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.

This is the study area associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

Region 5 Forest Health Treatment Priority MappingThe number of acres of forests burning at high severity in recent years, combined with the recent drought-induced tree mortality event of 2015-2016 have more than ever highlighted unsustainable forest health conditions in California. Urgency for implementing preventative landscape-level tree density and fuels reduction treatments to restore and maintain forest resiliency to wildfires and drought (bark beetles) has now become an emergency. To accomplish meaningful landscape level treatments, land managers must be able to prioritize areas of highest risk that are conducive to project implementation. Forest Health Protection has analyzed a variety of readily available corporate GIS data sets to identify areas that are considered most at risk to high levels of bark beetle-caused tree mortality, have a high likelihood of experiencing stand replacing wildfire and are accessible and appropriate for mechanical thinning. This product has been used on several R5 National Forests for 5-year planning, identifying cross collaboration, all lands opportunities, and guiding layout of new projects using the Farm Bill insect and disease treatment Categorical Exclusion authority under NEPA. This webmap illustrates areas deemed at high risk of tree mortality, due to bark beetles, on all lands throughout the state. These same areas should also be considered at a risk to high-severity wildfire due to overstocked conditions and generally high fuel loading from past tree mortality. The webmap is suitable for landscape-level planning, rather than stand-level planning, as the data used to identify priority treatment areas are not sufficiently detailed for use at the stand level. Ground verification of areas identified in the map as priorities for treatment is highly recommended. Areas mapped outside of USDA National Forest System lands may not reflect recent management activities. Basic consideration for classification as high priority for treatment required that areas:Have not suffered moderate or high severity wildfire since at least 1998;Have not been thinned by the USDA Forest Service since at least 2005;Have not experienced stand-replacing disturbance, owing to clear-cut or natural mortality, since at least 2005;Contain stands with 60% or higher relative stand density;Are dominated by trees with diameter at breast height (DBH) of 11” or more.Lands that met the basic conditions were then classified as high priority for treatment based on the species composition and density of the stands that they contain.Highest priority was assigned to locations with stands that contain:Pines principally, and have stand density index (SDI) of 220 or higher; OR Fir-dominated mixed conifer and white fir, have SDI 270 or higher, and historically contained mostly pines; OR Pine-dominated mixed conifers, and have SDI 270 or higher.Pine-dominated stands are typically associated with drier sites and often experience higher levels of tree mortality associated with high stand density, bark beetles, and drought.Second priority was assigned to locations with stands that:Contain fir-dominated mixed conifer and white fir, have SDI 330 or higher;Were not classified as highest priority.Fir-dominated stands found on more mesic sites can also experience elevated tree mortality associated with high stand density, bark beetles, and drought, though generally at a lower level than pine-dominated stands or fir-dominated stands growing on historically pine-dominated sites.Download the thinning priority layers displayed in this WebMap. In addition to what is displayed on this webmap, the download also includesThird priority including smaller DBH of 6" - 11" 50% relative stand density (dependent on dominant species)Regional Dominance Type for each priority pixel

We present a forest management map for Europe. Forest is classified in 5 distinct forest management classes: primary forest, nature management, multifunctional management, intensive forestry and plantations. Data on disturbance size, disturbance frequency, age, evenness, plantation species and primary forest is used to classify forest. The word document describes the procedure and provides a link to the data and scripts.

IV Forest Inventory of the Balearic Islands. Part of the IV Spanish Forest Map published by the Directorate General of Forest Policy and Rural Development Ministry of Agriculture, Food and Environment. Includes forest fuel model (Rothermel), the structure of the vegetation foerestal, the type of training trees, shrubs and type of training. Part of the Spanish forest map 1:25000. Have been contributed to the area of Data Bank of the nature of the Department of Environmental Quality and Assessment and environment.The project carried out between 2007 and 2017.

The USGS Governmental Unit Boundaries service from The National Map (TNM) represents major civil areas for the Nation, including States or Territories, counties (or equivalents), Federal and Native American areas, congressional districts, minor civil divisions, incorporated places (such as cities and towns), and unincorporated places. Boundaries data are useful for understanding the extent of jurisdictional or administrative areas for a wide range of applications, including mapping or managing resources, and responding to natural disasters. Boundaries data also include extents of forest, grassland, park, wilderness, wildlife, and other reserve areas useful for recreational activities, such as hiking and backpacking. Boundaries data are acquired from a variety of government sources. The data represents the source data with minimal editing or review by USGS. Please refer to the feature-level metadata for information on the data source. The National Map boundaries data is commonly combined with other data themes, such as elevation, hydrography, structures, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain boundaries data in either Esri File Geodatabase or Shapefile formats. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

The U.S. Forest Service Hazardous Fuels Treatments Web Map is used in the U.S. Forest Service Hazardous Fuels Treatments dashboard to display the regional accomplishments based on the number of non-wildfire acres treated for the fiscal year 2019. The dashboard displays the recent fuel treatment areas, total number of acres treated by category and the number of acres treated for hazardous fuels monthly progress report.

1.1.1 Forest Service Mapping Series 6 (FSMS6)

The 23 completed maps provide the distribution of indigenous forest vegetation for all of the North Island and the bulk of the South Island at a scale of 1:250,000. These maps were primarily compiled by Mr John Nicholls with some of the South Island maps compiled by Mr Dudley Franklin. Black and white aerial photographs, dating from 1948 to 1955 and at a scale of 15 chains per inch, supplemented by extensive ground truthing and some 16,000 National Forest Survey and Ecosurvey plots, were used to determine forest class boundaries. These were transferred to 1:63360 topographic maps. The maps were field checked and then copied for production by FRI graphics staff (Herbert 1997, pers. comm.).

Most maps were completed by the NZ Forest Service, with a small number being finished by the Ministry of Forestry and then by Landcare Research Ltd. Appendix 1 gives the list of maps digitised. The date of the photographs that were used to compile each map is not known exactly.

1.1.2 Forest Service Mapping Series 15 (FSMS15)

There are two FSMS15 comprising 1:1,000,000 maps of the North Island, and South Island (including Stewart Island). These were compiled by NZFS Conservancy and Head Office staff for the 1974 Forestry Development Conference. Forest boundaries for the 1:1,000,000 FSMS15 maps are significantly less accurate than those for the 1:250,000 FSMS6 maps (Herbert and Nicholls, 1997, pers. comm.). Data sources included existing FSMS6 maps (with 18 classes coalesced into eight super classes), local published and unpublished maps and local knowledge for areas not cover by the FSMS6. The Te Anau, Hauroko and Mataura FSMS6 series maps were substituted for by the South Island FSMS15 map.

1.1.3 Forest Service Type Map Series No. 2 (FSTM2)

These are a collection of detailed forest class maps at 1:63360 scale. Coverage is confined to parts of the central North Island.

### 1.1.4 Vegetation of Stewart Island

Mr Hugh Wilson (Wilson, 1987) developed a detailed map of the vegetation of Steward Island. Wilson’s Podocarp/hardwood forest, and rata-kamahi hardwood forest polygons (Types A 1-2, B3) were digitised.

1.2 Forest Class Description

There are eighteen forest classes described in the FSMS6 map series. These are described in Table 1. The source is Nicholls and Herbert (1995). FSMS15 has eight super classes and these are defined in Table 2.

*Table 1: Forest classes, codes and IPCC class

        (Dbase)

*Class Code IPCC Class

*Kauri A C

*Kauri -Softwoods-Hardwoods B M

*Kauri -Softwoods-Hardwoods-Beeches C M

*Softwoods L C

*Rimu-Matai-Hardwoods M M

*Rimu-Taraire - Tawa E M

*Rimu-Tawa D M

*Rimu-General Hardwoods F M

*Lowland Steepland and Highland Softwoods - Hardwoods G M

*Rimu-Tawa-Beeches H M

*Rimu - General Hardwoods - Beeches I M

*Highland Softwoods-Beeches J M

*Taraire-Tawa S B

*Tawa N B

*General Hardwoods P B

*Tawa Beeches O B

*General Hardwoods - Beeches T B

*Beeches K B

IPCC Class Definitions: C: Conifer, B: Broadleaf, M: Mixed.

Table 2: FSMS15 forest classes

      Dbase

Class code / FSMS6Classes Description IPCC Class

Kauri - Podocarp - Hardwood /A, B, C All forest containing kauri, including minor area of pure kauri and local occurrence of beech M

Podocarp L/ L Forest of abundant podocarps C

Lowland Podocarp - Hardwood 1/ D, E, F, M, pt. G Virgin or lightly logged podocarp - hardwood forest below the altitudinal limit of rimu M

Lowland Hardwood 2/ N, S, pt. P Residual and second growth forest below the altitudinal limit of rimu and minor areas of natural pure hardwood forest. B

Upland Podocarp - Hardwood 3/ Pts G, P Virgin or lightly logged podocarp - hardwood above the altitudinal limit of rimu and minor areas of natural pure hardwood forest.
M

Podocarp - Hardwood - Beech 4/ H, I Virgin or lightly logged forest of mixed podocarp - hardwood and beech below the altitudinal limit of rimu M

Hardwood - Beech 5/ O, T Residual or second growth forest and minor areas of natural pure hardwood - beech. B

Beech 6/ J, K Virgin and lightly logged or second-growth forests predominantly composed of beech B

Wilson Stewart Island 7/ Podocarp/hardwood forest, and rata-kamahi hardwood forest. M

2. METHODS

2.1 Digitising and Topology Generation

The maps were digitised by staff at the Forest Research Institute under standards listed in Appendix 2, using the Terrasoft Geographic Information System. The linear features that made up each forest class polygon are shared between two feature classes one, called NZFS6 which contains the national coverage, and the other based on the respective map sheet number. This allows themes to be developed for a national view and also for the individual map sheets.

The line work is topologically correct with no over-, or under- shoots.

Each polygon has a nationally unique identifier and which is linked to a dbase table containing a code letter which describes the forest vegetation class.

These maps were digitised for the purpose of providing indigenous forest vegetation cover for usage at a national scale. There has been no formal checking of the accuracy of the digitised linework. Any errors are considered to be insignificant for determining a 1990 indigenous forest vegetation baseline database. Each polygon was checked to confirm correct tagging. During that process any significant linear differences were noted and corrected.

2.2 Problems

2.2.1 Incorrect map details

In several places errors on the maps were found. Either the FSTM2 maps were consulted for greater detail where coverage existed or Mr John Nicholls was, personally, consulted and the error corrected.

2.2.2 Map source quality

Most FSMS6 maps where unused, unfolded sheets with only sheet 12 being an unused folded map. The FSMS15 South Island map was a well used map with significant fold lines. This map also had other printed information which made precise measurement of some forest class boundaries difficult.

Standards

This document defines the standards used for digitising the forest class maps (NZFS Map Series 6, FSMS15 and Wilson, 1987).

Source

The source of the FSMS6 data is the 1:125,000 flat map sheets, the FSMS15 maps and the Vegetation map contained in Wilson (1987).

Digitising

The following digitising standards were used.

A minimum of five points for registration should be selected from a rectangular range encapsulating the immediate digitising area. These points then should he entered into Convert and both the input and the resultant NZMG coordinates checked before the map is registered. The registration error should be (in Terrasoft) 0.00%. The media should be anchored firmly to the digitiser. The RMU laboratory should be used with the air conditioning turn on. Registration should occur at least twice a day, but occur more frequently if the humidity changes. All lines and polygon which represent a forest type needs to be captured irrespective of size. All intersections should have a node digitised. The two feature classes are NZFS6 and NZFS6_

Output

Shape must be identical

Theme creation

A Theme will be created for each map sheet. The national NZFS6 theme will be created by including the previously digitised map sheets and the FSMS15 and Wilson’s map. Polygon tags are to be corrected between the map sheets to make them all unique. All dangles and overlaps, and bad polygons are to be corrected.

Tagging

All polygons are to be tagged with a code representing the forest type. All sliver polygons are to be removed.

Checking

A plot should be created at the original scale and overlayed over the original map. Each polygon is checked to confirm correct tagging.

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 data collection is associated with the project: “Status and Trends of Deciduous Communities in the Bighorn Mountains”. It contains the project study area, model evaluation data, model input data, and model output data in the form of probability of occurrence rasters for deciduous and coniferous species, as well as a synthesis map. The aim of the study is to assess the current trends of deciduous communities in the Bighorn National Forest in north-central Wyoming. The data here represents phase I of the project, completed in FY2017. The USGS created a synthesis map of coniferous and deciduous communities in the Bighorn Mountains of Wyoming using a species distribution modeling approach developed in the Wyoming Landscape Conservation Initiative (WLCI) (Assal et al. 2015). The modeling framework utilized a number of topographic covariates and temporal remote sensing data from the early, mid and late growing season to capitalize on phenological differences in vegetation types. We used the program RandomForest in the R statistical program to generate probability of occurrence models for deciduous and coniferous vegetation. The binary maps were combined into a synthesis map using the procedure from Assal et al. 2015. In Phase II of this project (to be completed in FY2018 and 2019), the USGS will conduct a preliminary assessment on the baseline condition of riparian deciduous communities. This will be a proof-of-concept study where the USGS will apply a framework used in prior research in upland aspen and sagebrush communities to detect trends in riparian vegetation condition from the mid-1980s to present. Literature Cited Assal et al. 2015: https://doi.org/10.1080/2150704X.2015.1072289

This map displays forest treatments conducted by New Mexico Forestry Division and its partners including New Mexico Game & Fish. Forest treatments improve forest health, improve wildlife habitatsand reduce catastrophic fire risk in the wildland-urban interface (WUI)by increasing the defensible space around homes. Forest treatments are presented by state fiscal year (July 1 -June 30) and span from 2009 -2018.Data is compiled from a variety of sources, including N.M.State Forestry Division, N.M.Game & Fish, the N.M.Resource Geographic Information System Program Data Clearinghouse (RGIS), the U.S.GeologicalSurvey, and Bureau of Land Management.This mapping application is compatible on Chrome, Firefox,Internet Explorer(v11)and Safari, as well as mobile devices.

The Spanish Forest Map Web Service (MFE25_50) allows the visualisation and consultation of the data set of the Forest Map of Spain. The Forest Map of Spain (MFE) is the basic forest mapping at the state level, which includes the distribution of Spanish forest ecosystems. It is a project led by the General Directorate of Biodiversity, Forests and Desertification using a working methodology based on photointerpretation, with field verification. The MFE is a fundamental component of the Spanish Inventory of Natural Heritage and Biodiversity. The MFE provides detailed and homogeneous vector information for the entire Spanish territory of the structural type or main use of each tesela, the degree of coverage and the main mapped tree species, among others. It constitutes the cartographic basis of the National Forestry Inventory (IFN), and therefore, analogous to the IFN, has a continuous character and a periodicity of updating at least ten years. The most current Spanish Forest Map is the result of the combination of the old MFE50 with the new provinces that are being generated from the MFE25. The URL of the WMS Service Forest Map of Spain most current (MFE25_50 is: https://wms.mapama.gob.es/sig/Biodiversidad/MFE/wms.aspx The reference systems offered by this service are: — For geographical coordinates: CRS: 84, EPSG:4230 (ED50), EPSG:4326 (WGS 84), EPSG:4258 (ETRS 89). — For U.T.M coordinates: EPSG:32628 (WGS 84/UTM zone 28N), EPSG:32629 (WGS 84/UTM zone 29N), EPSG:32630 (WGS 84/UTM zone 30N), EPSG:32631 (WGS 84/UTM zone 31N), EPSG:25828 (ETRS 89/UTM zone 28N), EPSG:25829 (ETRS 89/UTM zone 29N), EPSG:25830 (ETRS 89/UTM zone 30N), EPSG:25831 (ETRS 89/UTM zone 31N), EPSG:23028 (ED50/UTM zone 28N), EPSG:23029 (ED50/UTM zone 29N), EPSG:23030 (ED50/UTM zone 30N), EPSG:23031 (ED50/UTM zone 31N).

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation. European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788. The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel. Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe. Dataset Record The forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB. The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given. In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below. Code Availability For the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629. Acknowledgements The computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.

Forest structure is strongly related to forest ecology and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand level approaches. In this paper an effective and simple method to map ITCs, and their stem diameter and above ground biomass is presented. ALS data were used to delineate ITCs and to extract ITCs' height and crown diameter; then using newly developed allometries the ITCs' DBH and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of individual DBH and AGB (R2 = 0.85 and 0.78, respectively) and of tree size distributions. The proposed method had a higher generalization ability compared to a standard area based method, in particular for the prediction of the Gini coefficient of DBHs. The delineation method used detected more than 50% of the trees with DBH >10 cm. The detection rate was particularly low for trees with DBH below 10 cm, but they represent a small amount of the total biomass. The Gini coefficient of the DBH distribution was predicted at plot level with R2 = 0.46. The approach described in this work, easy applicable in different forested areas, is an important development of the traditional area based remote sensing tools and can be applied for more detailed analysis of forest ecology and dynamics.

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Abstract: Old growth forest mapping from aerial photograph interpretation of canopy species regeneration and senescent growth stages. Scale 1:25,000. Bounded by NSW Morriset Forestry District. Boundaries include the New England Highway and Hunter River in the North,the Blue mountains and Wollemi National Park in the west and the Illawarra highway in the south. VIS_ID 4122

Purpose

To map old growth forest in the Morriset area.

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Digitised on screen over 1:250,000 scale topographic maps. Attributes were verified in the field and by NSW state forests. Stereoscopic interpretation using a range of stereoscopes with a variety of magnifications (eg. Topcon and Abrams stereoscopes with x10 magnification). Attributes table & codes - Information contained in the attribute table is: regrowth-juvenile and sapling, regrowth-pole, Mature-early mature & mature, senescent-late mature and overmature, disturbance code. The project was only concerned with pyrophytic vegetation consequently, vegetation that was <10% pyrophytic was coded with an O and rainforest was coded with an R. Mapping pathway - An api pathway was developed specifically for the BOGMP. O =0%.if vegetation obviously rainforest= R 1 =0-10%,if vegetetation obviously rainforest=R 2 =11-20% and 3 = 21-30% If understorey rainforest rather than grass or heath. On ecological grounds this should be called rainforest but there might be some debate over the upper part of class 3.= R 4 = 31-50% difficult to see the understorey, would require ground truthing. Could have rainforest in which case there would be an argument about whether to be treated as a separate vegetation type or as a serial stage of rainforest Discretionary(presence/absence of rainforest understorey) 5 =51-60% Could have rainforest elements but difficult to determine as rainforest. Eucalypt = 81-100% unlikely to be rainforest in understorey. Eucalypt The mapping pathway specified that in eucalypt forest, primary polygon primary polygon delineation was based on floristics then split firstly on structural differences, structure (% regrowth and senescence), secondly on height classes then regrowth size class, and tagged for relative stand density and disturbance indicators. No senescence was recorded for polygons outside of State Forest with >30% regrowth. Eucalyptus-dominated vegetation with <20% ccp was not delineated. Data recording. The aerial photographs were pre-prepared and supplied to interpreters with Effective areas and land tenure boundaries marked directly on to the photographs. The effective area of a photograph included all images closer to the centre of the photograph than to the centre of any other. The central old growth study area used recent logging disturbance maps provided by state forests. Land Cover - Vegetation Cover greater than 20% canopy cover Floristics - Classification into pyrophytic vegetation, <10% pyrophytic vegetation, and rainforest Strata - Mapping pathway delineates a code of rainforest or eucalypt according to understorey type in areas with discrepencies Growth Stage - Regeneration and senescence Multi-attribute Mapping - Native vegetation greater than 20% ccp delineated. Relative stand density for the regrowth component of the vegetation also identified. Special features identified (eg. exotic pine plantation). Land use / cover not identified. Survey Type point to plant transects. Inaccessible areas were assessed using aircraft. Information Collected Growth stage, disturbance and vegetation assessment Date of surveys -1996? Minimum Polygon Size -25 hectares Edge Matching - Not assessed Polygon Attribution - Comparison of the growth stage polygon codes and linework against a hard copy map and against the original linework on the aerial photographs.. Both a 10% random sample of the photographs,and all the photographs in a specific area were checked for coding and linework errors. Custodian - NPWS Date of map product -1996 Strengths - A validation process was implemented. Detailed growth stage information and disturbance information. Field checking was undertaken. Multi-attribute mapping with broad geographic coverage, relatively high quality data capture techniques, Weaknesses - The difference in ability of the interpreters (eg moist, high site quality forest types were more reliably mapped than other forest types).Field work was insufficient, confined to state forest tenure. High possibility of post mapping logging and disturbance.

Dataset Citation

NSW Office of Environment and Heritage (2015) Old Growth Forest Mapping Broad, Central, 1996. VIS_ID 4122 2015 20150116. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/85a296b9-0c03-4dec-a0c1-cb22debbdbd1.

This is a spatial layer showing Ministry of Forests Map Notation Polygons. These are polygonal spatial representation for a notation on the Forest Atlas which records the area of interest of other government agencies and individuals

The present study produced a new reference forest map for China from three land cover products for 2020. These datasets included the World Cover 2020 (ESA-2020), ESRI 2020 Land Cover (ESRI-2020), and the GlobeLand30 version of V2020 (GLC-2020). Within the production of the reference forest map for China, a pixel was assumed to represent forest when the same pixel among multiple land cover products showed forest properties, thereby decreasing the uncertainties of classification of forests at a large scale.

This publication contains a raster maps at 250 m resolution of the merchantable volume (m3/ha) of the mature Canadian forest available for harvesting in the next 20 years (2011 to 2031). The maps were produced from remote sensing products at a spatial resolution of 250 m on the MODIS pixel grid and 30 m on the Landsat pixel grid. More specifically, we used forest attribute data at the 250 m pixel for the years 2001 and 2011 (Beaudoin et al 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 at 30 m (Guindon et al. 2017 and 2018). The map of mature forests in Canada was prepared at the forest management unit (FMU) level and therefore exclude private lands. To be considered mature (i.e. available for cutting in the next 20 years), the forest pixels of Beaudoin et al. (2018) was to have a merchantable volume per ha equal to or greater than 80% of the average merchantable volume of the pixels that were harvested between 2001 and 2011 per forest management unit. A scientific article gives additional details on the methodology: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Can. J. For. Res. 48: http://www.nrcresearchpress.com/doi/10.1139/cjfr-2018-0080 Reference for this dataset: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/dd94871a-9a20-47f5-825b-768518140f35

Dataset of the IS BK 5 Ground Map for Forestry Site Sensing of NRW 1: 5.000. The data set gives the contents of all digitally processed large-scale ground maps, usually in scale 1: 5,000, again. For this purpose, the individual soil mapping projects (“procedures”) were integrated into a largely break-free overall package. Because the large-scale floor map was not created nationwide, the data set also shows white, uncharted areas. For these areas, medium-scale soil information can be extracted from the BK50 dataset. Each individual area is described upon retrieval of information from a GIS with regard to soil unit, simplified soil type, soil type group of the upper soil, dams, groundwater (former and current stage), soil worthy of protection, rootability, forest location characteristics, need for soil protection limescale, optimum land clearance, erodibility of the upper floor, capillary ascent of groundwater, usable field capacity, field capacity, air capacity, saturated water conductivity, leachability, cation exchange capacity and further evaluations.