The indicator uses areas identified as grasslands using LANDFIRE�s Biophysical Settings (BpS) raster dataset (US_140BPS_20180618) along with a cross-walk based on Reeves MC, Mitchell JE (2011) Extent of coterminous US rangelands: quantifying implications of differing agency perspectives. Rangel Ecol Manage 64:1�12 to identify BpS units that are likely to be grasslands during pre-European times. Presence of conifer tree species was developed using USGS National Landcover Database 2011 (NLCD) legend class �42 � Evergreen Forest�. The Evergreen forest class represents �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.���Grassland conifer encroachment is identified where raster pixels are identified as both �grasslands� and NLCD �Evergreen Forest�This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Reason for Selection Native grasslands and savannas are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands and savannas have significantly declined from their historical extent. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland and savanna birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential habitat. Input Data

Texas Ecological Mapping Systems: statewide raster, accessed 12-2023
Oklahoma Ecological Systems Map: download the raster, accessed 12-2023
Protected Areas Database of the United States (PAD-US): PAD-US 3.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement; PAD-US 4.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement
National Land Cover Database (NLCD): 2021 Land Cover, 2021 U.S. Forest Service (USFS) Tree Canopy Cover, 2013 Land Cover, and 2013 USFS Tree Canopy Cover
2020 LANDFIRE Biophysical Settings (BPS) [LF 2.2.0]
Southeast Blueprint 2024 landscape condition indicator
Southeast Blueprint 2024 extent

Known grasslands

Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University in Oct 2020 (available on request by emailing rua_mordecai@fws.gov); this is an improved version of the Known Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO) layer
Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailing rua_mordecai@fws.gov).
Grassland polygons from the Catawba Indian Nation, provided by Aaron Baumgardner, Natural Resources Director, in July 2023 (for more information email rua_mordecai@fws.gov)
Grassland polygons from two iNaturalist projects in Texas: erwin-park-prairie-restoration-area, stella-rowan-prairie
Southeastern Grasslands Institute polygons from selected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: 
    allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades, folder-six-glades

Mapping Steps

Combine all known grasslands polygons and convert to raster, assigning them a value of 7.
From the 2021 and 2013 NLCD landcover, create rasters that only include classes likely to have grasslands and savannas. The classes included are based on NLCD classes that overlap known grassland and savanna polygons. Any class that covered >1% of known grasslands and savannas is included: 31 Barren Land, 41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, 52 Scrub/Shrub, 71 Grassland/Herbaceous, 81 Pasture/Hay.
For those 2021 and 2013 selected landcover rasters, remove forest with ≥ 60% canopy cover using NLCD USFS Tree Canopy Cover for the corresponding year. This results in potential grassland and savanna rasters for 2021 and 2013.
Make a single potential grassland and savanna raster that only includes pixels that are potential grasslands and savannas in both 2013 and 2021. This removes temporary grasslands and savannas that result from clearcuts.
From the Texas and Oklahoma ecological systems maps, extract classes that predict areas invaded by mesquite, a non-native tree that spreads aggressively in the grasslands and savannas of the Southwest and disrupts natural ecosystems through its heavy water consumption. For Oklahoma, this is VegName = 'Ruderal Mesquite Shrubland'. For Texas, this is CommonName = 'Native Invasive: Mesquite Shrubland'. Combine these and use them to remove areas that are no longer grassland and savanna due to mesquite invasion. The resulting layer represents potential grasslands.
To identify potential grasslands and savannas in natural landscapes, use values 5 and 6 from the landscape condition indicator. Assign a value of 3 to any potential grassland pixel that receives a landscape condition score of 5 or 6. Assign all other potential grassland pixels a value of 2.
To identify likely grasslands and savannas, overlay the potential grasslands and savannas raster with select polygons from PAD-US 4.0. To pull out types of protected lands that commonly manage grasslands and savannas, we used GAP status, designation type, manager name, and easement holder. We also identified a number of protected areas directly by name that had important areas of grassland and savanna but weren’t captured by the other rules.
    GAP status (GAP_sts) 1 or 2: Gap status 1 and 2 refer to areas managed for biodiversity that are not subject to extractive uses like logging and mining. GAP status 2 is technically intended to encompass areas where disturbance events are suppressed, but in practice, most protected areas in the Southeast that are actively managing grasslands and savannas are classified as GAP status 2.
    Designation type (Des_Tp) of ‘NWR’, ‘MIL’, ‘NF’, or ‘NG’ (i.e. National Wildlife Refuge, military installation, National Forest, or National Grassland)
    Manager name (Mang_Name) of ‘RWD’ (i.e. Regional Water District)
    Local manager name (Loc_Mang) of 'Ducks Unlimited (Wetlands America Trust)'
    Easement holder (EsmtHldr) of 'Tall Timbers Research Station & Land Conservancy'
    Unit name (Unit_Nm) of ‘Point Washington State Forest’, ‘Pine Log State Forest’, ‘M. C. Davis - Seven Runs Creek Conservation Easement’, ‘Nokuse Plantation Conservation Easements’, ‘Tate's Hell State Forest’, ‘Box-R Wildlife Management Area’, ‘Aucilla Wildlife Management Area’, ‘Snipe Island Unit’, ‘Big Bend Wildlife Management Area’, ‘Goethe State Forest’, ‘Amelia Wildlife Management Area’, ‘Powhatan Wildlife Management Area’, ‘Cumberland State Forest’, ‘Appomattox-Buckingham State Forest’, ‘Haw River State Park’, ‘R. Wayne Bailey - Caswell Game Land’, ‘Medoc Mountain State Park’, ‘Embro Game Land’, ‘Dupont State Forest’, ‘Hanging Rock State Park’, ‘Bladen Lakes State Forest’, ‘Whitehall Plantation Game Land’, ‘Suggs Mill Pond Game Land’, ‘Bushy Lake State Natural Area’, ‘Pondberry Bay Plant Conservation Preserve’, ‘Green Swamp Game Land’, ‘Holly Shelter Game Land’, ‘Chowan Swamp Game Land’, ‘Brookgreen Gardens’, ‘Cary State Forest’, ‘Suwannee Ridge Mitigation Park Wildlife and Environmental Area’, ‘Adams-Alapha Ag & Conservation Easement’, ‘Twin Rivers State Forest’, ‘Chattahoochee Fall Line Wildlife Management Area’, ‘Enon Plantation’, or ‘Georgia-Alabama Land Trust Easement #214’, ‘Covington Wildlife Management Area’, ’ Magnolia Branch Wildlife Reserve’, ‘Little River State Forest’, or ‘Susan Turner Plantation’, or have the local name (Loc_Nm) 'Sandhills Game Land', 'Blackwater River State Forest', 'Three Lakes Wildlife Management Area', 'Herky Huffman/Bull Creek Wildlife Management

The purpose of this project was to map the estimated distribution of grassland communities of the Southern Great Plains prior to Euro-American settlement. The Southern Great Plains Rapid Ecoregional Assessment (REA), under the direction of the Bureau of Land Management and the Great Plains Landscape Conservation Cooperative, includes four ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. The REA advisors and stakeholders determined that the mapping accuracy of available national land-cover maps was insufficient in many areas to adequately address management questions for the REA. Based on the recommendation of the REA stakeholders, we estimated the potential historical distribution of 10 grassland communities within the Southern Great Plains project area using data on soils, climate, and vegetation from the Natural Resources Conservation Service (NRCS) including the Soil Survey Geographic Database (SSURGO) and Ecological Site ...

File-based data for download at https://www.grasslandsroadmap.org/ To address these challenges, the CGR provides a flexible strategic framework. This assessment map is core to implementing this framework. Partners and community members can use it to identify opportunities where short- and long-term conservation programs need to take place. This large-scale approach works best when partners work together by combining local priorities, resource concerns, and community will.

This map categorizes three areas of conservation to support and grow our core grasslands. By keeping the grass intact and “green-side up”, (shown in green on the map) these grassland areas can ensure food security, traditional cultural values and land overeignty for Indigenous Nations. Voluntary short- and long-term conservation programs and practices are needed to keep these core grasslands intact and support grass-based economies to help rural communities thrive.

Areas marked in yellow represent lands impacted by the spread of invasive woody vegetation and other annual species that negatively change the characteristic of these grasslands, and by areas under immediate threat of conversion to row-crop agriculture. Every effort should be made to ensure that these areas remain healthy, connected grasslands that benefit both rural communities and wildlife.

Purple indicates areas that need strategic investment that include, but are not limited to, removing invasive woody species, converting cropland on marginal soils back to grassland, and connecting to larger blocks of existing grassland.

To learn more about the map, explore data layers, and how you can help to support the health of this irreplaceable landscape visit www.grasslandsroadmap.org

Grasslands Risk Map Version 1.0 Released 10/21/2022 see https://www.grasslandsroadmap.org/

Appropriate use of data:

The Grasslands Risk Map provides a biome-level predictor of area in core grassland habitat, area under threat of conversion or encroachment to trees/woody shrubs, and area already converted/encroached. These data provide context for the top-two drivers of grassland loss over the past couple decades and can help guide national/international conservation priorities for grassland conservation of remaining core areas. In addition, these data are useful for understanding proximity to biome threats for regional/local conservation planners and as a guide for corresponding conservation action. It is recommended to integrate additional data layers/information at appropriate resolutions to further refine conservation actions and priorities at local scales (e.g., local resource concerns, species stronghold data, cultural resources, collaborative conservation groups, etc.) that complement biome-level. Attribute data:

Forest, natural (value 1000) Converted/altered Grasslands (Plowed/Encroached) (value 500) Vulnerable Grasslands (At Risk) (value 100) Core Grasslands (value 5) Data still in progress (value 5000)

The average relative difference of the mean inter-annual variability of vegetation production between a reference time period (1984-2014) and the current time period (2015-2019).

This web map and collection of data layers provides information on the location, extent, quality, and condition of species-rich grassland (SRG) sites in the Cairngorms National Park (CNP) - outputs from a joint project between NatureScot and the Cairngorms National Park Authority. Coverage includes the Spey, Avon, Livet and Dee river catchments.

Three sets of maps between them provide an almost complete coverage of the pre-settlement boundaries of natural temperate grasslands within the study area. They are the boundaries delineating grasslands of the Monaro (developed as part of broad vegetation mapping there (Costin, 1954; digitised by NPWS in 1998), the grassland boundaries in the ACT (digitised by ACT P&CS WRM) and the pre-settlement boundaries developed as a central program of this project, and covering a significant portion of South Eastern Highlands Bioregion. In Costin’s (1954) map of the vegetation of the Monaro region, the map units delineated represent Costin’s vegetation alliances. ACT vegetation was mapped by Pryor in 1939. This mapping included 5 alliances of woody species and one grassland alliance. This interpretation of ACT grassland areas has survived into modern mapping interpretations with some modifications. The current products mapped new boundaries for areas beyond the Monaro and ACT. This product present pre-European settlement grassland boundaries for an area covered by a series of 1:100 000 map sheets (Braidwood, Boorowa, Brindabella, Canberra (excluding the ACT section), Crookwell, Goulburn, Gunning and Yass). VIS_ID 4099 Data and Resources

The Tibetan Plateau is primarily composed of alpine grasslands. Spatial distributions of alpine grasses, however, are not well documented in this remote, highly uninhabited region. Taking advantage of the frequently observed moderate resolution imaging spectroradiometer (MODIS) images (500-m, 8-day) in 2010, this study extracted the phenological metrics of alpine grasses from the normalized difference vegetation index time series. With the Support Vector Machine, a multistep classification approach was developed to delineate alpine meadows, steppes, and desert grasses. The lakes, permanent snow, and barren/desert lands were also classified with a MODIS scene acquired in the peak growing season. With ground data collected in the field and aerial experiments in 2011, the overall accuracy reached 93% when alpine desert grasses and barren lands were not examined. In comparison with the recently published national vegetation map, the alpine grassland map in this study revealed smoother transition between alpine meadows and steppes, less alpine meadows in the southwest, and more barren/deserts in the high-cold Kunlun Mountain in the northeast. These variations better reflected climate control (e.g. precipitation) of different climatic divisions on alpine grasslands. The improved alpine grassland map could provide important base information about this cold region under the pressure of rapidly changing climate.

Murgia Alta: grassland change map (2012-2015).

A change map for the land cover class "GRASSLAND" in "Murgia Alta" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "GRASSLAND" from COPERNICUS Service VHR layer dated 2012 and at time T2 a Sentinel-2A image dated August 7th, 2015.

The map was produced at 20 meters spatial resolution and projected in WGS84/UTM33N.

The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.

The Overall Accuracy (OA) of the map was: OA=94.21%±0.10%.

[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).

[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257

This dataset provides polygon centroids (derived from CNPGrasslandMapping_2020to2022_Polygons dataset) providing results of grassland surveys undertaken in the Cairngorms National Park between 2020 and 2022 as part of a joint project delivered by NatureScot and the Cairngorms National Park Authority. The aim was to establish the location and extent of species-rich grassland (SRG) within enclosed (and formerly enclosed) farm land, up to a maximum altitudinal limit of 500 m, using a combination of remote sensing and targeted field survey. This dataset covers the Livet, Avon and Dee catchments.Patches of unimproved/semi-improved grassland, down to 0.04 ha in size, were identified and delineated by analysing high-resolution aerial photography (involving image segmentation and subsequent classification of the output). This provided a search map of polygons to visit in the field, targeting survey effort towards the areas where species-rich grassland was most likely to occur.The field survey was undertaken by contractors during July to September in 2020 and 2021, and July to October in 2022. For each polygon, grassland communities and their relative percentage cover were described using the National Vegetation Classification (NVC), and species-richness was assessed. Locations of any missed species-rich grassland, occurring outside the search map polygons, were captured in the field and added to the dataset.When species-rich grassland was encountered, additional detailed attributes describing the quality and condition of these stands were collected in the CNPGrasslandMapping_2020to2022_SRGAttributes dataset which can be joined/related to this one using the POLY_ID/ PARENT_POLY fields. Other notable habitats and plant species were captured in the CNPGrasslandMapping_2020to2022_TargetNotes dataset.There are 4930 polygons in total, of which 1482 (30%) were found to contain at least some species-rich grassland. The principal species-rich grassland NVC types were: dry – acid (U1d, U4c, U5c), neutral (MG2, MG3, MG5), calcareous (CG2, CG7, CG10, CG11); wet – purple moor-grass & rush pasture (M23a, M25c), tall-herb fen meadow (M27, M28). Polygons containing species-rich grassland can be identified using the SRG_PRES (yes/no) field.The dataset contains the following fields:NVC_1 to NVC_6 – NVC communities recorded in the polygon;COVER_1 to COVER_6 – relative percentage cover of each NVC community in the polygon;COMMENT – specific notes about vegetation in the polygon;SRG_PRES – presence of species-rich grassland in polygon (yes/no);NVC_LIST – list of all NVC communities in polygon and proportion cover of mosaic components;SRG_NVC – species-rich grassland NVC communities and proportion cover in the polygon;SRG_COVER – total percentage cover of species-rich grassland in polygon;POLY_HA – area of polygon (hectares);SRG_HA – area of species-rich grassland in polygon (hectares);SURV_YR – year of field survey;SURV_DATE – date of field survey;SURVEYOR – field surveyor;CATCHMENT – river catchment area;POLY_ID – unique polygon identifier.

This database and mapping tool was produced to allow the identification of sites important to this incredibly diverse range of grassland fungal species for which Scotland is important on a global scale. Promotion of this project could lead to a great amount of these vulnerable sites being managed for their waxcaps, leading directly to the conservation of biodiversity, including several species on the Scottish Biodiversity List.Included layers:Heatmap of grassland fungi to the 10km level. The fewest species per square is represented by the lightest colour and the highest species per square represented by darkest colour. ADVICE: This layer is ideal for giving an overview of records in the area, but it doesn’t mean the fungi are throughout the area, or that the whole area is unimproved grassland.Heatmap of grassland fungi to the 1km level. The fewest species per square is represented by the lightest colour and the highest species per square represented by darkest colour. ADVICE: 1km grid square layer may provide a false picture and a blank square does not necessarily mean that no grassland fungi are there. Accurate georeferencing of biological records before the age of GPS and specialist phone apps was rare with many blocks of records being given the same centroid grid reference. Also, it would be common for many recorders to record the first find of a species on a site and none thereafter. THE 10KM SQUARE SHOULD BE LOOKED AT ALONGSIDE THIS LAYER.Point layer showing the Waxcap Sites. Sites are based centroid grid references for a spread of records in the area. The sites do not have clear boundaries, and so some form of local habitat knowledge is needed to set actual site boundaries within the real-world boundaries of unimproved grassland.• RED: Any site passing any of the SSSI thresholds• AMBER: Any site not passing any of the SSSI thresholds but with more than 11 species of Hygrocybe s.l. or with more than 4 IUCN species or with more than 4 indicator species.• GREEN: Any other site that has records of grassland fungi.Complete metadata on spatialdata.gov.scot.Data was collected from data holders from January to July 2023This database uses SSSI lists from 2018 JNCC guidelinesThis database uses a 2013 Bolete Fungi Red ListTaxonomy choices are correct in 2023See report for full referencesThis database has collated records of grassland species from the various fungus record holding institutions, ‘cleaned’ them, classified ‘sites’, and then rates sites and grid squares their mycological diversity using both the SSSI guidelines and the CHEGD grassland fungi rating system to allow judgements on their richness. There is also data on other fungal features such as phenology, and the presence of Red Listed species. With the information in this database, sites that qualify for fungal SSSI designation can be identified, and tools such as interactive maps made to allow land users to recognise sites of importance.The database of information and the GIS layers were created by the contractor David Mitchel. This contractor has also made Grassland fungi databases for statutory nature organisations in Ireland, N. Ireland, Wales, and England, and the databases are intercompatible.The data was collected from a variety of recording groups, individuals and biological record centres. These sources can be found in the Research Report RR1372

This dataset shows neutral grassland habitat (which includes unimproved and species rich neutral grassland habitats) alongside the dispersal distances (half distance and maximum distance) for grassland. These dispersal distances help identify where there are primary and secondary opportunities as highlighted in the Grasslands Opportunity Areas shape-file. Primary opportunities (coloured red) appear within the maximum dispersal distance where there is already some migration of species between habitat patches occurring. They connect habitat patches that are closer together so tend to be the quickest win for achieving connectivity. Secondary opportunities (coloured orange) connect the maximum dispersal distances for each patch.This dataset is part of a suite of shape-files which make up the CSGN 2021 Habitat Connectivity Map. The map identifies areas of habitat (woodland, grassland, wetland and bog and heath) across central Scotland which should be protected and improved, as well as key sites for connecting these habitats so that species can move between them.The data is intended to support planners, developers, land managers and communities identify where there are areas of neutral grassland and the dispersal zones will help identify where there are opportunities to improve habitat connectivity. These opportunities are highlighted on the Grassland Opportunity Areas shape-file. The data will also support the Scottish Government’s commitments to protect and restore biodiversity and to develop nature-based solutions to the climate emergency.The existing habitats shown on the Connectivity Map are limited to ‘natural’ or ‘semi-natural’ habitats. This means that they have not been significantly modified by humans; they have natural characteristics with a range of associated plant and animal species. Therefore, the existing habitats shown on the map do not include coniferous forest plantations or urban green spaces such as parkland. The map does not show acid or calcareous grassland habitats, nor does it show coastal, intertidal or marine habitats. Watercourses and open water are not included within the model, but can be identified from the base map or through other GIS layers used for analysis. Information on the current condition and future targets for rivers, lochs, coastal waters and groundwater can be found on the SEPA water environment hub. It is recommended that each habitat type is initially viewed alongside the other habitat layers, to ensure that the full range of habitats in an area are included in the decision-making process.

This dataset contains a raster dataset showing areas’ contributing to the grassland network, including how important the area is for the network (based on habitat type and proximity to the next core area).

This dataset is part of a dataset series that establishes an ecosystem service maps (national scale) for a set of services prioritised through stakeholder consultation and any intermediate layers created by Environment Systems Ltd in the cause of the project. The individual dataset resources in the datasets series are to be considered in conjunction with the project report: https://www.npws.ie/research-projects/ecosystems-services-mapping-and-assessment

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland.

This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs.

The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.

This point dataset contains target notes recorded during grassland field surveys in the Cairngorms National Park undertaken between 2020 and 2022, as part of a joint project delivered by NatureScot and the Cairngorms National Park Authority. The aim was to establish the location and extent of species-rich grassland (SRG) within enclosed (and formerly enclosed) farm land, up to a maximum altitudinal limit of 500 m, using a combination of remote sensing and targeted field survey. This dataset covers the Livet, Avon and Dee catchments. Patches of unimproved/semi-improved grassland, down to 0.04 ha in size, were identified and delineated by analysing high-resolution aerial photography (involving image segmentation and subsequent classification of the output). This provided a search map of polygons to visit in the field, targeting survey effort towards the areas where species-rich grassland was most likely to occur. The field survey was undertaken by contractors during July to September in 2020 and 2021, and July to October 2022. For each polygon, grassland communities and their relative proportion cover were described using the National Vegetation Classification (NVC), and species-richness was assessed. Locations of any missed species-rich grassland, occurring outside the search map polygons, were captured in the field and added to the dataset. This information is stored in the associated CNPGrasslandMapping_2020to2022_Polygons dataset. When species-rich grassland was encountered, additional detailed attributes describing the quality and condition of these stands were collected in the CNPGrasslandMapping_2020to2022_SRGAttributes dataset. This dataset provides point locations, descriptions and photo attachments for other notable habitats and plant species recorded during the field survey, and contains the following fields:

CATEGORY – category of point record collected (habitat record, species record, photo point, target note); HAB_SPEC – habitat or species recorded; COMMENT – detailed description/notes; GRIDREF – grid reference of point; SURV_YEAR – year of field survey; SURV_DATE – date of field survey; SURVEYOR – field surveyor; CATCHMENT – river catchment area; POINT_ID – unique identifier. Complete project metadata on spatialdata.gov.scot

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland. This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs. The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.

A binary change map for the class "GRASSLAND" on Montado (Portugal) PA, detected by the Cross Correlation Analysis (CCA) [1,2] algorithm considering at time T1 the layer "GRASSLAND" from COPERNICUS Service layer dated 2012 and at time T2 a Sentinel-2A image dated 18 August 2016, at 20 meters spatial resolution, projected in WGS84/UTM29N.
The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.

[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).

[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257

This dataset provides global annual dominant class maps of grasslands (cultivated and natural/semi-natural) from 2000 to 2022 at 30-m spatial resolution. Produced by Land & Carbon Lab Global Pasture Watch initiative, the mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low …

description: This map depicts lands owned and/or administered by the U.S. Fish and Wildlife Service at Grasslands Wildlife Management Area.; abstract: This map depicts lands owned and/or administered by the U.S. Fish and Wildlife Service at Grasslands Wildlife Management Area.

This dataset contains the common names of the national forests and grasslands and their respective FS WWW URL information that is used for both display of the national forest and national grassland boundaries on any map product and for dynamic interactivity of the map. This dataset exhibits the following characteristics: 1. Granularity of the polygon features - The spatial extent of the national forests and the grasslands match the way the agency would like to communicate with the public. 2. Preferred /Common Name of the National Forest Units - The common names of the national forest and grassland match the preferred name column that is present in the common names decision table maintained by the FS Office of Communication. 3. Hyperlinks to FS WWW Home page - This column contains the national forest and their respective FS WWW URL information. This URL could be used on any interactive map applications to link users directly to a forest’s home page. Data Source - This dataset is derived from the following FS ALP (Automated Lands Program) Land Status Records System authoritative data sources: 1. Administrative Forest Boundaries 2. Proclaimed Forest Boundaries 3. Ranger District Boundaries 4. National Grassland Areas. The common names decision table maintained by the FS Office of Communication contains the common name and its respective Land Status Records System authoritative data source to be used for building the spatial polygon. The spatial polygons for every feature in this dataset comes from one or more authoritative data sources listed above. The process to create the common names dataset is reusing the already existing ALP names from the data sources listed above.

This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.