Bathymetric, topographic, and grain-size data were collected in May 2009 along a 33-mi reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach is located from river miles 29 to 62 at the confluence of the Colorado and Little Colorado Rivers. Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps.

Accurate evaluation of riparian forests depends on precise delineation of both bank to bank (active channel) and single-thread hydrography. Local land use and salmon recovery planners use hydrography as a reliable tool for understanding and managing watershed impacts across the state. Active channel mapping allows practitioners to delineate riparian zones, examine the shading effects of riparian vegetation, map the location, extent, and distribution of anadromous and resident fish as well as locate fish blocking culverts, map protective stream buffers, and accurately inventory existing hydrography (Hyatt et al, 2022).The manual provided in this package describes methods and procedures used to digitize active channel polygons from high resolution elevation data and high-resolution imagery. Methods like this have become necessary, as access to high resolution data has become easier. Included in this method is AC Tools, a Python script-based ArcGIS Pro Toolset that can be used to delineate channel bank and channel island contour lines along river mainstems and larger tributaries. Much of the method involves how to select those contours and create active channel polygons. Methods are also available for download at https://pspwa.box.com/s/3stokaav635odvd8k2dtkcigef5sbkr2Pilot results of this methodology were conducted in Stillaguamish, Queets, and the Entiat River, and are available at the Puget Sound Partnerships Spatial Data Hub.

Active Channel HydrographyThe “active channel” includes the wetted channels of rivers and streams as well as adjacent un-vegetated cobble and gravel bars that are inundated during high flows. In this method, the active channel is analogous to the “bankfull channel” (Leopold and Maddock 1953, Leopold et al 1964, Williams 1978) or the ordinary high-water mark line (OHWM), where the presence and action of waters are “so common and usual, and so long continued in ordinary years as to mark upon the soil or vegetation a character distinct from the abutting upland,”(WAC 220-660-030(111)). In places where this line cannot the delineated the ordinary high water line is delineated along the elevation of the mean annual flood for every three years.

There are many reasons for considering the boundary of the active channel network. A common use for delineating the active channel is to map the inner edge of the riparian zone (eg. Hyatt 2023). Riparian areas are transitional areas between land and aquatic ecosystems that include both lotic and lentic systems (Gregory et al, 1991). These zones can include the surface and subsurface water influences and human induced natural forces, understanding the active channel boundary thereby isn’t just important for managing fish populations and identifying habitat restoration sites, it is also important for land use planning and management.

Bathymetric, topographic, and grain-size data were collected from May 2013 to October 2016 along a 15-mi reach of the Colorado River in Glen Canyon National Recreation Area, Arizona by the U.S. Geological Survey Grand Canyon Monitoring and Research Center. The study reach is located from river miles -15 at the base of Glen Canyon Dam to 0 at Lees Ferry. Channel bathymetry was mapped using multibeam bathymetry collected in November 2014 and single beam bathymetry collected in February 2015, August 2015, February 2016, and June 2016. Subaerial topography was mapped using photogrammetry derived from aerial Imagery collected in May 2013 and ground-based total-stations collected in February 2015, October 2015, and October 2016. Bed-sediment grain-size data were collected in November 2014 using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps.

This dataset provides coral reef habitat mapping (detailed geomorphological zonation) and change detection maps spanning 2000-2011. First data output is: Oil spills monitored twice a week during 6 months (from July 1st to January 3rd 2011) leading to a total of 324 products (in netcdf/cdf formats along with png snapshots), including: Chlorophyll concentration, Low-Res Sea Surface Temperature, Hi-Res Sea Surface Temperature, Total Suspended Matter, Water Transparency and Significant Wave Height. Second data output is: Assessment of coral reef health and evolution focusing on four sites of interest (Aldabra, Tulear, Mayotte, Rodrigues). This dataset is one of the products produced under the 2008-2012 World Bank (WBG) European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2013.

This dataset provides coral reef habitat mapping (detailed geomorphological zonation) and change detection maps spanning 2000-2011. First data output is: Oil spills monitored twice a week during 6 months (from July 1st to January 3rd 2011) leading to a total of 324 products (in netcdf/cdf formats along with png snapshots), including: Chlorophyll concentration, Low-Res Sea Surface Temperature, Hi-Res Sea Surface Temperature, Total Suspended Matter, Water Transparency and Significant Wave Height. Second data output is: Assessment of coral reef health and evolution focusing on four sites of interest (Aldabra, Tulear, Mayotte, Rodrigues). This dataset is one of the products produced under the 2008-2012 World Bank (WBG) European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2013.

Bathymetric, topographic, and grain-size data were collected in April 2011 along a 27-mi (43.5 – km) reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach begins at river mile 61.1, about 0.6 -mi (1 –km) above the confluence of the Colorado and Little Colorado Rivers and ends at river mile 88.1 at the upstream boundary of the Bright Angel Rapid (Phantom Ranch boat beach). Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps. These data were collected by the Southwest Biological Science Center, Grand Canyon Monitoring and Science Center as a component of a larger effort to monit ...

These data provide an accurate high-resolution shoreline compiled from imagery of SACRAMENTO RIVER DEEP WATER SHIP CHANNEL, CA . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

These data provide an accurate high-resolution shoreline compiled from imagery of MATAGORDA SHIP CHANNEL, TX . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

These data provide an accurate high-resolution shoreline compiled from imagery of Pass-A-Grille Channel to Longboat Key, FL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Santa Barbara Channel map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Santa Barbara Channel map area data layers. Data layers are symbolized as shown on the associated map sheets.

Technical Reading for High School.Student Worksheet: https://nhedgis.maps.arcgis.com/home/item.html?id=c1a5b641068f44828501bf936660fe44Answer Key: https://nhedgis.maps.arcgis.com/home/item.html?id=e2dc89f38534477ca1a630b8ce0eb501

This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03. A detailed description is given here: Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016. Abstract: Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain. Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

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

The radio map, radio environment map (REM), or RSSI map, can visualize the information of invisible electromagnetic spectrum, and is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. It is useful for the abnormal spectral activity detection, radiation source localization, spectrum resource management, etc.

The performance of different REM construction methods should be compared based on the data under realistic scenarios. So we measured the signal strength under campus scenario by a spectrum sensing system. This project includes two datasets as 1) Raw received signal strength: Collecting RSSI data at the sampled positions in the ROI (117mX97m). 2) Constructed REM data: Recovery RSSI data at the unsampled positions and obtain a whole REM

The dataset has been applied and validated in the following references. [1]. Q. Zhu et al., DEMO Abstract: An UAV-based 3D Spectrum Real-time Mapping System, 2022 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), New York, NY, USA, 2022, pp. 1-2. [2]. Y. Zhao, et al. Temporal prediction for spectrum environment maps with moving radiation sources, IET Communications, vol. 17, no. 5, pp. 538–548, 2023. [3] J. Wang, et al., “Sparse Bayesian Learning-Based 3D Radio Environment Map Construction—Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery,” IEEE Transactions on Cognitive Communications and Networking, vol.10, pp.80-93, Feb. 2024. [4]. J. Wang, ea al. Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing, IEEE Transactions on Wireless Communications, IEEE Transactions on Wireless Communications, 2024, vol.23, no.10, pp.14560-14574, Oct. 2024. [5] Yang Huang, et al. Space-Based Electromagnetic Spectrum Sensing and Situation Awareness. Space Sci Technol. 2024;4:0109. DOI:10.34133/space.0109 [6]. Q. Gao, et al. Spatial Sensor Layout Optimization for Radio Environment Map Construction, 2024 IEEE Globecom Workshops, 2024, for publication

More details and instrucitons can be found in the guidemanual_measuredCompus_117m_97m.pdf.

These data provide an accurate high-resolution shoreline compiled from imagery of CALUMET HARBOR TO NORTH SHORE CHANNEL, IL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

This data was developed to represent Channel Markers and their associated attributes for the purpose of mapping, analysis, and planning. The accuracy of this data varies and should not be used for precise measurements or calculations.

The Urban Planning Code defines two types of areas for municipal maps: however, there are special cases:- Graphic documents may define sectors reserved for industrial or craft activities, in particular those incompatible with the neighbourhood of inhabited areas.- They delimit, where appropriate, the areas in which the reconstruction of a building destroyed by a disaster is not permitted.- The installations necessary for collective equipment, agricultural or forestry exploitation and the development of natural resources are not covered by the principle of inconstructibility resulting from a classification.

Global 3D Cardiac Mapping System Market size was $1.93 billion in 2024 and is predicted to grow to $3.79 billion by 2034, a CAGR of 8.80% from 2025 and 2034.

The National Map 1:25,000 is a topographic map of Switzerland representing the areas of traffic and settlements, the hydrography, the topography and the vegetation in great detail. The update is done by sector. The current status of the map is displayed by default in the federal map viewer. With the ‘journey through time’ function, all available data statuses since 1952 can be displayed. The detailed statuses of updates are shown in the layer "Division national map 25 Raster". The current National Map 1:25,000 is published in analogue format as a printed map and in digital format as the Swiss Map Raster or Swiss Map Vector.

The active channel of the Willamette River in western Oregon is the portion of the floodplain that regularly conveys water and bed-material sediment (typically sand and gravel) on an annual basis (Wallick and others, 2013). The active channel comprises features such as gravel bars, side channels, main channel and alcoves that contribute to the overall diversity of aquatic and floodplain habitats (for example, Hall and others, 2007; Harrison and others, 2011; Williams and others, 2020). Historically, a complex mosaic of active channel features supported a diverse array of fish and wildlife, but over the last 170 years, construction of flood control dams, bank stabilization structures, removal of large wood and conversion of floodplain forests to agriculture and other land uses has resulted in substantial reductions in the complexity and abundance of active channel features (Sedell and Froggatt, 1984; Hulse and others, 2002; Baker and others, 2004; Wallick and others, 2013; Gregory and others, 2019). For gravel-bed rivers like the Willamette River, active channel features such as gravel bars, side channels, and alcoves can be mapped remotely to evaluate the distribution of features along the river or to assess geomorphic changes over time. Digital geomorphic maps of features within the active channel of the Willamette River were developed to document channel conditions between the confluence of the Coast Fork and Middle Fork Willamette Rivers near Springfield (floodplain kilometer [FPKM] 228) and the head of the Newberg pool, where streamflow is backwatered by Willamette Falls, near Dayton (FPKM 80) in 2009 and 2016. This documentation describes the geomorphic mapping for the active channel of the Willamette River floodplain as mapped from aerial photographs acquired in the summer of 2009.