This web map contains the Bing Maps aerial imagery web mapping service, which offers worldwide orthographic aerial and satellite imagery. Coverage varies by region, with the most detailed coverage in the USA and United Kingdom. Coverage in different areas within a country also varies in detail based on the availability of imagery for that region. Bing Maps is continuously adding imagery in new areas and updating coverage in areas of existing coverage. This map does not include bird's eye imagery. Information regarding monthly updates of imagery coverage are available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Aerial service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Aerial from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

This web map contains the Bing Maps aerial imagery with labels web mapping service, which provides worldwide orthographic aerial and satellite imagery with roads and labels overlaid. Coverage varies by region, with the most detailed coverage in the USA and United Kingdom. Coverage in different areas within a country also varies in detail based on the availability of imagery for that region. Bing Maps is continuously adding imagery in new areas and updating coverage in areas of existing coverage. This map does not include bird's eye imagery. Information regarding monthly updates of imagery coverage are available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Hybrid service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Hybrid from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation Note: (1) Information on growing and non-growing seasons has been derived from the remote sensing product: https://lpdaac.usgs.gov/dataset_discovery/measures/measures_products_table/vipphen_ndvi_v004(2) Google provides full global coverage by images, in contrast to Bing. However, in many areas, these are Landsat-based images (from 1984 up to now). For more objective comparison with Bing imagery, we have excluded those areas from the analysis. Supplement to: Lesiv, Myroslava; See, Linda; Laso-Bayas, Juan-Carlos; Sturn, Tobias; Schepaschenko, Dmitry; Karner, Mathias; Moorthy, Inian; McCallum, Ian; Fritz, Steffen (2018): Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data. Land, 7(4), 118

This web map contains the Bing Maps Road web mapping service, which offers worldwide roads data. Street-level detail is available for 67 countries/regions, including 50 countries/regions in Europe, 4 countries in North America and the Caribbean, 3 countries in South America, 5 countries in the Asia/Pacific region, and 5 countries in Northern Africa. Detailed roads coverage information for Bing Maps is available at the Microsoft Developer's Center. Additional information about content updates for Bing Maps is available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Road service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Road from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Remotely-sensed imagery provides the foundation for mapping vegetation types and other land cover classes. Imagery taken by the GeoEye-1 satellite/sensor was acquired from LandInfo Worldwide Mapping, LLC. The product was delivered as bundled 50 cm panchromatic and 2 meter 4-band multispectral (R, G, B, and NIR) images. The imagery has a positional accuracy of <3 m. Specifications for the GeoEye acquisition included the following: Total area for new collection of 372 square kilometers, 10% or less cloud cover , 0-20 off-nadir angle guarantee, Acquisition dates between late May and late June, 2011 Imagery satisfying the requirements was successfully acquired for the BICA project area on June 15, 2011 and delivered to CSU in July 2011. Each image was delivered as a geo-referenced product mosaicked as a single scene/image. We created a 50 cm resolution pan-sharpened set of multispectral bands to use for interpretation of vegetation. The acquisition provided 4-band imagery during the peak growing season. Additional imagery supplementing interpretation included 30 cm true-color Google Earth/Bing imagery imported to ArcGIS using Arc2Earth™ software and older true-color imagery viewed using the Google Earth online viewer.

🇦🇹 오스트리아 English Bing Maps is a free online map service from Microsoft that allows you to view various spatial data and use spatial services. It is a further development of the MSN Virtual Earth and is part of the search engine Bing. The data and services are provided through the Bing Maps for Enterprise platform and include satellite and aerial images. In the so-called transit area (for public transport connections), stops and timetables of the Wiener Linien as well as several hundred other transport companies and networks in the world are mapped to form the largest existing transit network. In the future, the mapping of real-time connections is also planned in this context.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. the draft final map was subjected to a heads-up screen digitizing edit using the most recent aerial photography. Accordingly, we accessed recent imagery through 2012 Microsoft Corporation Bing Imagery, available via ESRI ArcGis 10.0. As with all Bing imagery, the exact image date is not provided, but a search of the Digital Globe library indicates three possible dates: 2009-01-13, 2011-11-18, 2012-01-09, or a combination thereof. We think that it is not likely that the 2012 imagery had been posted to Bing, and that the 2011 imagery is the most likely candidate. We were also able to bring directly in additional 2009 New Mexico county imagery, and 2005 NAIP color-infrared and natural-color imagery at 1 m resolution. During the final edit, the thematic composition and number of Level 1 and 2 map units were finalized and the final map product produced using NPS cartographic standards. While the minimum mapping requirements were at 1:24,000 scale with map unit delineations or polygons at 0.5 ha or larger, most of the final line work was completed at an operational scale between 1:12,000 and 1:3,000. Hence, polygons down to 0.25 ha were often maintained. For final map production, adjacent polygons of the same class were merged. Final map products included the geodatabase and a 1:44,000 poster map at Level 1 and 2.

Bing Maps is a free online map service from Microsoft that allows you to view various spatial data and use spatial services. It is a further development of the MSN Virtual Earth and is part of the search engine Bing. The data and services are provided through the Bing Maps for Enterprise platform and include satellite and aerial images. In the so-called transit area (for public transport connections), stops and timetables of the Wiener Linien as well as several hundred other transport companies and networks in the world are mapped to form the largest existing transit network. In the future, the mapping of real-time connections is also planned in this context.

Over the past few years, Bing Maps has generated high-quality building footprints leveraging AI and harnessing the power of computer vision to identify map features at scale. Applying Deep Neural Networks and ResNet34 to detect building footprints from Bing imagery. Ensuring the best outputs, noise and suspicious data are removed from the predictions.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Base Imagery used for mapping (acquired by MoRAP): April 2010, SPOT, color infrared, leaf-off, 10 m 2009 NAIP, leaf-on, true color, 1 m Additional Imagery acquired and viewed by MoRAP: 2012, leaf-on, Bing Imagery

Reference point samples used in the production of the global maps of annual grassland class and extent for 2000—2022 within the scope of the Global Pasture Wath initiative.

The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises 2.3M points visually classified (using Very High Resolution imagery) in:

1. Cultivated grassland,
2. Natural/semi-natural grassland
3. Other land cover

The file gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:

• cluster_id: Cluster id defined by k-means (FSCS),
• cluster_distance: Distance from the sample tile to center of the cluster (FSCS),
• cluster_size: Size of cluster (strata) defined by the FSCS,
• priority: Priority used by the visual interpretation,
• tile_id: Sample tile id,
• imagery: VHR reference images used by the visual interpretation,
• min_year: Minimum of year covered by the reference samples,
• max_year: Maximum of year covered by the reference samples,
• n_years: Number of years covered by the reference samples,
• n_samples_c1: Number of reference samples for "Cultivated grass" (1),
• n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),
• n_samples_c3: Number of reference samples for "Open Shrubland" (2),
• n_samples_c4: Number of reference samples for "Not grass" (3),
• n_samples_all: Total number of reference samples,

The file gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg provides individual points (with 60-m spatial support) and include the follow collumns:

• sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,
• x: Longitude in WGS84 (EPSG:4326),
• y: Latitude in WGS84 (EPSG:4326),
• vi_tile_id: 1-km tile id,
• tile_id: GLAD tile id (1x1 degree)
• imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),
• ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,
• year: Reference year of GPW samples (based on VHR image) and of other existing datasets,
• class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,
• class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,
• dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW)
  dataset_class: Original land cover class provided by the maintainer of existing dataset
• esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,
• glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,
• glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,
• gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),
• ml_cv_group: spatial block CV group (based on vi_tile_id),
• ml_type: specify if the sample was used for (1) training or (2) calibration.

The file gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg provides the grid samples (with 10-m spatial support) and include the follow collumns:

• tile_id: 1-km tile id,
• bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,
• bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,
• bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,
• google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,
• google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,
• google_image_end_date: End date of the Google Maps Images used in the visual interpretation,
• missing_image_date: No images available,
• same_image_bing_google: Images from the same date available in Google and Bing Maps.

The dataset was produced through the QGIS plugin Fast Grid Inspection.

Related resources

• Maps of dominant grassland:
  2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
• Probability maps of cultivated grassland:
  2000-2022 (All URLs)
• Probability maps of natural/semi-natural grassland:
  2000-2022 (All URLs)
• Grassland reference samples based on VHR imagery (2000–2022):
  GeoPackage files
• Global machine learning models (Random Forest):
  Parquet and joblib python files
• Reference sampling design derived by FSCV:
  GeoPackage and raster files
• Harmonized reference samples based on existing LULC dataset:
  GeoPackage and raster files
• Source code for reproducibility:
  GitHub release
  
• Mapping feedback tool:
  GeoWiki
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Support

For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watch

Map Data Services market has a significant presence globally, with a market size valued at XXX million in 2025. It is projected to expand at a CAGR of XX% during the forecast period, reaching XXX million by 2033. The growth of the market is primarily driven by the increasing demand for accurate and reliable map data for various applications such as navigation, location-based services, and urban planning. Additionally, the rise of autonomous vehicles and the adoption of advanced technologies like augmented reality and virtual reality are further contributing to the demand for map data. The key players in the Map Data Services market include Google, WikiMapia, Apple Maps, Here, Bing Maps, Navinfo, TomTom, Mapbox, Esri, AutoNavi, Baidu Apollo, Sanborn, Yandex, Azure Maps, OpenStreetMap, and ArcGIS. These companies offer a wide range of map data products and services to meet the diverse needs of various industries and consumers. The market is segmented by application, type, and region, providing a comprehensive overview of the industry landscape and competitive dynamics. North America, Europe, and Asia Pacific are the major regional segments of the market, with North America holding a significant share due to the presence of major technology companies and the adoption of advanced technologies.

The MSDI (Manchester Surface Drone Imagery) is a geo-imagery registration dataset.
The dataset consists of
a. 446 downward-facing drone images.
b. 89 forward-facing(45-degree) drone images.
c. 64 forward-facing (0-degree) drone images.
d. parameter matrix of drone camera and transformation matrix.
e. checkerboard images for camera calibration.

This dataset is collected by Mochuan Zhan for his MSC project: Registration of UAV Imagery to Aerial and Satellite Imagery
in the University of Manchester (2021/9 - 2022/9) which is supervised by Dr.Terence Patrick Morley. This project aims at
developing a system that could perform efficient UAV visual localization through image registration based on local feature
detectors and the technique of high-throughput computing.

Notice:
The corresponding satellite image from Google Map and Bing Map could be obtained by my program, Link:

https://doi.org/10.5281/zenodo.6977652

A Ground Control Point (GCP) selector is provided for user to select GCP and create file with small effort.
By registrating corresponding images, users could evaluate the performance of their registration techniques.

The Imagery contains images of 8 Areas in Manchester:
- Manchester Aquatics Center 80
- Manchester ASDA 76
- Manchester Bussiness School 37
- Manchester Energy Center 71
- Manchester Holy Name Church 58
- Manchester Hulme Park 47
- Manchester Hulme Part(0-degree) 64
- Manchester Hulme Part(45-degree) 89
- Manchester Metropolitan university 29
- Manchester Museum 48

Device Information:
- Drone brand: Parrot
- Drone model: Parrot Anafi

Software Information:
- Pix4DCapture
- FreeFlight6

Flight parameters:
- Height 100m
- Speed 5m/s
- overlap low

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The California Department of Fish and Wildlife (CDFW) Vegetation Classification and Mapping Program (VegCAMP) has created a fine-scale vegetation map of part of the range of the American Ranch and Chimineas Ranch tule elk herds. This section of the elk range is adjacent to the Carrizo Plain National Monument and the Chimineas Ranch Unit of the Carrizo Plain Ecological Reserve, both of which have been previously mapped. This map has been seamed to the vegetation map of the Chimineas Ranch completed by VegCAMP (VegCAMP 2010) and the map of the Carrizo Plain National Monument produced by the California Native Plant Society (Stout et al. 2013), and completes the range of these two elk herds in San Luis Obispo County, California. Like those maps, this mapping follows Survey of California Vegetation, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) standards (FGDC 2008, Jennings et al. 2009). The map legend is based on the classification in Stout et al. (2013), with slight modifications as discussed in Appendix C of the report, which is available here: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92951. Reconnaissance-level sampling of vegetation stands in the project area was conducted in the spring of 2013. Polygons were drawn using heads-up digitizing with true color 1-foot aerial imagery from August 1, 2007 as the map base. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and color infrared (CIR) 1-meter resolution data from 2010''2012, Bing imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of ½ acre. Mapping is to the NVC hierarchy Association, Alliance, or Group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Two sub-Group level mapping units were used in instances where the vegetation types could not differentiated on the imagery. The first mapping unit is composed of the Salvia leucophylla, Eriogonum fasciculatum, Artemisia californica, and Artemisia californica-Eriogonum fasciculatum Alliances; the second is composed of the Atriplex polycarpa and Atriplex canescens Alliances. Accuracy assessment (AA) data was collected in spring of 2014. Map accuracy was calculated to be 89 percent; corrections were made to the map based on the AA data to increase the final accuracy.

The collection includes beach coastlines from Southeastern Australia, specifically Victoria and New South Wales, used to train an image segmentation model using the U-Net deep learning architecture for mapping sandy beaches. The dataset contains polygons that represent the outline or extent of the raster images and polygons drawn by citizen-scientists. Additionally, we provide the trained model itself, which can be utilized for further evaluation or refined through fine-tuning. The resulting predictions are also available in Shapefiles format, which can be loaded to NationalMap.

This collection supplements the publication: Regional-Scale Image Segmentation of Sandy Beaches: Comparison of Training and Prediction Across Two Extensive Coastlines in Southeastern Australia (Yong et al. 2025) Lineage: The training dataset of citizen science-drawn beach outlines and polygons was sourced from OpenStreetMap (OSM) https://www.openstreetmap.org/). Tiled images along the coast were sourced from Microsoft Bing imagery to process new beach outlines, as it is also one of the main sources of imagery used for drawing features in OSM. Note, the original OSM data was licensed ODbL and should be considered when using the processed dataset, which required a Creative Commons Licence to be published in this portal. CC-BY was identified as the most suitable license in the portal to align with ODbL.

The saved deep learning model was trained on the dataset using a U-Net architecture, which is used to generate the predicted maps.

Analysis of ‘Vegetation - Carrizo Plain National Monument, Ecological Reserve and Adjacent Elk Range [ds1094]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7c438c18-d580-4f24-8f42-e38a4f19cfb5 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The California Department of Fish and Wildlife (CDFW) Vegetation Classification and Mapping Program (VegCAMP) has created a fine-scale vegetation map of part of the range of the American Ranch and Chimineas Ranch tule elk herds. This section of the elk range is adjacent to the Carrizo Plain National Monument and the Chimineas Ranch Unit of the Carrizo Plain Ecological Reserve, both of which have been previously mapped. This map has been seamed to the vegetation map of the Chimineas Ranch completed by VegCAMP (VegCAMP 2010) and the map of the Carrizo Plain National Monument produced by the California Native Plant Society (Stout et al. 2013), and completes the range of these two elk herds in San Luis Obispo County, California. Like those maps, this mapping follows Survey of California Vegetation, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) standards (FGDC 2008, Jennings et al. 2009). The map legend is based on the classification in Stout et al. (2013), with slight modifications as discussed in Appendix C of the report, which is available here: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=92951. Reconnaissance-level sampling of vegetation stands in the project area was conducted in the spring of 2013. Polygons were drawn using heads-up digitizing with true color 1-foot aerial imagery from August 1, 2007 as the map base. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and color infrared (CIR) 1-meter resolution data from 2010''2012, Bing imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of ½ acre. Mapping is to the NVC hierarchy Association, Alliance, or Group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Two sub-Group level mapping units were used in instances where the vegetation types could not differentiated on the imagery. The first mapping unit is composed of the Salvia leucophylla, Eriogonum fasciculatum, Artemisia californica, and Artemisia californica-Eriogonum fasciculatum Alliances; the second is composed of the Atriplex polycarpa and Atriplex canescens Alliances. Accuracy assessment (AA) data was collected in spring of 2014. Map accuracy was calculated to be 89%; corrections were made to the map based on the AA data to increase the final accuracy.

--- Original source retains full ownership of the source dataset ---

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. The primary imagery used for the base map for the project was 2016 60 cm National Aerial Imagery Program (NAIP) imagery. Additional imagery supporting the interpretation phase included current and historic true-color Google Earth and Bing Maps imagery, as well as 2015 4-band 30 cm imagery from Cornerstone Mapping Inc., and imagery from Digital Globe, Inc.

The California Department of Fish and Wildlife (Department) Vegetation Classification and Mapping Program (VegCAMP) created a fine-scale vegetation classification and map of the southern addition to the Departments Knoxville Wildlife Area (WA), Napa County, California following State Vegetation Survey, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) Standards (Grossman et al 1998). The vegetation classification was derived from data collected in the field following the Combined Rapid Assessment and Relevé Protocol during the periods November 18''20, 2013 and April 28''May 1, 2014. Vegetation polygons were drawn using heads-up manual digitizing using the 2011 Napa County 30-cm resolution color infrared (CIR) imagery as the base imagery. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and CIR 1-meter resolution data from 2009''2012, BING imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of 1/2 acre. Ponds, riparian types, and the one vernal pool on the WA that were visible on the imagery were mapped regardless of size, and streams were generally mapped if greater than 10 m wide (narrower portions may have been mapped to maintain the continuity of the streams). Mapping is to the NVC hierarchy association, alliance, or group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Both the existing (northern) and new addition (southern) portions of the Knoxville WA were mapped in 2002 as part of the Napa County vegetation map (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=14660). The 2002 map is at a coarse thematic resolution (alliance through macrogroup level) and vegetation in portions of the WA has changed since the 2004 Rumsey Fire, necessitating this map update. We have produced an updated version of the KWA portion of the 2002 map layer that uses the same spatial data, but added a crosswalk to the current classification and the upper levels of the current hierarchy. This map layer is included in the downloaded dataset for this map and an expanded metadata report for that crosswalk can be found at https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=164825.

Analysis of ‘Vegetation - Knoxville Wildlife Areas [ds2812]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9e92cb82-860f-48fd-bdb6-1db9ed5fd60b on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The California Department of Fish and Wildlife (Department) Vegetation Classification and Mapping Program (VegCAMP) created a fine-scale vegetation classification and map of the southern addition to the Departments Knoxville Wildlife Area (WA), Napa County, California following State Vegetation Survey, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) Standards (Grossman et al 1998). The vegetation classification was derived from data collected in the field following the Combined Rapid Assessment and Relevé Protocol during the periods November 18''20, 2013 and April 28''May 1, 2014. Vegetation polygons were drawn using heads-up manual digitizing using the 2011 Napa County 30-cm resolution color infrared (CIR) imagery as the base imagery. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and CIR 1-meter resolution data from 2009''2012, BING imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of 1/2 acre. Ponds, riparian types, and the one vernal pool on the WA that were visible on the imagery were mapped regardless of size, and streams were generally mapped if greater than 10 m wide (narrower portions may have been mapped to maintain the continuity of the streams). Mapping is to the NVC hierarchy association, alliance, or group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Both the existing (northern) and new addition (southern) portions of the Knoxville WA were mapped in 2002 as part of the Napa County vegetation map (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=14660). The 2002 map is at a coarse thematic resolution (alliance through macrogroup level) and vegetation in portions of the WA has changed since the 2004 Rumsey Fire, necessitating this map update. We have produced an updated version of the KWA portion of the 2002 map layer that uses the same spatial data, but added a crosswalk to the current classification and the upper levels of the current hierarchy. This map layer is included in the downloaded dataset for this map and an expanded metadata report for that crosswalk can be found at https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=164825.

--- Original source retains full ownership of the source dataset ---