The Forest Service National Maps experience page is designed to distribute and deliver maps to the Forest Service and public. Maps cover Forest Service lands. Map series include National; Regional; Admin; Forest; Ranger District and 24K or better known as FSTopo, and our historical product FSTopo Legacy.

A depiction of the boundary that encompasses a Ranger District. Metadata

This application was created to support the Mapping Existing Vegetation on Cordova Ranger District Vegetation Story Map. Dominance type, tree canopy cover, tall shrub canopy cover, and tree size maps were developed for Cordova Ranger District. The Cordova Ranger District (including other federal, state, native, and private land inholdings) was mapped through a partnership between the Geospatial Technology and Applications Center (GTAC) and the Chugach National Forest. The Chugach National Forest and their partners prepared the AOI classification system, identified the desired map units (map classes) and provided general project management. GTAC provided project support and expertise in vegetation mapping. A combination of reference data was used to inform the classification models that output the final maps. Federal and Private field personnel collected plot data on the ground. Classification models were used to characterize modeling units (mapping polygons) with the following vegetation attributes: 1) dominance type; 2) tree canopy cover; 3) tree size. The minimum map feature depicted on the map is 0.25 acres. All map products were designed according to the Forest Service mid-level vegetation mapping standards in order to be stored in the Forest GIS and National databases. This map product was generated primarily using data acquired prior to or in 2021. The field data used as reference information for this mapping project was primarily collected in the summer of 2021. Therefore, the final map can be considered indicative of the existing vegetation conditions found on the Cordova Ranger District in 2021.

This dataset is a subset of the dataset titled ALL CULTRAL POINTS, created by the Arkansas Highway and Transportation Department. Combinations of all of the cultural features are plotted when updating county and city maps in the Mapping Section at the Arkansas State Highway and Transportation Department

This is a georeferenced raster image of a printed paper map of the Ranger Lake, Ontario region (Sheet No. 041J13), published in 1964. It is the second edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1964 and the information on the map is current as of 1953. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

This is a georeferenced raster image of a printed paper map of the Ranger Lake, Ontario region (Sheet No. 041J13), published in 1957. It is the first edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1957. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

A depiction of the boundary that encompasses a Ranger District.

This is a georeferenced raster image of a printed paper map of the Ranger Lake, Ontario region (Sheet No. 041J13), published in 1995. It is the fifth edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1995 and the information on the map is current as of 1992. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

This is a georeferenced raster image of a printed paper map of the Ranger Lake, Ontario region (Sheet No. 041J13), published in 1995. It is the fourth edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1995 and the information on the map is current as of 1992. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

A map service depicting the boundary that encompasses a Ranger District. This map service provides display, identification, and analysis tools for determining current boundary information for Forest Service managers, GIS Specialists, and others. Metadata and Downloads

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

A map service depicting the boundary that encompasses a Ranger District. This map service provides display, identification, and analysis tools for determining current boundary information for Forest Service managers, GIS Specialists, and others. Metadata and Downloads

Dominant tree species map of Switzerland We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg). Access will be granted upon request. In total, the following 15 species were mapped: Abies alba, Acer pseudoplatanus, Alnus glutinosa, Alnus incana, Betula pendula, Castanea sativa, Fagus sylvatica, Fraxinus excelsior, Picea abies, Pinus cembra, Pinus mugo arborea, Pinus sylvestris, Quercus petraea, Quercus robur, Sorbus aucuparia. -br/--br/- Approach -br/--br/- Data - Swiss National Forest Inventory Data (stand species with - 60 % dominance in upper canopy; on at least more than 9 plots dominant) - Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI) - Digital elevation model (DEM) (swissalti3d, 5 m) - Biogeographical regions (Federal Office for the Environment FOEN) - Forest mask 2017 (Approach: Waser et al., 2015) -br/--br/- Modeling approach We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold. The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time. -br/--br/- Training data for modeling - 295 Sentinel-2, DEM & Biogeographical variables - 10525 tree species pixels -br/--br/- Selected variables for final model 1. EVI of 2020.05.16 2. NDMI of 2020.03.12 3. CIRE of 2020.04.16 4. NDMI of 2020.07.05 5. CCI of 2020.05.11 6. dem 7. CCI of 2020.08.14 8. NDMI of 2020.08.24 9. CCI of 2020.12.22 10. NDMI of 2020.04.21 11. NDMI of 2020.11.17 12. NDMI of 2020.08.09 13. CIRE of 2020.03.22 14. CIRE of 2020.08.09 14. CCI of 2020.11.02 15. CIRE of 2020.06.10 -br/--br/- Overall Accuracy of final model - 0.759 -br/--br/- Nationwide prediction - Predicted throughout forest mask 2017 (Approach: Waser et al., 2015) - Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts) - Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data -br/--br/- -br/--br/- Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing -br/--br/- -br/--br/- References - Kuhn, M. (2022). Classification and Regression Training. 6.0-93. - Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001 - Meyer, H., Milà, C., & Ludwig, M. (2022). CAST: 'caret' Applications for Spatial-Temporal Models. 0.7.0. - Ververidis, D., & Kotropoulos, C. (2005). Sequential forward feature selection with low computational cost. 2005 13th European Signal Processing Conference. - Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition. Forests, 6, 12, 4510–4528. - Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

This dataset shows the location of Commonwealth funded Indigenous Ranger Program. Information for this dataset was provided by National Indigenous Australians Agency (NIAA). Spatial data was created by Department of Climate Change, Energy, the Environment and Water.Further information and locations of Indigenous Protected Areas and Commonwealth funded Indigenous land and water management projects are detailed on a pdf that can be found at the website below.https://www.niaa.gov.au/indigenous-affairs/environment/indigenous-ranger-programs

These data display national forest and ranger district boundaries for Region 1, 2 and 4. Note that many private and state-owned parcels lie within the national forest boundaries depicted in these data. Note that these data do not conform entirely with official national forest boundaries: (1) the boundaries included here have been expanded to encompass national forest system lands that lie outside of the official forest boundary and (2) many small and discrete polygons that have no national forest ownership but were (and are) included in the officially designated forest boundary are not included in these data. To obtain actual boundaries use most accurate land management data layer.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed by combining crown TSR spatial data, information gathered from Rural Lands Protection Board (RLPB) rangers, and surveyed Conservation and Biodiversity data to compile a layer within 30 RLPB districts in NSW. The layer attempts to spatially reflect current TSRs as accurately as possible with conservation attributes for each one. Dataset History The initial process in production involved using the most up to date extract of TSR from the crown spatial layer as a base map, as this layer should reasonably accurately spatially reflect the location, size, and attributes of TSR in NSW. This crown spatial layer from which the TSR were extracted is maintained by the NSW Department of Lands. The TSR extract is comprised of approximately 25,000 polygons in the study area. These polygons were then attributed with names, IDs and other attributes from the Long Paddock (LP) points layer produced by the RLPB State Council, which contains approximately 4000 named reserves throughout the study area. This layer reflects the names and ID number by which the reserves were or are currently managed by the RLPB's. This layer was spatially joined with the TSR polygon layer by proximity to produce a polygon layer attributed with RLPB reserve names and ID numbers. This process was repeated for other small datasets in order to link data with the polygon layer and LP reserve names. The next and by far the most time consuming and laborious process in the project was transferring the data gathered from surveys undertaken with RLPB rangers about each reserve (location, spatial extent, name, currency conservation value and biodiversity). This spatial information was annotated on hard copy maps and referenced against the spatial join making manual edits where necessary. Edits were conducted manually as the reference information was only on hard copy paper maps. Any corrections were made to the merged layer to produce an accurate spatial reflection of the RLPB reserves by name and ID. This manual editing process composed the bulk of the time for layer production as all reserves in each RLPB district in the study area had to be checked manually. Any necessary changes had to then be made to correct the spatial location of the reserve and ensure the correct ID was assigned for attributing the conservation data. In approximately 80% of cases the spatial join was correct, although this figure would be less where long chains of TSR polygons exist. The majority of time was devoted to making the numerous additions that needed to be incorporated. A spreadsheet based on the LP point layer was attributed with the LP point [OBJECTID] in order to produce a unique reference for each reserve so that conservation and biodiversity value data could be attributed against each reserve in the spatial layer being produced. Any new reserves were allocated [OBJECTID] number both in the GIS and the spreadsheet in order to create this link. All relevant data was entered into the spreadsheet and then edited to a suitable level to be attached as an attribute table. Field names were chosen and appropriate an interpretable data formats each field. The completed spreadsheet was then linked to the shapefile to produce a polygon TSR spatial layer containing all available conservation and biodiversity information. Any additional attribute were either entered manually or obtained by merging with other layers. Attributes for the final layer were selected for usability by those wishing to query valuable Conservation Value (CV) data for each reserve, along with a number of administrative attributes for locating and querying certain aspects of each parcel. Constant error checking was conducted throughout the process to ensure minimal error being transferred to the production. This was done manually, and also by running numerous spatial and attribute based queries to identify potential errors in the spatial layer being produced. Follow up phone calls were made to the rangers to identify exact localities of reserves where polygons could not be allocated due to missing or ambiguous information. If precise location data was provided, polygons could be added in, either from other crown spatial layers or from cadastre. These polygons were also attributed with the lowest confindex rating, as their status as crown land is unknown or doubtful. In some cases existing GIS layers had been created for certain areas. Murray RLPB has data where 400+ polygons do not exist in the current crown TSR extract. According to the rangers interviewed it was determined the majority of these TSR exist. This data was incorporated in the TSR polygon by merging the two layers and then assigning attributes in the normal way, ie by being given a LP Name and ID and then updated from the marked up hard copy maps. In the confidence index these are given a rating of 1 (see confindex matrix) due to the unknown source of the data and no match with any other crown spatial data. A confidence index matrix (confindex) was produced in order to give the end user of the GIS product an idea as to how the data for each reserve was obtained, its purpose, and an indication to whether it is likely to be a current TSR. The higher the confindex, the more secure the user can be in the data. (See Confidence Index Matrix) This was necessary due to conflicting information from a number of datasets, usually the RLPB ranger (mark up on hard copy map) conflicting with the crown spatial data. If these conflicting reserves were to be deleted, this would lead to a large amount of information loss during the project. If additions were made without sufficient data to determine its crown status, currency, location, etc (which was not available in all cases) the end user may rely on data that has a low level of accuracy. The confindex was produced by determining the value of information and scoring it accordingly, compounding its value if data sources showed a correlation. Where an RLPB LP Name and ID point was not assigned to a polygon due to other points being in closer proximity these names and ID are effectively deleted from the polygon layer. In a number of cases this was correct due to land being revoked, relinquished and/or now freehold. In a number of cases where the TSR is thought to exist and a polygon could not be assigned due to no info available (Lot/DP, close proximity to a crown reserve, further ranger interview provided no info, etc etc). For these cases to ensure no information loss a points layer was compiled from the LP points layer with further info from the marked up hard copy maps to place the point in the most accurate approximate location to where the reserve is though to exist and then all CV data attached to the point. In many of these cases some further investigation could provide an exact location and inclusion in the TSR poly layer. The accuracy of the point is mentioned in the metadata, so that the location is not taken as an absolute location and is only to be used as a guide for the approximate location of the reserve. Topology checks were conducted to eliminate slivers in the layer and to remove duplicate polygons. Where two crown reserves existed on the same land parcel, the duplicate polygon was deleted and unique attributes (Crown Reserve Number, Type, and Purpose) were transferred. Once the polygon layer was satisfactorily completed, a list of the LP points not allocated to polygons was compiled. Any points (reserves) that were said to have been revoked or relinquished were then removed from this list to provide a list of those that are said to be current. An extract of the LP points layer was then produced with only the aforementioned points. These points were then attributed with the same conservation and biodiversity data as the polygon layer, in an attempt to minimise the amount of information loss. Dataset Citation "NSW Department of Environment, Climate Change and Water" (2010) Travelling Stock Route Conservation Values. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/198900d5-0d06-4bd0-832b-e30a7c4e8873.

Explore the historical Whois records related to ranger-cn.com (Domain). Get insights into ownership history and changes over time.

This dataset encompasses global soil erodibility (K) factor maps, with the K factor being estimated through the Wischmeier and Smith (1978) method. The equation incorporates permeability information crudely and indirectly, mainly relying on soil texture details, potentially overlooking factors like vegetation, biopores, and clay minerals. To address this limitation, we integrated measured Ksat values, representing soil hydraulic properties, into the Wischmeier and Smith (1978) soil texture-based K factor equation (referred to as KWischmeier factor) to formulate the Ksat-based soil erodibility (Kksat factor) map. Our dataset consists of approximately 6000 measured Ksat samples worldwide, linked with environmental covariates using the quantile random forest algorithm to generate 1 km spatial resolution maps for both Ksat and KWischmeier factors. Additionally, we calculated uncertainty for the Kksat and KWischmeier factor maps, represented by 90% prediction intervals (PI) through quantile calculations at 0.05 and 0.95. This uncertainty assessment was performed using the quantreg option in the R package ‘ranger’ (Wright and Ziegler 2015), termed as Uncertainty Kksat and Uncertainty KWischmeier. Comparisons were made between the Kksat and KWischmeier factor maps and Borrelli et al. (2017) KGloSEM factor map. The results indicated a reduction in Kksat factor values in tropical regions, highlighting differences in soil properties such as clay and iron. In contrast, other climate regions exhibited relatively minor changes compared to both the KWischmeier factor and Borrelli et al. 2017 KGloSEM factor map. Important notice: For European use, we recommend the European K-factor dataset Reference: Gupta, S., Borrelli, P., Panagos, P., Alewell, C., 2024. An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity. Science of The Total Environment 908, 168249. https://doi.org/10.1016/j.scitotenv.2023.168249 Datasets - Three raster maps are available plus two uncertainties maps: Title: K_factor_with_Ksat.tifDescription: The modified global soil erodibility map (Kksat factor) where the measured Ksat values were incorporated into the Wischmeier and Smith (1978) soil texture-based K factor equation. Title: K_factor_soiltexture_Wischmeier.tifDescription: The global soil erodibility map (KWischmeier factor raster) where the Wischmeier and Smith (1978) soil texture-based K factor equation was used without Ksat modification. Title: K_GloSEM_factor.tifDescription: The KGloSEM soil erodibility map (KGloSEM factor) was developed by Borrelli et al. (2017), where they employed soil texture as a proxy for permeability, similar to the KWischmeier factor. We used this existing map to compare it with the Kksat factor map. The original spatial resolution of this map was 250 m, however, we resampled it to 1 km for comparison. Title: K_factor_with_Ksat_error.tifDescription: The modified global soil erodibility map (Uncertainty raster of Kksat factor) errors represented by 90% prediction intervals (PI) through quantile calculations at 0.05 and 0.95. This uncertainty assessment was performed using the quantreg option in the R package ‘ranger’ (Wright and Ziegler 2015). Title: K_factor_soiltexture_Wischmeier_error.tifDescription: The global soil erodibility map (Uncertainty raster of KWischmeier factor) errors represented by 90% prediction intervals (PI) through quantile calculations at 0.05 and 0.95. This uncertainty assessment was performed using the quantreg option in the R package ‘ranger’ (Wright and Ziegler 2015). Spatial coverage: World , Extent: -180.00, -62.00: 180.00, 87.3Pixel size: 1km.Measurement Unit: t ha h ha-1 MJ-1 mm-1Projection: EPSG:4326 - WGS 84

This web map shows the Talladega Mountain Longleaf Conservation Partnership working landscape with reference to the Oakmulgee and Talladega Ranger Districts of Alabama National Forests. Clay, Coosa and Chilton Counties are also highlighted as target outreach counties for longleaf restoration projects. Local Implementation Teams work to bring landowners, managers, and other partners together to deliver results on the ground. These teams are typically centered on Significant Geographic Areas or Significant Sites identified in the Range-Wide Conservation Plan for Longleaf Pine (http://www.americaslongleaf.org/media/86/conservation_plan.pdf). They are responsible for identifying boundaries for focused restoration and maintenance activities, bringing key stakeholders together, defining and implementing priority management actions, and tracking and reporting results of local efforts. They coordinate with other Local Implementation Teams across the range as part of a network to share their approaches as well as help identify priority issues that need addressing at larger scales.

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:  Administrative Forest Boundaries  Proclaimed Forest Boundaries  Ranger District Boundaries  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. Go to this url for full metadata description: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.FSCommonNames.xml