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

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.

FQI values for forests.Forest vegetation data were collected annually at Long-term Monitoring Program plots using fixed plots. Floristic Quality Index values were calculated for each plot in each year. The % exotic represents the percentage of exotics species occurring in the plot out of the total number of species (both native and exotic). If a site contained more than one survey station an average was taken. Averages were calculated for each 5 year period and compared.Data is for period ending: 12/31/2019View the Natural Heritage System Reporting Key for detailed information about target setting and evaluation of current conditions and trends, including data collection and analysis methodologies.

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.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

Proclaimed Forest BoundariesThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Forest Service (USFS), displays proclaimed forest boundaries in the United States. Per the USFS, the USFS National Forests Dataset (U.S. 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.”Ozark National Forest, Ouachita National Forest & Holly Springs National ForestData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Original Proclaimed National Forests) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 13 (FS National Forest Dataset (US Forest Service Proclaimed Forests))OGC API Features Link: (Proclaimed Forest Boundaries - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information: FS National Forests Dataset (US Forest Service Proclaimed Forests)Support documentation: FS National Forests Dataset (US Forest Service Proclaimed Forests), MetadataFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Cadastre Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cadastre is defined as the "past, current, and future rights and interests in real property including the spatial information necessary to describe geographic extents. Rights and interests are benefits or enjoyment in real property that can be conveyed, transferred, or otherwise allocated to another for economic remuneration. Rights and interests are recorded in land record documents. The spatial information necessary to describe geographic extents includes surveys and legal description frameworks such as the Public Land Survey System, as well as parcel-by-parcel surveys and descriptions. Does not include federal government or military facilities."For other NGDA Content: Esri Federal Datasets

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 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 data set was created by the North Carolina Department of Agriculture and Consumer Services (NCDA&CS). This Forest (Tree) Land Cover data was derived from the North Carolina, 4 band, 2016, USDA National Agriculture Imagery Program (NAIP) imagery.It includes the entire state of NC, except Ft. Bragg. It is one (1) meter pixel resolution which makes hiding errors difficult. Some errors (incorrect classification) exists but we estimate the data is better than 90% accurate. When viewing this data, NCDA&CS highly recommends using aerials from 2016 for a base map. The original NAIP (raster) data was in TIF format (DOQQ tiles) and was natively in UTM projection.A decision rule supervised classification process was specifically designed around the tonal differences inherent in NAIP imagery. It used with spectral and textural (to separation grasslands from trees) information derived for each 4 band NAIP tile (quarter quad). A total of 3,564 tiles or 16 TBs of data were processed. The classification resulted in a 2-class classification schema. Class 1 is Forest/Trees and Class 2 Non-forest/trees. Class 2 is set to white/transparent by default. Texture processing was applied to reduce mixed pixel values between tree canopy, healthy grass and agriculture land areas. These features have similar vegetation spectral response and would otherwise result in a significant number of misclassified pixels. In many areas however, agriculture and grass land areas containing higher texture values still resulted in mixed canopy pixels. We assume this introduces around a 5% error or misclassification rate.

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

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.

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 Forest Service Basemap service is created, maintained, and produced by the U.S. Forest Service. The Forest Service Basemap is a scalable digital map product and can be used as background (or basemap) in web applications and GIS software.

The Forest Service Basemap is compiled from authoritative data sources from the US Forest Service, the US Geologic Survey (USGS), the Bureau of Land Management (BLM), the National Park Service (NPS), the US Fish and Wildlife Service (USFWS), The Census Bureau (US Census), The Federal Aviation Administration (FAA), North American Rail Network (NARN), and the Homeland Infrastructure Foundation Level Data (HIFLD- HERE) from the Department of Homeland Security (DHS).Click here to download a PDF of the Forest Service Basemap Style Guide.

Latest Update:

(Feb 2025)Updated Gulf label

Previous Updates:

(Dec 2024)

Enhancements to the December 2024 Forest Service Basemap update include the removal of woodland tint and railroad_HFLD. The addition of railroad_FRA, Airport_FAA, and Regional medium resolution glacier layer datasets. Minor symbology changes to county roads across HIFLD and MVUM datasets, as well as airfield_FAA. Mud flow symbology was added to LanformArea USGS, Rapid labels were added to NHD Area USGS. SQL queries were updated for USFS Trails Plus and American Indian Lands BLM. Scale adjustments were applied to medium lakes, removal of grey fill for private land on USFS Basic Ownership, removal of USFS Basic ownership 36K which was only showing the grey fill for private lands. PLLS and American Indian land visual scales extended, forest fill adjustments, ice mass and glacier symbology changed to white.

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

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

The El Pilar Project has been conducting research at El Pilar, Belize and Guatemala since 1993, and was founded on a base of survey work that goes back to 1983. This unusual archaeological program recognizes the present environment as a part of the ancient Maya past. Our mission is the preservation and conservation of endangered resources through local and international education. Addressing tensions between culture and nature, we use the past as a reference to build a responsible future. Weaving together traditional knowledge and practice with scientific inquiry and interpretation, we promote a deeper awareness of heritage through local partnership.

The University of California Santa Barbara (UCSB) Maya Forest GIS is an essential tool to organize and use the numerous geographic resources involved in our studies, and provide reliable datasets for the project.

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

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 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.

The forestry consulting services market is experiencing robust growth, driven by increasing demand for sustainable forest management practices and the rising need for efficient resource utilization. The market size in 2025 is estimated at $2.5 billion, reflecting a Compound Annual Growth Rate (CAGR) of approximately 7% from 2019 to 2024. This growth is fueled by several key factors: the expanding global population leading to increased timber demand, stricter government regulations promoting environmental conservation, and the growing adoption of advanced technologies like GIS and remote sensing in forestry operations. Furthermore, the increasing awareness of climate change and its impact on forests is driving demand for expert advice on carbon sequestration, forest health, and mitigation strategies. Key players like Atlas Information Management, Forest Resource Consultants, Inc., and others are actively shaping the market through innovative solutions and expanding service offerings. The market is segmented based on service type (e.g., forest inventory, sustainable forest management planning, environmental impact assessments), client type (e.g., government agencies, private landowners, timber companies), and geographic region. The forecast period from 2025 to 2033 projects continued expansion, with the market expected to reach approximately $4.2 billion by 2033. However, challenges remain, including fluctuations in timber prices, economic downturns impacting investment in forestry, and the scarcity of skilled professionals in the field. Despite these restraints, the long-term outlook remains positive, driven by the ongoing need for responsible forest management and the increasing recognition of forests' crucial role in mitigating climate change and biodiversity loss. The market will likely see consolidation among consulting firms, partnerships with technology providers, and a greater focus on data-driven solutions to optimize forest management practices. This will drive further innovation and specialization within the sector, enhancing the overall quality and effectiveness of forestry consulting services globally.