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

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.

Priority Composite Areas derived from Pennsylvania’s Forest Action Plan

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.

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.

Abandoned railroads and infrastructure from the anthracite coal mining industry are significant features in abandoned mine lands and are an important part of history; however, these features are often lost and masked by the passage of time and the regrowth of forests. The application of modern light detection and ranging (lidar) topographic analysis, combined with ground-truthing "boots on the ground" mapping, enable recovery of the location of these historical features. Waste rock piles and abandoned mine lands from historical mining locally appear as distinct features on the landscape depicted on the percent slope map. Abandoned, and in many places demolished, infrastructure such as breakers, turntables, rail beds, water tanks, tram piers, and bridge abutments, to name a few, were confirmed in the field and located with a global positioning system (GPS) receiver. This map captures the locations of many of the abandoned features from the coal mining industry near Forest City, Pennsylvania, and preserves a time that was an important part of the industrial revolution and a way of life that has been quiet for over half a century. The data layers should be used in conjunction with lidar data available separately at https://www.pasda.psu.edu.

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Durham, NC tree cover configuration and connectivity map categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). In this community, forest is only trees & forest. Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. 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).

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This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

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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.

This data set includes deforestation maps, located in the border between the west of Brazil and the north of Bolivia (corresponding to Sentinel-2's tile 20LKP). The source images for this dataset came from ESA's Sentinel-2A satellite. They were processed from top of the atmosphere to surface reflectance using the Sen2Cor 2.8 software and their clouds were masked using the algorithm Fmask 4.0. The K-Fold technique was used to select the best Random Forest (RF) model varying different combinations of Sentinel-2A bands and vegetation indices. The RF models were trained using the time series of 481 samples included in this data set. The two selected models that presented the highest median of F1 score for the Deforestation class were: 1) the combination of the blue, bnir, green, nnir, red, swir1, and swir2 bands (hereafter Bands); and 2) the combination of Enhanced Vegetation Index, Normalized Difference Moisture Index, and Normalized Difference Vegetation Index (hereafter Indices). Each RF model produced a deforestation map. During training, we used RF models of 1000 trees and the full depth of the Sentinel-2A time series, comprising 36 observations ranging from August 2018 to July 2019. To assess the map's accuracy, good practices were followed [1]. To determine the validation data set size (n), the user accuracy was conjectured using a bootstrapping technique. Two validation data sets (n=252) were collected independently to assess the maps' accuracy. For Deforestation, the Bands classification model has the highest values of the F1 score (93.1%) when compared with the Indices model (91.9%). The Forest and Other classes had better results of the F1 score using the Indices (85.8% and 82.2%, respectively) than using the Bands (85.3% and 78.7%, respectively). Our classifications have an overall accuracy of 88.9% for Bands and 84.9% for Indices, and the following user's and producer's accuracy for the models: Accuracy of classification using Bands: Deforestation: UA - 97.4% PA - 89.2% Forest: UA - 80.8% PA - 90.4% Other: UA - 80.2% PA - 77.3%

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

Forest stands on State Forest Land with the majority of the dominant and co-dominate trees less than 6 inches diameter and classified as a 1) DD:Aspen/Grey-(Paper)-Birch Forest. Includes both fully and understocked.

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.

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.

Priority areas identified in a Statewide Forest Assessment. Maps available for forest pest, wildfire, ecological, water, urban, and working forest themes, as well as an overall composite map.

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!