The Division of Forestry Geographic Information Systems home page provides information on GIS information, Spatial Data, GIS Web Applications depicting current wild land fire information and forest resource information for the entire state of Alaska.

Selected GIS data that encompass Coconino National Forest are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 12 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Coconino National Forest GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5209303

The FS National Forests Dataset (US Forest Service Proclaimed Forests) is a depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.Metadata and Downloads - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Original+Proclaimed+National+Forests

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

This dataset contains the White Mountain National Forest Boundary. The boundary was extracted from the National Forest boundaries coverage for the lower 48 states, including Puerto Rico developed by the USDA Forest Service - Geospatial Service and Technology Center. The coverage was projected from decimal degrees to UTM zone 19. This dataset includes administrative unit boundaries, derived primarily from the GSTC SOC data system, comprised of Cartographic Feature Files (CFFs), using ESRI Spatial Data Engine (SDE) and an Oracle database. The data that was available in SOC was extracted on November 10, 1999. Some of the data that had been entered into SOC was outdated, and some national forest boundaries had never been entered for a variety of reasons. The USDA Forest Service, Geospatial Service and Technology Center has edited this data in places where it was questionable or missing, to match the National Forest Inventoried Roadless Area data submitted for the President's Roadless Area Initiative. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.

This layer file consists of three related datasets:
- Statutory boundary polygons of State Forests
- Lands managed by the Division of Forestry within the statutory boundaries, known as Management Units
- Lands managed by the Division of Forestry outside of the statutory boundaries, known as Other Forestry Lands

State Forests - Statutory Boundaries:
This theme shows the boundaries of those areas of Minnesota that have been legislatively designated as State Forests ( http://www.dnr.state.mn.us/state_forests/index.html )

Minnesota's 58 state forests were established to produce timber and other forest crops, provide outdoor recreation, protect watersheds, and perpetuate rare and distinctive species of native flora and fauna. The mapped boundaries are based on legislative/statutory language and are described in broad terms based on legal descriptions. Private or other ownerships included inside a State Forest boundary are typically NOT identified in legislative language and subsequently are NOT mapped in this layer. It is important to note that these data do not represent public ownership. State Forest boundaries often include private land and should not be used to determine ownership. Ownership information can be found in State Surface Interests Administered by MNDNR or by Counties ( https://gisdata.mn.gov/dataset/plan-stateland-dnrcounty ) and the GAP Stewardship 2008 layer ( http://gisdata.mn.gov/dataset/plan-gap-stewardship-2008 ).

Data has been updated during 2009 by the MNDNR Forest Resource Assessment office.

State Forests - Management Units
This theme shows the land owned and managed by the Division of Forestry within the Statutory Boundaries. The shapes were derived mostly from county parcel data, where available, and from plat maps and other ownership resources. This data presents an approximate location of the land ownership and is intended for cartographic purposes only. It is not survey quality and should never be used to resolve land ownership disputes.

State Forests - Other Forest Lands
This theme shows State Forest lands outside of the State Forest Statutory Boundaries. It was derived from MNDNR's Land Records System PLS40 data layer. Sub-40 shapes are not represented. Partial PLS40 ownership is represented as a whole PLS40. This data is not survey quality and should never be used to resolve land ownership disputes.

The Oregon Department of Forestry's (ODF) GIS goal is to support the stewardship of Oregon's forests through the acquisition, analysis, distribution and display of geographic information. We are using ArcGIS Online as tool to help our state agency upload, collaborate, and expose geospatial data online. ODF was established in 1911. It is under the direction of the State Forester who is appointed by the State Board of Forestry. The statutes direct the state forester to act on all matters pertaining to forestry, including collecting and sharing information about the conditions of Oregon's forests, protecting forestlands and conserving forest resources.Our Agency tasks include: Fire protection for 16 million acres of private, state and federal forests.Regulation of forest practices (under the Oregon Forest Practices Act) and promotion of forest stewardship.The implementation of the Oregon Plan for Salmon and Watersheds.Detection and control of harmful forest insect pests and forest tree diseases on 12 million acres of state and private lands.Management of 818,800 acres of state-owned forestlands.Forestry assistance to Oregon's 166,000 non-industrial private woodland owners.Forest resource planning.Community and urban forestry assistance.Contact:Contact:Steve TimbrookGIS Data AdministratorAdministrative BranchInformation Technology Program - GIS UnitOregon Department of Forestrysteve.timbrook@odf.oregon.gov503.931.2755

Forest Health - Insect and Disease GIS data that encompass the Southwestern Region (Arizona, New Mexico) are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Lambert Conformal Conic 1st standard parallel: 32° 0' 0" 2nd standard parallel: 36° 0' 0" Central meridian: -108° 0' 0" Units: Meters Datum: NAD 1983 Resources in this dataset:Resource Title: Forest Health – Insect Disease GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprd3805189

Forest Preserve District of Cook County boundaries. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS, is required.

The Trails Layer is designed to provide information about National Forest System trail locations and characteristics to the public. When fully realized, it will describe trail locations, basic characteristics of the trail, and where and when various trail uses are prohibited, allowed and encouraged. Because the data readiness varies between Forests, each Forest will approve which level of attribute subset are published for that forest. Forests can provide no information or one of three attribute subsets describing trails. The attribute subsets include TrailNFS_Centerline which includes the location and trail name and number; TrailNFS_Basic which adds information about basic trail characteristics; and TrailNFS_Mgmt which adds information about where and when users are prohibited, allowed, and encouraged. When a Forest chooses to provide the highest attribute subset, TrailNFS_Mgmt, these attributes must be consistent with the Forest's published Motorized Vehicle Use Map (MVUM). Metadata for the individual Forest feature classes used to compile this feature class are available at data.fs.usda.gov/geodata/edw/dir_trails.php. Metadata

Matrix sites are large contiguous areas whose size and natural condition allow for the maintenance of ecological processes, viable occurrences of matrix forest communities, embedded large and small patch communities, and embedded species populations. The goal of the matrix forest selection was to identify viable examples of the dominant forest types that, if protected and allowed to regain their natural condition, would serve as critical source areas for all species requiring interior forest conditions or associated with the dominant forest types.

Selected GIS data that encompass Apache-Sitgreaves National Forests are available for download from this page. A link to the FGDC compliant metadata is provided for each dataset. All data are in zipped shapefile format, in the following projection: Universal Transverse Mercator Zone: 12 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Resources in this dataset:Resource Title: Apache-Sitgreaves National Forests GIS Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=stelprdb5202663

This is the web map for the Resources Viewer web experience that is on the Forestry GIS hub site. This map is intended to show the public locations of current and old sales, as well as upcoming sales. This map serves as a resource for public for be able to see proposed sales and may submit public comment based off the information in this map.

The European Primary Forest Database is a curated collection of (sub)-national and regional datasets on the distribution of primary forests in Europe. It contains geographical (GIS) data (point, polygons) on the location and boundaries of documented primary and old-growth forests in Europe

This is the final export from the Forest Inventory Module (FIM) system, retired on 6/29/2022.

This layer is a digital inventory of individual forest stands. The data is collected by MNDNR Foresters in each MNDNR Forestry Administrative Area, and is updated on a continuous basis, as needed. Most stands are field checked and their characteristics described. Follows internal MNDNR classification schema. This data originates from the MNDNR's "Forest Inventory Management" system (also referred to as FIM).

This resource was replaced by MNDNR Forest Inventory: https://gisdata.mn.gov/dataset/biota-dnr-forest-inventory

Trails within the Forest Preserve District of Cook County. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS, is required.

The data supporting the findings of this study are available and can be accessed via the Google Earth Engine platform. The Google Earth Engine code used for the calculation of indices, CO₂ removal, forest loss, and other metrics related to this study.The following elements have been shared:

Sentinel-2 and Planet NICFI Satellite Data: The images for January and August 2024 used for NDVI, GNDVI, EVI, SAVI, NDFI calculations. Google Earth Engine (GEE) Code: The script for calculating satellite-derived vegetation indices, CO₂ absorption, and forest loss, which is available in the repository. Figures and Python Code: The figures generated using Spyder and Python scripts are also available for reproduction and further analysis. The datasets used in this study, including the area of interest (Gandoca-Manzanillo Wildlife Refuge), were derived from public satellite data (Sentinel-2 and Planet NICFI) accessible via Google Earth Engine. Raw data can be accessed directly from the corresponding repositories. Supplementary materials, including the scripts and figures.

Forests to Faucets 2.0 builds upon the national Forests to Faucets(2011) by updating base data and adding new threats including wildfire, invasive pests, and future stresses such as climate-induced changes in land use and water quantity. The purpose of this project is to quantify, rank, and illustrate the geographic connection between forests and other natural cover (private and public), surface drinking water supplies, and the populations that depend on them–the ecosystem service of water supply. The project assesses subwatersheds across the US to identify those important to downstream surface drinking water supplies as well as evaluate a subwatersheds natural ability to produce clean water based on its biophysical characteristics: percent natural cover, percent agricultural land, percent impervious, percent riparian natural cover, and mean annual water yield. Using data from a variety of existing sources and maps generated through GIS analyses, the project uses maps and statistics to describe the relative importance of private forests and National Forest System lands to surface drinking water supplies across the United States. The data produced by this assessment provides information needed to identify opportunities for water market approaches or schemes based upon payments for environmental services (PES).September 2023 Update: Water yields (Q_YLD_MM; PER_Q40_45; PER_Q90_45; PER_Q40_85; PER_Q90_85) were updated to tie back to WASSI source data. All Forests to Faucets models and indices were recalculated. HUCs that did not have a corresponding water yield from WASSI were recalculated to the nearest HUC. AttributeDescriptionSourcesAcresHUC 12 AcresCalculated using ArcGISSTATESStatesWBD 2019HUC1212-digit Hydrologic Unit CodeWBD 2019NAME12-digit Hydrologic Unit NameWBD 2019HUTYPEHUC TypeWBD 2019From_HUC 12 From for routingWBD 2019ToHUC 12 To for routingWBD 2019 (edited by USDA FS)LevelHUC levelCalculated HUC level from outlet (1) to headwater (351)NLCDAcres of NLCDNLCDPER_NLCDPercent of HUC with NLCD DataCalculated using ArcGISFOREST_ACAcres of all forestNLCD Forest = 41,42,43, 90NLCDPER_FORPercent ForestCalculated using ArcGISAG_ACAcres of agricultural landNLCD = 81,82NLCDPER_AGPercent agricultural landCalculated using ArcGISIMPV_ACAcres of ImperviousNLCDPER_IMPVPercent ImperviousCalculated using ArcGISNATCOVER_ACAcres of Natural CoverNLCD = 11,12,41,42,43,51,52,71,90,95NLCDPER_NATCOVPercent Natural CoverCalculated using ArcGISRIPNAT_ACAcres of riparian natural coverSinan AboodPER_RIPNATPercent riparian natural coverCalculated using ArcGISQ_YLD_MM Mean Annual Water Yield in mm (Q) based on the historical time period (1961 to 2015). Baseline water yield for 2010.WASSI , Updated September 2023R_NATCOVNatural Cover score for APCWCalculated using ArcGISR_AGAgricultural land score for APCWCalculated using ArcGISR_IMPVImpervious Surface score for APCWCalculated using ArcGISR_RIPRiparian Natural Cover score for APCWCalculated using ArcGISR_QMean Annual Water Yield score for APCWCalculated using ArcGISAPCWAbility to Produce Clean Water (APCW)= (R_NATCOV+R_AG+R_IMPV+R_RIP) * R_QCalculated using ArcGISAPCW_RAPCW Score (0-100 Quantiles)Calculated using RGWNumber of groundwater water intakesSDWISSWNumber of surface water intakes (includes GU, groundwater under the influence of surface water)SDWISGW_POPNumber of groundwater water consumersSDWISSW_POPNumber of surface water consumers (includes GU, groundwater under the influence of surface water)SDWISGL_IntakesNumber of surface water intakes in the Great LakesSDWISGL_POPNumber of surface water consumers in the Great LakesSDWISSUM_POPP, Total number of Surface water consumers SW_POP+ GL_POPSDWISPRDrinking water protection model PRn = ∑(Wi Pi)Calculated using RPOP_DSSum of surface drinking water population downstream of HUC 12 ∑(SUM_POPn) **Cannot be summed across multiple HUC12 due to double counting down stream populations. Only accurate for an individual HUC12.Calculated using RIMPRaw Important Areas for Surface Drinking Water (IMP) Value. As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. Water yield (Q_YLD_MM) was estimating using the nearest HUC.USFS, Updated September 2023HUC_12_WASSIWASSI HUC numberWASSI, Updated September 2023

This feature class is comprised of DNR lands with the Resource Management (RMG) classification, identified primarily as General Use (Gu) and to a lesser extent Forestry (F) and Settlement (STL). The classifications were derived from the DNR planning process and the development of the Prince of Wales Area Plan and the Central Southeast Area Plan. Forest management was determined to be an allowed use in lands identified here.

Spot 5 pan-sharpened satellite image in the Standard Creek area of the Tanana Valley, Alaska. This image was acquired and processed as part of the "Vegetation and Community Mapping of the Tanana Valley" project, conducted cooperatively by the State of Alaska Department of Natural Resources, Division of Forestry and Tanana Chiefs Conference. Originator: State of Alaska, Dept. of Nat. Resources, Division of Forestry. Publication_Date: 3/21/05. Geospatial_Data_Presentation_Form: remote-sensing image.