The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This is a saved copy of the NWS Weather Watches and Warning layer, filtered just for wildfire related warnings.Details from the orginal item:https://www.arcgis.com/home/item.html?id=a6134ae01aad44c499d12feec782b386This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseUpdate FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described belowRECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area.RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats. Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format). Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data.

This submission contains a number of maps and shapefiles related to the Utah FORGE site. Examples include geologic maps (several variations) and GIS data for the Utah FORGE site outline. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

This service depicts National Park Service tract and boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated and vary by location. NPS assumes no liability for use of this data. The boundary polygons represent the current legislated boundary of a given NPS unit. NPS does not necessarily have full fee ownership or hold another interest (easement, right of way, etc...) in all parcels contained within this boundary. Equivalently NPS may own or have an interest in parcels outside the legislated boundary of a given unit. In order to obtain complete information about current NPS interests both inside and outside a unit’s legislated boundary tract level polygons are also created by NPS Land Resources Division and should be used in conjunction with this boundary data. To download this data directly from the NPS go to https://irma.nps.gov Property ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service (NPS) shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 10 countries of interest in Southwest Asia (area of study): Afghanistan, Cambodia, Laos, India, Indonesia, Iran, Nepal, North Korea, Pakistan, and Thailand. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feat ...

This product set contains high-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N) and Barrow Triangle (156.13 - 157.08 deg W, 71.14 - 71.42 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM). Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary and ArcInfo grid format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); a quarter-quadrangle index map for the 26 IFSAR tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format). Unmodified IFSAR data comprise 26 data tiles across UTM zones 4 and 5. The DSM and DTM tiles (5 m resolution) are provided in floating-point binary format with header and projection files. The ORRI tiles (1.25 m resolution) are available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on five DVDs, available through licensing only to National Science Foundation (NSF)-funded investigators. An NSF award number must be provided when ordering data.

This product set contains reduced-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of an Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM). Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format). The DSM and DTM data sets (20 m resolution) are provided in floating-point binary format with header and projection files. The ORRI mosaic (5 m resolution) is available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are available via FTP and CD-ROM.

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for the People's Republic of China. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feature classes from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facili ...

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for the 15 counties fully within the Northwest Florida Water Management District (Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Leon, Liberty, Okaloosa, Santa Rosa, Wakulla, Walton, and Washington counties). These files were compiled through a cooperative project between the U.S. Geological Survey and the Florida Department of Agriculture and Consumer Services, Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated lands that were verified during field surveying that started in May 2021 and concluded in August 2021. Field data collected were crop type, irrigation system type, and primary water source used. A map image of the shapefile is also provided. Previously published estimates of irrigation acreage for years since 1982 are included in summary tables.

Point feature class and related table containing the Precise Surveys measurement time series. Measurements include elevations, Northings and Eastings, distances, and point-to-point measurements. Northing and Easting measurements are in CA State Plane Coordinate systems, Elevations measurements are provided in NAVD88 or NGVD29. This dataset is for data exploration only. These measurements and point locations are not considered survey-grade since there may be nuances such as epochs, adjustments, and measurement methods that are not fully reflected in the GIS data. These values are not considered authoritative values and should not be used in-lieu of actual surveyed values provided by a licensed land surveyor. Related data and time series are stored in a table connected to the point feature class via a relationship class. There may be multiple table entries and time series associated to a single mark. Data was assembled through an import of Excel tables and import of mark locations in ArcGIS Pro. Records were edited by DOE, Geomatics, GDSS to resolve any non-unique mark names. This dataset was last updated 4/2024.

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described below RECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area. RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. Metadata

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for the fourteen counties that are fully or partially within the Suwannee River Water Management District, Florida compiled through a cooperative project between the U.S Geological Survey and the Florida Department of Agriculture and Consumer Services, Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated lands that were verified during field trips that started in January 2020 and concluded in December 2020, and the crop type, irrigation system type, and primary water source used. A map image of the shapefile is provided. Previously published estimates of irrigation acreage for years since 1982 are included in summary tables.

The Vegetation Technical Working Group (VTWG) of the Alaska Geospatial Council developed the Minimum Standards for Field Observation of Vegetation and Related Properties Version 1.1 (August 2022) to help ensure that vegetation data collected as part of independent vegetation survey, mapping, monitoring, and classification projects can support the production of a statewide vegetation map from quantitative data and the continued development of the U.S. National Vegetation Classification.

Wetlands are areas where water is present at or near the surface of the soil during at least part of the year. Wetlands provide habitat for many species of plants and animals that are adapted to living in wet habitats. Wetlands form characteristic soils, absorb pollutants and excess nutrients from aquatic systems, help buffer the effects of high flows, and recharge groundwater. Data on the distribution and type of wetland play an important role in land use planning and several federal and state laws require that wetlands be considered during the planning process.The National Wetlands Inventory (NWI) was designed to assist land managers in wetland conservation efforts. The NWI is managed by the US Fish and Wildlife Service.Dataset SummaryPhenomenon Mapped: WetlandsGeographic Extent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, American Samoa, and the Northern Mariana IslandsProjection: Web Mercator Auxiliary SphereVisible Scale: This layer preforms well between scales of 1:1,000,000 to 1:1,000. An imagery layer created from this dataset is also available which you can also use to quickly draw wetlands at smaller scales.Source: U.S. Fish and Wildlife ServiceUpdate Frequency: AnnualPublication Date: October 26, 2024This layer was created from the October 26, 2024 version of the NWI. The features were converted from multi-part to a single part using the Multipart To Singlepart tool. Features with more than 50,000 vertices were split with the Dice tool. The Repair Geometry tool was run on the features, using the OGC option.The layer is published with a related table that contains text fields created by Esri for use in the layer's pop-up. Fields in the table are:Popup Header - this field contains a text string that is used to create the header in the default pop-up System Text - this field contains a text string that is used to create the system description text in the default pop-upClass Text - this field contains a text string that is used to create the class description text in the default pop-upModifier Text - this field contains a text string that is used to create the modifier description text in the default pop-upSpecies Text - this field contains a text string that is used to create the species description text in the default pop-upCodes, names, and text fields were derived from the publication Classification of Wetlands and Deepwater Habitats of the United States.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for System Name = 'Palustrine' to create a map of palustrine wetlands only.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d mapUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Water Related Land Use (2002 to 2007) consists entirely of data generated from the "New Digital Method" and contains irrigation type and labels recorded in the attributes. This layer was combined from multiple basin layers to create the earliest state wide layer comprised entirely of the "New Digital Method" in which all basins have label and irrigation type information. When using multiple layers from these combined year state wide layers, please take care to verify that you do not duplicate data in certain basins due to how these layers have been generated.The water-related land use program is an effort by the Utah Division of Water Resources to quantify the acreages in the state of various land use types, especially those which are irrigated. Prior to 2017, land use was completed for a single basin each year. The present method is able to utilize historical line-work, attributes, and remotely sensed data to estimate acreage changes for the entire state in a single year.