Alaska is the largest U.S. state in terms of area and also contains some areas of the most untamed wildlife in North America. In 2012, there was around 91.8 million acres of forest area located in Alaska, more than any other U.S. state. U.S. lumber production The United States lumber industry has seen ups and downs over the last several years. In 2006, some 49.74 billion board feet of lumber were produced in the United States. Three years later this figure had decreased to around 30.2 billion board feet, the lowest it had been in recent years. By 2016, the production volume of lumber had recovered somewhat, reaching 41 billion board feet. U.S. national park system As a country with so much natural splendor, it only makes sense that there is a vast network of national parks and forests in the United States dedicated to preservation and public enjoyment. In 2018, the Golden Gate National Recreation Area in California was the leading unit in the national park system in terms of visitors. In addition, 58 percent of American campers intended to visit a national park in 2017.

The state of Amazonas had by far the most public forest area in Brazil in 2022. The state's public forest area accounted for more than 40 percent of Brazil's public forest area that year. The state of Pará followed with nearly 79 million hectares, which represents some 24 percent of Brazil's public forest area. Both states are in the Amazon biome. By comparison, Sergipe was the state with the least public forest area in 2022, with around 47,200 hectares. The "Cadastro Nacional de Florestas Públicas" (CNFP) was created in 2006 as a forest management and planning instrument, to gather georeferenced data on federal, state and municipal public forests. These forests are later categorized into different types according to the use, such as conservation, indigenous land, public rural settlements, military areas, and others.

As of 2022, Maine was the U.S. state that had the largest share of its land area covered in forest, amounting to over ** percent of its land being forested. Of its 19.7 million acres of land, about 17.6 million acres of Maine's land was forested as of 2022. New Hampshire had the second leading share of its overall land forested at that time, exceeding ** percent of its land area covered in forest.

Among its states, Haryana had the smallest percentage of forest cover in relation to its total geographical area in India. Punjab, another state in the northern region of India, was not far behind, with a forest cover of **** percent.

Forest cover in India

India, known for its diverse ecosystems, has over *** thousand square kilometers of forest cover, an integral component to its ecological balance. The classification of forest cover in India is determined by the density of the tree canopy, with moderately dense and open forests constituting the majority.

Biodiversity conservation

Hosting a rich diversity of life in its forests, wetlands, and marine regions, India has made considerable progress in conservation initiatives. The country has set up a range of protected zones, such as wildlife sanctuaries, national parks, and botanical gardens. These zones, crucial for preserving India’s biodiversity, implement both in-situ and ex-situ conservation strategies by providing habitat for a multitude of species.

The urban forest of Denton plays a crucial role in the livability and sustainability of the city. Denton’s 3.5 million trees impact everything from economic development to the overall health of the people that live, work, and play in Denton every day. A more comprehensive understanding about the urban forest’s structure, function, and associated value can promote effective policy development, sound management planning, and help set and anticipate future budgetary requirements. During the summer of 2016 the City of Denton and Keep Denton Beautiful partnered with Preservation Tree Services, Inc., Texas Trees Foundation, and Plan-It Geo, Inc. to perform the most detailed and comprehensive study of Denton’s urban forest resource ever completed. The State of the Denton Urban Forest Report provides detailed information to help Denton advance their understanding of their urban tree population and provides the framework to make more informed decisions about the future management of this important community asset. The data provided here lays the ground work for Denton becoming a more resilient city that is greener, cleaner, and cooler.

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

The national silviculture compendium is the first-ever compendium of silviculture treatments that cover most commercially operable forest types in the United States, built with input from a national team of silviculture experts from each National Forest System Region and Research Station of the USDA Forest Service. The compendium contains 240 silvicultural treatments, and 266 associated keyword component files (KCP) that are used with the Forest Service-supported Forest Vegetation Simulator (FVS), covering all regions and most commercial forest types in the United States in 2020. The treatments are based on current national forest plans and objectives but are relevant to other forested lands with similar conditions and management objectives. In part, the silvicultural compendium provides plausible real-world treatments to be used by planners, modelers, for training purposes (e.g., National Advanced Silviculture Program), and by others needing to simulate management-driven treatments with validated silvicultural parameters defined by silviculture experts from each region. Currently, KCP files for Regions 1-6 are available (Regions 8, 9 and 10 will be added as they are finalized). This data publication also includes information files such as a complete description of the 8 main treatment types and a list of each national forest and their organization code and region. Also included is a crosswalk between the KCP files and vegetation characteristics which can be used to apply KCPs to stands within each national forest in the United States, assigning treatments by biophysical setting, NVC and Forest Inventory and Analysis (FIA) cover type is included. Additionally, a table documenting a broad potential application of KCP files to appropriate National Vegetation Classifications (NVC) that are not represented in the compendium but exist within a given national forest is provided. This list of treatments by NVC and forest can be used to assign treatments to areas that are otherwise unassigned treatments within the compendium.The rapid pace of environmental and socioeconomic change poses a considerable challenge to managing for resilient and productive forests for the remainder of the 21st century. Nuanced management priorities will continue to evolve but the current challenges to address climate change, wildfires, insects and diseases, and invasive species will remain, and will require science-based and often active forest management to achieve resilient and productive forests. The national silviculture compendium was designed for use in large-scale modeling of forest treatments with potential application on over 300 million forested acres in the United States. Treatments are not intended to replace site-specific information or local expertise for actual on-the-ground plans but are generalized treatments that include parameters that are plausible for an identified objective, current condition, and desired condition. And although not intended to cover every conceivable situation, the silvicultural treatments were developed within the context of biophysical settings and forest types and address most of the conditions and objectives that prevail in each region across the country. Management needs that are addressed include forest restoration, fuel reduction, insects and disease resilience, timber production, wildlife habitat improvement, and many others. Moreover, almost all of the prescriptions were designated as improving multiple management objectives.

The treatments in the compendium are limited to one entry, or the first entry within a multi-step process (referred to as a silviculture system). For example, the uneven-aged group selection treatment here includes species to retain, the percentage of stand in harvested groups, the size of the groups, the residual density of the matrix, as well as other parameters, but it does not specify the length of the cutting cycle. The primary objective of the silviculture compendium is to test the effectiveness of meeting landscape, regional, and national goals in the short term (3 to 5 years) by simulating real-world silvicultural treatments used presently, which are also socially acceptable, ecologically viable, economically desirable, and consistent with regional and forest-level standards and guidelines.For more information about these data, see Schuler et al. (in press).

These data were published on 07/20/2023. On 06/04/2024, data for Region 4 were added along with minor metadata updates. On 04/11/2025, minor metadata updates were made as well as the addition of the Silviculture Compendium guide that covers the 240 silviculture treatments organized by Forest Service region.

The statistic displays the states with the largest forest cover area estimates in India in 2015. The forest area in the state of Kerala was estimated be approximately ****** square kilometers, while Madhya Pradesh was the state with the largest forest cover during the measured time period.

Open the Data Resource: https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_AdminAndOwnership/Forest_Stewardship_Priority_Areas/ImageServer With the Food, Conservation, and Energy Act of 2008 (the 2008 Farm Bill), Congress tasked states and territories to craft assessments of the forests within their boundaries and develop strategies to address threats and forest management opportunities. Now known as Forest Action Plans, these assessments and strategies provide an analysis of forest conditions and trends in the state and delineate priority forest landscape areas. They offer long-term plans for investing state, federal and other resources where they can be most effective in achieving national conservation goals by addressing the State and Private Forestry (SPF) national priorities and objectives: (1) Conserve working forest lands, (2) Protect forests from harm, and (3) Enhance public benefits from trees and forests. Administered by the U.S. Forest Service and implemented by state forestry agencies, the SPF Forest Stewardship Program encourages private forest landowners to manage their lands using professionally prepared Forest Stewardship plans. Participation in the Forest Stewardship Program requires that states and territories submit a raster dataset of priority areas specific to the Program (aligned with priority landscapes identified in Forest Action Plans) called Forest Stewardship Priority Areas, where they will focus their Program delivery efforts. Program performance measures include acres covered by active Forest Stewardship plans that are within Forest Stewardship Priority Areas.

The downloadable ZIP file contains an Esri File Geodatabases, Esri grids, and PDFs. Statewide Forest Resource Strategy ReportState Assessment of Forest Resources ReportIdaho State Assessment of Forest Resources - Priority AreasThe Statewide Assessment of Forest Resources (SAFR) is a geospatial analysis of forest conditions and trends in Idaho. The Idaho SAFR identifies seven main issues affecting Idaho forestlands (threats and potential benefits). Potential threats to forests include forest health decline, uncharacteristic wildfire, development pressure and recreation in undesignated areas. Potential benefits include sustainable wood-based forest resource markets, water quality & quantity, air quality, and wildlife habitat and biodiversity. A series of sub-issue datasets inform each of the primary issues. Together, statewide data and local knowledge identified areas in Idaho where these threats and benefits pointed to the highest need for investment and work. These areas of multiple high priority concerns and potential benefits were designated as Priority Landscape Areas (PLAs) and include urban, rural, and wildland urban-interface (WUI) lands. Note that the SAFR utilized the best available statewide data. Because the SAFR is statewide in scale, it does not identify every area in which an issue may be found. Local geospatial data may present a different characterization of the issues. The SAFR report describes each issue, the sub-issues each was comprised of, the data used and how it was modeled, and data considered but not used and why. In addition to the Idaho State Forest Resource Assessment report, information on the development of the SAFR, including meeting minutes, interim reports and more can be found on the SAFR website at: www.idl.idaho.gov/bureau/ForestAssist/safr_index.html. Geospatial data for the seven primary issues, the composite threats, composite benefits, final assessment map and priority landscape areas are also available on INSIDE Idaho. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2011.

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States. We matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016.

The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a code for cause of death where applicable. The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Because falling snags cause hazard to firefighting personnel and other forest users, in response to requests from the field, we provide a separate map that provides a rating of the severity of snag hazard based on the density and height of snags. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. The TreeMap 2016 dataset updates the TreeMap 2014 dataset to landscape conditions c2016. Prior to this imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB).

These data were published on 08/26/2021. On 02/01/2024, the metadata was updated to include reference to a recently published article and update URLs for Forest Service websites.

For more information about these data, see Riley et al. (2022).

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

TreeMap 2020 CONUS provides a tree-level model of the forests of the conterminous United States. Throughout the conterminous United States (CONUS), for each forested pixel in 30×30 meter (m) gridded landscape data for circa 2020, we identified and assigned the most similar Forest Inventory and Analysis (FIA) plot. We used a Random Forest machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE) and Daymet (Daymet). Predictor variables consisted of percent forest cover, vegetation height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), climatic variables (precipitation, shortwave radiation, snow water equivalent, maximum temperature, minimum temperature, vapor pressure, and vapor pressure deficit), and disturbance history (time since disturbance and disturbance type).

The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S., corresponding to landscape conditions circa 2020. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster of plot identifiers can be linked to the FIA databases available through the FIA DataMart to map hundreds of attributes available there, or to the comma-separated file included in this data publication to access a more limited set of tree-level attributes. The data files included in this publication also contain attributes for each tree in the plots that were assigned, including the FIA plot PLT_CN for the plot on which the tree was measured (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, the corresponding number of trees per acre it represents due to the study design, the status (live or dead), species, diameter, height, actual height (where broken), crown ratio and a code for cause of death where applicable. Previous versions of TreeMap have been validated for characteristics including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, and snag hazard category. Previous versions of TreeMap are being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. Prior to the TreeMap 2014 imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities. The TreeMap 2016 dataset (Riley et al. 2021, Riley et al. 2022) updated the 2014 version to include disturbance as a response variable, which improved accuracy in disturbed areas. The TreeMap 2020 CONUS dataset featured here updates the TreeMap 2016 dataset to landscape conditions circa 2020 and updates the methods by 1) using a different suite of climate variables in the imputation and 2) improving species composition assignments to prevent plots being imputed to areas where their existing vegetation type was not present, an issue which affected a small number of pixels in previous TreeMap versions.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB).

For more information about these data, see Riley et al. (2022).

With the Food, Conservation, and Energy Act of 2008 (the 2008 Farm Bill), Congress tasked states and territories to craft assessments of the forests within their boundaries and develop strategies to address threats and forest management opportunities. Now known as Forest Action Plans, these assessments and strategies provide an analysis of forest conditions and trends in the state and delineate priority forest landscape areas. They offer long-term plans for investing state, federal, and other resources where they can be most effective in achieving national conservation goals by addressing the State and Private Forestry (SPF) national priorities and objectives: 1) Conserve working forest lands, 2) Protect forests from harm, and 3) Enhance public benefits from trees and forests. Administered by the US Forest Service and implemented by State forestry agencies, the SPF Forest Stewardship Program encourages private forest landowners to manage their lands using professionally prepared Forest Stewardship plans. Participation in the Forest Stewardship Program requires that states and territories submit a raster dataset of priority areas specific to the Program - aligned with priority landscapes identified in Forest Action Plans - called Forest Stewardship Program Federal Investment Areas, where they will focus their Program delivery efforts. Program performance measures include acres covered by active Forest Stewardship plans that are within Forest Stewardship Priority Areas.

The Forest Legacy Program (FLP) is a federal grant program to protect forestlands from conversion to non-forest uses. The Vermont Department of Forests, Parks & Recreation working in conjunction with the USDA Forest Service is the State Lead Agency for Vermont's Forest Legacy Program. The federal Forest Legacy (16 U.S.C. Sec. 2103c) program was part of the 1990 Federal Farm Bill. The program acknowledges that most forested lands in the United States are held in private ownership and that those landowners are facing growing financial pressure to convert those lands to uses that would remove them from the forested land base. Much of this pressure arises from the demand for residential and commercial development.

EuroAmerican land use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Gridded (8x8km) estimates of pre-settlement (1800s) forests from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan) using 19th Century Public Land Survey System (PLSS) records provide relative composition, biomass, stem density, and basal area for 26 tree genera. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection. We compare pre-settlement to modern forests using US Forest Service Forest Inventory and Analysis (FIA) data to show the prevalence of lost forests, pre-settlement forests with no current analogue, and novel forests, modern forests with no past analogs. Differences between PLSS and FIA forests are spatially structured as a result of differences in the underlying ecology and land use impacts in the Upper Midwestern United States. Modern biomass is higher than pre-settlement biomass in northern Minnesota, northwestern and south central Wisconsin along the former prairie-forest border through Minnesota that was largely open savanna and the Big Woods of Minnesota. PLSS biomass was higher than today in northern Wisconsin and upper and lower Michigan due to shifts in species composition and, presumably, average stand age. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 29% of all FIA cells, while 25% of pre-settlement forests no longer exist in a modern context. Lost forests are centered around the forests of the Tension Zone, particularly in hemlock dominated forests of north-central Wisconsin, and in oak-elm-basswood forests along the forest-prairie boundary in south central Minnesota and eastern Wisconsin. Novel FIA forest assemblages are distributed evenly across the region, but novelty shows a strong relationship to spatial distance from remnant forests in the upper Midwest, with novelty predicted at between 20 to 60km from remnants, depending on historical forest type. The spatial relationships between remnant and novel forests, shifts in ecotone structure and the loss of historic forest types point to significant challenges to land managers if landscape restoration is a priority in the region. The spatial signals of novelty and ecological change also point to potential challenges in using modern spatial distributions of species and communities and their relationship to underlying geophysical and climatic attributes in understanding potential responses to changing climate. The signal of human settlement on modern forests is broad, spatially varying and acts to homogenize modern forests relative to their historic counterparts, with significant implications for future management.This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

Observations of the forests of the conterminous United States at the level of individual trees would be of utility for any number of applications, ranging from modelling the effect of wildland fire on terrestrial carbon resources to estimation of timber volume. While such observations do exist at selected spots such as established forest plots, most forests have not been mapped with this level of specificity. To fill the gap in tree-level mapping, we used a modelling approach that employed a random forests machine-learning technique. This technique was nearly identical to that employed by Riley et al. (2016), except that it used disturbance variables in addition to topographic and biophysical variables. This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. A set of predictor variables was used to train the random forests algorithm, which was then leveraged to extrapolate measurements across forested areas of the conterminous United States. Specifically, predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2014. These variables were present or were derived for both 1) the detailed reference data, which consisted of forest plot data from the U.S. Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. Of 67,141 single-condition FIA plots available to random forests, 62,758 of these (93.5%) were utilized in imputation to 2,841,601,981 forested pixels.

The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2014. This map is commonly known as "TreeMap 2014". The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart_access.html) or to the Microsoft Access Database and ASCII files included in this data publication to produce tree-level maps or to map other plot attributes. These files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the conterminous United States (CONUS) are currently available.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart.

These data were published on 07/02/2019. On 03/26/2021, the metadata was updated to include reference to a new publication. On 02/01/2024, some additional minor metadata updates were made and trees_CONUS_5_15_2019.mdb was removed from the package because it is an older format and the same content is included via text files.