This data set provides a means of identifying an x-y coordinate for the approximate center (centroid) of landnet units based on the corresponding standardized PLSS description (e.g., for PLSS Section this is DTRS -- Direction, Township, Range, and Section codes). This process is sometimes referred to as "protraction". The Landnet centroid shapefile includes coordinates in WTM83/91 and latitude/longitude expressed as decimal degrees or degrees, minutes and seconds.

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Loudoun County Parcel X,Y coordinates table. Available in Latitude and Longitude decimal degrees and Virginia State Plane North.

Please read: This table is part of the comprehensive Full Landonline Dataset and is designed for use only by data professionals who require the complex version of our property ownership and boundary data for advanced uses.

This table contains information about the different forms that coordinates can take within a datum. For example, Geocentric Cartesian (X, Y, Z); Topocentric Cartesian (East, North, Up): Geodetic (Latitude, Longitude) or (Latitude, Longitude, Height), Astronomic (Latitude, Longitude) Projection (North, East) in various projections, Orthometric Height, Ellipsoidal Height, Geoid Height, Gravitational Potential, etc.

Please refer to the LDS Full Landonline Data Dictionary and Models for detailed metadata about this table.

This dataset contains all basic primary school details including address, longitude/latitude, geo (XY) coordinates and eircode information

Summary Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads. Description FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing. Credits Federal Railroad Administration (FRA) Use limitations There are no access and use limitations for this item. Extent West -79.491008 East -75.178954 North 39.733500 South 38.051719 Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000 ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation * Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No Temporal keywords  2013 Theme keywords  Rail Theme keywords  Grade Crossing Theme keywords  Rail Crossings Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00 Presentation formats  * digital map Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor Contact information  ▼►Phone  Voice 202-366-DATA Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format Spatial representation type  * vector * Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348 Credits Federal Railroad Administration (FRA) ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Resource Maintenance ▼►Resource maintenance  Update frequency  annually Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item. Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree"

description: These data are .csv files of tagged sea otter re-sighting locations (henceforth, resights) collected in the field using a combination of VHF radio telemetry and direct observation using high powered (80x) telescopes. Sea otters were tracked by shore or boat-based observers from the date of tagging until the time of radio battery failure, the animal s death, or the end of the project, whichever comes first. The frequency of re-sighting was opportunistic, depending on logistical factors such as coastal access, but generally ranged from daily to weekly. Location coordinates are reported latitude and longitude as well as X and Y coordinates in the projection/datum California Teale-Albers NAD 1927. The file contains resight data for all tagged sea otters in the Santa Barbara Channel Study, collected over a period of 2.5 years. Fields include individual identification numbers, sex, age class, geographic coordinates, the date and time of the re-sighting location, and associated behavioral state of the individual at the time of re-sighting. These data support the followinf publication: Tinker, M.T., Tomoleoni, Joseph, LaRoche, Nicole, Bowen, Lizabeth, Miles, A. Keith, Murray, Mike, Staedler, Michelle, and Randell, Zach, 2017, Southern sea otter range expansion and habitat use in the Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 20171001 (OCS Study BOEM 2017-002), 76 p., http://doi.org/10.3133/ofr20171001.; abstract: These data are .csv files of tagged sea otter re-sighting locations (henceforth, resights) collected in the field using a combination of VHF radio telemetry and direct observation using high powered (80x) telescopes. Sea otters were tracked by shore or boat-based observers from the date of tagging until the time of radio battery failure, the animal s death, or the end of the project, whichever comes first. The frequency of re-sighting was opportunistic, depending on logistical factors such as coastal access, but generally ranged from daily to weekly. Location coordinates are reported latitude and longitude as well as X and Y coordinates in the projection/datum California Teale-Albers NAD 1927. The file contains resight data for all tagged sea otters in the Santa Barbara Channel Study, collected over a period of 2.5 years. Fields include individual identification numbers, sex, age class, geographic coordinates, the date and time of the re-sighting location, and associated behavioral state of the individual at the time of re-sighting. These data support the followinf publication: Tinker, M.T., Tomoleoni, Joseph, LaRoche, Nicole, Bowen, Lizabeth, Miles, A. Keith, Murray, Mike, Staedler, Michelle, and Randell, Zach, 2017, Southern sea otter range expansion and habitat use in the Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 20171001 (OCS Study BOEM 2017-002), 76 p., http://doi.org/10.3133/ofr20171001.

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.

The dataset contains pixel-based data of variables for Ethiopia and Kenya. Each data record (i.e. each row) is corresponding to one pixel of approximately 1 km2. The longitude and latitude coordinates of the pixel center (in decimal degree, GCS WGS 1984 datum) are the X and Y variables, respectively. Using these spatial coordinates of the pixels, the data represented in the file, or newly computed data from the data file, can be re-imported to a standard GIS software like ArcGIS (one raster layer is corresponding to one variable). For all variables, the value of -9999 indicates “no data”.

This data set consists of inferred accumulation rates from three radar layers (26, 35 and 41 thousand years old) in the Vostok Subglacial Lake region. Accumulation rates were inferred using Local-Layer Approximation (LLA), which assumes that the strain-rate history of a particle traveling through the ice sheet can be approximated by the vertical strain-rate profile at the current position of the particle, which the researchers assume to be uniform. Parameters include location, in latitude and longitude, polar stereographic coordinates, and local grid X and Y coordinates, along with layer age, in thousands of years (ka), and inferred accumulation rate (cm/a). The data cover a 150 by 350 km area.

Data are available via FTP, as a text file (.txt) with columns in comma separated value format.

Location of wifi hotspots in the city with basic descriptive information.

This dataset comprises high spatial- and temporal-resolution maps of coastal landfast sea ice (fast ice) distribution in the vicinity of the Cape Darnley Polynya in East Antarctica, in the June-November (winter-spring) periods of 2008 and 2009. The maps were derived from cross-correlation of pairs of spatially-overlapping Envisat Advanced Synthetic Aperture Radar (ASAR) images, using a modified version of the IMCORR algorithm to determine vectors of sea-ice motion (as described in Giles et al., 2011). Fast ice is then distinguished from moving pack ice by the fact that it is stationary. The raw ASAR WSM data (swath width 500 km) were processed using ENVI image processing software to produce geo-referenced images with a 75m pixel size. Use of SAR data ensures coverage uninterrupted by cloud cover or polar darkness.

Image pairs were chosen with a time separation between 2 and 21 days. IMCORR processing of the image pairs for mapping fast ice follows Giles et al (2011) – using a reference tile size of 32x32 pixels and a search tile size of 64 x 64 pixels. A land mask was applied to avoid contamination from matches on stationary features over the continental ice sheet. The grid spacing was set to 16 x 16 pixels, so the images were over-sampled by a factor of 2 to provide a more dense set of results.

Stationary fast ice vectors were chosen from the IMCORR results using a combination of the cluster search technique and a variation of the z-axis threshold technique as detailed in Giles et al (2011). The cluster search technique was applied to the IMCORR results from each image pair to derive the initial set of valid vectors – this set could contain both stationary fast ice vectors and non-stationary pack ice vectors. Due to registration errors in the image pairs, the stationary vectors will not necessarily be centred around zero, so using a simple window around the zero offset mark to differentiate the fast ice vectors was not possible. To select the stationary vectors, a 2D histogram was constructed from the X-Y vector displacements, and a 2D Gaussian was fitted to this histogram. The fast ice vectors will dominate because of the large image pair time separation and small search tile size, so the Gaussian peak should correspond to the centre of the stationary fast ice vectors. All vectors that are within 5 standard deviations of the Gaussian peak are tagged as valid fast ice vectors. This is a minor modification to the method of Giles et al (2011), who used a simple threshold cut on the z-axis of the 2D histogram to define the fast ice vectors.

Data format – one fully annotated (self-describing) netCDF file per image pair containing latitude/longitude coordinates of the stationary fast ice vectors.

This technique and dataset complement a lower resolution but longer-term dataset (2000-2014) derived from satellite MODIS visible and thermal infrared data. (AAS_4116_Fraser_fastice_mawson_capedarnley).

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.

This is a digital version of the grid reference map used to plot all sightings of Weddell seals in the Vestfold Hills. The point of origin is the same as the original map and each grid cell is numbered with the same numbering scheme. This can be used to plot any data using the same numbering scheme by joining (ArcInfo) or linking (ArcView) records to this coverage's polygon attribute table (pat) through the item GRIDREF.

The original map was a 1:100 000 map of the Vestfolds, provided by Harry Burton, with a grid drawn over it. The grid references were given as either six or four figure values on which field scientists are to plot their data.

This map has the following Antarctic Division drawing reference number:

M/75/05A

Some research with John Cox revealed that this grid was drawn up over a map digitised from another map with the following specifications:

Scale 1: 100 000 Date: 1958 (reprinted 1972) Projection: Polyconic Published by: Division of National Mapping, Canberra Reference number: NMP/58/084

Data are referenced to a 'grid' of 1 minute spacing in x axis and 30 second spacing in y axis. The point of origin is apparently 68 20 S 77 48 E. There are 45 rows and 47 columns.

The 'grid reference' is in fact in geographic coordinates (but using arbitrary units) so the projection of the original map became irrelevant.

The procedure adopted to create a new digital grid was as follows:

(Carried out in Arc/Info)

1. Generate a coverage using the original 'grid references'.
2. Tics were also generated using the corners of the 'grid reference' system.
3. A new coverage was created with tics at the same locations but given the true latitude/longitude vales.
4. The original coverage was then transformed to the new coverage based on the new tic values.
5. The new coverage was then projected from geographic coordinates to UTM metres.

The data locations were then viewed in Arc/Info using a coverage of the coastline supplied by the Mapping Officer, Antarctic Division. This had previously been determined to be in the UTM projection.

An offset was clearly visible between the data locations and the coastline. In order to determine whether the offset was more or less uniform, ten locations were plotted from the original data onto the original map using the 'grid'. Finally a manual corrected was made by moving all the data locations by a uniform distance of 508 metres north and 68 metres west.

Information from John van den Hoff, February 2019: The grid cells were originally labelled from 1 to 47 along the x axis and 1 to 45 along the y axis. The four digit values in the GRIDREF field of the attribute table are the x value followed by the y value. To avoid confusion between x and y values, the grid was later revised so that the y values were prefixed with a ‘1’ so for example 01 became 101. The GRIDREF_X and GRIDREF_Y fields have the x and y values of the revised grid. This needs to be kept in mind when data is sourced from field books. The map shows the revised grid.

This dataset represents States and equivalent entities, which are the primary governmental divisions of the United States. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded https://www.mrlc.gov/data.

The product provides modeled mean annual ground temperatures (MAGT) at the top of the permafrost for the Northern Hemisphere at 1 km spatial resolution. Permafrost probability (fraction values from 0 to 1) is assigned to each grid cell with MAGT < 0°C. Based on its permafrost probability each grid cell is classified as continuous, discontinuous and sporadic permafrost. The processing extent covers exposed land areas of Northern Hemisphere down to 25 ° latitude. The mean MAGT was validated with GTN-P and TSP borehole ground temperature data yielded RMS of 2.0 °C. According to the results permafrost (MAGT < 0 °C) covers 15 % of exposed land of the Northern Hemisphere.

The NetCDF files contain resampled data from GeoTiffs (see initial product guide) to approx. 5, 10 and 25 km spatial resolution (available as separate files). The data are in pretended regular lat-lon-grid from and contain both metric x and y coordinates in addition to geographic (latitude and longitude) coordinates. Each NetCDF file contains mean annual ground temperature (variable name: MAGT) dataset, permafrost occurrence probability dataset (variable name: PerProb) and standard deviation of mean annual ground temperature dataset (variable name: SD).

More Information about the product and it´s modelling method can be found in the product guide.