefs from WDB2.
• Physical region features: polygon and point labels of major physical features.
• Rivers and Lake Centerlines: ranked by relative importance. Includes name and line width attributes. Don’t want minor lakes? Turn on their centerlines to avoid unseemly data gaps.
• Lakes: ranked by relative importance, coordinating with river ranking. Includes name attributes.
• Glaciated areas: polygons derived from DCW, except for Antarctica derived from MOA. Includes name attributes for major polar glaciers.
• Antarctic ice shelves: derived from 2003-2004 MOA. Reflects recent ice shelf collapses.
• Bathymetry: nested polygons at 0, -200, -1,000, -2,000, -3,000, -4,000, -5,000, -6,000, -7,000, -8,000, -9,000,and -10,000 meters. Created from SRTM Plus.
• Geographic lines: Polar circles, tropical circles, equator, and International Date Line.
• Graticules: 1-, 5-, 10-, 15-, 20-, and 30-degree increments. Includes WGS84 bounding box.

Time series of brightness temperatures (T(B)) from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are examined to determine ice phenology variables on the two largest lakes of northern Canada: Great Bear Lake (GBL) and Great Slave Lake (GSL). T(B) measurements from the 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset/melt-onset and ice-on/ice-off dates on both lakes. The 18.7 GHz (H-pol) channel is found to be the most suitable for estimating these ice dates as well as the duration of the ice cover and ice-free seasons. A new algorithm is proposed using this channel and applied to map all ice phenology variables on GBL and GSL over seven ice seasons (2002-2009). Analysis of the spatio-temporal patterns of each variable at the pixel level reveals that: (1) both freeze-onset and ice-on dates occur on average about one week earlier on GBL than on GSL (Day of Year (DY) 318 and 333 for GBL; DY 328 and 343 for GSL); (2) the freeze-up process or freeze duration (freeze-onset to ice-on) takes a slightly longer amount of time on GBL than on GSL (about 1 week on average); (3) melt-onset and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL (DY 143 and 183 for GBL; DY 135 and 157 for GSL); (4) the break-up process or melt duration (melt-onset to ice-off) lasts on average about three weeks longer on GBL; and (5) ice cover duration estimated from each individual pixel is on average about three weeks longer on GBL compared to its more southern counterpart, GSL. A comparison of dates for several ice phenology variables derived from other satellite remote sensing products (e.g. NOAA Interactive Multisensor Snow and Ice Mapping System (IMS), QuikSCAT, and Canadian Ice Service Database) show that, despite its relatively coarse spatial resolution, AMSR-E 18.7 GHz provides a viable means for monitoring of ice phenology on large northern lakes.

Purpose of the Caribbean Ecoregional AssessmentThe Caribbean is one of the worlds epicenters of biological diversity and species endemism with literally thousands of plants and animals found nowhere else on earth. Conservation has proven a challenge in this large, diverse, and globally-important area one the Nature Conservancy is addressing through a strong on-the-ground presence led by country programs that have science-based conservation strategies. To address these problems and opportunities, The Nature Conservancy initiated a Regional Conservation Assessment for the Greater Caribbean Basin, designed to examine regional biodiversity and the associated threats and conservation opportunities. This follows a worldwide trend of recognizing the need to examine about and manage for the maintenance of functioning ecosystem processes and populations across appropriately large regions to help slow widespread environmental changes. To facilitate this approach, we have assembled, into a standard, seamless GIS database, the biological and socio-economic data necessary to analyze the regional-scale context of Caribbean biodiversity.We identified and mapped a range of coarse-filter targets that represent a full spectrum of terrestrial, freshwater and marine biodiversity using combinations of biophysical factors (such as climate, geology, major habitat type, elevation, depth etc.). Mapping Caribbean biodiversity provides the basis for conservation decision making. Coarse-filter mapping at the level of ecological communities, ecosystems and landscapes is an efficient method to represent all essential elements of biodiversity across the entire region. We assessed human impact in two ways: expert judgments and mapping of the relative intensity of human impacts. Local experts provided judgments on the condition of targets and this information is combined with maps of human activities in order to determine relative human impacts. Distribution of human activities is a critical factor in conservation and resource management. Not all human activities are threats to biodiversity and determining relative human impact and predicting ecological health is necessary for sound management. We suggest that by providing the latest analytical tools and comprehensive biodiversity and socio-economic data, we can assist conservation organizations, local communities and governments that are striving to meet their national or local conservation missions and leverage and enhance ongoing conservation efforts. These data and tools can be used to enable sound, pragmatic conservation decisions at-scale. In this way, this assessment will serve to enhance and unify ongoing local and national conservation efforts and provide a common vision of conservation success throughout the Greater Caribbean. We suggest that use of the data and tools can facilitate strategic partnerships amongst both local and regional organizations across the basin a key to achieving lasting results. We hope to put in place a long-term information system that promotes the protection of the regions irreplaceable terrestrial, freshwater, coastal and marine biodiversity. We have designed simple data management systems to promote long-term use and dynamic updates of the database. Information is archived in a standardized structure on a freely accessible spatial warehouse using simple, robust systems that are easily and accessible to partners and stakeholders. Standardization and open access promotes updateable archiving systems so that new information can be easily integrated and compared with existing information and also facilitations information sharing and collaboration.

Rapid changes in the densely distributed lakes on the Tibetan Plateau (TP) reflect the responses of terrestrial water resources to climate change. Timely and accurate monitoring of lake dynamics is essential for formulating adaptation strategies to manage water and protect public facility safety sustainably. Interfered by the numerous glaciers and snow mountains and limited by the acquisition and computing capacities of massive satellite data, annual inventories of all the lakes ranging from mini to large on the TP are still lacking. Here, we annually mapped approximately 9,000 lakes over 0.1 km2 on the TP during 1991-2023，using all the Landsat imagery, a robust algorithm for detecting surface water according to multiple spectral indices, and the cloud computing platform. We revealed a rapid expansion of lakes with significant spatial heterogeneity, with 6,590 newly increased and 2,851 disappeared lakes found. The total lake areas (554.1 km2/yr) and numbers (77.9/yr) continuously and significantly increased in the period. The growth in lake numbers dominated by small lakes mainly happened before 2005, while the increases in lake areas dominated by large lakes lasted the whole period after 1995. The most significant increases in lake areas and numbers happened in the north of the Inner Basin, the hotspot of lake changes identified in the study. The dataset is expected to promote our understanding of the complete lake evolution process and the dynamic response of the cryosphere to the changing climate. The method proposed is also applicable to continuously monitoring the dynamics of lakes with higher accuracies in other alpine regions around the world.