The Oregon Department of Forestry has three programs that are outlined by district boundaries. Use this hosted feature layer to display one or more in webmaps. Current June 7, 2019.Contact:Steve TimbrookGIS Data AdministratorAdministrative BranchInformation Technology Program - GIS UnitOregon Department of Forestrysteve.timbrook@odf.oregon.gov503.931.2755

The Oregon Department of Forestry's (ODF) GIS goal is to support the stewardship of Oregon's forests through the acquisition, analysis, distribution and display of geographic information. We are using ArcGIS Online as tool to help our state agency upload, collaborate, and expose geospatial data online. ODF was established in 1911. It is under the direction of the State Forester who is appointed by the State Board of Forestry. The statutes direct the state forester to act on all matters pertaining to forestry, including collecting and sharing information about the conditions of Oregon's forests, protecting forestlands and conserving forest resources.Our Agency tasks include: Fire protection for 16 million acres of private, state and federal forests.Regulation of forest practices (under the Oregon Forest Practices Act) and promotion of forest stewardship.The implementation of the Oregon Plan for Salmon and Watersheds.Detection and control of harmful forest insect pests and forest tree diseases on 12 million acres of state and private lands.Management of 818,800 acres of state-owned forestlands.Forestry assistance to Oregon's 166,000 non-industrial private woodland owners.Forest resource planning.Community and urban forestry assistance.Contact:Contact:Steve TimbrookGIS Data AdministratorAdministrative BranchInformation Technology Program - GIS UnitOregon Department of Forestrysteve.timbrook@odf.oregon.gov503.931.2755

An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.Downloads available: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Administrative+Forest+Boundaries

Overview

ORS 477.490 requires Oregon Sate University (OSU) and the Oregon Department of Forestry (ODF) to develop a statewide wildland-urban interface (WUI) map that will be used in conjunction with the statewide wildfire hazard map (ORS 477.490) by the Oregon State Fire Marshal to determine on which properties defensible space standards apply (ORS 476.392) and by the Building Codes Division to determine to which structures home hardening building codes apply (ORS 455.612).

Rules directing development of the WUI are listed in OAR-629-044-1011 and 629-044-1016. A comprehensive description of datasets and geospatial processing is available at https://hazardmap.forestry.oregonstate.edu/understand-map. The official statewide WUI map is available on the Oregon Wildfire Risk Explorer at https://tools.oregonexplorer.info/viewer/wildfire.

Following is an overview of the data and methods used develop the statewide WUI map.

Wildland-Urban Interface

Creating a statewide map of the WUI involved two general steps. First, we determined which parts of Oregon met the minimum building density requirements to be classified as WUI. Second, for those areas that met the minimum building density threshold, we evaluated the amount and proximity of wildland or vegetative fuels. Following is a summary of geospatial tasks used to create the WUI.

1. Develop a potential WUI map of all areas that meet the minimum density of structures and other human development - According to OAR 629-044-1011, the boundary of Oregon’s WUI is defined in part as areas with a minimum building density of one building per 40 acres, the same threshold defined in the federal register (Executive Order 13728, 2016), and any area within an Urban Growth Boundary (UGB) regardless of the building density. Step One characterizes all the locations in Oregon that could be considered for inclusion in the WUI on building density and UGB extent alone. The result of Step One was a map of potential WUI which was then further refined into final WUI map based on fuels density and proximity in Step Two.

   1. Compile statewide tax lots.

   2. Map all eligible structures and other human development.

   3. Simplify structure dataset to no more than one structure per tax lot

   4. Calculate structure density and identify all areas with greater than one structure per 40 acres

   5. Add urban growth boundaries to all the areas that meet the density requirements from the previous step.

2. Classify WUI based on amount and proximity of fuel. The WUI is also defined by the density and proximity of wildland and vegetative fuels (“fuels”). By including density and proximity of fuels in the definition of the WUI, the urban core is excluded, and the focus is placed on those areas with sufficient building density and sufficient fuels to facilitate a WUI conflagration. Consistent with national standards, we further classified the WUI into three general classes to inform effective risk management strategies. The following describes how we refined the potential WUI output from step one into the final WUI map.

   1. Intermix WUI: Areas that met the minimum building density threshold in step one and which had at least 50% vegetative or wildland fuel cover were classified as Intermix WUI

   2. Interface WUI: Interface WUI includes areas that met the minimum building density threshold in step one, and which had less than 50% vegetative and/or wildland fuel cover but were within 1.5 miles of a large patch (≥ 2 sq. miles) of at least 75% vegetation and/or wildland fuels

   3. Occluded WUI includes areas that met the minimum building density threshold in step one, and which had less than 50% vegetative and/or wildland fuel cover but were within 1.5 miles of a moderate patch (1 – 2 sq. miles) of at least 75% vegetation and/or wildland fuels.

Detailed geospatial processing steps are described in the technical guide available at https://hazardmap.forestry.oregonstate.edu/understand-map

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset consists of repeat vegetation cover maps of multiple Willamette River restoration sites where restoration activities were implemented to increase the area of floodplain forests. Beginning in the early 21st century, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats for native fish (Keith and others, 2022). For much of the Willamette River floodplain, direct enhancement of floodplain habitats through restoration activities is needed because the underlying hydrologic, geomorphic, and vegetation processes that historically created and sustained complex floodplain habitats have been fundamentally altered by dam construction, bank protection, large wood removal, land conversion, and other influences (for example, Hulse and others, 2002; Wallick and others, 2013). Floodplain forest vegetation cover was derived from R Random Forest classification of 2009, 2011, 2018, and 2020 aerial imagery at three large-scale floodplain planting restoration sites along the Willamette River: Harkens Lake (river kilometer [RKM] 153-154.5), Snag Boat Bend (RKM 144-147), and Luckiamute State Natural Area (RKM 108-111). The overall goals and approaches for the repeat mapping are based on a previously published effectiveness monitoring framework for Willamette River restoration activities (Keith and others, 2022). The repeat mapping datasets include GIS layers defining two classes of vegetation cover (forest and not-forest, condensed from six cover classes: forest, not-forest (agriculture), not-forest (other), water, shadow in forest, and shadow in non-forested areas). This mapping can be used to support an assessment of changes to floodplain forest vegetation cover at sites along the Willamette River floodplain where restoration activities were implemented from 2012 to 2020 to increase the area of native floodplain forest vegetation.

Forest resources in Washington and Oregon were surveyed in the 1930s by employees of the USDA Forest Service, Pacific Northwest Forest Experiment Station. As part of this process, forest cover maps were created on paper at an original scale of 1:253,440. Forest and land cover types recorded include classifications such as: agricultural, balsam fir mountain hemlock, cedar-redwood, deforested burns, Douglas-fir, hardwood, juniper, lodgepole pine, non-forest pine mix, ponderosa pine, recent cutover, spruce-hemlock, subalpine and non-commercial, water, etc. An additional subcategory classification is also provided which gives additional insight into tree size classes for conifers or species group for hardwoods. These forest and land cover types are provided as both a shapefile and geopackage for Washington and Oregon combined.The 1928 McSweeney-McNary Forestry Research Act (P.L. 70-466, 45 Stat. 699-702) directed the Secretary of Agriculture to make and keep current a comprehensive inventory and analysis of the nation's forest resources. The decision was made to begin the nationwide survey with the Douglas-fir region and shortly thereafter to expand to the other forested lands of Washington and Oregon. Surveys were conducted between 1930 and 1936. Results of these surveys were reported in many formats including quarter state maps (4 maps per state) as well as many printed reports.The history of this project and copies of some of the early results as well, were published in Harrington (2003) which included a CD with a digital map (an ArcView GIS shapefile) for all of Washington and Oregon.

This is a dataset download, not a document. The Open Document button will start the download.This data layer is an element of the Oregon GIS Framework. This data layer represents the Existing Vegetation data element. This statewide grid was created by combining four independently-generated datasets: one for western Oregon (USGS zones 2 and 7), and two for eastern Oregon (USGS zones 8 and 9; forested and non-forested lands), and selected wetland types from the Oregon Wetlands geodatabase. The landcover grid for zones 2 and 7 was produced using a modification of Breiman's Random Forest classifier to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to build two predictive models for the forested landcover classes, and the nonforested landcover classes. The grids resulting from the models were then modified to improve the distribution of the following classes: volcanic systems and wetland vegetation. Along the eastern edge, the sagebrush systems were modified to help match with the map for the adjacent region. Additional classes were then layered on top of the modified models from other sources. These include disturbed classes (harvested and burned), cliffs, riparian, and NLCD's developed, agriculture, and water classes. A validation for forest classes was performed on a withheld of the sample data to assess model performance. Due to data limitations, the nonforest classes were evaluated using the same data that were used to build the original nonforest model. Two independent grids were combined to map landcover in adjacent zones 8 and 9. Tree canopy greater than 10% (from NLCD 2001), complemented with a disturbance grid, served as a mask to delineate forested areas. A grid of non-forested areas was extracted from a larger, regional grid (Sagemap) created using decision tree classifier and other techniques. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another and mosaicked. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. The portion of this original grid corresponding to USGS map zones 8 and 9 was extracted and split into three mapping areas (one for USGS zone 8, two for USGS zone 9: Northern Basin and Range in the south, Blue Mountains in the north) and modified to improve the distribution of the following classes: cliffs, subalpine zone, dunes, lava flows, silver sagebrush, ash beds, playas, scabland, and riparian vegetation. Agriculture and urban areas were extracted from NLCD 2001. A forest grid was generated using Gradient Nearest Neighbor (GNN) imputation process. GNN uses multivariate gradient modeling to integrate data from regional grids of field plots with satellite imagery and mapped environmental data. A suite of fine-scale plot variables is imputed to each pixel in a digital map, and regional maps can be created for most of the same vegetation attributes available from the field plots. However, due to lack of sampling plots in the southern half of zone 9, the GNN model proved unreliable there; forest data from Landfire were used instead. To compensate for known under-representation of wetlands, selected wetland types from the Oregon Wetlands Geodatabase (version 2009-1030) were converted to raster and overlaid (replaced) pixel value assignments from the previous steps just detailed. See Process Steps for more information. The ecological systems were crosswalked to landcover (based on Oregon landcover standard, modified from NLCD 2001) and to wildlife habitats (based on integrated habitats used in the Oreg

This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.

Oregon Ownership and Admin Boundaries managed by Oregon Department of Forestry (2021).This includes Public Ownership, Counties, ODF Forest Protection Districts, and ODF Units, for the entire State of Oregon. This is only an export of the master data and is not updated on a regular schedule. Please see the source data to ensure accuracy and ensure it is up to date. Last updated on 7/11/2021, BRM..Useful Links:www.oregon.gov/odfhttps://www.oregon.gov/ODF/AboutODF/Pages/MapsData

Wilderness areas are federally-owned public lands managed by the federal government through four agencies, the Bureau of Land Management, Fish and Wildlife Service, Forest Service, and National Park Service. When the National Wilderness Preservation System started in 1964, only 54 wilderness areas were included. Since then, the system has grown nearly every year to include more than 800. The time component of this service is based on the year in which the wilderness was originally designated (additions may have occurred in subsequent years). Overall, however, only about 5% of the entire United States—an area slightly larger than the state of California— is protected as wilderness. Because Alaska contains just over half of America's wilderness, only about 2.7% of the contiguous United States—an area about the size of Minnesota—is protected as wilderness. To learn more about wilderness areas, visit Wilderness Connect, the authoritative source for wilderness information online. Wilderness Connect also publishes two other map resources:An interactive wilderness map allows visitors to search for and explore all wilderness areas in the United States. Fact-filled storymaps on the benefits of wilderness illustrate how wilderness protects values including clean water, wildlife habitat, nearby recreation, cultural sites and more.

Although wilderness areas are federally-owned, some areas contain non-federal parcels within their boundaries. Non-federal lands within some wilderness areas are included as part of this feature dataset as a separate layer. Termed inholdings or edgeholdings, these lands are privately-owned or owned by local governments, state governments or Indigenous Nations. Hundreds of inholdings and edgeholdings exist across the wilderness system. Generally, however, they are small compared to the size of the wilderness itself. Since the rules and regulations that apply to wilderness areas do not apply to these non-federally-owned parcels, it is important for wilderness visitors to know their location to avoid trespassing where access is not allowed. The owners of inholdings and edgeholdings can develop these parcels (as long as developments do not affect the character of the surrounding wilderness lands) and they retain special and limited access to them, sometimes, but not always, by motorized means.

The purpose of this map is to assist in wildland fire fighting activities. The data that is displayed is to help locate important features for fire fighting and navigate difficult back country roads and trails. This map contains Lookouts, Summits, Fire Stations, Feature Points, ODF Offices, ODF Communication Sites, Railroad Mileposts, Highway Mileposts, Gates, Water Source, Springs, Electric Transmission Lines, Railroads, Roads, Trails, Streams, Protection Boundaries- Unit and District, USFS District Lines, Structural Fire Protection District, Statewide Waterbodies, Scenic Waterways, Rangeland Protection Associations, Counties, Townships, Sections, Wilderness, Land Management and City Limits .This map is published as a map service (with hillshade), raster tile package (with hillshade), and vector tile package (without hillshade).

Fire perimeters 2000-2024. The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the final fire perimeters datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, and CalFire. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.2000-2023 fire perimeters were sourced from “InterAgencyFirePerimeterHistory All Years View” and 2024 fire perimeters were sourced from “WFIGS Interagency Fire Perimeters”, both of which are hosted on NIFC. This layer has been clipped to contain all fires that partially or completely occurred in Oregon and restricted to fires with a discovery date on or after 1/1/2000 for use in the SageCon Landscape Planning Tool on Oregon Explorer. QA/QC was performed to eliminate duplicate polygons based on incident names, however, some duplicate records may exist in the dataset because some fires had multiple incident names. The attributes table has been condensed to Incident name, polygon source, fire year, and GIS acres for simplicity.

This data set is obtained from the Forestry Group at Oregon State and it is an input to the analysis presented in this data set. Digital GNN imputation maps are provided as 30-m-resolution ArcGIS grids, where the grid value is a unique plot number that links to the plot database. Selected vegetation variables from the plot database are joined as items in the grid to facilitate viewing and exploratory spatial analysis. Metadata for the vegetation variables are included with the grids and in the plot database. Dates for maps developed from GNN species-size models are determined by the vintage of the satellite imagery used in their development.

The Private Forest Accords Steep Slope data includes 3 layers, which are available for download:1) Debris Flow Traversal Areas with the highest 20% shown by red channels and highest 50-20% displayed as orange channels (symbology generated on the Trav_Prop field, with highest 20% categorized from 0.8-1.0 and highest 50-20% categorized from 0.5-0.8).2) Debris Flow Traversal Area Subbasins, which circumscribe the highest 20% Debris Flow Traversal Areas (dashed line).3) Designated Sediment Source Areas, which are within each subbasin and display the highest 33% of Sediment Source Areas greater than ¼ acre in size. These are indicated as blue and coral polygons (symbolized on the TriggerSource field). The coral polygons indicate the Designated Sediment Source Areas that include Trigger Sources (TriggerSource field = True). Blue polygons indicate areas without Trigger Sources (TriggerSource field = False).

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework. Seed zones for Alaska Yellow Cedar, Cottonwood, Douglas Fir, Engelmann Spruce, Grand Fir, Incense Cedar, Jeffrey Pine, Lodgepole Pine, Noble Fir, Pacific Silver Fir, Pacific Yew, Ponderosa Pine, Port Orford Cedar, Red Alder, Sitka Spruce, Sugar Pine, Western Hemlock, Western Red Cedar, Western White Pine, and basic zones for other species in Oregon. Zones are based on genetic variation patterns obtained by evaluating genotypes of trees from locations in the region. Wherever possible, the zone lines follow natural boundaries such as crests of mountain ranges, ridge tops, rivers -or physical boundaries, such as highways and railroads, since it would be impossible for cone collectors to recognize any other type of boundary.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

description: FIRE1850_POLY: One of a series of four maps showing the state of forests in the northern coastal area of Oregon. They show the change in stand age over time due to fires. This dataset shows conditions in 1850.; abstract: FIRE1850_POLY: One of a series of four maps showing the state of forests in the northern coastal area of Oregon. They show the change in stand age over time due to fires. This dataset shows conditions in 1850.

The Oregon Sand Dunes are the largest expanse of coastal sand dunes in North America. The Oregon Dunes National Recreation Area is located on the Oregon Coast, stretching approximately 40 miles north from the Coos River in North Bend, to the Siuslaw River, in Florence. The NRA is part of Siuslaw National Forest and is administered by the United States Forest Service. The Oregon Dunes are featured in Stories in Stone - Geologic Resources of Our National Forests and this map was created to support that project. The app allows you to explore the area using either aerial photography, LiDAR imagery or both.