The PGC Map Catalog holds an archive of historical and contemporary polar maps. PGC obtains libraries of paper maps, scans them at a very high resolution, and provides digital copies in many formats, most with accompanying georeferencing information.

Elk management zones. Updated boundaries as of 2013.

The PGC Hunting Boundaries Layer is a spatial dataset used for managing and regulating hunting activities across Pennsylvania. It provides critical geographic information for hunters, conservationists, and wildlife managers by defining various regulated hunting areas. The areas included are special regulation areas, elk hunt zones, elk management areas, pheasant release locations, duck zones, goose zones, and controlled goose areas.

Pennsylvania Game Commission administrative boundaries for public use.PGC Regions and Commissioner Districts are derived from the county boundaries provided by PennDOT's Open Data to insure topologically correct lines.Wildlife Management Units are used to manage all game, except elk, waterfowl, and other migratory game birds. The large-scale units are based on habitat and human-related land characteristics. Human population density, public/private land ownership, recognizable physical features such as major roads and rivers, and land use practices such as agriculture, timber, and development were considered when establishing the physiographic boundaries of Wildlife Management Units. Prior to the implementation of Wildlife Management Units in 2003, game animals were managed using smaller, species-specific management units. Six game species, each with 2 to 67 species-specific management units were originally combined into 21 larger Wildlife Management Units. Though the larger units come with more habitat variability, they provide data sets adequate for management recommendations without added data collection effort, they give hunters larger areas to hunt, and they provide boundaries that are easy to see. Wildlife Management Units are established for the long term and periodically reviewed for adjustments.

Line feature to show roads on Game Lands. These include Public, Private and Administrative roads.

© Pennsylvania Game Commission This layer is a component of PennsylvaniaGameCommission.

This layer is a component of PennsylvaniaGameCommission.

Formally recognized trail system managed by the Pennsylvania Game Commission or another entity through an agreement with the Pennsylvania Game Commission.

This layer is a component of PennsylvaniaGameCommission.

Credit report of Pgc Polska Grupa Ceramiczna contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

The commissioner districts represent the responsible areas for each of the commissioners.

The Transforming Neighborhoods Initiative (TNI) focuses and aligns government efforts in six geographic areas throughout the county with the goal to improve the quality of life. The map below represents the areas that are part of this initiative. This map is refreshed when needed.

description: The 25 April 2015 Mw 7.8 Gorkha earthquake and its aftershocks triggered about 25,000 landslides over an area of more than 30,000 km2 in the Greater and Lesser Himalaya of Nepal and China. In order to understand the relation among landslide location, earthquake shaking, topography, tectonic geologic and climatic setting, earthquake-triggered landslides were mapped using high-resolution (<1m pixel resolution) pre- and post-event satellite imagery. Source and runout areas were differentiated and mapped separately. The data accompany an interpretive paper published in the journal Geomorphology. The published products are separate ESRI ArcMap 10.2.2 shapefiles that comprise: (i) mapped landslide source areas, (ii) mapped landslide full areas (source, transport and deposit area combined), (iii) the extent of geographic areas in which mapping was completed, (iv) obscured areas in which the mapping is incomplete because of the lack of clear, undistorted satellite data from post-earthquake dates, and (v) image quality designation for mapped regions. 24,915 landslide areas were mapped in the full20170209.shp file (full areas) compared to 24,795 landslide areas in the source20170209.shp file (source areas). This small discrepancy in total number arises because of image distortion and partial cloud cover. One hundred forty two of the full areas lack an identifiable source area. Additionally, 10 full areas have 2 corresponding source areas because the full area could not be divided into 2 separate runout areas due to image distortion; 12 other source areas do not have corresponding full areas. This work was supported by a National Science Foundation (NSF) RAPID award from the Geomorphology and Land Use Dynamics program to West (EAR-1546630) and Clark (EAR-1546631), partially supported by NSF Geomorphology and Land Use Dynamics program (EAR-1640894 to West and EAR-1640797 to Clark and Zekkos), University of Michigan internal award to Clark and Zekkos (MCubed 2.0 Project ID 917) and a Swiss Federal Institute of Technology (ETH) Research Commission research grant (ETH-15 15-2) awarded to Gallen. We thank Paul Morin from the PGC (Polar Geospatial Center) for providing imagery access and support for acquiring satellite data through a NGA (National Geospatial-Intelligence Agency) cooperative agreement with NSF. We also thank Kristen Cook, William Greenwood, Julie Bateman, Bibek Giri, Maarten Lupker and John Galetzka for their assistance during a 2015 field expedition to Nepal.; abstract: The 25 April 2015 Mw 7.8 Gorkha earthquake and its aftershocks triggered about 25,000 landslides over an area of more than 30,000 km2 in the Greater and Lesser Himalaya of Nepal and China. In order to understand the relation among landslide location, earthquake shaking, topography, tectonic geologic and climatic setting, earthquake-triggered landslides were mapped using high-resolution (<1m pixel resolution) pre- and post-event satellite imagery. Source and runout areas were differentiated and mapped separately. The data accompany an interpretive paper published in the journal Geomorphology. The published products are separate ESRI ArcMap 10.2.2 shapefiles that comprise: (i) mapped landslide source areas, (ii) mapped landslide full areas (source, transport and deposit area combined), (iii) the extent of geographic areas in which mapping was completed, (iv) obscured areas in which the mapping is incomplete because of the lack of clear, undistorted satellite data from post-earthquake dates, and (v) image quality designation for mapped regions. 24,915 landslide areas were mapped in the full20170209.shp file (full areas) compared to 24,795 landslide areas in the source20170209.shp file (source areas). This small discrepancy in total number arises because of image distortion and partial cloud cover. One hundred forty two of the full areas lack an identifiable source area. Additionally, 10 full areas have 2 corresponding source areas because the full area could not be divided into 2 separate runout areas due to image distortion; 12 other source areas do not have corresponding full areas. This work was supported by a National Science Foundation (NSF) RAPID award from the Geomorphology and Land Use Dynamics program to West (EAR-1546630) and Clark (EAR-1546631), partially supported by NSF Geomorphology and Land Use Dynamics program (EAR-1640894 to West and EAR-1640797 to Clark and Zekkos), University of Michigan internal award to Clark and Zekkos (MCubed 2.0 Project ID 917) and a Swiss Federal Institute of Technology (ETH) Research Commission research grant (ETH-15 15-2) awarded to Gallen. We thank Paul Morin from the PGC (Polar Geospatial Center) for providing imagery access and support for acquiring satellite data through a NGA (National Geospatial-Intelligence Agency) cooperative agreement with NSF. We also thank Kristen Cook, William Greenwood, Julie Bateman, Bibek Giri, Maarten Lupker and John Galetzka for their assistance during a 2015 field expedition to Nepal.

The PGC High Mountain Asia Imagery ViewerThis map contains imagery mosaic layers for the Himalaya region of High-Mountain Asia.The 50-cm panchromatic imagery layer was produced by the Polar Geospatial Center and is a mosaic of thousands of commercial satellite imagery scenes (e.g. WorldView-1, WorldView-2, WorldView-3). Refer to the "cutlines" index layer for specific imagery metadata (DigitalGlobe Catalog ID, sensor, acquisition date, etc.).The 15-meter pansharpened Landsat 8 mosaic layer was produced using Google Earth Engine to minimize clouds and snow cover. Dates range from 2013-present. This is a true-color (red, green, blue) product and should not be used for scientific analysis. Its is available as a tiled map service.An account with the PGC is required to view the high resolution satellite imagery mosaic. Accounts are only available to United States-funded researchers with an active, valid research award from the National Science Foundation or NASA High-Mountain Asia program for work in this region.Please contact pgc@umn.edu for more information on obtaining an account or apply for one here. Please include your U.S. federal funding information (funding agency, award number, Principal Investigator, etc.)Imagery © 2018 DigitalGlobe, Inc.

Text to accompany geological maps that are supplemental to 'Re-visiting the structural and glacial history of the Shackleton Glacier region of the Transantarctic Mountains, Antarctica'. (New Zealand Journal of Geology and Geophysics)

Supplemental data: These maps of the Gondwana succession (Devonian to Triassic) are based on a limited distribution of stratigraphic measurements (see Collinson and Elliot 1986), PGC/USGS aerial photograph interpretation, and field photographs. The interpreted geology is superimposed on USGS 1:250,000 topographic maps (Cloudmaker, Shackleton Glacier and Liv Glacier sheets), which have a relatively small number of occupied survey stations and intersected points, and a 200 m contour interval. These supplemental maps are also archived as part of the SCAR digital geological map of Antarctica (Cox et al. 2019). Illustrated faults are based on: 1) field observations, 2) observed offsets visible on air photos, 3) elevation differences of 200-300 m between adjacent outcrops of key horizons (e.g. the Buckley-Fremouw contact), 4) elevation differences of stratigraphic units of 750+ m determined from the topographic maps and REMA data. Fig. S1. Geological map of the Ramsey Glacier area. Fig. S2. Geological map of the Anderson Heights area. Fig. S3. Geological map of the Transantarctic Mountains front from Mount Wade to Mount Smithson, with an inset of the Cape Surprise area. The Cape Surprise region is modified from Barrett (1965) and Miller et al. (2001). Inferred fault north of Mount Munson from Miller et al. (2010). Dotted lines on the areas off the southern (lower) edge of the Shackleton Glacier topographic map are contours taken from the adjacent Liv Glacier topographic map. Fig. S4. Geological map of the Mount Heekin, Mount Rosenwald, and Bennett Platform region. Sirius Group locations are from Hambrey et al. (2003). The lateral extent of the Sirius Group (Bennett Platform Formation) at Matador Mountain was not reported in Hambrey et al. (2003). Fig. S5. Geological map of the lower McGregor Glacier region. Fig. S6. Geological map of the upper McGregor Glacier region. Fig. S7. Geological map of the upper Shackleton Glacier area (modified from fig. 3 in Elliot and Collinson 2022). Sirius Group locations are taken from McGregor (1965), Hambrey et al. (2003), and Elliot and Collinson (2022). The approximate location of the glacigenic strata illustrated by McGregor (1965, figs. 3, 4) is based on the observation that the Roberts Massif is visible in the background of his fig. 3.

Peatlands cover 3% of the global land surface, yet store 25% of the world’s soil organic carbon. These organic-rich soils are widespread across permafrost regions, representing nearly 18% of land surface and storing between 500 and 600 petagrams of carbon (PgC). Peat (i.e., partially decomposed thick organic layers) accumulates due to the imbalance between plant production and decomposition often within saturated, nutrient deficient, and acidic soils, which limit decomposition. As warmer and drier conditions become more prevalent across northern ecosystems, the vulnerability of peatland soils may increase with the susceptibility of peat-fire ignitions, yet the distribution of peatlands across Alaska remains uncertain. Here we develop a new high-resolution (20 meter (m) resolution) wall-to-wall ~1.5 million square kilometer (km2) peatland map of Alaska, using a combination of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM). Machine learning classifiers were trained and tested using peat cores, ground observations, and sub-meter resolution image interpretation, which was spatially constrained by a peatland suitability model that described the extent of terrain suitable for peat accumulation. This product identifies peatlands in Polar, Boreal, and Maritime ecoregions in Alaska to cover 26,842 (4.6%), 69,783 (10.4%), and 13,506 (5.3%) km2, respectively.

Important Note: This item is in mature support as of July 2024 and will retire in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This web application enables the exploration of Arctic elevation based on the 2m resolution Arctic Digital Elevation Models (DEM) created by the Polar Geospatial Center. The app displays multiple different renderings as well as profiles of the data. In many areas the coverage is available from multiple dates and the app displays temporal profiles as well as computing the differences. The current datasets consisting of 2m DEMs, cover the Arctic from 60*N to the Pole and will gradually, and incrementally be replaced with better 2m versions as they are produced during 2018. The elevations are digital surface models photogrammetrically generated from stereo satellite imagery and have not been edited to create terrain heights. The current datasets are preliminary and are known to contain some errors and artifacts. As more control becomes available, the elevation values will be refined and adjusted. The original PGC datasets have been adjusted according to the PGC proposed correction parameters, to give WGS84 ellipsoidal heights, but available in this service also as orthometric heights computed using the EGM2008 geoid separation. Details on how the DEMs are generated and their use can be found in ArcticDEM datasets. The DEMs were created from DigitalGlobe, Inc., imagery and funded under National Science Foundation awards 1043681, 1559691, and 1542736.The app also provides access to the Arctic Landsat imagery that is updated daily and also served through ArcGIS Online.Quick access to server functions defined for the following elevation derivatives are provided:Hillshade – Hillshaded surface generated dynamically on elevation layer, with a solar azimuth of 315 degrees and solar altitude of 45 degreesMulti-Directional Hillshade – Multi-directional hillshaded surface generated dynamically on elevation layer, computing hillshade from 6 different directionsElevation Tinted Hillshade – Elevation tinted hillshade surface generated dynamically on elevation layerSlopeMap – A color visualization of Slope surface generated dynamically on elevation layer, where flat surfaces is gray, shallow slopes are yellow and steep slopes are orangeAspectMap - A color visualization of aspect generated dynamically on elevation layerContour – Dynamically generated contours with specified contour intervals and options for smoothing to create more cartographically pleasing contours.The Time tool enables access to a temporal time slider and temporal profile for a selected point. The Time tool is only accessible at larger zoom scales. The Identify tool enables access to elevation, slope and aspect values for the specified point as well as information on the source image and links to download the source data. From the app it is also possible to export defined areas of the DEMs. These can be exported in user defined projections and resolutions. The Bookmark tool link to pre-selected interesting locations.For more information on the underlying services see Arctic DEM layer.The application is written using Web AppBuilder for ArcGIS accessing imagery layers using the ArcGIS API for JavaScript.

AbstractA continuous, smoothed contour dataset at 500 m intervals for all land south of 60°S, excluding the Balleny Islands. The vertical datum of the contours is EGM2008. Contours are extracted primarily from the PGC Reference Elevation Model of Antarctica (REMA) v1.1 with certain islands filled from Copernicus WorldDEM. Peter I Øy contours are from the Norwegian Polar Institute. Sources of individual line segments are contained in the attribute table and full compilation information is given in the lineage statement.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related high resolution dataset at 100 m intervals is also published via Living Atlas.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageAll processing described here was performed in ArcGIS Pro version 2.6.A composite Digital Elevation Model (DEM) was created comprising of three datasets from the Reference Elevation Model of Antarctica v1.1: ‘REMA_100m_peninsula_dem_filled’, ‘REMA_100m_dem’ and ‘REMA_200m_dem_filled’. These DEMs were first converted from ellipsoidal height to height above EGM2008 geoid and then mosaicked together in respective order at 100 m spatial resolution. This 100 m DEM was smoothed by performing ‘Focal Statistics’ using a 40x40 cell size.500 m contours were extracted and all contours with a height <1m were deleted, as well as erroneous offshore contours. In certain locations, primarily some islands on the Antarctic Peninsula, REMA data was insufficient to produce contours. In these places, contours were produced from the ‘Copernicus WorldDEM 90m’ DEM and smoothed by 4 km using a PAEK smoothing algorithm. Contours for Peter I Øy were incorporated from the Norwegian Polar Institute Data at 100 m intervals: 500 m intervals were extracted and smoothed by 800 m, to match the appropriate resolution of the main contours.All contours were merged together and lines <5 km in length were deleted. Further lines <20 km were deleted in ‘non-mountainous’ regions, so as to avoid deleting small mountain peak contours but to still simplify the main dataset. These regions were interpreted manually using the hillshade of the DEM used to produce the contours.Original DEM sources and citations:REMA: Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019 , 2019Copernicus WorldDEM: produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.Norwegian Polar Institute (2014). Map data / kartdata Peter I Øy 1:50 000 (P50 Kartdata). Norwegian Polar Institute. https://doi.org/10.21334/npolar.2014.29105abcCitationPlease cite this item as: 'Gerrish, L., Fretwell, P., & Cooper, P. (2020). Medium resolution vector contours for Antarctica (7.3) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/0779002b-b95d-432f-b035-b952c36aa5c9'. If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, accessed [year]'

Elk management zones. Updated boundaries as of 2013.

Wildlife Management Units are used to manage all game, except elk, waterfowl, and other migratory game birds. The large-scale units are based on habitat and human-related land characteristics. Human population density, public/private land ownership, recognizable physical features such as major roads and rivers, and land use practices such as agriculture, timber, and development were considered when establishing the physiographic boundaries of Wildlife Management Units. Prior to the implementation of Wildlife Management Units in 2003, game animals were managed using smaller, species-specific management units. Six game species, each with 2 to 67 species-specific management units were originally combined into 21 larger Wildlife Management Units. Though the larger units come with more habitat variability, they provide data sets adequate for management recommendations without added data collection effort, they give hunters larger areas to hunt, and they provide boundaries that are easy to see. Wildlife Management Units are established for the long term and periodically reviewed for adjustments.

Feature service index of available REMA2 strip DEMs built by the Polar Geospatial Center at the university of Minnesota. DEMs are extracted from MaxarInc., imagery using the SETSM algorithm at 2-meter resolution. Download URLs are included.

The PA Department of Conservation and Natural Resources (DCNR) and PA Game Commission (PGC) have teamed up to create an interactive map specifically for hunters. Collectively, State Forest Land and Gamelands comprise over 3.7 million acres of public forest open to hunting in Pennsylvania. Hunters can use this map to:View public forests open to hunting.Search hunting seasons and bag limits across different parts of the state.Display hunting hours (starting/ending times) across different parts of the state.Add personal GPS data to the map (waypoints and tracklogs).View different types of wildlife habitat across public forest lands, including mature oak forests, meadows, food plots, openings, winter thermal (coniferous) cover, and young aspen forest.See where recent timber harvests have occurred on public forest lands.Get deer management assistance program (DMAP) information for state forest lands.Add map layers associated with chronic wasting disease (CWD).Identify where bear check stations are located and get driving directions.Display the elk hunting zones and get information about them.Get the location of gated roads opened for hunters on public forest lands and when those gates will be opened.Analyze graphs and trends in antlerless/antlered deer harvests and antlerless license allocations from 2004 to the present.