AndreaBozzo/ceres-open-data-index huggingface dataset:
FWS NWRS Marine Protected Areas

Provides a dataset of Marine Protected Areas (MPAs) within the U.S. Fish and Wildlife Service National Wildlife Refuges. The dataset includes polygons for each National Wildlife Refuge with a distinction between area that are Marine/Estuarine and those that are Terrestrial. This dataset is a dissolved version of the USFWS Marine Protected Areas - Parcels dataset. The Marine Protected Areas Center is a partnership between the National Oceanographic and Atmospheric Administration (NOAA) and the Department of the Interior (DOI) to support effective MPA management, including data on the status and trends for MPAs in the U.S. Learn more about MPAs by visiting https://marineprotectedareas.noaa.gov/. Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Team Lead, [richard_easterbrook@fws.gov]
Public Safety Emergency Community Name

Emergency Community boundaries delineated as a means for identifying communities for emergency dispatching purposes. In September of 1997 the Borough recognized the need for a standardized map delineating community boundaries for the entire Borough and adopted Ordinance 97-119. The Planning and Land Use Departments gathered existing maps of community councils, postal routes and planning areas and consolidated them by forming an official Community Name and Boundary Map. These community names were adopted for E911 purposes to allow for unique community identification as part of an address.
Tongass National Forest – Prince of Wales Island – Vegetation Mapping – Legacy Vegetation Data

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2<sup>nd</sup> order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source. <o:p></o:p> All legacy data underwent a rigorous quality assurance/quality control process using high resolution imagery. Each site was reviewed for segment homogeneity, if the site was relatively uniform in vegetation characteristics, and if the assigned vegetation group, vegetation type, and tree size class was appropriate. Source USFS - previous vegetation and ecology projects.
Tongass National Forest – Prince of Wales Island – Vegetation Mapping - Segmentation Examples

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2<sup>nd</sup> order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source. <o:p></o:p>
Tongass National Forest – Prince of Wales Island – Vegetation Mapping - Field Data Collection

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2<sup>nd</sup> order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source. <o:p></o:p> Approximately 804 sites were collected during the summer of 2018 by Forest Service personnel. Field sites were selected using several criteria to ensure that representative vegetation conditions were sampled. First, an attempt was made to locate sites in relatively homogeneous areas as perceived from high resolution aerial imagery. Second, sites were large enough (one acre or greater) to capture variation in geospatial data to provide reasonable statistics for a particular sample. Third, sites were placed within 0.25 mile of a road or trail to facilitate accessibility in the field. Spectral and topographic data and an existing landcover map were also used to help capture the range of conditions anticipated to occur within the project area.
FWS HQ NWRS Region Hydrological Influence

This dataset represents the region of hydrological influence (RHI) for National Wildlife Refuge System lands. RHI is defined as the area that is most likely influencing the hydrology and water quality encompassing the refuge. Many of these boundaries are merged USGS unique hydrologic units (e.g., HUC-6, 8, 10, 12) or generated using hydrologic modeling techniques. Please see the ‘Comment’ field for more information. Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Team Lead, [richard_easterbrook@fws.gov]
Cadastral Tax Map Base

Base map page boundaries of the Mat-Su Borough Tax Map Page Index. The entire Borough is divided into a series of "base maps" and "index" or "grid maps". Base maps are given names that represent the geographical area represented (similar to USGS quad mapping) and index maps are numbered sequentially within the base map. The result is a base map with a two-character name (for example: ("WA" for Wasilla) and numbered index maps (usually numbered "1" thru "16"). The Mat-Su Borough tax map set is published using these pages. Furthermore, data such as the PARCELS coverages are divided into smaller files based on map page and later appended together to form one seamless file as part of the data processing procedures.
Cadastral Tax Map Grid

Index map page boundaries of the Mat-Su Borough Tax Map Page Index. The entire Borough is divided into a series of "base maps" and "index" or "grid maps". Base maps are given names that represent the geographical area represented (similar to USGS quad mapping) and index maps are numbered sequentially within the base map. The result is a base map with a two-character name (for example: ("WA" for Wasilla) and numbered index maps (usually numbered "1" thru "16"). The Mat-Su Borough tax map set is published using these pages.
Environment Waterbodies Ln MSB

This data contains the hydrology data for the Matanuska-Susitna Borough, AK. It is based upon the MSB tax parcel maps, orthoimagery, and data obtained from United States Geological Survey quad sheets. Data contains streams and rivers (except seasonal or intermittent streams), lakes, and islands. Much of the data doesn't match current aerial imagery and should only be used as a general representation water features. Not recommended for display at a scale below 1:6000 in areas along the road system and 1:63360 in other areas.
Infrastructure Mileposts

Milepost locations for the Matanuska-Susitna Borough (MSB) roads (Infrastructure__Roads_MSB). GIS ESRI Shapefile. Preliminary locational information was collected by Alaska Department of Transportation (DOT). The remaining mileposts were gathered from historical mileposts datasets within MSB as well as calculated values to fill in any gaps within the milepost dataset. Milepost Data was downloaded from the Alaska Department of Transportation (DOT) on 05/02/2016. All milepost that were decimal (20.3) were removed from this layer as well as milepost that were derived for the divided Parks HWY and GLENN HWY.
Environmentally_Threatened_Communities_Table_AGOL

Tabular data for environmentally threatened communities, created from the sql VIEW combining all the layers. Contains Flood, Erosion, Permafrost rankings and groups, Total Weighted Scores, and Threat Rankings from the Threatened Communities 2019 report. Contains NFIP, Risk MAP, MLT, ANSCA status. Contains Best Practice Scores, year, and season. Contains hazard descriptions.
Administrative MSB Boundary

This data contains the corporate boundary of the Matanuska-Susitna Borough. It is based upon the official legal description of the Borough which makes references to the Public Land Survey System, Latitude/Longitude Coordinates, and adjoining boundaries of the original Mount McKinley National Park as defined on U.S. Survey 2177. The data provides Borough staff, other agencies, and the general public with a clearly defined boundary that is consistent with other data (i.e. section line data). It is meant to be an improvement upon previous boundaries that were made available as part of the TIGER/line program of the U.S. Census Bureau.
Tongass National Forest Beach Buffer

This feature class represents combined 200ft and 1000ft beach buffers derived from Intertidal_PL. The process for creating this combined buffer is as follows: Select from Intertidal_PL where Description in ( 'CHK' , 'INT', 'EST', 'UIT' ) and buffer it 1000 ft, then dissolve all, so it's just a big ol' blob. Use the same selected set from Intertidal_PL and buffer it 200 feet, then dissolve all. Then select from Intertidal_PL where Description in ( 'CHK' , 'SW', 'EST' ) and erase those areas from the big buffered blobs. (I do not erase the INT areas because that might leave gaps and slivers along the shoreline above the water.) Add a field called Buff1000ft to the 1000 foot buffer and populate it with 'Y'. Add a field called Buff200ft to the 200 foot buffer and populate it with 'Y'. Identity the 200 ft buffer onto the 1000 ft buffer. Select where the field Buff200ft is blank and populate it with 'N'. (This would be the area within the 1000 ft buffer that falls outside of the 200 ft buffer.) The resulting buffer polygons can end up being wider than 1000ft or 200ft respectively in some places (beach…etc), but the intent of the polygons is to go landward 1000ft/200ft – the additional width is due to the beach/island/peninsula type stuff. The reason I originally started choosing to buffer CHK, INT, EST, and UIT was because I was concerned that buffering only INT might lead to odd gaps.
Tongass National Forest Land Type Association

LTAs charactarize landscapes of 1 to 10 square miles and depict broad patterns of geology, geomorphology, soils and vegetation. They were created by nesting existing R10 soil mapping and the NRCS STATSGO general soil map within the Ecological Subsections. All of the LTAs were scrutinized and refined by a team of Tongass Ecologists and Soil Scientists. The Ecological Subsection lines in this feature class are different than the Ecosubsections feature class. They were refined as part of the LTA mapping effort. All descriptions are in a document callled "Landtype Associations of the Tongass National Forest" https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fseprd524076.pdf
Tongass National Forest Roadless Areas 2001

This dataset contains the Inventoried Roadless Areas that were used in the Final Environmental Impact Statement for the 2001 Roadless Area Conservation Rule. The EIS analysis team used this spatial data to assess the impacts of roadless area alternatives on Forest Service policies, use of the National Forests and the surrounding environment. It was used for analysis in combination with national characterization layers, such as ambient human population, forest mortality risk to insects and diseases, current land cover types, and others. All of these datasets include the entire lower 48 states and Alaska, and are coarse resolution. The public also had a need to know where IRAs were located in their area and across the nation. The data was used to create a set of detailed maps published both on the web and in hard copy form, (Volume2, Roadless Area Conservation EIS). NOTE: The Idaho and Colorado Roadless Areas boundaries, represented in separate datasets, supersede the 2001 Roadless Area Boundaries.
Tongass National Forest Size Density Model Output

This data represents the output of the Size Density Model which classifies vegetation into 7 categories. This dataset is a derived data set using Tongass National Forest Covertypes, Tongass National Forest Hydric Soils, NWI Hydric Soils, and Aspect. They NWI Hydric soils were only used where the forest had not mapped soils such as Private Lands and Wilderness areas.There is a white paper that should go along with this metadata. T:\FS\Reference\GeoTool\r10_tnf\Metadata\SizeDensity\final_SizeDensity_white_paper_03262009.pdf
Tongass National Forest Timber Value Comparison Unit

The dataset contains polygons which were created during development of the first Tongass National Forest Land Management Plan to summarize and analyze data on smaller areas within the TNF. February 2016 additional fields were added to identify the Trout Unlimited Watershed polygons that were identified by VCU. There were initially 77 polygons identified, which were later refined to 16 to show the High Value Fish Watersheds. For further information, please refer to the following websites: 1. [http://www.americansalmonforest.org/the-details.html] 2. [http://www.americansalmonforest.org/uploads/3/9/0/1/39018435/the-tongass-77-a-closer-look-2_3_15.pdf] 3. [Tongass Advisory Committe Final Recommendations Document] March 2016, polygons were added and deleted based upon the Sealska Land adjustments. There are now 73 VCU identified.
Tongass National Forest Young Growth Inventory Plots

The State of Alaska and the Forest Service entered into a Challenge Cost-share agreement in June 2015, to complete a timber stand inventory in young-growth forest. This work supports collecting, analyzing, and using forest resource information to implement sound, sustainable forest management practices across Southeast Alaska, while offering training and developing job opportunites for rural residents in natural resource fields. This layer depicts the field-sampled plots for the timber cruise, as well as plot status and date completed. These points are sampled on a standard grid, created using the Alaska Region Stand Exam Preparation (StExPrep) program.
Tongass National Forest Young Growth Inventory Stands

The State of Alaska and the Forest Service entered into a Challenge Cost-share agreement in June 2015, to complete a timber stand inventory in young-growth forest. This work supports collecting, analyzing, and using forest resource information to implement sound, sustainable forest management practices across Southeast Alaska, while offering training and developing job opportunites for rural residents in natural resource fields. This layer depicts the polygons that are planned to be field-sampled for the timber cruise.
Alaska Energy Authority Regions

Energy Regions as defined by Alaska Energy Authority for Regional Energy Planning. "Regional Energy Planning is a way for Alaskans to determine their energy priorities and formulate a concrete, implementable, fundable energy plan. The plans should address energy needed for Electricity, Heating and Transportation. Each energy region can craft a specific action plan to ensure a less expensive, more reliable, efficient and sustainable energy future. Each planning effort will include regional stakeholders, evaluate alternatives and provide a prioritized action plan of projects that can be funded." (Alaska Energy Authority, Regional Energy Planning) Source: Alaska Energy Authority This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: [AEA Regional Energy Planning]
Tongass National Forest – Prince of Wales Island – Vegetation Mapping Feature Layer

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2<sup>nd</sup> order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source. <o:p></o:p>