This work is licensed under the Esri Master License Agreement.Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.
Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.
This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.
", "keyword": [ "GLDAS", "NASA", "hydrology", "earth observations", "landscape", "climate", "weather" ], "issued": "2017-09-28T18:12:28.000Z", "modified": "2026-10-27T17:38:19.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_hydro", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.0000,-89.9900,179.0000,89.9900", "license": " This work is licensed under the Esri Master License Agreement.
Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Raster, mosaic dataset, or image service. (Preferred cell size is 30 meters.)
Classified raster with the same classes as in the National Land Cover Database (NLCD) 2016.
Note: The classified raster contains 20 classes based on a modified Anderson Level II classification system as used by the National Land Cover Database.
This model has an overall accuracy of 77 percent. The table below summarizes the precision, recall and F1-score of the model on the validation dataset.
| Class | Collection 2 Level 2 Imagery | Collection 1 Level 1 Imagery | ||||
| Precision | Recall | F1 Score | Precision | Recall | F1 Score | |
| Open Water | 0.96 | 0.97 | 0.96 | 0.95 | 0.97 | 0.96 |
| Perennial Snow/Ice | 0.86 | 0.69 | 0.77 | 0.49 | 0.94 | 0.64 |
| Developed, Open Space | 0.51 | 0.38 | 0.44 | 0.43 | 0.38 | 0.4 |
| Developed, Low Intensity | 0.52 | 0.46 | 0.49 | 0.47 | 0.48 | 0.47 |
| Developed, Medium Intensity | 0.54 | 0.5 | 0.52 | 0.49 | 0.54 | 0.51 |
| Developed, High Intensity | 0.67 | 0.54 | 0.6 | 0.55 | 0.68 | 0.61 |
| Barren Land | 0.76 | 0.59 | 0.66 | 0.6 | 0.77 | 0.68 |
| Deciduous Forest | 0.74 | 0.81 | 0.78 | 0.78 | 0.76 | 0.77 |
| Evergreen Forest | 0.77 | 0.82 | 0.79 | 0.8 | 0.82 | 0.81 |
| Mixed Forest | 0.56 | 0.47 | 0.51 | 0.5 | 0.53 | 0.51 |
| Shrub/Scrub | 0.82 | 0.82 | 0.82 | 0.84 | 0.81 | 0.83 |
| Herbaceous | 0.78 | 0.74 | 0.76 | 0.79 | 0.77 | 0.78 |
| Hay/Pasture | 0.7 | 0.74 | 0.72 | 0.67 | 0.75 | 0.71 |
| Cultivated Crops | 0.87 | 0.91 | 0.89 | 0.91 | 0.9 | 0.9 |
| Woody Wetlands | 0.7 | 0.68 | 0.69 | 0.67 | 0.68 | 0.68 |
| Emergent Herbaceous Wetlands | 0.72 | 0.54 | 0.62 | 0.54 | 0.61 | 0.57 |
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/content/esri::land-cover-classification-landsat-8" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=5ff454d2e9ea420ba1f810076ce70ec2", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/datasets/esri::sentinel-2-imagery-bathymetric-with-dra", "title": "Sentinel-2 Imagery: Bathymetric with DRA", "description": "Retirement Notice: This item is currently in beta and will be retired in December 2026. A replacement item has not been identified at this time. It is not recommended for production use, as functionality and content are subject to change without notice.
Sentinel-2, 10 and 60m Multispectral 13-band imagery, rendered on-the-fly. Available for visualization and analytics, this Imagery Layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.
This imagery layer can be used for multiple purposes including but not limited to bathymetric mapping applications, changing lands and marine environmental monitoring.
Geographic Coverage
Temporal Coverage
Image Selection/Filtering
Analysis Ready
Visual Rendering
Multispectral Bands
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
Additional Notes
Data Source
Sentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , EarthExplorer or the Copernicus Open Access Hub to download the scenes.
", "keyword": [ "sentinel2", "sentinel", "imagery", "multispectral", "temporal", "sentinel2 on aws", "amazon", "MS", "bathymetric", "bathymetric with dra", "marine", "esa", "sentinel-2", "retiring-2026-12" ], "issued": "2018-05-02T15:47:01.000Z", "modified": "2026-05-21T19:27:01.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.9973,-66.8050,179.9973,72.9957", "license": "If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Retirement Notice: This item is currently in beta and will be retired in December 2026. A replacement item has not been identified at this time. It is not recommended for production use, as functionality and content are subject to change without notice.
Sentinel-2, 10m Multispectral 13-band imagery, rendered on-the-fly. Available for visualization and analytics, this Imagery Layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.
This imagery layer can be used for multiple purposes including but not limited to vegetation, land cover, plant health, deforestation and environmental monitoring.
Geographic Coverage
Temporal Coverage
Image Selection/Filtering
Analysis Ready
Visual Rendering
Multispectral Bands
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
Additional Notes
Data Source
Sentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , EarthExplorer or the Copernicus Open Access Hub to download the scenes.
", "keyword": [ "sentinel2", "sentinel", "imagery", "multispectral", "temporal", "sentinel2 on aws", "amazon", "MS", "ndvi", "ndvi classified", "vegetation", "esa", "sentinel-2", "retiring-2026-12" ], "issued": "2018-05-02T15:47:11.000Z", "modified": "2026-05-21T19:26:42.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.9973,-66.8050,179.9973,72.9957", "license": "If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2018 World Ocean Atlas.
The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representing epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies.
The EMUs are an initial objective partitioning of the ocean using longterm historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanic environment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.
", "keyword": [ "open science", "emu", "ecological marine units", "ocean", "marine", "ecosystem", "ExU" ], "issued": "2016-09-13T20:29:05.000Z", "modified": "2026-02-19T22:46:27.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_oceans", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-180.0000,-71.3458,180.0000,83.7386", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/apps/esri::ecological-marine-unit-emu-explorer" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://livingatlas.arcgis.com/emu" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/datasets/esri::multispectral-landsat", "title": "Multispectral Landsat", "description": "This layer includes Landsat GLS, Landsat 8, and Landsat 9 imagery for use in visualization and analysis. This layer is time enabled and includes a number band combinations and indices rendered on demand. The Landsat 8 and 9 imagery includes nine multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.
Geographic Coverage
Temporal Coverage
Product Level
Image Selection/Filtering
Visual Rendering
Multispectral Bands
| \n Band \n | \n \n Description \n | \n \n Wavelength\n (µm) \n | \n \n Spatial\n Resolution (m) \n | \n
| \n 1 \n | \n \n Coastal\n aerosol \n | \n \n 0.43 - 0.45 \n | \n \n 30 \n | \n
| \n 2 \n | \n \n Blue \n | \n \n 0.45 - 0.51 \n | \n \n 30 \n | \n
| \n 3 \n | \n \n Green \n | \n \n 0.53 - 0.59 \n | \n \n 30 \n | \n
| \n 4 \n | \n \n Red \n | \n \n 0.64 - 0.67 \n | \n \n 30 \n | \n
| \n 5 \n | \n \n Near Infrared\n (NIR) \n | \n \n 0.85 - 0.88 \n | \n \n 30 \n | \n
| \n 6 \n | \n \n SWIR 1 \n | \n \n 1.57 - 1.65 \n | \n \n 30 \n | \n
| \n 7 \n | \n \n SWIR 2 \n | \n \n 2.11 - 2.29 \n | \n \n 30 \n | \n
| \n 8 \n | \n \n Cirrus (in\n OLI this is band 9) \n | \n \n 1.36 - 1.38 \n | \n \n 30 \n | \n
| \n 9 \n | \n \n QA Band\n (available with Collection 1)* \n | \n \n NA \n | \n \n 30 \n | \n
*More about the Quality Assessment Band
TIRS Bands
| \n Band \n | \n \n Description \n | \n \n Wavelength\n (µm) \n | \n \n Spatial\n Resolution (m) \n | \n
| \n 10 \n | \n \n TIRS1 \n | \n \n 10.60 - 11.19 \n | \n \n 100 * (30) | \n
| \n 11 \n | \n \n TIRS2 \n | \n \n 11.50 - 12.51 \n | \n \n 100 * (30) | \n
*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.
Additional Usage Notes
Data Source
Landsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted in Amazon Web Services as part of their Public Data Sets program.
For information, see Landsat 8 and Landsat 9.
", "keyword": [ "Landsat on AWS", "Landsat", "Multispectral", "Multitemporal", "imagery", "landsat 8", "temporal", "MS", "landsat 9" ], "issued": "2015-03-19T16:21:14.000Z", "modified": "2026-02-06T21:52:35.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-180.0000,-65.3235,180.0000,65.3126", "license": "
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.Geographic Information Systems (GIS) can help in solving some of the biggest challenges when it come to disasters by:
Retirement Notice: This item is in mature support as of November 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.
The National Water Model (NWM) is a new product from the National Weather Service that forecasts streamflow volume and velocity over the entire continental United States. It is a hydrologic model that predicts the flow in every river reach of the National Hydrography Dataset, mathematically modeling physical processes like snowmelt, infiltration and the movement of water through soil layers in order to determine how much of the NWS precipitation forecast becomes runoff, then routing that runoff through the river network. This is the medium term forecast, which is run once a day, predicting streamflow over the next ten days at three hour intervals.
This work is licensed under the Esri Master License Agreement.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.
Quick access to the following band combinations and indices is provided:
This work is licensed under the Esri Master License Agreement.
This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen months
Quick access to the following band combinations and indices is provided:
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
", "keyword": [ "imagery", "Sentinel", "Sentinel-2", "multispectral", "temporal", "multitemporal", "earth" ], "issued": "2018-05-22T22:05:42.000Z", "modified": "2025-08-29T00:50:18.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.Worldview uses NASA's Global Imagery Browse Services (GIBS) to rapidly retrieve its imagery for an interactive browsing experience. While Worldview uses OpenLayers as its mapping library, GIBS imagery can also be accessed from Google Earth, NASA WorldWind, and several other clients. We encourage interested developers to build their own clients or integrate NASA imagery into their existing ones using these services.
Comments/suggestions/problem reports are welcome via Earthdata Support. View frequently asked questions (FAQ) about Worldview.
Source Information Obtained Oct 18, 2018 from https://earthdata.nasa.gov/worldview
The vision of GEO is to realize a future\nwhere decisions and actions, for the benefit of humankind, are informed by\ncoordinated, comprehensive and sustained Earth Observation information and\nservices.
\n\nWith this in mind, the GEO Global Water\nSustainability Initiative (GEOGLOWS) is working to provide relevant, actionable\ninformation about water that promotes the use of earth observations while\nstrengthening observational networks in local operational frameworks.
\n\n", "keyword": [ "hydrological forecasting", "Flood", "Stream flow forecasting", "Water", "agua", "Inundación", "Sistemas de pronóstico hidrológico global", "Sequía", "GEOGLOWS", "GEO", "GEOSS", "Hydrology", "Hidrología", "Americas", "AmeriGEO", "AmeriGEOSS", "Climate", "Climate Change", "Climate Indicators", "Climate Initiatives", "GeoPathways", "GeoPathways 2021-2022", "Inter-American", "NASA", "NOAA", "Open Science", "Science", "USGS" ], "issued": "2018-12-03T22:07:37.000Z", "modified": "2021-11-18T00:41:52.000Z", "publisher": { "name": "AmeriGEO" }, "contactPoint": { "@type": "vcard:Contact", "fn": "AngelicaGutierrez_amerigeoss", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/documents/amerigeoss::global-stream-flood-forecasting-kyotoplenary-final" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=c078a168b1d44fbf88f0454deb32b1ad", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::global-agriculture-disaster-assessment-system-gadas-data-hub", "title": "Global Agriculture Disaster Assessment System (GADAS) Data Hub", "description": "{{description}}", "keyword": [ "Agriculture", "Disaster", "Elevation", "Volcano", "volcanic", "droughts", "typhoon", "hurricane", "eruption", "irrigation", "water", "infrastructure", "land cover", "crop", "vegetation", "AmeriGEOSS", "GEOSS", "GEO", "UNGGIM-Americas", "Application", "disaster", "hazard", "EuroGEOSS", "Americas", "AmeriGEO", "Climate", "Climate Change", "Climate Indicators", "Climate Initiatives", "GeoPathways", "GeoPathways 2021-2022", "Inter-American", "NASA", "NOAA", "Open Science", "Science", "USGS" ], "issued": "2018-10-23T13:02:23.000Z", "modified": "2021-11-18T00:41:52.000Z", "publisher": { "name": "AmeriGEO" }, "contactPoint": { "@type": "vcard:Contact", "fn": "rich_amerigeoss", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::global-agriculture-disaster-assessment-system-gadas-data-hub" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://hub-fasgis.opendata.arcgis.com/" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=c8997996dba34793911305ef46b7b45b", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::global-visualization-viewer-glovis", "title": "Global Visualization Viewer (GloVis", "description": "GloVis
The USGS Global Visualization Viewer (GloVis) is an online search and order tool for selected satellite data. Through a graphic map display, the user can select any area of interest and immediately view all available browse images for the specified location. From the browse image viewer page, the user may either navigate to view adjacent scene locations or select a new area of interest. GloVis also offers additional features such as cloud cover limits, date limits, user-specified map layer displays, scene list curation, and access to metadata. The viewer provides access to Thermal Infrared (TIR) and Visible and Near Infrared (VNIR) data from the LP DAAC’s ASTER L1T data product. A selection of data collected by Landsat satellites and other remote sensing instruments are also available. A full listing of available data products can be found in the GloVis FAQ’s.
The International Production Assessment Division (IPAD) is part of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS), an agency within the US Department of Agriculture (USDA). FAS-IPAD uses satellite imagery and remote sensing data to assist in its agricultural estimates of global crop conditions. The division provides monthly estimates of area, yield and production for 17 distinct commodities in over 160 countries around the world, including post-disaster assessments. GADAS is a powerful visualization tool based on an ArcGIS platform that enables FAS-IPAD analysts, and other users, to rapidly assess real-time crop conditions using a wide variety of data layers from a multitude of sources.
GADAS integrates a vast array of highly detailed data streams to include daily precipitation data, vegetation index, crop masks, land cover data, irrigation and water data, elevation and infrastructure, political data, and much more. In addition, FAS-IPAD has partnered with the Pacific Disaster Center (PDC) in Hawaii to incorporate real-time data streams into GADAS for worldwide monitoring, tracking, and pre- and post-disaster agricultural assessments resulting from hurricanes, typhoons, tsunamis, floods, droughts, earthquakes and volcanic eruptions.
You may want to begin exploring GADAS for the many things it can be used for, such as:
We welcome your feedback on how GADAS has worked or is working for you, and are enthusiastic about expanding the data layers, utilization, and future development of this very powerful GIS tool. Please contact us at OGA.IPAD@fas.usda.gov to provide your valued comments…we look forward to hearing from you!
Here’s a screenshot centered over the northern Atlantic Ocean:
", "keyword": [ "Disaster", "Agriculture", "Elevation", "Volcano", "volcanic", "droughts", "typhoon", "hurricane", "eruption", "irrigation", "water", "infrastructure", "land cover", "crop", "vegetation", "AmeriGEOSS", "GEOSS", "GEO", "UNGGIM-Americas", "Americas", "AmeriGEO", "Climate", "Climate Change", "Climate Indicators", "Climate Initiatives", "GeoPathways", "GeoPathways 2021-2022", "Inter-American", "NASA", "NOAA", "Open Science", "Science", "USGS" ], "issued": "2018-10-23T12:36:36.000Z", "modified": "2021-11-18T00:41:51.000Z", "publisher": { "name": "AmeriGEO" }, "contactPoint": { "@type": "vcard:Contact", "fn": "AmeriGEOSS", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::global-agriculture-and-disaster-assessment-system-gadas" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://geo.fas.usda.gov/GADAS/index.html" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=37ad3058effb4cc5bc230b629d232738", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/maps/RHSB::mega-volcanoes-and-buffers", "title": "Mega Volcanoes and buffers", "description": "{{description}}", "keyword": [ "rhsb", "mega volcanoe", "volcanoe", "volcano", "apocalypse", "buffers" ], "issued": "2015-04-24T20:25:27.000Z", "modified": "2020-11-18T08:31:14.000Z", "publisher": { "name": "Royal High School Bath" }, "contactPoint": { "@type": "vcard:Contact", "fn": "raphael.heath", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-180.0000,-65.0160,180.0000,78.9295", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/maps/RHSB::mega-volcanoes-and-buffers" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=a3c245d93ca640d5a40fd9c37a0b27fb", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::tethys-streamflow-prediction-service", "title": "Tethys- Streamflow Prediction Service", "description": "{{description}}", "keyword": [ "Water" ], "issued": "2020-09-23T23:00:28.000Z", "modified": "2020-09-23T23:03:43.000Z", "publisher": { "name": "AmeriGEO" }, "contactPoint": { "@type": "vcard:Contact", "fn": "rich_amerigeoss", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::tethys-streamflow-prediction-service" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tethys-staging.byu.edu/apps/geoglows-hydroviewer/" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=090e51d12f334c5e9593afe4d82bea86", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/apps/noaa::noaa-coastal-imagery-viewer", "title": "NOAA Coastal Imagery Viewer", "description": "Abstract: The imagery posted on this site is in response to \nIntegrated Ocean and Coastal Mapping requirements. The aerial \nphotography missions were conducted by NOAA’s National Geodetic Survey, \nRemote Sensing Division. Images were acquired from various altitudes \nranging from 3,500 to 7,500 feet, using a Trimble Digital Sensor System \n(DSS) at a 37.5 degree look angle.The data provided on this site is for informational and planning purposes only.
Absolutely no accuracy or completeness guarantee is implied or intended.
This work is licensed under the Web Services and API Terms of Use. Billions in real estate investments
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Coastal storms and other meteorological phenomenon can have a significant impact on how high water levels rise and how often. The inundation analysis program is extremely beneficial in determining the frequency (or the occurrence of high waters for different elevations above a specified threshold) and duration (or the amount of time that the specified location is inundated by water) of observed high waters (tides). Statistical output from these analyses can be useful in planning marsh restoration activities. Additionally, the analyses have broader applications for the coastal engineering and mapping community, such as, ecosystem management and regional climate change. Since these statistical outputs are station specific, use for evaluating surrounding areas may be limited.
The data input for this tool is 6-minute water level data time series and the tabulated times and heights of the high tides over a user specified time period, relative to a desired tidal datum or user-specified datum. The data output of this tool provides summary statistics, which includes the number of occurrences of inundation above the threshold (events) and length of duration of inundation of each events above the threshold elevation for a specified time period. In addition to summary statistics, graphical outputs are provided using three plots. The first plot is a histogram of frequency of occurrence relative to the threshold elevation, the second plot is a histogram of the frequency of duration of inundation, and the third plot is an X-Y plot of frequency of elevation versus duration of inundation for each event. Input data time series are presently limited to the verified data from a set of operating and historical tide stations in the NOAA CO-OPS data base.
Overview
Download here the 2018-2019 update on the GEO Global Water Sustainability.
This Initiative consolidates the positive elements of the water activities in the first phase of GEO and ensures that strong coordination and commitment is in place for links between data, information, knowledge, and applications and policy. There are many contributions being made to these goals by various GEO Member countries but these contributions are unlikely to be included in planning or in synthesis or to be linked with policy at the global scale unless they are part of a larger framework for coordinating water within and beyond GEO. Thus, when mature, GEOGLOWS could provide a coordination framework for all water initiatives under the GEO programme.
This Initiative is intended to facilitate the use of Earth observation assets to contribute to mitigating water shortages, excesses and degraded quality arising from population growth, climate change and industrial development. The Initiative is relevant to GEO Strategic Objectives and follows the model of GEO projects. It will achieve its objectives by facilitating collaborations, enabling projects, and encouraging conversations that will strengthen these programmes, increasing awareness of related activities, and promoting the leverage of existing or planned activities.
The Initiative will develop knowledge based on an analysis of Essential Water Variables (EWVs) and use the knowledge to inform applications related to minimizing Basin and Regional Risk, policies related to enhancing global water sustainability and capacity building through regional programs and alliances such as AmeriGEOSS. To achieve this objective it strives to enhance observational programs, innovate on ways to make more effective use of data, adhere to GEO principles of good data management, and implement the policy of free and open data exchange.
Activities for the period
This Initiative will involve the collection, processing, and analysis of data to produce a better understanding of water processes, improved predictions, and insights into strategies for more effective water management; models and tools that will be applied in the provision of monitoring and prediction services for water management; and general evidence and support for policy development. The scope of this work will initially cover the responsibility areas of the member nations involved and will provide the global overviews needed to make the connections between data and policy. The scientific approaches are supported by the enhancement of data collection and processing, the improvement of data services, and the expansion of capabilities to use the data by all segments of society and in all parts of the world.
The GEOGLOWS framework includes activities focusing on:
User engagement
End users will be engaged in the definition of needs for data, products and services. Emerging applications such as water quality applications and extreme event monitoring should lead to new services. Once user requirements are further clarified, an evaluation of global data centers’ current holdings should be carried out to determine where new initiatives and services are required. They will assist in the conduct of gap analysis to clarify the adequacy of existing products and services and the need for new services.
AmeriGEOSS functions as a User Engagement element through the surveys it undertakes at its meetings and training sessions to address this need. Other user engagement will be achieved through continuous active participation in the Integrated Global Water Cycle Observations (IGWCO) Community of Practice (CoP) where emphasis will be placed on engagements with users around the world. In addition GEOGLOWS will organize side events at the GEO Work Planning Symposium and Plenary meetings to inform experts in other SBAs of their activities and to stimulate cross-SBA activities. GEOLGLOWS members will be encouraged to organize scientific sessions and town halls at international science meetings to expand their activities.
Future plans
In addition to suggesting a strategy for coordination, this Initiative will also provide a set of recommendations for implementation including preliminary proposals for new activities. Part of this will involve expanding the geographical scope of GEOGLOWS by bringing more GEO Members into the Initiative.
Resources
Leadership
Contributors
Members: Brazil, Canada, Colombia, Costa Rica, EC, Germany, Mexico, the Netherlands, USA.
Participating Organizations: CODATA, WMO.
Others: Barbados, El Salvador, International Association of Hydrological Sciences (IAHS).
Linkages across the Work Programme
Initiatives: AmeriGEOSS; Earth Observations in Service of the 2030 Agenda for Sustainable Development.
Leadership & Contributors (this list is being populated)
Role | Name | Member/PO | Implementing Entity | |
|---|---|---|---|---|
Lead | Rose Alabaster | n/a | rose.alabaster@gmail.com | |
Lead | Angelica Gutierrez | United States | NOAA | angelica.gutierrez@noaa.gov |
Contributor | Stephan Dietrich | Germany | BfG | dietrich@bafg.de |
Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.
SATELLITE IMAGERY+
YOUR ALGORITHMS+
REAL WORLD APPLICATIONSExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.
EXPLORE TIMELAPSEThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.
EXPLORE DATASETSThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.
EXPLORE THE APIUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.
LEARN ABOUT THE CODE EDITORScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.
SEE CASE STUDIES
The Smithsonian's "Eruptions, Earthquakes, & Emissions" web application (or "E3") is a time-lapse animation of volcanic eruptions and earthquakes since 1960. It also shows volcanic gas emissions (sulfur dioxide, SO2) since 1978 — the first year satellites were available to provide global monitoring of SO2. The eruption data are drawn from the Volcanoes of the World (VOTW) database maintained by the Smithsonian's Global Volcanism Program (GVP). The earthquake data are pulled from the United States Geological Survey (USGS) Earthquake Catalog. Sulfur-dioxide emissions data incorporated into the VOTW for use here originate in NASA's Multi-Satellite Volcanic Sulfur Dioxide L4 Long-Term Global Database. Please properly credit and cite any use of GVP eruption and volcano data, which are available via a download button within the app, through webservices, or through options under the Database tab above. A citation for the E3 app is given below.
Clicking the image will open this web application in a new tab.
Global Volcanism Program, 2016. Eruptions, Earthquakes & Emissions, v. 1.0 (internet application). Smithsonian Institution. Accessed 19 Oct 2018 (https://volcano.si.edu/E3/).
VEI is the "Richter Scale" of volcanic eruptions. Assigning a VEI is not an automated process, but involves assessing factors such as the volume of tephra (volcanic ash or other ejected material) erupted, the height the ash plume reaches above the summit or altitude into the atmosphere, and the type of eruption (Newhall and Self, 1982). VEIs range from 1 (small eruption) to 8 (the largest eruptions in Earth's entire history).
For information about volcanic eruptions before 1960, explore the GVP website, where we catalog eruption information going back more than 10,000 years. This E3 app only displays eruptions starting in 1960 because the catalog is much more complete after that date. For most eruptions before the 20th century we rely on the geologic record more than historical first-hand accounts — and the geologic record is inherently incomplete (due to erosion) and not fully documented.
The E3 app displays emissions of sulfur dioxide gas (SO2) from erupting volcanoes, including the mass in kilotons. Even though water vapor (steam) and carbon dioxide gas (see more about CO2 below) are much more abundant volcanic gases, SO2is the easiest to detect using satellite-based instruments, allowing us to obtain a global view. There is no universally accepted "magnitude" scale for emissions; the groupings presented here were chosen to best graphically present the relative volumes based on available data.
You are seeing a time-lapse movie of satellite measurements of SO2 associated with a particular emission event. These SO2 clouds, or plumes, are blown by winds and can circle the globe in about a week. As plumes travel, they mix with the air, becoming more dilute until eventually the concentration of SO2 falls below the detection limit of satellites. Earth's entire atmosphere derives from outgassing of the planet — in fact, the air you breathe was once volcanic gas, and some of it might have erupted very recently!
E3 shows volcanic gas emissions captured from satellite-based instruments, which were first deployed in 1978. NASA launched the Total Ozone Mapping Spectrometer (TOMS) in 1978, which provided the first space-borne observations of volcanic gas emissions. Numerous satellites capable of measuring volcanic gases are now in orbit.
The most abundant gases expelled during a volcanic eruption are water vapor (H2O in the form of steam) and carbon dioxide (CO2). Sulfur dioxide (SO2) is typically the third most abundant gas. Hydrogen gas, carbon monoxide and other carbon species, hydrogen halides, and noble gases typically comprise a very small percentage of volcanic gas emissions. So why can't we show H2O and CO2 in the E3 app? Earth's atmosphere has such high background concentrations of H2O and CO2 that satellites cannot easily detect a volcano's signal over this background "noise." Atmospheric SO2 concentrations, however, are very low. Therefore volcanic emissions of SO2 stand out and are more easily detected by satellites. Scientists are just beginning to have reliable measurements of volcanic carbon dioxide emissions because new satellites dedicated to monitoring CO2 have either recently been launched or have launches planned for the coming decade.
We don't really know. CO2, carbon dioxide, is the dominant form of carbon in most volcanic eruptions, and can be the dominant gas emitted from volcanoes. Humans release more than 100 times more CO2 to the atmosphere than volcanoes (Gerlach, 2011) through activities like burning fossil fuels. Because of this, the background levels of CO2 in the atmosphere have risen to levels that are so high (greater than 400 parts per million, or 0.04%) that satellites cannot easily detect the CO2 from volcanic eruptions. Scientists are able to estimate the amount of carbon flowing from Earth's interior to exterior (the flux) by measuring carbon emissions directly at volcanic vents and by measuring the carbon dissolved in volcanic rocks. Scientific teams in the Deep Carbon Observatory (one of the supporters of E3) are working to quantify the flux of carbon from Earth's interior to exterior.
No, not in modern times. The dominant effect of volcanic eruptions is to cool the planet in the short term. This is because sulfur emissions create aerosols that block the sun's incoming rays temporarily. While volcanoes do emit powerful greenhouse gases like carbon dioxide, they do so at a rate that is likely 100 times less than humans (Gerlach, 2011). Prior to human activity in the Holocene (approximately the last 10,000 years), volcanic gas emissions did play a large role in modulating Earth's climate.
Eruptions and earthquakes occur at Earth's plate boundaries — places where Earth's tectonic plates converge, diverge, or slip past one another. The forces operating at these plate boundaries cause both earthquakes and eruptions. For example, the Pacific "Ring of Fire" describes the plate boundaries that surround the Pacific basin. Around most of the Pacific Rim, the seafloor (Earth's oceanic crust) is "subducting" beneath the continents. This means that the seafloor is being dragged down into Earth's interior. You might think of this as Earth's way of recycling! In this process, ocean water is released to Earth's solid rocky mantle, melting the mantle rock and generating magma that erupts through volcanoes on the continents where the plates converge. In contrast, mid-ocean ridges, chains of seafloor volcanoes, define divergent plate boundaries. The Mid-Atlantic Ridge that runs from Iceland to the Antarctic in the middle of the Atlantic Ocean is one example of a divergent plate boundary. Earth's crust is torn apart at the ridge, as North and South America move away from Europe and Africa. New lava erupts to fill the gap. This lava cools, creating new ocean crust. All these episodes where solid rock collides or is torn apart generate earthquakes. And boom! You have co-located eruptions and earthquakes. To learn more about plate margins using E3, watch this video.
E3 is a successor to the program Seismic/Eruption developed by Alan Jones (Binghamton University). That program was one of the first to show the global occurrence of earthquakes (USGS data) and eruptions (GVP data) through space and time with animations and sound. The program ran in the Smithsonian's Geology, Gems, and Minerals Hall from 1997 to 2016, and was also available on the "Earthquakes and Eruptions" CD-ROM. E3 builds upon Seismic/Eruption with the addition of emissions data and automated data updates.
The application is currently showing 2,065 eruptions from 334 volcanoes. It is also showing 360 emission activity periods from 118 different volcanoes. In addition, there are 67 animations available showing the movement of SO2 clouds from 44 volcanoes.
The application checks for updates once a week. Earthquake data, being instrumentally recorded, is typically very current. Eruption data, which relies on observational reports and analysis by GVP staff, is generally updated every few months; however, known ongoing eruptions will continue through the most recent update check. Emissions data is collected by satellite instruments and also must be processed by scientists, so updates will be provided as soon as they are available following an event, on the schedule with eruption updates.
To run the map, your computer and browser must support WebGL. For more information on WebGL, please visit https://get.webgl.org to test if it should work.
Source Obtained from http://volcano.si.edu/E3/
", "keyword": [ "Hazards", "Disasters", "Volcano", "Volcanic", "AmeriGEOSS", "GEOSS", "GEO", "Earthquakes", "Emissions", "Sulfur Dioxide", "UNGGIM-Americas", "Application", "disaster", "hazard", "EuroGEOSS" ], "issued": "2018-10-19T12:13:15.000Z", "modified": "2018-11-11T13:48:59.000Z", "publisher": { "name": "AmeriGEO" }, "contactPoint": { "@type": "vcard:Contact", "fn": "AmeriGEOSS", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::eruptions-earthquakes-emissions" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://volcano.si.axismaps.io" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=61a7eb3f37344191914ecdde6db8a038", "landingPage": "https://communities-amerigeoss.opendata.arcgis.com/apps/amerigeoss::landsatlook-viewer", "title": "LandsatLook Viewer", "description": "
The LandsatLook Viewer is a prototype tool that was developed to allow rapid online viewing and access to the USGS Landsat image archives. This viewer allows you to:
We welcome feedback and input for future versions of this Viewer! Please provide your comments or suggestions .
About the ImageryThis viewer provides visual and download access to the USGS LandsatLook "Natural Color" imageproduct archive.
BackgroundThe Landsat satellites have been collecting multispectral images of Earth from space since 1972. Each image contains multiple bands of spectral information which may require significant user time, system resources, and technical expertise to obtain a visual result. As a result, the use and access to Landsat data has been historically limited to the scientific and technical user communities.
The LandsatLook “Natural Color” image product option was created to provide Landsat imagery in a simple user-friendly and viewer-ready format, based on specific bands that have been selected and arranged to simulate natural color. This type of product allows easy visualization of the archived Landsat image without any need for specialized software or technical expertise.
LandsatLook ViewerThe LandsatLook Viewer displays the LandsatLook Natural Color image product for all Landsat 1-8 images in the USGS archive and was designed primarily for visualization purposes.
The imagery within this Viewer will be of value to anyone who wants to quickly see the full Landsat record for an area, along with major image features or obvious changes to Earth’s surface through time. An area of interest may be extracted and downloaded as a simple graphic file directly through the viewer, and the original full image tile is also available if needed. Any downloaded LandsatLook image product is a georeferenced file and will be compatible within most GIS and Web mapping applications.
If the user needs to perform detailed technical analysis, the full bands of Landsat source data may also be accessed through direct links provided on the LandsatLook Viewer.
Image ServicesThe imagery that is visible on this LandsatLook Viewer is based on Web-based ArcGIS image services. The underlying REST service endpoints for the LandsatLook imagery are available at https://landsatlook.usgs.gov/arcgis/rest/services/LandsatLook/ImageServer .
Useful linksLandsat- Landsat Mission (USGS)- Landsat Science (NASA)LandsatLook- Product Description- USGS Fact Sheet- LandsatLook image services (REST)Landsat Products- Landsat 8 OLI/TIRS- Landsat 7 ETM+- Landsat 4-5 TM- Landsat 1-5 MSS- Landsat Band DesignationsLandsatLook images are full-resolution files derived from Landsat Level-1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. It is recommended that these images not be used in image analysis.
LandsatLook image files are included as options when downloading Landsat scenes from EarthExplorer, GloVis, or the LandsatLook Viewer (See Figure 1).
Figure 1. LandsatLook and Level-1 product download options
The LandsatLook Natural Color image is a .jpg composite of three bands to show a “natural” looking (false color) image. Reflectance values were calculated from the calibrated scaled digital number (DN) image data. The reflectance values were scaled to a 1-255 range using a gamma stretch with a gamma=2.0. This stretch was designed to emphasize vegetation without clipping the extreme values.
Landsat 1-3 MSS = Bands 7,5,4
The LandsatLook Thermal image is a one-band gray scale .jpg image that displays thermal properties of a Landsat scene. Image brightness temperature values were calculated from the calibrated scaled digital number (DN) image data. An image specific 2 percent clip and a linear stretch to 1-255 were applied to the brightness temperature values.
Landsat 1-5 MSS = not available
LandsatLook Quality images are 8-bit files generated from the Landsat Level-1 Quality band to provide a quick view of the quality of the pixels within the scene to determine if a particular scene would work best for the user's application. This file includes values representing bit-packed combinations of surface, atmosphere, and sensor conditions that can affect the overall usefulness of a given pixel. Color mapping assignments can be seen in the tables below.
For each Landsat scene, LandsatLook Quality images can be downloaded individually in .jpg format, or as a GeoTIFF format file (_QB.TIF) within the LandsatLook Images with Geographic Reference file.
| Landsat Collection 1 LandsatLook 8-bit Quality Images Designations | ||||||
| Landsat 8 OLI/TIRS | Landsat 7 ETM+, Landsat 4-5 TM | Landsat 1-5 MSS | Color | |||
| Bit | Description | Bit | Description | Bit | Description | |
| 0 | Designated Fill | 0 | Designated Fill | 0 | Designated Fill |
|
| 1 | Terrain Occlusion | 1 | Dropped Pixel | 1 | Dropped Pixel |
|
| 2 | Radiometric Saturation | 2 | Radiometric Saturation | 2 | Radiometric Saturation |
|
| 3 | Cloud | 3 | Cloud | 3 | Cloud |
|
| 4 | Cloud Shadow | 4 | Cloud Shadow | 4 | Unused |
|
| 5 | Snow/Ice | 5 | Snow/Ice | 5 | Unused |
|
| 6 | Cirrus | 6 | Unused | 6 | Unused |
|
| 7 | Unused | 7 | Unused | 7 | Unused | Unused |
Table 1. Landsat Collection 1 LandsatLook 8-bit Quality Images Designations
LandsatLook Images with Geographic ReferenceThe LandsatLook Image with Geographic Reference is a .zip file bundle that contains the Natural Color, Thermal, and the 8-bit Quality images in georeferenced GeoTiff (.TIF) file format.
Additional Information About LandsatLook Images
Many geographic information systems and image processing software packages easily support .jpg images. To create these files, Landsat data is mapped to a 1-255 range, with the fill area set to zero (if a no-data value is set to zero, the compression algorithm may introduce zero-value artifacts into the data area causing very dark data values to be displayed as no-data).
", "keyword": [ "AmeriGEOSS", "GEO", "GEOSS", "Imagery", "USGS", "Landsat", "Sentinel-2", "Landsat WRS-1", "Landsat 8", "Landsat 7", "Landsat 4", "Landsat 5" ], "issued": "2018-11-09T16:41:56.000Z", "modified": "2018-11-09T16:59:00.000Z", "publisher": { "name": "USGS" }, "contactPoint": { "@type": "vcard:Contact", "fn": "AmeriGEOSS", "hasEmail": "mailto:support@amerigeo.org" }, "accessLevel": "public", "spatial": "", "license": "Use the USGS EarthExplorer (EE) to search, download, and order satellite images, aerial photographs, and cartographic products. In addition to data from the Landsat missions and a variety of other data providers, EE provides access to MODIS land data products from the NASA Terra and Aqua missions, and ASTER level-1B data products over the U.S. and Territories from the NASA ASTER mission. Registered users of EE have access to more features than guest users.
The EarthExplorer user interface is an online search, discovery, and ordering tool developed by the United States Geological Survey (USGS). EarthExplorer supports the searching of satellite, aircraft, and other remote sensing inventories through interactive and textual-based query capabilities. Through the interface, users can identify search areas, datasets, and display metadata, browse and integrated visual services within the interface.
The distributable version of EarthExplorer provides the basic software to provide this functionality. Users are responsible for verification of system recommendations for hosting the application on your own servers. By default, this version of our code is not hooked up to a data source so you will have to integrate the interface with your data. Integration options include service-based API's, databases, and anything else that stores data. To integrate with a data source simply replace the contents of the 'getDataset' and 'search' functions in the CWIC.php file.
Distribution is being provided due to users requests for the codebase. The EarthExplorer source code is provided "As Is", without a warranty or support of any kind. The software is in the public domain; it is available to any government or private institution.
The software code base is managed through the USGS Configuration Management Board. The software is managed through an automated configuration management tool that updates the code base when new major releases have been thoroughly reviewed and tested.
The concept for the Geohazard Supersites and Natural Laboratory was conceived during the 3rd International Geohazards workshop held in November 2007 at ESA-ESRIN. In 2010 it was established as an initiative within the Group on Earth Observation (GEO), and is organised as a voluntary international partnership which aims to improve geophysical scientific research and geohazard assessment, promoting rapid and effective uptake of scientific results for enhanced societal benefits in Disaster Risk Reduction (DRR). The focus of the initiative are areas with important scientific problems and high risk levels: the Supersites and the Natural Laboratories.
WHO ARE WE?
The space agencies under the Committee for Earth Observation Satellites (CEOS) support the initiative by contributing satellite imagery at no cost over the Supersite areas, within a specific quota agreed in advance.
In situ data providers, as geophysical monitoring institutes and research infrastructure consortiums, provide open access to data and research products, and contribute in kind resources for the management of the Supersites and their data infrastructures.
The international scientific community exploits the openly shared EO data to investigate the volcanic or tectonic processes ongoing at the Supersites. They in turn are expected to share the results of their research in a reusable format.
The end-users and decision makers involved in Disaster Risk Management receive updated scientific information which can greatly benefit their Prevention or Response activities at the Supersites.
Details on the composition of our Partnership are available here.
WHY GSNL
The incomplete knowledge of seismic and volcanic phenomena and the slow uptake of scientific results by decision makers often increase the consequences of eruptions and earthquakes, especially in less developed countries. The lack of knowledge may be caused by lack of observations, but can also be due to a difficult access to existing data and reusable scientific results. Other relevant causes could be the lack of local capacities, technologies, resources.
By establishing a Supersite, the local scientific community can increase the level of international collaboration and attention on important scientific questions. The advancements generated by the focused scientific investigations will be transferred to end-users and decision makers by the Supersite Coordinator, ensuring a smooth and rapid uptake of information.
HOW GSNL WORKS
Supersite proposals are first evaluated by the GSNL Scientific Advisory Committee and independent reviewers. The CEOS Data Coordination Team (DCT) collects the various space agencies' commitments to support the Supersite image requests (with archive data and new acquisitions). The space agencies then provide open access to satellite imagery for the Supersite scientific community. The in situ data providers (which in most cases are the Supersite Coordinators) commit to provide open access to ground-based data. The Supersite data and resources are open for the international scientific community (licensing may apply), and this stimulates collaboration, knowledge transfer, capacity building, and generation of new scientific results, which are shared within the community.
The research results which are relevant to geohazard assessment are then communicated by the Supersite Coordinator to the end-users and decision makers, using the national institutional channels.
GSNL OBJECTIVES
 |  |
The Global Wildfire Information System is a joint initiative of the GEO and the Copernicus Work Programs. TheGlobal Wildfire Information System (GWIS) aims at bringing together existing information sources at regional and national level in order to provide a comprehensive view and evaluation of fire regimes and fire effects at global level.
GWIS builds on the ongoing activities of the European Forest Fire Information System (EFFIS), the Global Terrestrial Observing System (GTOS) Global Observation of Forest Cover- Global Observation of Land Dynamics (GOFC-GOLD) Fire Implementation Team (GOFC Fire IT), and the associated Regional Networks, complementing existing activities that are on-going around the world with respect to wildfire information gathering. The development of GWIS is supported by the partner organizations and space agencies. Support to GWIS was just launched by NASA through its ROSES program
Access to worldwide information on wildfires is available through the GWIS viewer at http://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html
Reference: http://gwis.jrc.ec.europa.eu/
This map contains a number of world-wide dynamic image services providing\naccess to various Landsat scenes covering the landmass of the World for\nvisual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).
The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled\nso that the applications can restrict the displayed scenes to a specific date range.
This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.
Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.
For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.
*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.
For more information on each of the individual layers, see
http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ;
http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ;
http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6
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"landingPage": "https://communities-amerigeoss.opendata.arcgis.com/datasets/amerigeoss::global-volcano-risk-by-country",
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"title": "US1hz050 1:15M poly",
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"format": "ArcGIS GeoServices REST API",
"mediaType": "application/json",
"accessURL": "https://earthquake.usgs.gov/arcgis/rest/services/haz/US1hz050_2008/MapServer/102"
},
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"mediaType": "text/csv",
"accessURL": "https://communities-amerigeoss.opendata.arcgis.com/api/download/v1/items/8c5677ee1ef04456ae7ae6e4fb66f440/csv?layers=102"
},
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"description": "Probabilistic seismic-hazard maps were prepared for the conterminous United States portraying peak horizontal acceleration and horizontal spectral response acceleration for 0.2- and 1.0-second periods with probabilities of exceedance of 10 percent in 50 years and 2 percent in 50 years. This particular data set is for horizontal spectral response acceleration for 1.0-second period with a 10 percent probability of exceedance in 50 years. All of the maps were prepared by combining the hazard derived from spatially smoothed historic seismicity with the hazard from fault-specific sources. The acceleration values contoured are the random horizontal component. The reference site condition is firm rock, defined as having an average shear-wave velocity of 760 m/sec in the top 30 meters corresponding to the boundary between NEHRP (National Earthquake Hazards Reduction program) site classes B and C.\n\nFor more information online visit:\nhttp://pubs.usgs.gov/sim/3195/,\nhttp://pubs.usgs.gov/of/2008/1128/, and \nhttp://earthquake.usgs.gov/hazards/",
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"landingPage": "https://communities-amerigeoss.opendata.arcgis.com/maps/amerigeoss::puerto-gaitan-meta",
"title": "Puerto Gaitan, Meta",
"description": "{{description}}",
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"landingPage": "https://communities-amerigeoss.opendata.arcgis.com/documents/amerigeoss::streamflow-prediction-tool-argentina",
"title": "Streamflow Prediction Tool Argentina",
"description": "{{description}}",
"keyword": [
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"issued": "2018-10-16T13:48:18.000Z",
"modified": "2018-11-09T01:30:38.000Z",
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"accessURL": "https://arcg.is/1Saamb"
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"landingPage": "https://communities-amerigeoss.opendata.arcgis.com/datasets/amerigeoss::quake-data-2017-oct-nov",
"title": "Quake Data 2017 Oct-Nov",
"description": "{{description}}",
"keyword": [
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"modified": "2018-11-09T01:30:38.000Z",
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"identifier": "https://www.arcgis.com/home/item.html?id=b0d3fd52dbf9439fac94f4505cabf1a3",
"landingPage": "https://communities-amerigeoss.opendata.arcgis.com/documents/amerigeoss::advanced-webinar-radar-remote-sensing-for-land-water-disaster-applications",
"title": "Advanced Webinar: Radar Remote Sensing for Land, Water, & Disaster Applications",
"description": "Optical satellite remote sensing depends on cloudless, well-illuminated areas to produce quality data. This is especially problematic for collecting data during nighttime or when there is cloud cover. Radar is an ideal sensor because of its ability to “see” the surface through clouds or regardless of day or night conditions. In addition, the radar signal can penetrate through the vegetation canopy and provide information about conditions underneath, such as whether there is flooding. Also, techniques such as interferometry can track surface deformation on the order of centimeters, such as displacement caused by earthquakes.
This webinar series builds on ARSET's previous webinar: Introduction to Synthetic Aperture Radar. The training will focus on different techniques such as time-series, polarimetry, and interferometry for mapping and monitoring disasters, water and land cover applications such as deforestation, crops, flooding, and earthquakes.
La teledetección satelital óptica depende de áreas sin nubes y bien iluminadas para producir datos de calidad. Esto es especialmente problemático para la recolección de datos durante la noche o cuando hay cobertura nubosa. El radar es un sensor ideal debido a su habilidad de “ver” la superficie a través de las nubes o independientemente si es de día o de noche. Además, la señal de radar puede penetrar a través del dosel de vegetación y proporcionar información sobre las condiciones en la superficie, como por ejemplo si el área esta inundada. Las técnicas como la interferometría pueden detectar deformación en la superficie en escala de centímetros, como por ejemplo el desplazamiento causado por terremotos.
Esta serie en línea tiene como base la capacitación ARSET: Introducción al Radar de Apertura Sintética. Esta capacitación se enfocará en diferentes técnicas tales como series temporales, polarimetría e interferometría para el mapeo y monitoreo de aplicaciones relacionadas a desastres y cobertura terrestre como la deforestación, los cultivos, las inundaciones y los terremotos.
SOURCE: https://arset.gsfc.nasa.gov/disasters/webinars/advanced-SAR-18
By the end of this training, attendees will be able to:
Al concluir esta capacitación, los(las) participantes podrán:
Remote sensing users from local, regional, state, federal, and international organizations interested in using SAR for terrestrial applications such as inundation mapping, land cover land use change studies, and surface deformation for volcanic and earthquake activity.
ParticipantesUsuarios de la teledetección de organizaciones locales, regionales, estatales, federales e internacionales interesados en utilizar SAR para aplicaciones terrestres como el mapeo de inundaciones, estudios de cambios en la cobertura terrestre y el uso del suelo y deformación superficial para actividad volcánica y terremotos.
This session will cover synthetic aperture radar (SAR) applications for mapping land cover and land cover change, including deforestation. We will address the challenges encountered when mapping these types of applications, and how to best address them.
Primera Sesión: 7 de agosto
Esta sesión cubrirá aplicaciones de radar de apertura sintética (SAR por sus siglas en inglés) para el mapeo de la cobertura terrestre y sus cambios, incluyendo la deforestacion. Abordaremos los retos que enfrentamos al mapear este tipo de aplicaciones y la mejor manera de superarlos.
This session will cover using SAR for mapping flooded areas and flood dynamics in natural and urban environments.
Segunda Sesión: 9 de agosto
Esta sesión cubrirá el uso de SAR para el mapeo de áreas inundadas y la dinámica de inundaciones en ambientes naturales y urbanos.
This session will cover using cover using SAR for mapping crop growth.
Tercera Sesión: 14 de agosto
Esta sesión cubrirá el uso de SAR para el mapeo de el crecimiento de cultivos.
This session will cover using interferometric SAR to map surface deformation related to earthquakes.
Sesión Cuatro: Agosto 16
Esta sesión cubrirá el uso de SAR interferométrico para mapear deformación en la superficie terrestre relacionada con terremotos.
The CEOS Water Portal Concept
The goal of the CEOS Water Portal Project is to provide assistance to scientists and general users (or non-researchers) from the water domain in the development of data services associated with data integration and distribution.
ScopeThe CEOS Water Portal is built around several key concepts:
CEOS Water Portal Data Partners include the National Aeronautics & Space Administration (NASA) and the National Oceanic & Atmospheric Administration (NOAA).
Access the CEOS Water Portal.
New Water Portal Architecture
For several years in 2000s, WGISS Test Facility for CEOP (WTF-CEOP) project was tackled by the lead of JAXA to meet the demand of CEOP user community to develop the data integration services. With the fulfillment of the task, the project ended in 2009.
The WTF-CEOP successfully having demonstrated the advantages of data integration for CEOP, the request for enhanced services for water communities was motivated by Dr.Toshio Koike, the lead scientist of CEOP, to push the data integration forward and improve the accessibility to the hydrological data, or water relevant data in broader sense, which are distributed globally.
JAXA has taken the roll to expand the“WTF-CEOP” prototype system into “CEOS Water Portal ”, in which more variety of datasets and features are explored. Another essential element is that of the use by not only scientists but also by non-researchers who are dealing with those data in their daily work, such as river administrators etc.
The lead organization representing the CEOS WGISS agencies in the CEOS Water Portal Project is JAXA, with participation by NASA and NOAA. Other WGISS members and WGISS affiliates may join at a future date. The project lead is Satoko Miura, JAXA.
Take a look at the Water Portal Presentation slides presented at recent WGISS meetings and WGISS meeting minutes or download the Water Portal Implementation Plan for more information.
| Date | Meeting Title | Presentation Slides | Minutes |
| Sep 29, 2015 | WGISS-40 | ||
| May 13, 2015 | WGISS-39 | ||
| Oct 01, 2014 | WGISS-38 | ||
| Apr 17, 2014 | WGISS-37 | pdf-1, pdf-2 | |
| Sep 17, 2013 | WGISS-36 | pdf-1, pdf-2 | |
| May 9, 2013 | WGISS-35 | pdf-1, pdf-2 | |
| Sep 25, 2012 | WGISS-34 | ||
| Apr 25, 2012 | WGISS-33 | ||
| Feb 24, 2012 | IGWCO 8th | ||
| Sep 27, 2011 | WGISS-32 | ||
| Jun 16, 2011 | WGISS-31 |
Communication and Coordination
Portal development will continue through the end of March, 2016, at which point it becomes fully operational from April, 2016 onward.
Planned enhancements to the Portal include architecture renovation, which is to be complete by the end of March, 2015. The new architecture focuses on leveraging an open source catalog broker software (GI-cat) as well as outside catalog servers (or brokers), which will greatly simplify the total architecture and reduce the labor of catalog management without sacrificing service quality for users.
For more information, feel free to contact Satoko Miura (Japan Aerospace Exploration Agency, JAXA).
Source: http://ceos.org/ourwork/workinggroups/wgiss/past-activities/water-portal/
Hurricane Katrina of August, 2005, is remembered as one of the most destructive and influential storms in United States history. The densely populated city of New Orleans, one of many areas around the Gulf Coast to face catastrophic damage, endured extreme flooding and physical destruction when several levees and other flood prevention features guarding the city broke down. Many evacuated the city and fled to far corners of the country, and a large portion of these evacuees were unable to resettle in New Orleans after the storm. Dealing with the aftermath of Hurricane Katrina involved many immense challenges, but ten years later, one can see the effects of a decade of hard work in restoring this historic city. This series of maps tracks New Orleans through these ten years of change.
The story map uses the Esri Story Map Series app,
The story was produced by Esri in collaboration with the Smithsonian Institution. The story can also be found on the Smithsonian Website. Data for each map was taken from the following sources:
Katrina Diaspora: 2006 American Community Survey 1-year Estimates, State-to-State Migration Flows, NHC, NOAA, NWS.
Flooding: Terrestrial lidar datasets of New Orleans levee failures from Hurricane Katrina, August 29, 2005: U.S. Geological Survey Data Series, NASA Earth Observatory, NOAA National Geodetic Survey.
Population Shift: The Data Center analysis of data from U.S. Census 2000 Summary File 1 (SF1) and U.S. Census 2010 Summary File 1 (SF1)
Steady Restoration: The Data Center analysis of Valassis Residential and Business Database
Neighborhood Reference Map: City of New Orleans GIS Department
A GRID-Arendal Story Map: The Spread of Sargassum
In recent years Sargassum has increasingly been seen as a pest, washing up in vast quantities on beaches on both sides of the Atlantic. There are two species of Sargassum involved in this phenomenon - Sargassum natans and Sargassum fluitans. They are both examples of the free-floating variety that characteristically form the large algal rafts found in the Sargasso Sea.
Something happened in 2011 to produce an ocean-wide explosion of Sargassum that stretched from the Caribbean to the coast of Africa. Unprecedented quantities of seaweed came ashore on coastlines on both sides of the Atlantic. Previously there had been sporadic episodes of the seaweed washing up on beaches, but nothing before on this scale.
Similar events have been observed since the 2011 great Sargassum pileup. There is uncertainty about whether the “golden tides”, as they are often referred to, will be an annual occurrence. However 2015 has seen a severe influx of Sargassum on both sides of the Atlantic, similar in magnitude to the 2011 event. Learn more about Sargassum in this Story Map.
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No use of this graphic may be made for resale or any other commercial purpose whatsoever without prior permission in writing from GRID-Arendal. Applications for such permission, with a statement of the purpose and extent of the reproduction, should be addressed to grid@grida.no.
More graphics produced by GRID-Arendal can be found on our GRAPHICS page.
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| Very High Human Development: | 0.736 and higher |
| High Human Development: | 0.615 to 0.735 |
| Medium Human Development: | 0.494 to 0.614 |
| Low Human Development: | 0.493 and lower |
| Very High Human Development: | 0.736 and higher |
| High Human Development: | 0.615 to 0.735 |
| Medium Human Development: | 0.494 to 0.614 |
| Low Human Development: | 0.493 and lower |
| Very High Human Development: | 0.736 and higher |
| High Human Development: | 0.615 to 0.735 |
| Medium Human Development: | 0.494 to 0.614 |
| Low Human Development: | 0.493 and lower |
This is the 20160330 situation report on the Ebola Response.
This interactive map journal shows the evolution of the outbreak and the global response. The report contains a review of the epidemiological situation based on official information reported by ministries of health, and an assessment of the response measured against the core Roadmap indicators where available. Substantial efforts are ongoing to improve the availability and accuracy of information about both the epidemiological situation and the implementation of response measures.
The html version of the latest situation report is available on the WHO website here.
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