This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This Vegetation product (Bands 1 2 1 | Red, Near Infrared, Red) shows the differences in type and density of vegetation. Irrigated agriculture appears bright green, whereas forests are a darker green and lawns are a muted green. Bare soils are light purples to white, and water is dark purple to black. Clouds are light grey to white. There are two Vegetation products, one for each satellite (Aqua and Terra). Each product is a surface reflectance product with a 250 meter resolution. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this layer is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.IMPORTANT NOTICE: On August 16, 2020, Aqua MODIS experienced an anomaly with the Formatter-Multiplexer Unit (FMU). As a result, imagery was not produced from August 16, 2020 through September 2, 2020.

This dataset is available for download from: Wetlands (File Geodatabase).

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader land cover raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log

Version 1.1 (January 26, 2023)

• Full resolution of wetlands replaced a coarser resolution version that was previously shared. Also, file type changed from polygon to raster (feature service to tile layer service).

This is a subset of the flood images that is curated by Strategic Communications and is used in public applications highlighting recovery.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 27 threshold values of temperature for the contiguous United States for 1950-2013. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2013. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, _location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

Mature Support Notice: This item is in mature support as of March 2022. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This Burn Scars and Flooding band composition (Bands 7 2 1 | Shortwave Infrared, Near Infrared, Red) is designed to distinguish between burned areas and bare soil, this composition is effective at detecting recent burn scars, which will appear red to reddish-brown. Water will appear dark blue to black, making it an effective tool for delineating inundated areas. This map shows the 250 meter corrected reflectance product from both satellites that carry a MODIS, Aqua and Terra. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis. Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service. This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service. Note on Time: The image service supporting this layer is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

This app is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsThis layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireNote about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to downscaled climate projections for 27 threshold values of temperature for the contiguous United States for 2 SSP climate scenarios from 1950-2100. These services are intended to support analysis of climate exposure for custom geographies and time horizons. Sixteen downscaled global circulation models (GCMs) were chosen to be included in a weighted ensemble, optimized for the contiguous United States. More details on the models included in the ensemble and the weighting methodologies can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 2005 to 2100. Additionally, a modeled history runs from 1950 - 2005. The modeled history and future projections have been merged into a single time series. These annual increments support deriving a temporal average, such as a decadal or thirty-year period centered on a specific year. These time steps should not be used to make predictions about conditions for a specific year, especially at a pixel-level. Climate ScenariosClimate models use estimates of future greenhouse gas concentrations and human activities to predict overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Two different SSPs are presented here: 2-4.5 and 5-8.5. The 2- or 5- represents the socioeconomic growth model. The 4.5 or 8.5 number indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP2-4.5 aligns closest with the international targets of the COP-26 agreement for no greater than 2oC average global warming. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, depicting a worst-case scenario if reductions in greenhouse gasses are not undertaken. Variable DefinitionsSee the variable list and definitions here. Additional ServicesThree versions of the gridded climate projections are available from CRIS:LOCA2 Ensemble: a statistically downscaled 6-km resolution model. LOCA2 has SSP2-4.5, 3-7.0 and 5-8.5STAR-ESDM Ensemble: a statistically downscaled 4-km resolution model. STAR-ESDM has SSP2-4.5 and 5-8.5NCA5 Blended Ensemble: a merging of LOCA2 and STAR-ESDM ensembles at a 6-km resolution, as was done for the 5th National Climate Assessment (2023). NCA Blended Ensemble has SSP2-4.5 and 5-8.5Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable, SSP, and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.

University of Vermont Spatial Analysis Laboratory

This dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:

• Existing tree canopy percent
• Possible tree canopy - vegetation percent
• Relative percent change
• Absolute percent change

For more information, please see the 2021 Tree Canopy Assessment.

This aerial imagery mosaic dataset was collected on behalf of the Washington State Department of Natural Resources (WA DNR) during the summer of 2022 in order to map the extent of floating kelp canopies in Washington State. Areas covered include the northern outer coast of Washington, the US portion of the Strait of Juan de Fuca, North Puget Sound, the San Juan Islands, Saratoga Passage & Whidbey Island Basin, WA DNR managed aquatic reserves (Cherry Point AR, Cypress Island AR, and Smith & Minor Islands AR), Admiralty Inlet, Tacoma Narrows, and Squaxin Island. Aerial surveys were conducted during low-tide, slack-current conditions identified by WA DNR for kelp forest monitoring, with some exceptions. This imagery has four bands (Band_1=red, Band_2=green, Band_3=blue, and Band_4=near-infrared) and was collected at 6-inch resolution. Floating kelp is readily visualized using an NGB band combination (near-infrared, green, blue), but can also be seen in some places with standard RGB imagery (red, green, blue). Imagery from this project are distributed as compressed (lossy) MrSID files for accessibility and ease of visualization, and are not suitable for image classification and analysis. These mosaics are a subset of the imagery products generated by this project, please contact the WA DNR Nearshore Habitat Program about other products that are available. These data support WA DNR's statewide long-term monitoring goals by providing high-resolution and large-area visualization of floating kelp canopies.For more information and inquiries about products available at specific locations, contact Danielle Claar, WA DNR, Nearshore Habitat Program at: danielle.claar@dnr.wa.gov or nearshore@dnr.wa.gov.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual 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 so 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

Mature Support Notice: This item is in mature support as of June 2021. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. This map shows water states for the planet, featuring NASA Global Imagery Browse Services band composition (Bands 3 6 7) which shows the different physical states that water can take, such as ice, snow or liquid. The map is time enabled and shows the period from January 1 to December 31, 2013 by two week intervals. The map includes bookmarks for different continents and other places of interest for easy navigation. This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This Water States product (Bands 3 6 7 | Blue, Shortwave Infrared, Shortwave Infrared) effectively discriminates between the various states water can take, such as clouds, snow, ice, and liquid. Ice or snow appears bright red, liquid water appears dark red to black. This band composition also tells you something about the makeup of clouds. Where ice crystals are present in clouds, they will appear reddish-orange or peach; liquid water clouds are white. Vegetation is green and deserts are light blue-green. There is one Water States product which is from the Terra satellite. It is a 250 meter corrected reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction. NASA Global Imagery Browse Services (GIBS) This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis. Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service. This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 16 threshold values of precipitation for the contiguous United States for 1950-2013. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2013. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This Burn Scars and Flooding band composition (Bands 7 2 1 | Shortwave Infrared, Near Infrared, Red) is designed to distinguish between burned areas and bare soil, this composition is effective at detecting recent burn scars, which will appear red to reddish-brown. Water will appear dark blue to black, making it an effective tool for delineating inundated areas. There are four Burn Scars and Floods products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this layer is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.IMPORTANT NOTICE: On August 16, 2020, Aqua MODIS experienced an anomaly with the Formatter-Multiplexer Unit (FMU). As a result, imagery was not produced from August 16, 2020 through September 2, 2020.

This raster file represents land within the Mountain Home study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. To build a Random Forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 27 threshold values of temperature for the contiguous United States for 1950-2023. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2023. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.