AuthorityIn the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas.The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies.Previous MethodsThe land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information.After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets.In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS).For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking.In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes.Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data.Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side.The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location.Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map.Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles.The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI).Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process.Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present MethodologyUsing the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. 2017 marked the first year of using the CDL Method for the whole state of Utah. This method utilizes the Cropland Data Layer from the USDA's National Ag. Statistics Service which provides acreage estimates major commodities and to produce crop-specific geo-referenced products at 30m resolution. The CDL Method utilizes past line work digitized by the division and reconciles changes that may have occurred, including new crop types or ag-to-urban conversions.Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed.Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies.In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas.Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county.During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state.Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Based primarily on the most recent release of LANDFIRE v2.0.0, this generalized land cover dataset provides full coverage of California including to the three nautical mile limit offshore. It represents a ground condition of 2016 divided into 30mx30m cells across the entire state. The state is grouped into the following land cover classes: forests, shrublands and chaparral, grasslands, croplands, wetlands, seagrasses and seaweeds, developed lands, and sparsely vegetated lands. The mapped area has been extended offshore to three nautical miles. Lakes, reservoirs, rivers, and oceans that do not overlay seagrasses and seaweeds are identified in as “open water.” LANDFIRE v.2.0.0 provides the source for much of the land cover and is an integrated dataset with many layers. The Existing Vegetation Type (EVT) and Biophysical Settings (BPS) layers provide inputs to this data set. The EVT layer contains data on life form (tree, shrub, herb, developed, agriculture, sparse, barren, snow-ice, or water), a named vegetation type, and notes on recent disturbance. These are used to assign a likely generalized land cover type to each pixel. This result is then refined using the BPS layer to suggest the land cover that might exist in recently disturbed (fire or logging) areas absent that disturbance. These results are then supplemented through the creation of a seagrasses and seaweeds dataset by combining data on the presence of eelgrass and kelp canopy and replacing the water category with seagrasses and seaweeds where it is present.These data result from the integration of remote sensing (satellite imagery analysis), with field data, using computer algorithms under the oversight of the LANDFIRE team or the teams developing the seagrass and kelp maps. Errors are expected in all data and while every attempt is made to minimize and understand them, they cannot be eliminated. As a result, the cells in the data represent an estimate of what is on the ground at that specific location. Validation techniques used in the production of the data help identify and allow for correction of gross errors, but individual pixels, or even small groupings of them may differ from real world conditions. Similarly, while efforts are made to be consistent with the selection of the source satellite data, the difference between seasons or a wet versus dry year do impact the final maps, notably water and wetlands.Data SourcesLANDFIRE: LANDFIRE Existing Vegetation Type layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].LANDFIRE: LANDFIRE Biophysical Setting layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].Bell, T, K. Cavanaugh, D. Siegel. 2020. SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing) ver 13. Environmental Data Initiative. https://doi.org/10.6073/pasta/5d3fb6fd293bd403a0714d870a4dd7d8. Accessed 2021-04-08. (Data extraction performed by T. Bell April 8, 2021)Eelgrass Survey GIS Data version 2.0 (2017, updated 2020), National Marine Fisheries Service West Coast Region. Available: https://www.sfei.org/data/eelgrass-survey-gis-data#sthash.u94SjLu7.afUwqGJA.dpbs [Accessed: April 6, 2021)

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified.This layer displays land cover for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web mercator, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief.MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover.51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is periodically saturated with or covered with water.95. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80% of vegetative cover and the soil or substrate is

This land cover map is a subset of the National Land Cover Dataset (NLCD) produced by the Multi-Resolution Land Characteristics (MRLC) Consortium (USGS, EPA, NOAA, and USFS) 1/6/1999. The NLCD was produced in order to provide a consistent, land cover data layer for the conterminous U.S. utilizing early 1990s Landsat Thematic Mapper data. The raster map depicts the land cover of the Oak Ridge Reservation at a 30m spatial resolution. Yang et al. (2001) found the thematic accuracy for the MRLC land cover map for the eastern U.S. to be 59.7% at Anderson Level II thematic detail and 80.5% at Anderson Level I.

The NLCD classification scheme (based on Anderson et al. 1976) is as follows -

Water - All areas of open water or permanent ice/snow cover. 11. Open Water - all areas of open water, generally with less than 25% cover of vegetation/land cover. 12. Perennial Ice/Snow - all areas characterized by year-long surface cover of ice and/or snow.

Developed Areas characterized by a high percentage (30 percent or greater) of constructed materials (e.g. asphalt, concrete, buildings, etc). 21. Low Intensity Residential - Includes areas with a mixture of constructed materials and vegetation. Constructed materials account for 30-80 percent of the cover. Vegetation may account for 20 to 70 percent of the cover. These areas most commonly include single-family housing units. Population densities will be lower than in high intensity residential areas. 22. High Intensity Residential - Includes highly developed areas where people reside in high numbers. Examples include apartment complexes and row houses. Vegetation accounts for less than 20 percent of the cover. Constructed materials account for 80 to100 percent of the cover. 23. Commercial/Industrial/Transportation - Includes infrastructure (e.g. roads, railroads, etc.) and all highly developed areas not classified as High Intensity Residential.

Barren - Areas characterized by bare rock, gravel, sand, silt, clay, or other earthen material, with little or no green vegetation present regardless of its inherent ability to support life. Vegetation, if present, is more widely spaced and scrubby than that in the green vegetated categories; lichen cover may be extensive. 31. Bare Rock/Sand/Clay - Perennially barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, beaches, and other accumulations of earthen material. 32. Quarries/Strip Mines/Gravel Pits - Areas of extractive mining activities with significant surface expression. 33. Transitional - Areas of sparse vegetative cover (less than 25 percent of cover) that are dynamically changing from one land cover to another, often because of land use activities. Examples include forest clearcuts, a transition phase between forest and agricultural land, the temporary clearing of vegetation, and changes due to natural causes (e.g. fire, flood, etc.).

Forested Upland - Areas characterized by tree cover (natural or semi-natural woody vegetation, generally greater than 6 meters tall); tree canopy accounts for 25-100 percent of the cover. 41. Deciduous Forest - Areas dominated by trees where 75 percent or more of the tree species shed foliage simultaneously in response to seasonal change. 42. Evergreen Forest - Areas dominated by trees where 75 percent or more of the tree species maintain their leaves all year. Canopy is never without green foliage. 43. Mixed Forest - Areas dominated by trees where neither deciduous nor evergreen species represent more than 75 percent of the cover present.

Shrubland - Areas characterized by natural or semi-natural woody vegetation with aerial stems, generally less than 6 meters tall, with individuals or clumps not touching to interlocking. Both evergreen and deciduous species of true shrubs, young trees, and trees or shrubs that are small or stunted because of environmental conditions are included. 51. Shrubland - Areas dominated by shrubs; shrub canopy accounts for 25-100 percent of the cover. Shrub cover is generally greater than 25 percent when tree cover is less than 25 percent. Shrub cover may be less than 25 percent in cases when the cover of other life forms (e.g. herbaceous or tree) is less than 25 percent and shrubs cover exceeds the cover of the other life forms.

Non-Natural Woody - Areas dominated by non-natural woody vegetation; non-natural woody vegetative canopy accounts for 25-100 percent of the cover. The non-natural woody classification is subject to the availability of sufficient ancillary data to differentiate non-natural woody vegetation from natural woody vegetation. 61. Orchards/Vineyards/Other - Orchards, vineyards, and other areas planted or maintained for the production of fruits, nuts, berries, or ornamentals.

Herbaceous Upland - Upland areas characterized by natural or semi-natural herbaceous vegetation; herbaceous vegetation accounts for 75-100 percent of the cover. 71. Grasslands/Herbaceous - Areas dominated by upland grasses and forbs. In rare cases, herbaceous cover is less than 25 percent, but exceeds the combined cover of the woody species present. These areas are not subject to intensive management, but they are often utilized for grazing.

Planted/Cultivated - Areas characterized by herbaceous vegetation that has been planted or is intensively managed for the production of food, feed, or fiber; or is maintained in developed settings for specific purposes. Herbaceous vegetation accounts for 75-100 percent of the cover. 81. Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops. 82. Row Crops - Areas used for the production of crops, such as corn, soybeans, vegetables, tobacco, and cotton. 83. Small Grains - Areas used for the production of graminoid crops such as wheat, barley, oats, and rice. 84. Fallow - Areas used for the production of crops that do not exhibit visable vegetation as a result of being tilled in a management practice that incorporates prescribed alternation between cropping and tillage. 85. Urban/Recreational Grasses - Vegetation (primarily grasses) planted in developed settings for recreation, erosion control, or aesthetic purposes. Examples include parks, lawns, golf courses, airport grasses, and industrial site grasses.

Wetlands - Areas where the soil or substrate is periodically saturated with or covered with water. 91. Woody Wetlands - Areas where forest or shrubland vegetation accounts for 25-100 percent of the cover and the soil or substrate is periodically saturated with or covered with water. 92. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for 75-100 percent of the cover and the soil or substrate is periodically saturated with or covered with water.

Citations Anderson, J. F., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. In: U.S. Geological Survy Professional Paper. 964 (pp.28). Washingont, DC: U.S. Geological Survey.

Yang, L., S. V. Stehman, J. H. Smith, and J. D. Wichham. 2001. Thematic accuracy of MRLC land cover for the eastern United States. Remote Sensing of Environment, 76:418-422.

Date of Images:6/24/2024, 6/25/2024Date of Next Image:UnknownSummary:Scientists at NASA's Marshall Space Flight Center created these water extents in June 2024 using PlanetScope imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the Cropland Data Layer (CDL).It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets.Suggested Use:This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from CDL that appear to have water and are potentially flooded.Blue (1): Known WaterRed (2): Flooded DevelopedGreen (3): Flooded VegetationOrange (4): Flooded Cropland/GrasslandGray (5): Clouds/Cloud Shadow(0): No DataSatellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on the right side of page.WMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/iowa_flood_202406/Iowa_Floods_Surface_Water_Extent_Planet/MapServer/WMSServer

Low with neighborhood-nature, agriculture and water (>0.5ha). The layer shows the open spaces that are natural, agricultural or water and also have a recreational use. We mean anything larger than 0.5 ha (or 5,000 m2) (smaller does not exist): - agricultural or agricultural greenery, unless completely inaccessible - natural greenery, unless completely inaccessible: e.g. Hobokense Polder, Wolvenberg - water: For example, Galgenweel, Burchtse Weel, Docks, Scheldt Recreational shared use means that this space can be experienced, via accessible paths in or along the area. Only passive forms of recreation are allowed on these accessible paths. This data layer is updated annually in January. (see also geodata portal - https://geoportaal.antwerpen.be/portal/home/search.html?q=experience green , open geodata portal - http://portaal-stadantwerpen.opendata.arcgis.com/datasets?q=experience green , quantified in city in figures - https://stadincijfers.antwerpen.be/Databank/Jive/?workspace_guid=1afba9b7-ae0e-442b-9a49-d82c97f7cf0f )

The data was developed for the USGS Water-Use and Data Research program grant opportunities G20AS00053 and G21AS00258, combined with fundnig from Oregon Water Resources Department to improve estimates of water use from irrigated lands in Oregon. These data contain attributes of irrigation status, irrigation source type, crop type, irrigation method, assumed irrigation efficiency, irrigation water source, evapotranspiration (ET) data from OpenET, and effective precipitation developed using the USBR ET Demands model. Thee data were aggregated in order to further the development of estimates of applied water at the field-scale. Methods A single set of draft field boundaries for all agricultural lands were developed to represent the maximum extent of irrigated lands from 1985-2020 (digitized at the 1:5,000 scale). The approach used for this task was relatively straight forward yet time consuming and required careful attention to detail to avoid numerous potential pitfalls. Agricultural field boundaries were developed within a GIS system by modifying existing 2007 USDA Common Land Unit (CLU) data, OWRD drawn field boundaries (e.g., Malheur Lake Basin) and developing field boundaries from scratch where needed. This entailed: 1) using Common Land Unit (CLU) as-is where the quality and representativeness of the linework was deemed suitable; 2) modifying the CLU data to eliminate duplicates, overlaps, and slivers within the linework, and make representative of maximum agricultural extent; 3) manually digitizing new field boundaries where they do not currently exist; and 4) QAQC all results. Crop type and irrigation status rasters and field-level summaries were derived from the USDA Cropland Data Layer (CDL) (USDA, 2019) and the open-source IrrMapper model (Ketchum et al., 2020). IrrMapper uses a Random Forest (RF) modeling approach to predict four land classes of irrigated agriculture, dryland agriculture, uncultivated lands, and wetlands at an annual time step, and at 30 m spatial resolution across the Western U.S. IrrMapper was used in this project to produce rasters of these classes for 2016-2022. For the attribution of agricultural field boundaries, the native IrrMapper values were aggregated into 2 classifications; a value of ‘1’ representing irrigated conditions and ‘0’ representing non-irrigated conditions. For each year, mapped field polygons were included in HUC-12 ET and irrigated acreage summaries if the irrigation status value was greater than 0.4 (40% of IrrMapper pixels in polygon are classified as irrigated). Crop type classification was based on the mode (i.e., majority) of CDL crop type pixels contained by the individual field geometry. Irrigation system type was determined based on available data including OWRD place of use, water right, and water source information, high-resolution aerial images, and expert knowledge of agricultural practices in Oregon. The primary sources of imagery used for irrigation system type attribution was sourced from OSIP acquired in 2017 and 2018 at ~0.3m (1 ft pixel resolution) (State of Oregon: Oregon Geospatial Enterprise Office - Oregon Statewide Imagery Program, n.d.) and the 2020 series of aerial imagery from the National Agriculture Imagery Program (NAIP) (National Agriculture Imagery Program (NAIP), 2019) acquired at 60 cm (2 ft pixel resolution). Fields were attributed using the following irrigation system types: 0 - Developed/No longer irrigated; 1 - Sprinkler-Pivot-Linear; 2 - Sprinkler-Other (Wheel Line, Hand Line, Solid Set, Big Gun, Travelling Gun, Pods); 3 - Flood-Uncontrolled (Wild Flood) and No Apparent Irrigation Equipment; 4 - Flood-Controlled (Land Leveling, Borders, Basins, Furrows); 5 - Micro (Micro Sprinklers, Drip Lines, Subsurface Drip). An irrigation efficiency value, assumed to represent the ratio of ET of applied water divided by the total applied water, was assigned to each agricultural field based on the system type attribute. Average values of irrigation efficiency for each system type category were based on values in the Washington Department of Ecology Report “Determining Irrigation Efficiency and Consumptive Use” (Washington State Department of Ecology, 2005). Fields digitized by the DRI team were attributed by OWRD staff with one of the following irrigation source types: groundwater irrigated (GW), surface water irrigated (SW), or a combination of groundwater and surface water (GW&SW). The geometries represented in the shapefile are attributed using the following categories: 1 =GW irrigated, 2 = SW irrigated, and 3 = Combination. Estimates of irrigation application rates were developed using spatially averaged field-scale OpenET ensemble ET estimates, effective precipitation developed from ET Demands, and irrigation efficiency attributes collected by OWRD. Application rates were estimated as: Application Rate = (ET – effective precipitation) / irrigation efficiency) This approach resulted in many timesteps where effective precipitation was greater than ET, which resulted in negative Net ET. This negative Net ET was interpreted as a surplus of water contained within the represented unit of soil. As vegetation response lags irrigation activity, it is a certainty that irrigation or precipitation events occur during one calendar month, with a corresponding increase in ET and vegetation vigor observed in the following month. To account for this asynchronous relationship, negative Net ET was carried over to the following calendar month. This carry-over was repeated until positive Net ET values accounted for the surplus water condition. The applied water calculation was initialized using data developed prior to the 2016 water year, therefore all data associated with 2016 is considered valid. Citations: Beamer, J., & Hoskinson, M. (2021). Historical Irrigation Water Use and Groundwater Pumpage Estimates in the Harney Basin, Oregon, 1991-2018. State of Oregon Water Resources Department. Bromley, M.; Minor, B. A.; Pearson, C.; Beamer, J.; Dunkerly, C. W.; Ott, T.; Huntington, J. L.; Hoskinson, M. (2023). Evapotranspiration, Net Irrigation Water Requirements, and Reservoir Evaporation Estimates for Oregon. Desert Research Institute – Draft report prepared for Oregon Water Resources Department. Melton, F. S., Huntington, J. L., Grimm, R., Herring, J., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Senay, G. B., Volk, J., Hain, C., Johnson, L., Ruhoff, A., Blankenau, P., Bromley, M., Carrara, W., Daudert, B., Doherty, C., Dunkerly, C., Friedrichs, M., Guzman, A., Halverson, G., Hansen, J., Harding, J., Kang, Y., Ketchum, D., Minor, B., Morton, C., Ortega-Salazar, S., Ott, T., Ozdogan, M., ReVelle, P. M., Schull, M., Wang, C., Yang, Y., & Anderson, R. G. (2021). OpenET: Filling a critical data gap in water management for the western United States. JAWRA Journal of the American Water Resources Association, 58(6): 971-994. https://doi.org/10.1111/1752-1688.12956 National Agriculture Imagery Program (NAIP). (2020). [Data set]. DOI/USGS/EROS. https://catalog.data.gov/dataset/national-agriculture-imagery-program-naip State of Oregon: Oregon Geospatial Enterprise Office - Oregon Statewide Imagery Program. (n.d.). https://www.oregon.gov/geo/Pages/imagery.aspx USDA NRCS. (1993). Part 623 National Engineering Handbook, Chapter 2, Irrigation Water Requirements. Washington State Department of Ecology, 2005, Determining Irrigation Efficiency and Consumptive Use: Washington State Department of Ecology GUID-1210.

The debate over the best agricultural practices for biological conservation often focuses on the degree to which agricultural lands should be interspersed with desirable habitats versus protecting lands entirely from production. It is important to understand the benefits agriculture provides for wildlife because it is consuming an increasing proportion of the landscape. We evaluated the nesting ecology of breeding ducks within a mosaic of flood-irrigated conservation areas and agricultural lands in hay production. We assessed how habitat features at two spatial scales across these lands were related to nest site selection, nest density, and nest survival of multiple duck species. Birds selected nest sites with higher visual obstruction, a higher proportion of shrubs around the nest, and less bare ground, but we did not detect evidence of selection per se at larger spatial scales. Nest density was marginally higher along linear features, including irrigation ditches and riparian stretches, but nest survival remained similar across land-use types and habitat features. This system is representative of many agricultural landscapes around the globe and highlights the ways agroecosystems can be managed to maintain habitat suitability for wildlife on working lands. Methods Study System We studied a system of flood-irrigated basins in north-central Colorado, USA, to evaluate duck reproductive success across agricultural working lands (Figure 1). The North Platte Basin (hereafter North Park) is a high-elevation (2500 m on average) intermountain basin characterized by sagebrush (Artemesia spp.) steppe and riparian corridors used as sources of water to flood irrigate hay meadows (by diverting water into irrigation ditches). The Intermountain West of North America spans 11 states and is comprised of many of these high-elevation basins associated with river and groundwater-fed wetlands. While many are still associated with flood irrigation, some have predominantly transitioned to sprinkler-based irrigation systems to use water more efficiently (e.g., the San Luis Valley of Colorado). Agricultural production is typically comprised of large cattle ranches that also actively produce high-quality, flood-irrigated hay that is harvested each year. In North Park, harvested meadows consist primarily of Timothy hay (Phleum pretense), and are flooded in May, dried anywhere from July to August, and then harvested from July to September. Because of the short growing season, a single cut of hay each year is typical. The system also has public land parcels along riparian areas that are spared the annual harvest of typical agricultural operations, primarily Arapaho National Wildlife Refuge (NWR). This NWR was created in 1967 to benefit migratory and breeding ducks as mitigation for the conversion of high-quality duck breeding habitat in the Prairie Pothole Region of North America to high-intensity agriculture production in the 1960s and 1970s (Doherty et al. 2018). The NWR flood-irrigates wet meadows that are not cut, and that typically exhibit more diverse vegetation communities than Timothy hay meadows, including forbs, sedges, rushes, and grasses interspersed by small areas of greasewood shrubs (Sarcobatus vermiculatus) and sagebrush. In addition to the NWR, there are also state wildlife areas (SWAs) on which managers flood irrigate to create wetland habitat, as well as waterfowl management areas (WMAs) managed by the Bureau of Land Management (BLM) specifically for breeding ducks. Wetland habitats on the parcels of public land included in the study are comprised of large water storage reservoirs with variable amounts of submerged aquatic vegetation, basin wetlands with rings of emergent vegetation, and irrigated meadows consisting of graminoids and occasionally robust emergent vegetation (e.g., cattails [Typha spp.] and bulrush [Scirpus spp.]). Data Collection and Processing We searched systematically for duck nests from 20 April until 1 August 2018-2023. Study sites included five private ranches on which agricultural production was predominantly focused on cattle and hay. Additionally, we included Arapaho NWR, Lake John SWA, and Hebron WMA, which are multi-use parcels of public land spared from extractive agricultural production but subject to light cattle grazing. We searched randomly selected nest plots across land-use types in addition to searching opportunistically between plots. After overlaying a grid with 8-ha grid cells on the wet meadows of Arapaho NWR using a geographic information system (GIS; Esri ArcGIS Pro 2.8.0), we randomly selected 16 square plots to sample portions of the large expanses of the irrigated meadow. However, plots on private lands followed the natural boundaries of hay meadows, which were often smaller and more easily definable (Figure 2). As a result, plots on private land varied in size and number, but we still delineated them based on landscape features in a GIS and randomly selected a subset to search each year. Access to ranches also varied across years, which altered the number of plots we could search. The number of plots we searched on private ranches varied from five during a pilot year to 131, and plots ranged in size from 0.14-35.83 ha, averaging 6.44 ha. Additionally, we randomly selected 500-m length sections of riparian areas (n=40) and irrigation ditches (n=25) across the study area, searching within a 200-m buffer of the edges, and systematically searched the perimeter of all basin wetlands out to a radius of 200 m. We display an example ranch in Figure 2, which shows the layout of selected plots of several wetland habitats. We report the total area (ha) of each habitat type in the study area in Table 1 alongside the area of each habitat in our sampling frame, including land associated with accessible ranches and focal parcels of public land. Finally, we report the area within that sampling frame that we searched annually to illustrate which habitats were represented in our search plots relative to the area available. We searched plots 1-5 times per year and used a combination of rope drags (on foot; Higgins et al. 1969) and systematic foot searches to flush laying and incubating hens off of the nests, marking the location with a global positioning system (GPS) device. We recorded search effort each year (date searched and the number of people searching a plot) and used a GIS framework to compute the area in ha of each plot, whether the plot contained or its centroid was within < 200 m of a basin wetland, and the composition of rasterized habitat classes within each plot based on the 2021 National Land Cover Database (NLCD) layer. We identified the species incubating each nest as the hen flushed and used the size and color of the eggs to verify the identification. We candled several eggs in each nest to calculate the nest initiation date by backdating from the date the nest was located based on the embryonic stage of development and the number of eggs in the nest (Klett et al. 1986). As incubating hens typically cover their eggs with down feathers upon leaving the nest, we also covered eggs after each nest visit and placed two pieces of grass across the top of the nest in an “x” shape to determine whether the hen returned to the nest or abandoned after disturbance. We monitored each nest approximately every five to seven days, noting its incubation status, hen presence or absence, full clutch size, and ultimately nest fate. Regardless of whether a nest failed (i.e., all eggs were eaten by a predator or abandoned by the hen) or was successful (i.e., at least one egg hatched), we conducted vegetation surveys on the estimated or actual hatch date (McConnell et al. 2017). We calculated the hatch date based on the stage of embryonic development of the eggs during each nest visit and the average incubation time for each species. For successful nests, we conducted surveys the day after ducklings left the nest. Vegetation surveys occurred at the nest bowl and at four randomly selected points within a 200-m radius of the nest bowl to evaluate fine-scale (i.e., third-order; Johnson 1980, Eichholz and Elmberg 2014, Kaminski and Elmberg 2014) metrics of habitat selection. Surveys included visual estimation of percent cover within a 1-m Daubenmire frame (Daubenmire 1959). We estimated the percent cover of bare ground, litter (dead vegetation from the previous growing season), water, grasses, forbs, shrubs, sedges, and rushes, and we allowed the total percent cover to sum to more than 100% because the vegetation was often layered vertically. We also assigned each nest to a categorical habitat type at the time of measurement and measured visual obstruction by noting the lowest decimeter visible on a 1-m Robel pole from each cardinal direction and averaged the four values (Robel et al. 1970). Habitat types were classified based on the dominant vegetation within 200 m of the nest and included riparian, shrub-scrub, emergent marsh (dominated by robust vegetation like cattails), graminoid meadow, graminoid meadow interspersed by shrubs, Timothy hay meadow, and irrigation ditch, which was used when a nest was within 3 m of the inner channel of an irrigation ditch. We separated graminoid meadows from graminoid meadows interspersed with shrubs because shrubs may provide perches for avian predators from which duck nests may be more easily located (Thompson et al. 2012, Coates et al. 2021, Peterson et al. 2022). We measured broad-scale habitat characteristics using a GIS to evaluate the drivers of nest site selection at a larger scale. We created ~10000 random points across the study area (i.e., within the sampling frame indicated by the delineated boundaries in Figure 1) in all habitats where we consistently searched for nests. We calculated the distance of each random point and nest site to the nearest irrigation ditch, river, open water (i.e., ponds, marshes, or reservoirs), road, harvested hay meadow,

This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. This dataset is meant to provide information for resource planning and assessments across multiple agencies and serves as a consistent base layer for a broad array of potential users and multiple end uses. This dataset presents the 2014 agricultural land use, managed wetlands, and urban boundaries for all 58 counties in California. This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. Delineated from 2014 NAIP Imagery. The data are derived from a combination of remote sensing and agronomic analysis and ground verification.

This dataset is a line and a polygon feature-based layer compiled at 1:24,000 scale that includes water quality classification information for surface waters for all areas of the State of Connecticut. The Surface Water Quality Classifications and the Ground Water Quality Classifications are usually presented together as a depiction of water quality classifications in Connecticut. Water Quality Classifications, based on the adopted Water Quality Standards, establish designated uses for surface and ground waters and identify the criteria necessary to support those uses. This edition of the Surface Water Quality Classifications is based on the Water Quality Standards adopted on February 25, 2011. Surface Water means the waters of Long Island Sound, its harbors, embayments, tidal wetlands and creeks; rivers and streams, brooks, waterways, lakes, ponds, marshes, swamps, bogs, federal jurisdictional wetlands, and other natural or artificial, public or private, vernal or intermittent bodies of water, excluding groundwater. The surface waters includes the coastal waters as defined by Section 22a-93 of the Connecticut General Statutes and means those waters of Long Island Sound and its harbors, embayments, tidal rivers, streams and creeks, which contain a salinity concentration of at least five hundred parts per million under the low flow stream conditions as established by the Commissioner of the Department of Environmental Protection. The Surface Water Quality Classes are AA, A, B, SA and SB. All surface waters not otherwise classified are considered as Class A if they are in Class GA Ground Water Quality Classifications areas. Class AA designated uses are: existing or proposed drinking water, fish and wildlife habitat, recreational use (maybe restricted), agricultural and industrial supply. Class A designated uses are: potential drinking water, fish and wildlife habitat, recreational use, agricultural and industrial supply. Class B designated uses are: fish and wildlife habitat, recreational use, agricultural and industrial supply and other legitimate uses including navigation. Class B* surface water is a subset of Class B waters and is identical in all ways to the designated uses, criteria and standards for Class B waters except for the restriction on direct discharges. Coastal water and marine classifications are SA and SB. Class SA designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for direct human consumption, recreation and other legitimate uses including navigation. Class SB designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for transfer to approved areas for purification prior to human consumption, recreation and other legitimate uses including navigation. There are three elements that make up the Water Quality Standards which is an important element in Connecticut's clean water program. The first of these is the Standards themselves. The Standards set an overall policy for management of water quality in accordance with the directive of Section 22a-426 of the Connecticut General Statutes. The policies can be simply summarized by saying that the Department of Environmental Protection shall: Protect surface and ground waters from degradation, Segregate waters used for drinking from those that play a role in waste assimilation, Restore surface waters that have been used for waste assimilation to conditions suitable for fishing and swimming, Restore degraded ground water to protect existing and designated uses, Provide a framework for establishing priorities for pollution abatement and State funding for clean up, Adopt standards that promote the State's economy in harmony with the environment. The second element is the Criteria, the descriptive and numerical standards that describe the allowable parameters and goals for the various water quality classifications. The final element is the Classification Maps which identify the relationship between designated uses and the applicable Standards and Criteria for each class of surface and ground water. Although federal law requires adoption of Water Quality Standards for surface waters, Water Quality Standards for ground waters are not subject to federal review and approval. Connecticut's Standards recognize that surface and ground waters are interrelated and address the issue of competing use of ground waters for drinking and for waste water assimilation. These Standards specifically identify ground water quality goals, designated uses and those measures necessary for protection of public and private drinking water supplies; the principal use of Connecticut ground waters. These three elements comprise the Water Quality Standards and are adopted using the public participation procedures contained in Section 22a-426 of the Connecticut General Statutes. The Standards, Criteria and Maps are reviewed and revised roughly every three years. Any change is considered a revision requiring public participation. The public participation process consists of public meetings held at various locations around the State, notification of all chief elected officials, notice in the Connecticut Law Journal and a public hearing. The Classification Maps are the subject of separate public hearings which are held for the adoption of the map covering each major drainage basin in the State. The Water Quality Standards and Criteria documents are available on the DEP website, www.ct.gov/dep. The Surface Water Quality Classifications is a line and polygon feature-based layer is based primarily on the Adopted Water Quality Classifications Map Sheets. The map sheets were hand-drawn at 1:50,000-scale in ink on Mylar which had been underprinted with a USGS topographic map base. The information collected and compiled by major drainage basin from 1986 to 1997. Ground Water Quality Classifications are defined separately in a data layer comprised of polygon features. The Ground and Surface Water Quality Classifications do not represent conditions at any one particular point in time. During the conversion from a manually maintained to a digitally maintained statewide data layer the Housatonic River and Southwest Coastal Basins information was updated. A revision to the Water Quality Standards adopted February 25, 2011. These revisions included eliminating surface water quality classes C, D, SC, SD and all the two tiered classifications. The two tiered classifications included a classification for the present condition and a second classification for the designated use. All the tiered classifications were changed to the designated use classification. For example, classes B/A and C/A were changed to class A. The geographic extent of each the classification was not changed. The publication date of the digital data reflects the official adoption date of the most recent Water Quality Classifications. Within the data layer the adoption dates are: Housatonic and Southwest Basins - March 1999, Connecticut and South Central Basins - February 1993, Thames and Southeast Basins - December 1986. Ground water quality classifications may be separately from the surface water quality classifications under specific circumstances. This data is updated.

The International Production Assessment Division (IPAD) is part of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS), an agency within the US Department of Agriculture (USDA). FAS-IPAD uses satellite imagery and remote sensing data to assist in its agricultural estimates of global crop conditions. The division provides monthly estimates of area, yield and production for 17 distinct commodities in over 160 countries around the world, including post-disaster assessments. GADAS is a powerful visualization tool based on an ArcGIS platform that enables FAS-IPAD analysts, and other users, to rapidly assess real-time crop conditions using a wide variety of data layers from a multitude of sources.GADAS integrates a vast array of highly detailed data streams to include daily precipitation data, vegetation index, crop masks, land cover data, irrigation and water data, elevation and infrastructure, political data, and much more. In addition, FAS-IPAD has partnered with the Pacific Disaster Center (PDC) in Hawaii to incorporate real-time data streams into GADAS for worldwide monitoring, tracking, and pre- and post-disaster agricultural assessments resulting from hurricanes, typhoons, tsunamis, floods, droughts, earthquakes and volcanic eruptions.You may want to begin exploring GADAS for the many things it can be used for, such as:Global agricultural monitoring and commodity forecastingComparative climatic and satellite-derived vegetation analysisEnvironmental change detection studies and analysisDrought monitoringNatural disaster assessment and analysisTracking current and historical disaster eventsHighlighting regional risk posed by natural disastersSpatial modeling of potential disaster impactsDelineation of major land-use categories worldwideRegional planning and climate-resilience studiesProgram or project-specific data archive and data miningWe welcome your feedback on how GADAS has worked or is working for you, and are enthusiastic about expanding the data layers, utilization, and future development of this very powerful GIS tool. Please contact us at OGA.IPAD@fas.usda.gov to provide your valued comments…we look forward to hearing from you!Here’s a screenshot centered over the northern Atlantic Ocean:

This digital dataset defines the model grid and altitudes of the top of the 10 model layers and base of the model simulated in the transient hydrologic model of the Central Valley flow system. The Central Valley encompasses an approximate 50,000 square-kilometer region of California. The complex hydrologic system of the Central Valley is simulated using the USGS numerical modeling code MODFLOW-FMP (Schmid and others, 2006), which estimates dynamically integrated supply-and-demand components of irrigated agriculture as part of the simulation of surface-water and ground-water flow based on MODFLOW-2000. This application is referred to here as the Central Valley Hydrologic Model (CVHM) (Faunt, 2009). Utilizing MODFLOW-FMP, the CVHM simulates groundwater and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central Valley on a monthly basis from 1961-2003. The total active modeled area is an approximately 20,334 square-miles on a finite difference grid comprising 441 rows, 98 columns. Slightly less that 50 percent of the cells are active. It has a uniform horizontal discretization of 1x1 square mile and is oriented parallel to the valley axis, 34 degrees west of north (Faunt, 2009).The thickness of model layers is derived by sequentially subtracting the altitudes of the uppermost to the lowermost model layers. Most model layers range in thickness from 15 to more than 300 meters, and thickness generally increases with depth (Faunt, 2009). The upper 3 model layers are used to simulate the relatively shallow flow through basin-fill sediments.
Layers 4 and 5 are used to represent the Corcoran Clay Member of the Tulare Formation. The lower 5 layers are used to simulate the confined deeper portion of the basin-fill sediments. The CVHM is the most recent regional-scale model of the Central Valley developed by the U.S. Geological Survey (USGS).The CVHM was developed as part of the USGS Groundwater Resources Program (see "Foreword", Chapter A, page iii, for details).

Gering mit Nachbarschaftsnatur, Landwirtschaft und Wasser (> 5 ha). Die Schicht zeigt die offenen Flächen, die natürliche, landwirtschaftliche oder wasserwirtschaftliche Flächen sind und auch mit der gemeinsamen Nutzung für Freizeitzwecke vertraut sind. Wir verstehen alles über 5 ha (bzw. 50,000 m²) (kleiner ist nicht vorhanden): landwirtschaftlich oder landwirtschaftlich grün, sofern nicht völlig unzugänglich – natürliche Grün, sofern nicht völlig unzugänglich: beispielsweise Hobokense Polder, Wolvenberg – Wasser: so bedeutet z. B. Galgenweel, Burchtse Weel, Dokken, Schelde Recreative Co-use, dass dieser Raum durch zugängliche Wege innerhalb oder entlang des Gebiets navigiert, erlebt werden kann. Auf diesen barrierefreien Wegen sind nur passive Erholungsformen zulässig. Diese Datenschicht wird jährlich im Januar aktualisiert. (siehe auch Geodatenportal – https://geoportaal.antwerpen.be/portal/home/search.html?q=belevingsgroen, offenes Geodatenportal – http://portaal-stadantwerpen.opendata.arcgis.com/datasets?q=belevingsgroen, Zahlenangaben der Stadt in Zahlen)

The amount of water in soil is based on rainfall amount, what proportion of rain infiltrates into the soil, and the soil's storage capacity. Available water storage is the maximum amount of plant available water a soil can provide. It is an indicator of a soil’s ability to retain water and make it sufficiently available for plant use. Available Water Storage is a capacity estimate for the top 150 centimeters of soil. It is calculated from the difference between soil water content at field capacity and the permanent wilting point adjusted for salinity and fragments.Available water storage is used to develop water budgets, predict droughtiness, design and operate irrigation systems, design drainage systems, protect water resources, and predict yields. Available water storage is an important input into hydrologic models including the Soil and Water Assessment Tool (SWAT) - a water quality model that is designed to assess non-point and point source pollution at the river basin scale. Available water storage can also be used as an indication of a soil's drought susceptibility, for water recharge modeling, to assess a soil's ability to support crops, and for many other purposes.Dataset SummaryPhenomenon Mapped: Amount of water a soil can hold that is available to plantsUnits: MillimetersCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Mosaic Projection: Web Mercator Auxiliary SphereSource: Natural Resources Conservation ServicePublication Date: November 2023Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the for the contiguous United States and Alaska. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Puerto Rico, the U.S. Virgin Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, Republic of the Marshall Islands, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for available water storage is derived from the gSSURGO map unit aggregated attribute table field: Available Water Storage 0-150cm Weighted Average (aws0150wta).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "available water storage" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "available water storage" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Soils vary widely in their ability to retain or drain water. The rate at which water drains into the soil has a direct effect on the amount and timing of runoff, what crops can be grown, and where wetlands form. In soils with low drainage rates water will pond on the soil's surface.This layer summarizes soil drainage rates in eight classes:Excessively drained: Water is removed very rapidly. The occurrence of internal free water commonly is very rare or very deep. The soils are commonly coarse-textured and have very high hydraulic conductivity or are very shallow.Somewhat excessively drained: Water is removed from the soil rapidly. Internal free water occurrence commonly is very rare or very deep. The soils are commonly coarse-textured and have high saturated hydraulic conductivity or are very shallow.Well drained: Water is removed from the soil readily but not rapidly. Internal free water occurrence commonly is deep or very deep; annual duration is not specified. Water is available to plants throughout most of the growing season in humid regions. Wetness does not inhibit growth of roots for significant periods during most growing seasons. The soils are mainly free of the deep to redoximorphic features that are related to wetness.Moderately well drained: Water is removed from the soil somewhat slowly during some periods of the year. Internal free water occurrence commonly is moderately deep and transitory through permanent. The soils are wet for only a short time within the rooting depth during the growing season, but long enough that most mesophytic crops are affected. They commonly have a moderately low or lower saturated hydraulic conductivity in a layer within the upper 1 m, periodically receive high rainfall, or both.Somewhat poorly drained: Water is removed slowly so that the soil is wet at a shallow depth for significant periods during the growing season. The occurrence of internal free water commonly is shallow to moderately deep and transitory to permanent. Wetness markedly restricts the growth of mesophytic crops, unless artificial drainage is provided. The soils commonly have one or more of the following characteristics: low or very low saturated hydraulic conductivity, a high water table, additional water from seepage, or nearly continuous rainfall.Poorly drained: Water is removed so slowly that the soil is wet at shallow depths periodically during the growing season or remains wet for long periods. The occurrence of internal free water is shallow or very shallow and common or persistent. Free water is commonly at or near the surface long enough during the growing season so that most mesophytic crops cannot be grown, unless the soil is artificially drained. The soil, however, is not continuously wet directly below plow-depth. Free water at shallow depth is usually present. This water table is commonly the result of low or very low saturated hydraulic conductivity of nearly continuous rainfall, or of a combination of these.Very poorly drained: Water is removed from the soil so slowly that free water remains at or very near the ground surface during much of the growing season. The occurrence of internal free water is very shallow and persistent or permanent. Unless the soil is artificially drained, most mesophytic crops cannot be grown. The soils are commonly level or depressed and frequently ponded. If rainfall is high or nearly continuous, slope gradients may be greater.Subaqueous Soils: Free water is above the soil surface. Internal free water occurrence is permanent, and there is a positive water potential at the soil surface for more than 21 hours of each day. The soils have a peraquic soil moisture regime.For more information on the classifications see the Soil Survey Manual section on Soil Water.Dataset SummaryPhenomenon Mapped: Drainage Class of SoilsGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024Data from the gNATSGO database was used to create the layer for the for the contiguous United States and Alaska. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Puerto Rico, the U.S. Virgin Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, Republic of the Marshall Islands, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for drainage class is derived from the gSSURGO map unit aggregated attribute table field Drainage Class - Dominant Condition (drclassdcd).What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "drainage class" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "drainage class" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.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.

Download .zipThis coverage was extracted from the 1994 statewide land cover inventory of Ohio produced by Bruce R. Motsch and Gary M. Schaal of the Ohio Department of Natural Resources.

The land cover inventory for the State of Ohio was produced by the digital image processing of Landsat Thematic Mapper Data. The Thematic Mapper is a multi-spectral scanner that collects electromagnetic radiation reflected from the earth's surface in the visible, near infrared and mid-infrared wavelength bands. The resolution of the Thematic Mapper data is a 30 meter by 30 meter cell. The computer analysis of the data isolates unique spectral classes that relate to land cover characteristics.

The land cover inventory was produced from Thematic Mapper data acquired in September and October 1994. The data was classified into the general land cover categories of urban, agriculture/open urban areas, shrub/scrub, wooded, open water, non-forested wetlands and barren.

The land cover information reflects the conditions of the satellite data during the specific year and season the data was acquired. The Thematic Mapper data was processed using ERDAS image processing software. The data was originally created in raster format and georeferenced to Universal Transverse Mercator (UTM) zone 17 coordinates NAD27. The data can be combined with other georeferenced digital data layers.

The data is also available in its original ERDAS image format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis coverage was extracted from the 1994 statewide land cover inventory of Ohio produced by Bruce R. Motsch and Gary M. Schaal of the Ohio Department of Natural Resources.

The land cover inventory for the State of Ohio was produced by the digital image processing of Landsat Thematic Mapper Data. The Thematic Mapper is a multi-spectral scanner that collects electromagnetic radiation reflected from the earth's surface in the visible, near infrared and mid-infrared wavelength bands. The resolution of the Thematic Mapper data is a 30 meter by 30 meter cell. The computer analysis of the data isolates unique spectral classes that relate to land cover characteristics.

The land cover inventory was produced from Thematic Mapper data acquired in September and October 1994. The data was classified into the general land cover categories of urban, agriculture/open urban areas, shrub/scrub, wooded, open water, non-forested wetlands and barren.

The land cover information reflects the conditions of the satellite data during the specific year and season the data was acquired. The Thematic Mapper data was processed using ERDAS image processing software. The data was originally created in raster format and georeferenced to Universal Transverse Mercator (UTM) zone 17 coordinates NAD27. The data can be combined with other georeferenced digital data layers.

The data is also available in its original ERDAS image format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis coverage was extracted from the 1994 statewide land cover inventory of Ohio produced by Bruce R. Motsch and Gary M. Schaal of the Ohio Department of Natural Resources.

The land cover inventory for the State of Ohio was produced by the digital image processing of Landsat Thematic Mapper Data. The Thematic Mapper is a multi-spectral scanner that collects electromagnetic radiation reflected from the earth's surface in the visible, near infrared and mid-infrared wavelength bands. The resolution of the Thematic Mapper data is a 30 meter by 30 meter cell. The computer analysis of the data isolates unique spectral classes that relate to land cover characteristics.

The land cover inventory was produced from Thematic Mapper data acquired in September and October 1994. The data was classified into the general land cover categories of urban, agriculture/open urban areas, shrub/scrub, wooded, open water, non-forested wetlands and barren.

The land cover information reflects the conditions of the satellite data during the specific year and season the data was acquired. The Thematic Mapper data was processed using ERDAS image processing software. The data was originally created in raster format and georeferenced to Universal Transverse Mercator (UTM) zone 17 coordinates NAD27. The data can be combined with other georeferenced digital data layers.

The data is also available in its original ERDAS image format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov