Depicts the area planned and accomplished acres treated as a part of the timber harvest program of work, funded through the budget allocation process and reported through the FACTS database. Activities are self-reported by Forest Service Units. Metadata

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the portions of land in four condensed maps which illustrate the kind of forest maps that were being prepared from air photographs with a minimum of groundwork by the Forestry Branch of the Department of Northern Affairs and National Resources in the 1950s. Such maps not only show more detail than can be shown on a general forest regions map but also enable sample areas to be located which, when investigated on the ground, provide estimates of timber volumes. These maps are also of value to those responsible for forest protection and the suppression of forest fires. The first of the maps reproduced here illustrates an area of almost continuous forest in the rough terrain of the Alberta foothills (from sheet 82 0/14 - Marble Mountain). The second shows forested areas broken only by a few scattered farms (from sheet 31 0/10 - Mitchinamecus River, Quebec). The third shows an area almost equally divided between farm and forest (from sheet 21 J/7 - Napadogan, New Brunswick) The remaining map represents a farming district with scattered woodlots (from sheet 31 H/1 - Memphremagog, Quebec).

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

Forests cover large areas of Canada but only some of these forests are actively managed. The Map of Forest Management in Canada provides a generalized classification of forest management in Canada, including: protected areas, Treaty/Settlement Lands (including Treaty Lands identified in Final Agreements, Land Claim Agreements and Settlements), Indian Reserves, other federal reserves (including military training areas), provincial and territorial reserves and restricted use areas, private lands, short- and long-term Crown forest tenure areas and areas with no current Crown timber dispositions. The Managed Forest Map of Canada dataset provides a wall-to-wall classification of lands in Canada. It does not differentiate areas of forest from non-forest. The Managed Forest Map of Canada differs from maps defining the area designated as “managed forest” for greenhouse gas inventory reporting purposes and does not replace those maps. Instead, the Managed Forest Map of Canada shows areas that are currently managed, as of June 2017, and provides generalized management type classification for those areas. Collaborating agencies plan to update the dataset periodically as needed, and remain open to receiving advice from experts concerning refinement priorities for future versions.

Map showcasing the planned timber activities including Deck Sales in dotted yellow, Stormy Diamond Thinning in red polygons Units, and Harvest Units in mustard polygons, and Stormy Diamond Sale Area Boundary in green hatched polygons.

We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.

These maps were prepared and published by the Mapping Branch of the Forestry Commission of New South Wales. They contain information relating to State forests and timber reserves.


(SR Map Nos.52563-70). 8 maps.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

This data collection contains various historical features within Alberta related to the Dominion Forestry Branch (DFB), and the early days of the Alberta Forest Service (AFS). The data collection consists of the following: Historical Forest Reserve Boundaries (1900 to 1930). Timber Berth boundaries (1910 to 1923). Early Ground and Aerial Fire Patrol Routes. Air Stations (1920s). Locations of DFB Forest Surveys (1910 to 1915). AFS Forest and District Boundaries (circa 1938). These datasets provide approximate locations of the features they represent and are intended for use in medium to small-scale mapping. No spatial accuracy tests were conducted on any of the data. Features derived from land locations tied in the Alberta Township System (ATS) are likely to be accurate providing there were no errors in transcribing the locations. However features digitized from old maps will have poor spatial accuracy ranging from several kilometres to a few hundred metres, depending on the map scale, map projection, and the accuracy of the features on the source map.

This is a Web Map that displays the Timber Harvests on NF lands from 1820-Present.

The young trees mapping project developed a machine learning methodology using remote sensing to identify restocked stands where saplings persist in healthy numbers. The approach uses an eight-year timeframe since planting, crucial for verifying government grant compliance. Automating this methodology ensures easy replication and model transferability across years by training on multi-year data, making it resilient to climatic variations. Validation has confirmed the model’s accuracy, recommending high-confidence thresholds for restock classification. In the future, integration with the National Forest Inventory will enhance woodland mapping, accelerating updates and improving national indicators for forest extent and connectivity.

The aim of the young trees mapping project was to develop a machine learning methodology using remote sensing data, to identify stands where trees have been planted and saplings persist in healthy numbers. This was conducted within restock contexts across a specific timeframe, currently eight years since planting. This timeframe is significant because funding provided by government grants for planting can be reclaimed if it can be demonstrated that the funding has not been utilised by the landowner. Furthermore, the restock status of clearfell polygons has the potential to improve the accuracy of extent and connectivity environmental indicators developed as part of the Tree Health Resilience Strategy (THRS). The aim of this part of the project was to automate the methodology in such a way that it can be easily replicated, and to make the model transferable across years. Specifically, to train the model using multiple years of data, which makes the model agnostic to variable annual climactic conditions. The model is both robust and accurate, as demonstrated by the validation. It is recommended that only polygons with over 95% and under 5% confidence are treated as restocked or not restocked with any certainty. Outside of these limits confidence scores are only indicative of the restock status. In the future, the model is likely to be implemented as part of the National Forest Inventory (NFI) woodland map creation procedure. This will result in accelerated turnover of polygon labels from clearfell to young trees, where appropriate and will provide an important improvement to a national indicator for woodland extent and connectivity. Attribution statement: © Forestry Commission copyright and/or database right 2024. All rights reserved.

We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m² for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.

For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent.

A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % the South-Eastern area.

The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.

The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.

Corresponding authors: Jukka.Miettinen@vtt.fi, Johannes.Breidenbach@nibio.no

This map was created from two State Forestry Department (SFD) 2010 maps, supplemented by information from Environmental Impact Assessments (EIAs) for re-entry logging by specific licensees. Where not already identified in available SFD maps, names of licensees associated with individual numbered licenses have (where possible) been obtained from company documents and regional perspective maps from EIAs.Permit issuance dates and official areas in hectares are from various sources, including EIAs. Identities of corporate groupings to which individual licensee companies belong are based on various sources, including EIAs and official company documents.This map does not include Timber Licenses in Sarikei, Betong, Sri Aman, Samarahan and Kuching Divisions in the west of Sarawak, though there are known to be few such licenses in those Divisions. It is possible that some licenses shown on this map may have expired or been amended since 2010; other new licenses not shown here may also have been created. Individual licensees may also have joined or split from the named corporate groups. This map also does not include 'Belian Timber' Licenses.A number of the Timber Licenses are split into non-contiguous parts. In some cases separate boundary entries are given for each part; these are annotated 'Part 1' etc in the title. These 'Part' are not official names, but rather naming conventions used in the GIS cleaning of the data.

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.

Working Forest Management Plans (WFMPs) and Working Forest Harvest Notices (WFNs) approved by the California Department of Forestry and Fire Protection for landowners with less than 10,000 acres of land, not primarily engaged in the manufacture of forest products. Objectives include maintaining, restoring, or creating uneven aged managed timber stand conditions, achieving sustained yield, and promoting forestland stewardship that protects watersheds, fisheries and wildlife habitats, and other important values on non-federal lands in California. WFMPs are living documents that do not expire. For more information, see Subchapter 7, Article 6.95, Working Forest Management Plan, of the California Forest Practice Rules. Actual timber operations are conducted under Working Forest Harvest Notices (WFNs). Data from 2020 to present. This data set is in the California Teale Albers NAD83 meters projection (TA83).Link to the California Forest Practice Rules: https://bof.fire.ca.gov/regulations/bills-statutes-rules-and-annual-california-forest-practice-rules/For additional information on specific plans see the California Timber Regulation and Environmental Evaluation System (CalTREES): https://caltreesplans.resources.ca.gov/caltrees/Default.aspx

Noxubee National Wildlife Refuge Forest Compartment 26 Maps 1961. Also contains locations of known RCW cavity trees.

The map service (WMS Group) presents the digital geodata from the forestry sector of the Saarland.:Location mapping in the forest

The map was created in 1986 by the State Forestry Corps, in collaboration with the Province of Bologna, on 70 sections of the Regional Technical Cartography corresponding to the areas most occupied by forest surfaces in hilly, submountainous and mountainous areas of the Province of Bologna, all south of the Via Emilia. The cartography was created following the enactment of the Law of 8 August 1985, n. 431 entitled "Conversion into law with amendments of the decree law of 27 June 1985, n° 312 concerning urgent provisions for the protection of areas of particular environmental interest" (Galasso Law) and therefore it was necessary to know the location and the extension of the woods in order to be able to ensure the protection that the law provided for these areas. The synergy of the forces in the hands of the then Province of Bologna and the State Forestry Corps made it possible to carry out this project through the identification and return cartography, on a scale of 1:10,000, of the woods surveyed in the area directly by the staff of the State Forestry Corps. The individual forest polygons were implemented with information relating to the forms of government and the main species characterizing the forest populations. The Map reports, for the Municipality of Imola alone, also the Parks and Gardens annexed to historic Villas, some of which are protected by the Ministerial Decree of 1 August 1985 (Galassini). The paper material was recovered and returned in both raster and vector digital format by the Metropolitan City of Bologna . The recovery work is described on the website https://www.cittametropolitana.bo.it/pianificazione/recupero_carta_forestale_1986

The geodata visualize the findings (location information) of the forestry site exploration in the Free State of Saxony. Mapping unit is the site form. It brings together similar individual sites with the same ecological value. The characteristics of a site form are recorded independently of each other. Its most important subunits are the local soil and water balance forms (subdivided for groundwater, congestion and relief-related (leakage water imprinted) water balance forms). The local floor shapes are encrypted by 3 to 4-digit letter combinations. The immediately following digits symbolize the characteristics of water house shapes. The large number of subunit combinations is further aggregated for practical forestry applications. With regard to tree species selection and growth conditions, equivalent site types are merged into a site type group. The site type groups are the basic site reference units for forest planning and measures. The site shape group consists of the soil moisture level, the nutrient strength level (trophyte) and a moisture number and is primarily represented by the combination of two uppercase letters and a number on the maps. The ‘substrate moisture level’ was developed as a further evaluation feature. It is essentially a derivation from the mapping characteristics of the local soil forms and associated soil physical measured values. The existing forest site mapbook is the result of a uniform method development for East Germany since the 1950s and the mapping based on it in several stages. On the basis of the Saxon Forest Act, the Free State of Saxony is responsible, regardless of the type of forest ownership, for updating the forest location map, including the location-related processing of the forests. The implementation is the responsibility of the state enterprise Sachsenforst. The corresponding explanation can be found at https://www.forsten.sachsen.de/rasterdienste/Legendenbilder/bemerkungen_standortskarte.pdf