Pennsylvania State Forest boundary with forest district and proposed and official wild/natural area boundaries embedded.

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.

Pennsylvania State Forest Wild & Natural Areas

description: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the Pennsylvania State Plane Coordinate System (North Zone) and Lambert Conformal conic projection. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.; abstract: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the Pennsylvania State Plane Coordinate System (North Zone) and Lambert Conformal conic projection. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

This data depicts a route system for Forest, state, and township roads within or in close proximity to the Allegheny National Forest (ANF). Routed roads consist of one single arc for each numbered road. Routed roads have been calibrated to include measure values for mileposts.

This layer is sourced from maps.pasda.psu.edu.

Use our interactive map to discover State Park, State Forest, and Geology information. Search for locations geographically or by name/amenity, learn interesting facts, get driving directions, print maps, get the weather for your destination and much more! We recently added ADA Accessible features on State Parks and Forests, as well as State Park Campground information!

Welcome to PA DCNR's open data portal. This site provides access to all of our published GIS data, which includes over 130 datasets relative to state parks, state forests, geology, recreational opportunities and more! This site provides access to all published GIS spatial data that you can map, style, chart, download or share! Select the link below to explore.

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Portions of State Forest Land classified as either a 1) O1:Natural Herbaceous Area, 2) O2:Cultivated Herbaceous Area, 3) O3:Agriculture Herbaceous Area, or 4) OM:Miscellaneous Herbaceous Area.

This EnviroAtlas dataset describes the percentage of a 15-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Timbersales categorized as "removals" include clearcuts and overstory removals and result in early successional habitat and a complete regeneration of the forest. Timber harvests categorized as "shelterwood/seed tree" are partial cuttings with a goal to establish new tree seedlings in the understory, and they are often followed by a removal treatment in later years. Other types of timber harvest (such as improvements, two-age, salvage only, single tree selection, etc) are not included. Most timbersales dating back to 2005 are included.

Detected deforestation (2008–2018) between our and Global Forest Change’s land cover maps.

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The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Olympic National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 2,980 plots representing 151 vegetation associations and 46 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel.

We calculated deforested area here using the Global Forest Change dataset.

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

This data sets include yearly maps of land cover classification for the state of Mato Grosso, Brasil, from 2001 to 2016, based on MODIS image time series at 250 meter spatial resolution. Ground samples consisting of 2,115 time series with known labels are used as training data for a support vector machine classifier. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of Mato Grosso, Brazil's agricultural frontier. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 93% and the following user's and producer's accuracy for the land cover classes: Cerrado: UA - 99% PA - 98% Fallow_Cotton UA - 100% PA - 100% Forest UA - 99% PA - 98% Pasture UA - 95% PA - 96% Soy-Corn UA- 87% PA - 97% Soy-Cotton UA - 99% PA - 94% Soy-Fallow UA - 100% PA - 100% Soy-Millet UA- 84% PA - 84% Soy-Sunflower UA - 85% PA - 85%

This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.

Foliar chemistry values were obtained from two important native tree species (white oak (Quercus alba L.) and red maple (Acer rubrum L.)) across urban and reference forest sites of three major cities in the eastern United States during summer 2015 (New York, NY (NYC); Philadelphia, PA; and Baltimore, MD). Trees were selected from secondary growth oak-hickory forests found in New York, NY; Philadelphia, PA; and Baltimore, MD, as well as at reference forest sites outside each metropolitan area. In all three metropolitan areas, urban forest patches and references forest sites were selected based on the presence of red maple and white oak canopy dominant trees in patches of at least 1.5 hectares with slopes less than 25%, and well-drained soils of similar soil series within each metropolitan area. Within each city, several forest patches were selected to capture the variation in forest patch site conditions across an individual city. All reference sites were located in protected areas outside of the city and within intermix wildland-urban interface landscapes, in order to target similar contexts of surrounding land use and population density (Martinuzzi et al. 2015). Several reference sites were selected for each city, located within the same protected area considered representative of rural forests of the region. White oaks were at least 38.1 cm diameter at breast height (DBH), red maples were at least 25.4 cm DBH, and all trees were dominant or co-dominant canopy trees. The trees had no major trunk cavities and had crown vigor scores of 1 or 2 (less than 25% overall canopy damage; Pontius & Hallett 2014). From early July to early August 2015, sun leaves were collected from the periphery of the crown of each tree with either a shotgun or slingshot for subsequent analysis to determine differences in foliar chemistry across cities and urban vs. reference forest site types. The data were used to invstigate whether differences in native tree physiology occur between urban and reference forest patches, and whether those differences are site- and species-specific. A complete analysis of these data can be found in: Sonti, NF. 2019. Ecophysiological and social functions of urban forest patches. Ph.D. dissertation. University of Maryland, College Park, MD. 166 p. References: Martinuzzi S, Stewart SI, Helmers DP, Mockrin MH, Hammer RB, Radeloff VC. 2015. The 2010 wildland-urban interface of the conterminous United States. Research Map NRS-8. US Department of Agriculture, Forest Service, Northern Research Station: Newtown Square, PA. Pontius J, Hallett R. 2014. Comprehensive methods for earlier detection and monitoring of forest decline. Forest Science 60(6): 1156-1163.

Forest stands on State Forest Land with the majority of the dominant and co-dominate trees greater than 12 inches diameter and classified as either a 1) AD:Dry-Oak-Mixed-Hardwood-Forest, 2) AH:Dry-Oak-Heath-Forest, or 3) AR:Red-Oak-Mixed-Hardwood-Forest. Includes both fully and understocked.