DDOT's Urban Forestry Division (UFD) is the primary steward of Washington DC's ~175,000 public trees and has a mission of keeping this resource healthy, safe, & growing. Trees in the city are critical to our well-being. Visit trees.dc.gov for more information.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and NRM Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Data for each individual unit must be verified and proved consistent with the published MVUMs prior to publication.The Forest Service's Natural Resource Manager (NRM) Infrastructure (Infra) is the agency standard for managing and reporting information about inventory of constructed features and land units as well as the permits sold to the general public and to partners. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_MVUM_01/MapServer/1 Metadata For complete information, please visit https://data.gov.

Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:

• NetworkID: A unique ID for each connected road network.
• SegLenM: The length of the road segment (in meters).
• NetLenM: The length of the connected road network (in meters).
• Month: The road segment opening month.
• Year: The road segment opening year.
• MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.

Additional information

• More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
• Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
• The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
• The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380" target="_blank" rel="noopener">https://doi.org/10.1016/j.rse.2024.114380

Citation

Please cite the following when referring to this dataset:

Record of self-reported stewardship activity on DPR trees performed by members of the public. This dataset can be joined to the Forestry Tree Points dataset (https://data.cityofnewyork.us/Environment/Forestry-Tree-Points/hn5i-inap/data) by joining the TreeId from this dataset to OBJECTID from Forestry Tree Points.

This map layer shows the forestry management zones established by the Columbus Recreation and Parks Department, Urban Forestry program. The forestry management zones are based upon static geographical features (rivers and major highways) or the centerline of prominent streets. In cases where a waterway is used as a border, the border is the center line of respective waterway or the centerline of a branch of the waterway located to the appropriate side of an island within the waterway. In the case where a streets is used as a border, the border is the centerline of the street. For a street with medians, the border is standardized along the same side of the medians along the entire length of the street. All exterior borders conform to the City of Columbus (OH) corporate boundary, Some interior forestry zone borders conform to the borders of adjoining cities and townships.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only trails with the symbol value of 5-12, 16, 17 are Forest Service System trails and contain data concerning their availability for motorized use. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the EDW. Click this link for full metadata description: Metadata _This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

A public app to view the status of forestry logging roads.Web map: https://soa-dnr.maps.arcgis.com/home/item.html?id=42ff431fdeb24c8cb2d111f706137061(Map owned by Andrew Allaby)

Trees captured in 1999. Contract NCPC 93-02. This document describes the planimetric map production for the 350 tiles located in Washington DC and the surrounding states of MD and VA.

The locations of right of way trees maintained by the City of Madison Urban Forestry Department. Data is used to coordinate trimming and maintenance of trees as well as aiding in planning public works construction projects. NOTE: Data last updated 03/26/2025Field descriptions are as follows:• Species Common Name – Common species name• Species Scientific Name – Scientific species name• Notes – Tree/Site notes• Inventory Date – Last inventory date• Last Inspection Date – Last inspection date• Site ID – Unique ID for planting site• Growth Space Size – Available growth area around site• Tree Diameter – Diameter breast height (DBH)• Status – Site active yes/no• Treeguard Present – Treeguard present yes/no• Grate Size – Size of grate around tree, if present

Current number of times a given tree has been marked as a favorite by registered users of the NYC Street Tree Map (nyc.gov/parks/treemap).

This dataset can be joined to the Forestry Tree Points dataset (https://data.cityofnewyork.us/Environment/Forestry-Tree-Points/k5ta-2trh) by joining the TreeId to OBJECTID from Forestry Tree Points.

Live data feed: https://www.nycgovparks.org/tree-map-feeds/favorite-trees.json

This dataset does not represent all trees in Raleigh. The data is updated weekly by Urban Forestry staff using the ArcGIS Field Maps application.This layer has a filter where type =/ duplicate, to remove any unnecessary data.Fields shown in this Open Data layer:- Street Name- Common Name- Diameter

A map service depicting Forest Service roads and trails that are designated for motor vehicle use under the official U.S. Government Code of Federal Regulations for identifying designated roads and trails (36 CFR 212.56). Road and Trail MVUM. The difference between MVUM_01 and MVUM_02 is that MVUM_02 has trails and roads in the MVUM Symbology group labeled while MVUM_01 does not. Additional roads, such as highways, county roads or public roads, are included for mapping purposes. This map service shows the specific types of motorized vehicles allowed on the designated routes and their seasons of use. Data used in this map service are designed to be consistent with the MVUM (Motor Vehicle Use Map). The road and trail data are compiled from the GIS Data Dictionary data and Infra tabular data that the U.S. Forest Service administrative units have prepared for the creation of their MVUMs. This data is published and refreshed on a unit by unit basis as needed and approved by the individual units in order to stay in sync and consistent with the published MVUMs. Only roads with the symbol value of 1,2,3, 4, 11, 12 are Forest Service System roads and contain data concerning their availability for OHV use. Only trails with the symbol value of 5-12, 16, 1. are Forest Service System trails and contain data concerning their availability for motorized use.�Metadata and Downloads

Information about roads under MNRF jurisdiction may fall within the responsibility of several different ministry branches and including: * Forests Division * Parks, Lands & Waters Branch * Resource access roads The dataset also contains roads not under the jurisdiction of the MNRF like municipal roads and provincial highways. These are sourced from the Ontario Road Network. *[MNRF]: Ministry of Natural Resources and Forestry

Forest resource roads (FRR) are actively used for accessing forest resources. FRRs are tracked and managed in a road management database. These roads do not represent a complete road network, they should be viewed in conjunction with the Roads -50k - Canvec dataset. Many FRRs are gated with controlled access. There are four categories of roads in the dataset: Forest Resource Roads (act); Forest Resource Access (non-act), Public Access (non-act) and auxiliary roads: Forest resources road (Act): a road constructed, modified or maintained for the purpose of providing access for forest resources harvesting or management of forest resources that is authorized under subsection 32(1) of the Act, or a road designated as forest resources road under section 73 of the Regulation. These roads were constructed or designated after the Forest Resources Act and Regulation were enacted. Forest resource access (non-Act): a road constructed, modified or maintained for the purpose of providing access for forest resources harvesting or management of forest resources that is considered a capital investment by the Government of Yukon, Department of Energy, Mines and Resources, Forest Management Branch. These roads were constructed prior to the enactment of the Forest Resources Act. Public Access (non act): a pre-existing public road. In this road network all Public Access is part of the forest resource access network. Auxiliary access: a variety of access types that do not conform to the above descriptions and may include: research forest trails, skid trails, heritage trails, trails that access forest resources. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

The National Forest Inventory (NFI) woodland map covers all forest and woodland area over 0.5 hectare with a minimum of 20% canopy cover, or the potential to achieve it, and a minimum width of 20 metres. This includes areas of new planting, clearfell, windblow and restock. The woodland map excludes all 'tarmac' roads and active railways, and forest roads, rivers and powerlines where the gap in the woodland is greater than 20 meters wide.All woodland (both urban and rural), regardless of ownership, is 0.5 hectare or greater in extent, with the exception of Assumed woodland or Low density areas that can be 0.1 hectare or greater in extent. Also, in the case of woodland areas that cross the countries borders, the minimum size restriction does not apply if the overall area complies with the minimum size.Woodland less than 0.5 hectare in extent, with the expectation of the areas above, will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features.The woodland map is updated on an annual basis and the changes in the woodland boundaries use the Ordnance Survey MasterMap® (OSMM) as a reference where appropriated.The changes in the canopy cover have been identified on:Sentinel 2 imagery taken during spring/summer 2023 or colour aerial orthophotographic imagery available at the time of the assessment;New planting information for the financial year 2022/2023, from grant schemes and the sub-compartment database covering the estate of Forestry England, Forestry and Land Scotland and Natural Resources Wales;Woodland areas, greater than 0.5 hectares, are classified as an interpreted forest type (IFT) from aerial photography and satellite imagery. Non-woodland areas, open areas greater than 0.5 hectare completely surrounded by woodland are described according to open area types.IFT categories are Conifer, Broadleaved, Mixed mainly conifer, Mixed mainly broadleaved, Coppice, Coppice with standards, Shrub, Young trees, Felled, Ground prep, Cloud \ shadow, Uncertain, Low density, Assumed woodland, Failed, Windblow.IOA categories are Open water, Grassland, Agricultural land, Urban, Road, River, Powerline, Quarry, Bare area, Windfarm, Other vegetation.For further information regarding the interpreted forest types (IFT) and the interpreted open areas (IOA) please see NFI description of attributes available on www.forestresearch.gov.uk

1.     INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). 2.     FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023: Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by Remote Sensing. Correspondence regarding these data can be directed to: Sean Sloan Department of Geography, Vancouver Island University, Nanaimo, B.C, Canada sean.sloan@viu.ca;  ..., 1.     INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limi..., , # Satellite images and road-reference data for AI-based road mapping in Equatorial Asia

https://doi.org/10.5061/dryad.bvq83bkg7

1. INTRODUCTION For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).   2. FURTHER INFORMATION The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023:  Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by...

This dataset does not represent all trees in Raleigh. The data is updated weekly by Urban Forestry staff using the ArcGIS Field Maps application.This layer has a filter where type =/ duplicate, to remove any unnecessary data.Fields shown in this Open Data layer:- Street Name- Common Name- Diameter

A map service on the www depicting existing National Forest System Roads (NFSR) that are under the jurisdiction of the U.S. Forest Service. Each feature represents a segment of a road, along which all of the attributes are the same. A road is a motor vehicle travel way over 50 inches wide, unless classified and managed as a trail. National Forest System Roads Metadata Road and Trail data in the Enterprise Data Warehouse (EDW) are kept current by daily updates from forest SDE geodatabases located in NRM and VDC. The EDW map services data layers are kept current with the EDW data. If a road or trail is added one day in a forest SDE geodatabase, the next day it will show up in the SDE EDW publication layer and dynamic map services. This applies to the following map services:https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_RoadBasic_01/MapServerhttps://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_TrailNFSPublish_01/MapServer

The Map Service (WFS Group) presents data from the field of forestry: This dataset contains the road network in the state forest as well as participating municipal and private forests classified according to NavLog route classes.

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)