Contained within the 1st Edition (1906) of the Atlas of Canada is a map that shows the northern limits of approximately 40 different tree species in Canada, including an extension into the Northern U.S. Using green lines the map displays the northern limits of the principal trees found within the Southern Forest. Blue lines indicate the northern, and in a few incidences the southern, limits of the principal trees found within the Northern Forest. Red lines show the limits of the trees within the Cordilleran Forest. For this map, and the Atlas of Canada Forests map, the line of division between the Northern and Southern Forests has been taken as the northern limit of red and white pine. These trees are assigned to the Northern Forest, including those whose limit is south of the pine tree limits within the Southern Forest. The map also includes, rivers, major bodies of water, and a few tree types labeled in specific locations.

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

The dataset presents the estimated occurrence of less common tree species (other than pine and spruce) in the form of thematic maps covering entire area of Finland. The maps series represent the following years: 1994, 2002, 2009 and 2015. The tree species maps are based on geostatistical interpolation of field measurements from national forest inventory sample plots and satellite image-based forest resource estimates. The occurrence data is presented as the average volume (m3/ha) of the tree species in forestry land. The tree species maps are available as ESRI polygon shapefiles where Finland is divided into 1 x 1 km2 square polygons for which the tree species data is estimated. Koordinaattijärjestelmä: ETRS89 / ETRS-TM35FIN (EPSG:3067)

This data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones. SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology. # Limitations 1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates. 2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates. 3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version. 4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions. Updates of this dataset will eventually be available on this metadata page. # Details on the product development and validation can be found in the following publication: Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118 # Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available: • NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer • Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies • Height (meters): vegetation height • Crown closure (%): percentage of pixel covered by the tree canopy • Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)

This study’s objective was to use a reduced major axis (RMA) regression to develop a model that would predict the height of loblolly pine trees in plantations using values from NAIP point clouds and apply the model to a statewide map of pines for Virginia, North Carolina, and Tennessee. The NAIP point clouds were first normalized by subtracting the ground elevation DEM from the point cloud z values. Then a 5m x 5m grid was created for each state and the 90th percentile of height was extracted for each grid cell. These grid cells were then converted to a raster and mosaiced into one large raster for each state. Next, using National Landcover Dataset (NLCD) information, the raster was extracted for pixels only classified as evergreen (class 42). Lastly, the predicted pine height (PPH) model (PPH = 0.81 + (0.88 * 90th Percentile of Height)) was applied to the extracted raster. It should be noted that the model, and therefore the maps, are most applicable to areas of loblolly pine that are not located in areas where heavy thinning (removal of a large portion of trees) has occurred.

This data product contains raster maps of live tree aboveground biomass (tons/pixel) for Ponderosa pine (Pinus ponderosa), 2014-2018. 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 dense time series of Landsat imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species aboveground biomass to create maps of tree species abundance and distribution at a 30-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 the mean of the nearest neighbors based on proximity in a feature space derived from the model.

This data product contains raster maps of live tree aboveground biomass (tons/pixel) for lodgepole pine (Pinus contorta), 2014-2018. 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 dense time series of Landsat imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species aboveground biomass to create maps of tree species abundance and distribution at a 30-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 the mean of the nearest neighbors based on proximity in a feature space derived from the model.

Description of plot condition classes based on impact by single or multiple disturbances across the plots (N = 1,432).

Where 2004 and 2011 are the centroid-year of first (1997–2010) and last (2003–2018) inventory intervals, respectively. Each pair of variables were tested using the dependent paired-samples t-test at α = 0.05 level (N = 1,432).

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

Reason for SelectionOpen longleaf pine forests once spanned nearly 90 million acres across the Southeast, supporting a rich community of wildlife and plants. Fire suppression, deforestation, and conversion to ecosystems dominated by loblolly and slash pine have dramatically reduced the extent of longleaf and caused the decline of many associated species. In addition, pine and prairie birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture opportunities to conserve, restore, and manage open pine habitat. This indicator also promotes consistency with the longleaf and open pine ecosystem priorities of the East Gulf Coastal Plain Joint Venture (EGCPJV).Input DataBird priorities from Prioritization of areas for open pine ecosystem restoration in the Southeastern United States (bird_priority.tif); read the project final report; read a news article about the project; explore the data in the EGCPJV Open Pine Decision Support ToolEstimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018).2019 National Land Cover Database (NLCD): Land coverBase Blueprint 2022 extentSoutheast Blueprint 2023 extentMapping StepsReproject the EGCPJV bird priority data to NAD 1983 Contiguous USA Albers (EPSG 5070).If an area intersects an EPA Estimated Floodplain, change the value to NoData.If an area is something besides Evergreen Forest or Mixed Forest in the 2019 NLCD, change the value to NoData. Because the bird priority layer was made with older landcover data, this step helps remove areas that do not currently have pine trees.The EGCPJV bird data provides a raster with scores of 0-100 representing the relative potential for open pine bird response to conservation action. Reclassify this raster as shown in in the legend below.Add zero values to represent the extent of the source data and to make it perform better in online tools. Assign a value of 0 to any pixel that had a value ≥ 0 in the original bird priorities layer but was converted to NoData above.Clip to the spatial extent of Base Blueprint 2022.As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:5 = High priority for open pine conservation for focal bird species (Bachman’s sparrow, red-cockaded woodpecker, Henslow’s sparrow, red-headed woodpecker, Northern bobwhite, and brown-headed nuthatch) (score >80-100)4 = Medium-high priority (score >60-80)3 = Medium priority (score >40-60)2 = Medium-low priority (score >20-40)1 = Low priority (score 0-20)0 = Not a priority (not identified as upland pine)Known IssuesThe EGCPJV’s open pine bird model includes frequently inundated floodplain areas. These areas are unlikely to be potential open pine habitat. To address this in the indicator, we removed areas within the EPA estimated floodplain. This indicator prioritizes some areas outside of the floodplain that were not historically open pine habitat.The 2019 NLCD classes used to exclude non-pine areas of the open pine bird model (evergreen forest and mixed forests) do not exclusively target pine trees. For example, the evergreen class can include other evergreen tree species like Eastern red cedar or southern magnolia. In addition, the NLCD likely misclassifies other types of land cover as evergreen or mixed forests. As a result, this indicator may leave in some areas prioritized in the bird model that are not actually pine. Conversely, the NLCD also likely misclassifies some areas of pine as other land cover classes, which could cause pine areas prioritized in the model to be inadvertently excluded from the indicator.This indicator does not capture areas where planting new pine stands in existing agricultural areas would benefit open pine birds. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedGrand, J.B., and Kleiner, K.J., 2017, Prioritization of areas for open pine ecosystem restoration in the Southeastern United States: U.S. Geological Survey data release, [https://doi.org/10.5066/F7P26WN2]. Grand, James B. and Kevin J. Kleiner. 2016. Prioritizing Landscapes for Longleaf Pine Conservation. Report provided by the Cooperative Fish and Wildlife Research Unit Program under agreement with the U.S. Fish and Wildlife Service. U.S. Department of Interior, Fish and Wildlife Service, Cooperator Science Series FWS/CSS-119-2016, National Conservation Training Center. [https://digitalmedia.fws.gov/digital/collection/document/id/2131]. Open Pine Decision Support Tool. [https://scagulf.shinyapps.io/opdst/]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].

Factsheet: https://tukmangeospatial.egnyte.com/dl/Mkqw33JUoi The San Luis Obispo County enhanced lifeform map is a 30-class land use and land cover map of San Luis Obispo County, reflecting the state of the landscape in summer, 2022. The enhanced lifeform map is a draft data product and will be updated and finalized when the fine scale vegetation map is released. The enhanced lifeform map is a foundational input to the fine scale vegetation map. The fine scale vegetation map will have significantly more floristic detail than the enhanced lifeform map; woody vegetation communities in the fine scale map will be mapped to the alliance level of the National Vegetation Classification.Table 1 shows the formats available and the links for downloading the draft enhanced lifeform map.The draft enhanced lifeform map has 30 classes, which are shown in table 2, along with the mapped acreage of each class and a brief description of the mapping rules for each class. The map includes 127,115 polygons.Table 1. Draft enhanced lifeform dataset availability for San Luis Obispo County DescriptionLinkFile GDB Feature Classhttps://vegmap.press/slo_elf_fgdbArcGIS Pro Layer Packagehttps://vegmap.press/slo_elf_layer_pkg Table 2. Draft enhanced lifeform classes and acreages, San Luis Obispo CountyClassDescriptionAcresAlkali Grasses and ForbsAreas where interior alkali herbaceous vegetation is at least 10% absolute cover; absolute tree and shrub cover is less than 10%2,088Aquatic VegetationFreshwater stands dominated by aquatic, floating or submerged plants25Barren and Sparsely VegetatedAreas where shrub, forest, and herbaceous cover are each less than 10% absolute cover and the area is best characterized as bare land15,127Deciduous HardwoodAreas where tree species are at least 10% absolute cover; hardwoods either: strongly dominate the tree canopy (>70% relative tree cover when with redwood or doug-fir) or co-dominate (>40% relative tree cover with pines or cypress); deciduous hardwoods dominate hardwood cover (>50% relative hardwood cover)139,463DevelopedManmade developed areas greater than 0.2 acres; areas include irrigated lawns, heavily landscaped garden and patio areas, bocce courts, tennis courts, sport courts, developed horse riding arenas, baseball fields, soccer fields, golf courses, swimming pools, and playground areas41,823EucalyptusAreas where tree species are at least 10% absolute cover and Eucalyptus spp. dominates tree cover (>50% relative tree cover)3,693Evergreen HardwoodAreas where tree species are at least 10% absolute cover; hardwoods either: strongly dominate the tree canopy (>70% relative tree cover when with redwood or doug-fir) or co-dominate (>40% relative tree cover with pines and cypress); evergreen hardwoods dominate hardwood cover (>50% relative hardwood cover)217,613Freshwater Herbaceous WetlandAreas that are depressional, wet all year long, and exhibit obvious herbaceous wetland vegetation.; absolute tree and shrub cover are both less than 10%4,450HerbaceousAreas where upland herbaceous vegetation is at least 10% absolute cover; absolute tree and shrub cover is less than 10%945,147Intensively Managed HayfieldArea is an intensively managed hayfield that is mechanically turned over every year3,835Irrigated PastureArea is an irrigated pasture7,292Major RoadArea is a major road3,675Non-native ForestAreas where tree species are at least 10% absolute cover; tree cover dominated by ornamental non-native species (>50% relative tree cover)4,184Non-native HerbaceousAreas where herbaceous vegetation is at least 10% absolute cover; non-native herbaceous species dominate the herbaceous stratum; absolute tree and shrub cover are both less than 10%781Non-native ShrubAreas where shrub species are at least 10% absolute cover; absolute tree cover is less than 10%; relative shrub cover is dominated by non-native species313Nursery or Ornamental Horticulture AreaArea is a nursery or horticultural area142Orchard or GroveArea is an orchard or grove of fruit or nut trees14,491Pine/CypressAreas where tree species are at least 10% absolute cover; native pine (gray pine, knobcone pine, ponderosa pine, and native Monterey pine) and native cypress species strongly dominate tree cover (>60% relative tree cover)56,679Postfire ForestArea that was identified as a forest lifeform in NAIP imagery that has burned recently enough or at a high, canopy destroying severity that it prevents photo interpreters from confidently identifying the vegetation community in 2022 NAIP imagery. This class is limited to areas that burned in 2019 to 2022.1,410Postfire ShrubArea that was identified as a shrub lifeform in NAIP imagery that has burned recently enough or at a high enough severity that it prevents photo interpreters from confidently identifying the vegetation community in 2022 NAIP imagery. This class is limited to areas that burned in 2019 to 2022.4,032Redwood/Doug FirAreas where tree species are at least 10% absolute cover; Douglas fir and/or Redwood co-dominate or dominate tree cover (>30% relative tree cover)19Riparian ForestAreas where tree species are at least 10% absolute cover; obligate riparian tree genera (alder, willow, cottonwood, ash, sycamore) dominate tree cover (>50% relative tree cover)14,802Riparian ShrubAreas where woody riparian shrub species are at least 10% absolute cover; obligate riparian genera (e.g., shrubby willow trees) dominate shrub cover (>50% relative shrub cover)9,006Row CropsAreas that are either active annual or perennial row crops or are tilled and prepped for planting of row crops or are in between plantings. Row crops include annual crops like lettuce, spinach, corn, etc. and perennial crops such as strawberries, raspberries, lavender, or actively managed Christmas tree farms. Temporary greenhouses should be classified as row crops.36,112Salt MarshSalt marsh areas dominated by salt-tolerant wetland species524ShrubArea where native upland woody shrubs are at least 10% absolute cover; absolute tree cover is less than 10%559,758Tidal MudflatAreas of mudflat (<10% vegetated cover) in the intertidal zone of salt marshes84True FirAreas where tree species are at least 10% absolute cover; Santa Lucia fir co-dominate or dominate tree cover (>30% relative tree cover)43VineyardArea is a vineyard50,041WaterWater covers the area15,027 Total:2,151,679Methods:The San Luis Obispo County enhanced lifeform map was created using "expert systems" rulesets developed in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object- based image classification techniques in a process called “Object Based Image Analysis (OBIA)”. In contrast with the machine learning approaches that will be used to make San Luis Obispo County’s fine scale vegetation map, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key input datasets for the enhanced lifeform ruleset include: National Aerial Imagery Program (NAIP) .6 meter, 4-band imagery (2022), a LiDAR derived Canopy Height Model (various dates), and other LiDAR derived landscape metrics.After it is produced using Ecognition, the preliminary enhanced lifeform map is manually edited by expert photointerpreters. Manual editing fixes errors where the automated methods produced incorrect results. Edits are made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.Minimum Mapping Units:Table 3 shows the minimum mapping units (MMUs) for San Luis Obispo County’s draft enhanced lifeform map.Table 3. Minimum mapping units by feature type – San Luis Obispo County Draft Enhanced LifeformFeature TypeMinimum Mapping UnitAgricultural Classes1/4 AcreUpland Woody Vegetation1/2 AcreRiparian Woody Vegetation1/4 AcreUpland Herbaceous Classes1/2 AcreWetland Herbaceous Classes1/4 AcreBare Land1/2 AcreDeveloped1/5 AcreWater400 square feetRelated Datasets:The impervious surface map, a separate product, will provide very detailed delineations of impervious surfaces, with a minimum mapping unit of 500 square feet (200 for buildings). Impervious surfaces will be mapped using the following classes:BuildingsDirt and Gravel RoadsPaved RoadsOther PavedOther Dirt/GravelMappers:The vegetation mapping team for this enhanced lifeform map included the following mappers:Laura AskimBrittany BurnettEddie FitzsimmonsBrian KrebElliot KuskulisDylan LoudonTyler McCarthyJulia MurphyMark Tukman

This dataset describes the type and location of trees located in Ballarat.The information was collected as the tree were registered.The intended use of the information is to inform the public of the locations of Ballarat's trees.This dataset is typically updated monthly on an automated schedule.

This data shows the locations and conditions of White Pine trees in Madison Borough, New Jersey. Using data from the Town of Madison & NJDEPB GIS (2022), it categorizes the trees' conditions and explores their relationship to nearby open spaces. This helps in assessing how environmental factors influence the health of White Pines in the area. From the data, we can see that:

1. Good Condition (2 trees):

   • Both trees are located in the eastern part of Madison Borough.
   • One is near the border with Florham Park Borough.
   • The other is close to the border with Chatham Borough.
   • Both are in areas with a lower percentage of open space (lighter green or white areas).

2. Fair Condition (51 trees):

   • These constitute the majority of the White Pines.
   • They are widely distributed throughout Madison Borough.
   • Some are in areas with a high percentage of open space (dark green), particularly in the western and eastern parts of the borough.
   • Others are in more urbanized areas (white areas on the map).

3. Poor Condition (17 trees):

   • Most are clustered in the southern part of Madison Borough, near Garfield Avenue.
   • This area seems to have less open space compared to other parts of the borough.
   • A few are also scattered in the northern and eastern parts.

4. Dead Trees (2 trees):

   • One is located in the western part of Madison Borough, in an area with a high percentage of open space.
   • The other is in the central part, in an area with less open space.

5. Vacancy (1 tree):

   • Located in the western part of Madison Borough, near the border with Harding Township.
   • It's in an area with a high percentage of open space.

Overall, this analysis suggests that: - Trees in good condition are found in more urbanized areas, possibly indicating better care or more favorable conditions. - Most fair condition trees are distributed throughout, suggesting adaptability to various environments. - Poor-condition trees are concentrated in the southern, more urbanized part, possibly due to environmental stress or less care. - The presence of dead trees and vacancies in high open space areas could indicate natural causes or lack of management in these less developed zones.

This information can guide urban forestry management, helping prioritize areas for tree care, replacement, or new plantings based on tree health and environmental factors like open space availability.

This publication includes a black and white georeferenced 1903 map of Cooper River holdings of the E.P. Burton Company from the publication “Working plan for forest lands in Berkeley County, South Carolina”. The map includes the area of the Santee Experimental Forest.The 1903 map of Cooper River holdings shows the stock of pine and cypress trees in stands within and around the Santee Experimental Forest.Original map is currently archived at the National Archives in Atlanta, Georgia.

Change classes for total basal area, total aboveground biomass, and species diversity across the plots between 2004 and 2011, where 2004 and 2011 are the centroid-year of first (1997–2010) and last (2003–2018) inventory intervals, respectively (N = 1,432).

Change classes for basal area ratio, aboveground biomass ratio, basal area coefficient of variation and aboveground biomass coefficient of variation of longleaf pine across the plots between 2004 and 2011, where 2004 and 2011 are the centroid-year of first (1997–2010) and last (2003–2018) inventory intervals, respectively (N = 1,432).

Probability and uncertainty maps showing the potential and realized distribution for the Scots pine (Pinus sylvestris, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:

• 2000 - 2002,
• 2002 - 2006,
• 2006 - 2010,
• 2010 - 2014,
• 2014 - 2018,
• 2018 - 2020.

Files are named according to the following naming convention, e.g:

• veg_pinus.sylvestris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3

with the following fields:

• theme: e.g. veg,
• species code: e.g. pinus.sylvestris,
• species distribution type: e.g. anv (= actual natural vegetation),
• species estimation method: e.g. eml,
• species estimation type: e.g. md ( = model deviation),
• resolution in meters e.g. 30m,
• reference depths (vertical dimension): e.g. 0..0cm,
• reference period begin end: e.g. 2000..2002,
• reference area: e.g. eumap,
• coordinate system: e.g. epsg3035,
• data set version: e.g. v0.3.

For each species is then easy to identify probability and uncertainty distribution maps:

• veg_pinus.sylvestris_anv.eml_md: model uncertainty for realized distribution
• veg_pinus.sylvestris_anv.eml_p: probability for realized distribution
• veg_pinus.sylvestris_pnv.eml_md: model uncertainty for potential distribution
• veg_pinus.sylvestris_pnv.eml_p: probability for potential distribution

Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format.

If you would like to know more about the creation of the maps and the modeling:

• watch the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL)
• access the repository with our R/Python scripts and follow the instructions (GitLab)
• access the repository with the training dataset (Zenodo)
• read the tutorial with executable code on our GitBook

A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on PeerJ. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.

Most of the central New England landscape was cleared for agriculture in the mid-19th century and then naturally reforested into "secondary forests" with the abandonment of agricultural land. Some sites, often poorly drained, remained forested, but were usually subjected to intensive fuelwood cutting or logging and are termed "primary forests." The Hemlock Woodlot was never cleared for agriculture, but has a history of cutting and natural disturbance. The hemlock woodlot is located in the center of Harvard Forest's Prospect Hill Tract, adjacent to a spruce-blackgum swamp. Soils are moist and rocky, with a thick organic layer. Hemlock dominates tree species composition (62% by basal area), with hardwoods and scattered large white pine comprising the remainder. Most of the trees are 100-150 years old, with a few hemlock trees up to 230 years old. While the site was never cleared for agriculture, it was logged several times and chestnut blight removed a chestnut-dominated overstory in the 1910s. The 0.72 ha stem-mapped plot is at the center of a 4-ha hemlock-dominated forest. This plot serves as a major reference site and is part of a network of hemlock forests that are being intensively sampled as the hemlock woolly adelgid arrives.

Scion’s spatial projections of the height productivity of radiata pine (Pinus radiata) developed at a 25 m resolution. Height productivity is displayed as Site Index (units are metres) which is the mean top height at age 20 years, where mean top height is defined as the mean height of the 100 largest trees, by diameter. Using a national dataset (n = 3,676 plots) a regression kriging model (multiple regression and kriging of the residuals) was used to predict Site Index from environmental surfaces and this model was used to produce the displayed map. A validation undertaken on a test dataset not used for model fitting showed the model of Site Index has a coefficient of determination (_R_2) of 0.80, an RMSE of 2.08 m, and a percentage RMSE of 6.9%. A detailed description of the modelling methods and results is given in Watt et al. (2021). The displayed spatial projections masked large lakes and a forest located in the central North Island.

We are grateful to the forestry companies with radiata pine permanent sample plots who granted permission to use this dataset for constructing the displayed map. The New Zealand Strategic Science Investment Fund (SSIF) was used to fund this project.

The presented surface is intended only as a guide for afforestation and growers should take into account the impact of microsite when choosing an appropriate species. These surfaces were derived from permanent sample plot data, which typically comprise plots of well managed trees, that do not have any unstocked areas and are sited away from exposed ridges. Consequently, we recommend that presented values of Site Index are reduced by 15% when applied to standard forestry sites.

Watt, M.S., Palmer, D.J., Leonardo, E.M.C, Bombrun, M. (2021) Use of advanced modelling methods to estimate radiata pine productivity indices. Forest Ecology and Management, 479, 118557.