We developed a completely new free geospatial dataset and substituted all Spearfish (SD) examples in the previous editions with this new, much richer North Carolina (NC, USA) data set. This data set is a comprehensive collection of raster, vector and imagery data covering parts of North Carolina (NC), USA (map), prepared from public data sources provided by the North Carolina state and local government agencies and Global Land Cover Facility (GLCF).

This data is packaged as a GRASS location as well as SHAPE/GeoTIFF/KML/ArcGRID files. See also http://www.grassbook.org/data_menu3rd.php for download.

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

Source: Fire and Resource Assessment Program (FRAP)

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

This is a lidar DEM at 1m resolution, in EPSG:3358, extracted from the GRASS GIS North Carolina Dataset.

Original CREDITS.txt:

This OSGeo sample dataset for research, development and education was
prepared thanks to agencies providing public access to geospatial
data. We are especially grateful to the North Carolina (NC) Center for
Geographic Information and Analysis, Wake County GIS, NC State Climate
Office, NC Department of Transportation, USGS and NASA for making
their data available. Advice and assistance with the data set by
Julia Harrell, Silvia Terziotti, Robert Austin, Adeola Dokun, Jeff
Essic, and Doug Newcomb, and computer system assistance by Micah Colon
are greatly appreciated. Martin Spott is acknowedged for processing
of geonames.org data for NC.

The processing of MODIS time series (separate MAPSET) was kindly
supported by telascience.org, we are grateful to John Graham for
granting access to these computational resources.

August 2007 Helena Mitasova
Markus Neteler
http://www.grassbook.org

The Geological Atlas of the Western Canada Sedimentary Basin was designed primarily as a reference volume documenting the subsurface geology of the Western Canada Sedimentary Basin. This GIS dataset is one of a collection of shapefiles representing part of Chapter 27 of the Atlas, Geological History of the Williston Basin and Sweetgrass Arch, Figure 5, Sweetgrass Arch and Williston Basin Tectonic Elements. Shapefiles were produced from archived digital files created by the Alberta Geological Survey in the mid-1990s, and edited in 2005-06 to correct, attribute and consolidate the data into single files by feature type and by figure.

Grass Lake School Dictrict 36

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

GRASS Upland local network

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

Dunegrass (Leymus leymus) is a native plant found on spits and berms. This dataset summarizes dunegrass occurrence using data from the ShoreZone Inventory. Dunegrass is classified as being patchy (less than 50%) or continuous (greater than 50%) along each unit of shoreline (an area with similar physical characteristics). The majority of the shoreline is described by line data, polygon features exist in some areas with extensive shallows. The ShoreZone Inventory includes all saltwater shorelines statewide. It was completed between 1994 and 2000 using aerial videography collected at low tide. The complete ShoreZone Inventory can be found under Download Data.

GRASS Upland core network

Version InformationThe data is updated annually with fire perimeters from the previous calendar year.Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. A duplicate 2020 Erbes fire was removed. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. There were 2,132 perimeters that received updated attribution, the bulk of which had IRWIN IDs added. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020). YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres._Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria:~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage 2002: 10 acres timber, 50 acres brush, 300 acres grass, damages or destroys three or more structures, or does $300,000 worth of damage~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Large and Damaging Redbook Fire data criteria:1979: Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981: 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992: 1981 criteria plus 1500 acres agricultural products, or destroys three residence or one commercial structure or does $300,000 damage1993: 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2006: 300 acres or larger and burned at least: 30 acres of timber, or 300 acres of brush, or 1,500 acres of woodland, or 1,500 acres of grass, or 1,500 acres of agricultural products, or 3 or more structures destroyed, or $300,000 or more dollar damage loss.2008: 300 acres and largerYear# of Missing Large and Damaging Redbook Fires197922198013198115198261983319842019855219861219875619882319898199091991219921619931719942219959199615199791998101999720004200152002162003520042200512006112007320084320093201022011020124201322014720151020162201711201862019220203202102022020230Total488Enumeration of fires in the Redbook that are missing from Fire Perimeter data. Three letter unit code follows fire name.1979-Sylvandale (HUU), Kiefer (AEU), Taylor(TUU), Parker#2(TCU), PGE#10, Crocker(SLU), Silver Spur (SLU), Parkhill (SLU), Tar Springs #2 (SLU), Langdon (SCU), Truelson (RRU), Bautista (RRU), Crocker (SLU), Spanish Ranch (SLU), Parkhill (SLU), Oak Springs(BDU), Ruddell (BDF), Santa Ana (BDU), Asst. #61 (MVU), Bernardo (MVU), Otay #20 1980– Lightning series (SKU), Lavida (RRU), Mission Creek (RRU), Horse (RRU), Providence (RRU), Almond (BDU), Dam (BDU), Jones (BDU), Sycamore (BDU), Lightning (MVU), Assist 73, 85, 138 (MVU)1981– Basalt (LNU), Lightning #25(LMU), Likely (MNF), USFS#5 (SNF), Round Valley (TUU), St. Elmo (KRN), Buchanan (TCU), Murietta (RRU), Goetz (RRU), Morongo #29 (RRU), Rancho (RRU), Euclid (BDU), Oat Mt. (LAC & VNC), Outside Origin #1 (MVU), Moreno (MVU)1982- Duzen (SRF), Rave (LMU), Sheep’s trail (KRN), Jury (KRN), Village (RRU), Yuma (BDF)1983- Lightning #4 (FKU), Kern Co. #13, #18 (KRN)1984-Bidwell (BTU), BLM D 284,337, PNF #115, Mill Creek (TGU), China hat (MMU), fey ranch, Kern Co #10, 25,26,27, Woodrow (KRN), Salt springs, Quartz (TCU), Bonanza (BEU), Pasquel (SBC), Orco asst. (ORC), Canel (local), Rattlesnake (BDF)1985- Hidden Valley, Magic (LNU), Bald Mt. (LNU), Iron Peak (MEU), Murrer (LMU), Rock Creek (BTU), USFS #29, 33, Bluenose, Amador, 8 mile (AEU), Backbone, Panoche, Los Gatos series, Panoche (FKU), Stan #7, Falls #2 (MMU), USFS #5 (TUU), Grizzley, Gann (TCU), Bumb, Piney Creek, HUNTER LIGGETT ASST#2, Pine, Lowes, Seco, Gorda-rat, Cherry (BEU), Las pilitas, Hwy 58 #2 (SLO), Lexington, Finley (SCU), Onions, Owens (BDU), Cabazon, Gavalin, Orco, Skinner, Shell, Pala (RRU), South Mt., Wheeler, Black Mt., Ferndale, (VNC), Archibald, Parsons, Pioneer (BDU), Decker, Gleason(LAC), Gopher, Roblar, Assist #38 (MVU)1986– Knopki (SRF), USFS #10 (NEU), Galvin (RRU), Powerline (RRU), Scout, Inscription (BDU), Intake (BDF), Assist #42 (MVU), Lightning series (FKU), Yosemite #1 (YNP), USFS Asst. (BEU), Dutch Kern #30 (KRN)1987- Peach (RRU), Ave 32 (TUU), Conover (RRU), Eagle #1 (LNU), State 767 aka Bull (RRU), Denny (TUU), Dog Bar (NEU), Crank (LMU), White Deer (FKU), Briceburg (LMU), Post (RRU), Antelope (RRU), Cougar-I (SKU), Pilitas (SLU) Freaner (SHU), Fouts Complex (LNU), Slides (TGU), French (BTU), Clark (PNF), Fay/Top (SQF), Under, Flume, Bear Wallow, Gulch, Bear-1, Trinity, Jessie, friendly, Cold, Tule, Strause, China/Chance, Bear, Backbone, Doe, (SHF) Travis Complex, Blake, Longwood (SRF), River-II, Jarrell, Stanislaus Complex 14k (STF), Big, Palmer, Indian (TNF) Branham (BLM), Paul, Snag (NPS), Sycamore, Trail, Stallion Spring, Middle (KRN), SLU-864 1988- Hwy 175 (LNU), Rumsey (LNU), Shell Creek (MEU), PG&E #19 (LNU), Fields (BTU), BLM 4516, 417 (LMU), Campbell (LNF), Burney (SHF), USFS #41 (SHF), Trinity (USFS #32), State #837 (RRU), State (RRU), State (350 acres), RRU), State #1807, Orange Co. Asst (RRU), State #1825 (RRU), State #2025, Spoor (BDU), State (MVU), Tonzi (AEU), Kern co #7,9 (KRN), Stent (TCU), 1989– Rock (Plumas), Feather (LMU), Olivas (BDU), State 1116 (RRU), Concorida (RRU), Prado (RRU), Black Mt. (MVU), Vail (CNF)1990– Shipman (HUU), Lightning 379 (LMU), Mud, Dye (TGU), State 914 (RRU), Shultz (Yorba) (BDU), Bingo Rincon #3 (MVU), Dehesa #2 (MVU), SLU 1626 (SLU)1991- Church (HUU), Kutras (SHF)1992– Lincoln, Fawn (NEU), Clover, fountain (SHU), state, state 891, state, state (RRU), Aberdeen (BDU), Wildcat, Rincon (MVU), Cleveland (AEU), Dry Creek (MMU), Arroyo Seco, Slick Rock (BEU), STF #135 (TCU)1993– Hoisington (HUU), PG&E #27 (with an undetermined cause, lol), Hall (TGU), state, assist, local (RRU), Stoddard, Opal Mt., Mill Creek (BDU), Otay #18, Assist/ Old coach (MVU), Eagle (CNF), Chevron USA, Sycamore (FKU), Guerrero, Duck1994– Schindel Escape (SHU), blank (PNF), lightning #58 (LMU), Bridge (NEU), Barkley (BTU), Lightning #66 (LMU), Local (RRU), Assist #22 & #79 (SLU), Branch (SLO), Piute (BDU), Assist/ Opal#2 (BDU), Local, State, State (RRU), Gilman fire 7/24 (RRU), Highway #74 (RRU), San Felipe, Assist #42, Scissors #2 (MVU), Assist/ Opal#2 (BDU), Complex (BDF), Spanish (SBC)1995-State 1983 acres, Lost Lake, State # 1030, State (1335 acres), State (5000 acres), Jenny, City (BDU), Marron #4, Asist #51 (SLO/VNC)1996- Modoc NF 707 (Ambrose), Borrego (MVU), Assist #16 (SLU), Deep Creek (BDU), Weber (BDU), State (Wesley) 500 acres (RRU), Weaver (MMU), Wasioja (SBC/LPF), Gale (FKU), FKU 15832 (FKU), State (Wesley) 500 acres, Cabazon (RRU), State Assist (aka Bee) (RRU), Borrego, Otay #269 (MVU), Slaughter house (MVU), Oak Flat (TUU)1997- Lightning #70 (LMU), Jackrabbit (RRU), Fernandez (TUU), Assist 84 (Military AFV) (SLU), Metz #4 (BEU), Copperhead (BEU), Millstream, Correia (MMU), Fernandez (TUU)1998- Worden, Swift, PG&E 39 (MMU), Chariot, Featherstone, Wildcat, Emery, Deluz (MVU), Cajalco Santiago (RRU)1999- Musty #2,3 (BTU), Border # 95 (MVU), Andrews,

GIS-based computer generated real-time landscape models, and other computer generated static images were produced and used alongside photographs in more in-depth interviews and focus groups. (Some elements of this dataset are not part of this data submission due to copyright restrictions, though images may be included in the report). The study is part of the NERC Rural Economy and Land Use (RELU) programme. Future policies are likely to encourage more land use under energy crops: principally willow, grown as short rotation coppice, and a tall exotic grass Miscanthus. These crops will contribute to the UK's commitment to reduce CO2 emissions. However, it is not clear how decisions about appropriate areas for growing the crops, based on climate, soil and water, should be balanced against impacts on the landscape, social acceptance, biodiversity and the rural economy. This project integrated social, economic, hydrological and biodiversity studies in an interdisciplinary approach to assessing the impact of converting land to Miscanthus grass and short-rotation coppice (SRC) willows. Two contrasting farming systems were focused on: the arable-dominated East Midlands; and grassland-dominated South West England. The public attitudes questionnaire data from this study are available at the UK Data Archive under study number 6615 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

RHESSysWorkflows provides a series of Python tools for performing RHESSys data preparation workflows. These tools build on the workflow system defined by EcohydroLib and RHESSysWorkflows. This notebook uses custom datasets and is for advanced users comfortable with a linux terminal and using text editor such as Vi.

Dead Run, USGS gage 01589330 is located at Franklintown MD. The general steps to use RHESSys workflows

1 Register DEM 2 Import Gage 3 Download soil data 4 Prepare Land Cover data 5 Download LAI data 6 Create a new GRASS GIS Location 7 Import RHESSys code and compile (automatically or manually) 8 Import Climate data 9 Delineate watershed 10 Generate Patch map 11 Process soil maps 12 Generate derived landcover maps 13 Generate Rules and Reclassify 14 Generate template 15 Create world 16 Create flow table 17 Initializing vegetation carbon and nitrogen stores 18 Creating a RHESSys TEC file 19 Running RHESSys models

This notebook is built on RHESSys sample workflow and RHESSys Workflow at Coweeta, NC examples. Not all steps are documented. Here we focus on explaining new or modifications.

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

DefinitionThis indicator identifies grassland condition within the Midwest Landscape. It prioritizes areas based on the percentage of perennial forbs and grasses within an area. Pixels can take the following values:NoData – 0-20% perennial grass and forbs1 – 20-40% perennial grass and forbs2 – 40-60% perennial grass and forbs3 – 60-80% perennial grass and forbs4 – 80-100% perennial grass and forbsSelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values.Input Data & Mapping StepsThis indicator originates from the Rangeland Analysis Platform (RAP) cover data. To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, and selected and reclassified band 4 of RAP cover data into 5 classes: NoData – 0-20% perennial grass and forbs, 1 – 20-40% perennial grass and forbs, 2 – 40-60% perennial grass and forbs, 3 – 60-80% perennial grass and forbs, 4 – 80-100% perennial grass and forbs. Finally, we removed highly altered areas using our Highly Altered Areas mask. For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.

This dataset exceeds the size and feature limits of the Shapefile format, so is unavailable on the Natural England Open Data Geoportal in that format. Please select ESRI File Geodatabase or another format to download.The Priority Habitat Inventory is a spatial dataset that maps priority habitats identified in the UK Biodiversity Action Plan and listed as being of principal importance for the purpose of conserving or enhancing biodiversity, under Section 41 of the Natural Environment and Rural Communities Act (2006).Habitats mapped in the PHIThe PHI currently maps 27 terrestrial and freshwater priority habitats across England.

Priority Habitat Name

HabCode

Blanket bog

BLBOG

Calaminarian grassland

CALAM

Coastal & floodplain grazing marsh

CFPGM

Coastal saltmarsh

SALTM

Coastal sand dunes

CSDUN

Coastal vegetated shingle

CVSHI

Deciduous woodland

DWOOD

Limestone pavements

LPAVE

Lowland calcareous grassland

LCGRA

Lowland dry acid grassland

LDAGR

Lowland fens

LFENS

Lowland heathland

LHEAT

Lowland meadows

LMEAD

Lowland raised bog

LRBOG

Maritime cliff & slope

MCSLP

Mountain heath & willow scrub

MHWSC

Mudflats

MUDFL

Purple moor grass & rush pastures

PMGRP

Reedbeds

RBEDS

Saline lagoons

SLAGO

Traditional orchards

TORCH

Upland calcareous grassland

UCGRA

Upland hay meadows

UHMEA

Upland heathland

UHEAT

Upland flushes, fens & swamps

UFFSW

Lakes

LAKES

Ponds

PONDS

Non Priority Habitats mapped in the PHIThe PHI also includes four habitat classes which are not priority habitats, but which hold potential importance for conservation of biodiversity in England. These can indicate a mosaic of habitat which may contain priority habitats, have restoration potential and/or contribute to ecological networks. Where evidence indicates the presence of unmapped or fragmented priority habitats within such polygons, these are attributed as additional habitats.

Non-Priority Habitat Name

HabCode

Description

Fragmented heath

FHEAT

This refers to areas of degraded and relict upland heathland, typically in a mosaic with acid grassland that fails to meet the Upland Heathland priority habitat definition.

Grass moorland

GMOOR

This includes large areas of upland grassland, which may contain mosaics of priority habitat, but tends to be species-poor, grass dominated acid grassland above the moorland line.

Good quality semi-improved grassland

GQSIG

This includes grasslands with biodiversity value that do not meet priority grassland habitat definitions.

No main habitat

NMHAB

In some cases, a priority habitat may be present within a polygon, but its extent may be less than the minimum mapping unit, or it may not be accurately mappable.

Feature Descriptions and CodesFor some polygons the PHI contains additional information about the main habitats in the form of feature descriptions and corresponding feature codes. These are new fields to the PHI and currently only sparsely populated. We expect the use of these fields to expand over coming updates with new features and codes.

Feature Description

Feature Code

Priority ponds and lakes

Oligotrophic lakes

OLIGO

Dystrophic lakes

DYSTR

Mesotrophic lakes

MESOT

Eutrophic standing waters

EUTRO

Ice age pond

ICEAG

Pond with floating mats

PWFLM

Deciduous woodland

Upland oakwood

UPOWD

Lowland beech and yew woodland

LBYWD

Upland mixed ashwoods

UMAWD

Wet woodland

WETWD

Lowland mixed deciduous woodland

LMDWD

Upland birchwoods

UPBWD

Ancient semi natural woodland

ASNWD

Plantations on ancient woodland

PAWDS

Grassland

Countryside Stewardship Option

CSOPT

Waxcap grassland

WAXCP

Heathland

Dry heathland

DRYHL

Wet heathland

WETHL

Coastal sand dunes

Dunes under coniferous woodland

CWDUN

Dunes under deciduous woodland

DWDUN

General

Degraded

DEGRD

Spatial framework: Wherever possible habitats are mapped to polygons in OS Mastermap. These polygons are merged or split where necessary to create resulting habitat patches.Coverage: EnglandUpdate Frequency: The PHI is updated twice a year.Metadata: Full metadata can be viewed on data.gov.uk.Uses include: National planning and targeting for nature recovery; agri-environment scheme targeting; local development planning; Local Nature Recovery Strategies.Contact: If you have any questions or feedback regarding the Priority Habitats’ Inventory, please contact the Habitats’ Inventory Project Team at the following email address.HabitatInventories@naturalengland.org.ukAttributes

Alias

Field name

Example Value

Description

Main habitats

MainHabs

Lowland dry acid grassland, Lowland heathland

Name(s) of habitat(s) present in the polygon.

Habitat codes

HabCodes

LDAGR, LHEAT

List of codes(s) representing main habitat(s) present in the polygon.

Habitat feature descriptions

FeatDesc

Dry heathland

Additional information about the nature of the habitat or features present.

Habitat feature codes

FeatCodes

DRYHL

List of code(s) corresponding to the habitat feature descriptions.

Other habitat classifications

OtherClass

Phase1(D5)

Additional habitat classification information relating to main habitats.

Additional habitats present

AddHabs

GQSIG, LFENS

List of code(s) for additional habitats that may be present within the polygon.

Primary data sources

PrimSource

Natural England's SSSI database ENSIS (LDAGR), Northumberland County Council Phase 1 Survey 2003 (LHEAT)

List of primary sources for the main habitats present in the polygon, with corresponding HabCode in brackets.

Area in hectares

AreaHa

0.14

Polygon area in hectares rounded to one decimal place.

Publication version

Version

July_24

Date of publication for the current PHI update: Month_Year.

Unique ID

UID

PHIDXXXXXXXXXX _YYYYYYYYYYY

Unique ID for the polygon based on XY location coordinates.

Spatial and Attribute Metadata and Licensing informationSpatial Metadata - Priority Habitats Inventory.pdfAttribute Metadata - Priority Habitats Inventory.pdfLayer File - PHI.lyrFull metadata can be viewed on data.gov.uk.

Download: hereA satellite imagery classification of Southern Saskatchewan based mainly on 1994 Landsat5 imagery. Developed by the Saskatchewan Research Council after 1997. Background: A group of Provincial and Federal Agencies formed a partnership in March of 1997 to share the cost of obtaining satellite imagery and interpreting this imagery to create a landcover dataset for the agricultural portion of Saskatchewan. The partnership included Saskatchewan Research Council (SRC), Saskatchewan Agriculture and Food (SAF), Saskatchewan Crop Insurance (SCI), Saskatchewan Property Management Corporation (SPMC), Environment Canada, the Prairie Farm Rehabilitation Administration (PFRA) and Saskatchewan Environment Resource Management (SERM). The University of Regina was also involved as an 'in kind' partner providing research services in the area of land cover classifications, accuracy assessment and data conversions. The Partnership Agreement required SRC (partner doing the bulk of data processing) to provide digital files for each of 328 1:50,000 NTS map sheets. The digital files included not only raw imagery, but also one file for each map sheet where the imagery was classified into 24 landcover types. The accuracy of this classification was to be demonstrated by SRC to be at least 90 per cent correct. In addition to the data processing done by SRC, SPMC provided the necessary positional control data (road intersection coordinates) and verified the positional accuracy of the final product. The other partners provided feedback to SRC on classification errors, which improved the overall accuracy of the final product.

Classification

Value

No Data

0

Crop Land

1

Hay Crops (Forage)

2

Native Dominant Grass Lands

3

Tall Shrubs

4

Pasture (Seeded Grass Lands)

5

Hardwoods (Open Canopy)

6

Hardwoods (Closed Canopy)

7

Jack Pine (Closed Canopy)

8

Jack Pine (Open Canopy)

9

Spruce (Close Canopy)

10

Treed Rock

13

Recent Burns

14

Revegetating Burns

15

Cutovers

16

Water Bodies

17

Marsh

18

Herbaceous Fen

19

Mud/Sand/Saline

20

Shrub Fen (Treed Swamp)

21

Treed Bog

22

Open Bog

23

Slopes

25

Slopes

26

1. No Data1. Crop Land - All lands dedicated to the production of annual cereal, oil seed and other specialty crops, and typically cultivated on an annual basis. 2. Hay Crops (Forage) - Alfalfa and alfalfa/tame grass mixtures. 3. Native Dominant Grass Lands - Native dominant grasslands/may contain tame grasses and herbs. 4. Tall Shrubs - Communities containing both low and tall shrub, snowberry, saskatoon, chokecherry, buffaloberry, and willow. 5. Pasture (Seeded Grass Lands) - Grassland dominated by tame grass species. 6. Hardwoods (Open Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 10-30% crown closure. 7. Hardwoods (Closed Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 30-100% crown closure. 8. Jack Pine (Closed Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 30-100% crown closure. 9. Jack Pine (Open Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 10-30% crown closure. 10. Spruce (Close Canopy) - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 11. Spruce: Open Canopy - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 12. Mixed Woods - All softwood/hardwood mixtures. 13. Treed Rock - Areas of exposed bedrock with generally less then 10% tree cover. Dominant species are Jack Pine and Black Spruce. 14. Recent Burns - All areas that have been recently burned over by wildfires. 15. Revegetating Burns - Burns with a regrowth of commercial timber generally 1-5 metres in height. 16. Cutovers - Areas where commercial timber has been completely or partially removed by logging operations. 17. Water Bodies - Consists of all open water - lakes, rivers, streams, ponds, and lagoons. 18. Marsh - Dominated by sedge and wetland grasses. 19. Herbaceous Fen - Fens dominated by herbaceous species. 20. Mud/Sand/Saline 21. Shrub Fen (Treed Swamp) - Fens dominated by shrubby species. 22. Treed Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. The bogs have 25% or more canopy by trees greater than one metre tall. The primary species is black spruce. 23. Open Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. 24. Farmstead - Farmstead types, towns, cities, Exposed areas with little or no vegetation or Cloud coverage. 25. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification.26. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification.