This is the input data 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

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.

We present a forest management map for Europe. Forest is classified in 5 distinct forest management classes: unmanaged forest, close-to-nature forestry, combined objective forestry, intensive forestry and very intensive forestry. Data on disturbance area, disturbance frequency, forest age, forest age evenness, fast-growing tree species and primary forest is used to classify forest.

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.

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.

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

For animals with spatially complex behaviours at relatively small scales, the resolution of a global positioning system (GPS) receiver location is often below the resolution needed to correctly map animals’ spatial behaviour. Natural conditions such as canopy cover, canyons or clouds can further degrade GPS receiver reception. Here we present a detailed, high-resolution map of a 4.6 ha Neotropical river island and a 8.3 ha mainland plot with the location of every tree >5 cm DBH and all structures on the forest floor, which are relevant to our study species, the territorial frog Allobates femoralis (Dendrobatidae). The map was derived using distance- and compass-based survey techniques, rooted on dGPS reference points, and incorporates altitudinal information based on a LiDAR survey of the area.

This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas.

Note: This map service contains generalized NFS Land Unit boundaries to help with map service performance. Data in this service is not as accurate as the Automated Lands Program published data and will not accurately represent the boundary.National Forest System Land Unit original accurate data can be downloaded from here.An NFS Land Unit is nationally significant classification of Federally owned forest, range, and related lands that are administered by the USDA Forest Service or designated for administration through the Forest Service. NFS Land Unit types include proclaimed national forest, purchase unit, national grassland, land utilization project, research and experimental area, national preserve, and other land area. Each NFS Land Unit is identified by a National Forest Fiscal Identifier (NFFID) code, a unique 4-digit number that is used for accounting purposes.

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.

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 RecordThe 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 AvailabilityFor 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.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

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

The Forest Service National Maps experience page is designed to distribute and deliver maps to the Forest Service and public. Maps cover Forest Service lands. Map series include National; Regional; Admin; Forest; Ranger District and 24K or better known as FSTopo, and our historical product FSTopo Legacy.

The global map of forest types provides a spatially explicit representation of primary forest, naturally regenerating forest and planted forest (including plantation forest) for the year 2020 at 10m spatial resolution. The base layer for mapping these forest types is the extent of forest cover of version 1 of the …

This web map contains data layers viewable for eighteen mid-level existing vegetation maps (1:100,000) prepared for the Tongass National Forest to provide up-to-date and more complete information about vegetative communities, structure, and patterns across the project area. Over 18 million acres were mapped through a partnership between the US Department of Agriculture Forest Service Field Services & Innovation Center – Geospatial Office (GO), the Tongass National Forest, and the Alaska Regional Office. The Tongass National Forest and their partners prepared the regional classification system, identified the desired map units (map classes) and provided general project management. GO provided project support and expertise in vegetation mapping. Maps are available for the six integrated vegetation attributes: project map group, project vegetation type, Tongass NF map group, Tongass NF vegetation type, NVC division, and NVC macrogroup. For forested areas, maps are available for the twelve forest structure metrics: tree canopy cover, tree canopy cover class, tree size, biomass (Mg/ac) for trees ≥2” diameter at breast height (DBH), crown competition factor (CCF), gross board feet (GBF), quadratic mean diameter (QMD) for trees ≥2” DBH, QMD for trees ≥9” DBH, rumple index, stand density index (SDI) for trees ≥9” DBH, trees per acre (TPA) for trees ≥1’ tall, and TPA for trees ≥6” DBH. The minimum map feature depicted on the map is 0.25 acres. The map products conform to the mid-level mapping standards referenced in the Existing Vegetation Classification, Mapping, and Inventory Technical Guide (Nelson et al. 2015). For more detailed information on mapping methodology please see the Tongass National Forest Existing Vegetation Project Report or the individual area project reports.

The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*. The forest maps from the first inventory are available at a scale of 1/20,000. They cover almost all of the territory south of the 52nd parallel. Each file covers an area of approximately 250 km2. These digital maps correspond to the black and white paper maps with a dimension of 125 cm X 75 cm that have been digitized and georeferenced. They illustrate forest stands. They were prepared from the photo-interpretation of aerial photos on a scale of 1/15,000. Main components: •outline of forest stands; • type of vegetation (forest species, density, height and stage of development, origin); • disturbances; • nature of the terrain (peatlands, gravel, etc.); • territorial subdivisions; • territorial subdivisions; • hydrography (lakes, rivers, streams, streams, swamps, etc.); • disturbances; • nature of the terrain (peatlands, gravel, etc.); • territorial subdivisions; • hydrography (lakes, rivers, streams, swamps, etc.); • topography (level curves). The units of measurement shown on the maps in the first inventory are those of the English imperial system of measurement.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. * Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 * Please cite this dataset as: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 * This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple), acer saccharinum (silver maple), acer saccharum (sugar maple), acer spicatum (mountain maple), acer spp. (unidentified maple), alnus rubra (red alder), alnus spp. (unidentified alder), arbutus menziesii (arbutus), betula alleghaniensis (yellow birch), betula papyrifera (white birch), betula populifolia (gray birch), betula spp. (unidentified birch), carpinus caroliniana (blue-beech), carya cordiformis (bitternut hickory), chamaecyparis nootkatensis (yellow-cedar), fagus grandifolia (american beech), fraxinus americana (white ash), fraxinus nigra (black ash), fraxinus pennsylvanica (red ash), juglans cinerea (butternut), juglans nigra (black walnut), juniperus virginiana (eastern redcedar), larix laricina (tamarack), larix lyallii (subalpine larch), larix occidentalis (western larch), larix spp. (unidentified larch), malus spp. (unidentified apple), ostrya virginiana (ironwood, hop-hornbeam), picea abies (norway spruce), picea engelmannii (engelmann spruce), picea glauca (white spruce), picea mariana (black spruce), picea rubens (red spruce), picea sitchensis (sitka spruce), picea spp. (unidentified spruce), pinus albicaulis (whitebark pine), pinus banksiana (jack pine), pinus contorta (lodgepole pine), pinus monticola (western white pine), pinus ponderosa (ponderosa pine), pinus resinosa (red pine), pinus spp. (unidentified pine), pinus strobus (eastern white pine), pinus sylvestris (scots pine), populus balsamifera (balsam poplar), populus grandidentata (largetooth aspen), populus spp. (unidentified poplar), populus tremuloides (trembling aspen), populus trichocarpa (black cottonwood), prunus pensylvanica (pin cherry), prunus serotina (black cherry), pseudotsuga menziesii (douglas-fir), quercus alba (white oak), quercus macrocarpa (bur oak), quercus rubra (red oak), quercus spp. (unidentified oak), salix spp. (unidentified willow), sorbus americana (american mountain-ash), thuja occidentalis (eastern white-cedar), thuja plicata (western redcedar), tilia americana (basswood), tsuga canadensis (eastern hemlock), tsuga heterophylla (western hemlock), tsuga mertensiana (mountain hemlock), tsuga spp. (unidentified hemlock), ulmus americana (white elm), unidentified needleaf, unidentified broadleaf, broadleaf species, needleaf species, unknown species

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 data set indicates the location of intact and degraded primary forests across Indonesia for the years 2000, 2005, 2010, and 2012. Primary forest consists of mature natural forest cover that has not been completely cleared in recent history (30 years or more) and exists in a contiguous block of 5 ha or more. Primary forest cover was mapped using Landsat composites and multi-temporal metrics as input data to a two-step supervised classification. The first step was a per-pixel classification of areas with tree canopy cover of 30% and above for the 2000 reference year. A second per-pixel classification procedure was performed to separate primary forest from other tree cover for 2000; contiguous areas of 5 ha and greater were retained as primary forest. A limited editing of this classification was performed to remove older plantations and adjust other forest formations that could not be identified using the per-pixel classifier, but could be identified in photo-interpretive contexts. Primary forests were subsequently characterized into primary intact and primary degraded subclasses using the GIS-based buffering approach of the Intact Forest Landscapes (IFL). To create the IFL layer, buffers of roads, settlements and other signs of human landscape alteration were used to identify degraded areas within zones of primary forest cover. IFL mapping employed cloud-free Landsat mosaics to quantify changes in primary intact forest extent. The map of primary intact and primary degraded forest cover types corresponds to the Indonesia Ministry of Forestry’s primary and secondary forest cover types.

As part of an effort of the World Resources Institute Global Restoration Initiative to map forest and landscape restoration opportunities, the map of potential forests represents an estimate of where forests would grow under current climate conditions and without human influence. The main source of data for defining potential forest extent is the terrestrial ecoregions of the world (Olson et al. 2001). Each ecoregion was classified as belonging to one of four categories: dense forests, open forests, woodlands, or non-forest, depending on its description (including current and potential vegetation) and its proportion of different forest types, with additional input from the following datasets: current forest extent; bioclimatic zoning and original forest cover extent; and a forest distribution map produced by modeling based on global climate variables and elevation (Hansen et al. 2013, Zomer et al. 2007). The dataset is based on significant simplifications due to limited availability of globally-consistent data. The maps are at a relatively coarse scale and should only be used to estimate potential forest coverage at regional or global scale. Estimates of potential forest coverage are based on current climate conditions in the absence of human disturbance.