This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations.

The radars used during the SRTM mission were actually developed and flown on two Endeavour missions in 1994. The C-band Spaceborne Imaging Radar and the X-Band Synthetic Aperture Radar (X-SAR) hardware were used on board the space shuttle in April and October 1994 to gather data about Earth's environment. The technology was modified for the SRTM mission to collect interferometric radar, which compared two radar images or signals taken at slightly different angles. This mission used single-pass interferometry, which acquired two signals at the same time by using two different radar antennas. An antenna located on board the space shuttle collected one data set and the other data set was collected by an antenna located at the end of a 60-meter mast that extended from the shuttle. Differences between the two signals allowed for the calculation of surface elevation.

Endeavour orbited Earth 16 times each day during the 11-day mission, completing 176 orbits. SRTM successfully collected radar data over 80% of the Earth's land surface between 60° north and 56° south latitude with data points posted every 1 arc-second (approximately 30 meters).

Two resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive:

1 arc-second (approximately 30-meter) high resolution elevation data offer worldwide coverage of void filled data at a resolution of 1 arc-second (30 meters) and provide open distribution of this high-resolution global data set. Some tiles may still contain voids. The SRTM 1 Arc-Second Global (30 meters) data set will be released in phases starting September 24, 2014. Users should check the coverage map in EarthExplorer to verify if their area of interest is available.

3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60° north and 56° south latitude.

[Summary provided by the USGS.]

The Shuttle Radar Topography Mission (SRTM) successfully collected Interferometric Synthetic Aperture Radar (IFSAR) data over 80 percent of the landmass of the Earth between 60 degrees North and 56 degrees South latitudes in February 2000. The mission was co-sponsored by the National Aeronautics and Space Administration (NASA) and National Geospatial-Intelligence Agency (NGA). NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of SRTM data and forwarded partially finished data directly to NGA for finishing by NGA's contractors and subsequent monthly deliveries to the NGA Digital Products Data Wharehouse (DPDW). All the data products delivered by the contractors conform to the NGA SRTM products and the NGA Digital Terrain Elevation Data (DTED) to the Earth Resources Observation & Science (EROS) Center. The DPDW ingests the SRTM data products, checks them for formatting errors, loads the SRTM DTED into the NGA data distribution system, and ships the public domain SRTM DTED to the U.S. Geological Survey (USGS) Earth Resources Observation & Science (EROS) Center.

Two resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive:

1 arc-second (approximately 30-meter) high resolution elevation data are only available for the United States.

3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60° north and 56° south latitude.

[Summary provided by the USGS.]

The Shuttle Radar Topography Mission (SRTM) is a collaborative effort from NASA (National Aeronautics and Space Administration) and NGA (National Geospatial-Intelligence Agency) as well as DLR (Deutsches Zentrum für Luft-und Raumfahrt) and ASI (Agenzia Spaziale Italiana). SRTM was flown aboard the Endeavour space shuttle in February 2000 to provide a high-resolution Digital Elevation Model (DEM). The SRTM instrumentation consisted of the Spaceborne Imaging Radar-C (SIR-C) with an additional antenna to form a 60 meters long baseline. As a result of the SRTM mission, several DEM versions have been released since 2003, which differ in terms of data processing and procedures applied for the filling of voids (areas not or poorly observed by the SRTM radar observations).

SRTM v3.0 (SRTM Plus) is the newest version, published in 2015 by NASA as a part of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, which incorporates topographic data to fill the gaps or voids in earlier versions of SRTM data. For the void filling with the Delta Surface Fill algorithm, ASTER DEMs have been used as auxiliary data source, or interpolations have been applied. Many variants of DEM are available in SRTM v3.0, with SRTMGL1 being one of the key products from SRTM v3.0. ‘GL1’ on its name stands for “Global 1-arc second”. It provides regularly spaced DEM grids of 1 arc-second (approximately 30 meters) and covering 80% of Earth’s landmass, between 60° North and 56° South. This product is divided into 1° x 1° latitude and longitude tiles in “geographic” projection, as shown here.

A typical file of the SRTMGL1 dataset requires 25 MB memory (without compression) and stores exactly one 1°x1° tile; it contains 3,601 lines and 3,601 columns, which sum up to around 100 GB (compressed) and 350 GB (uncompressed) for the global data set of 14297 tiles. Individual tile names refer to the latitude and longitude of southwest (lower left) corner of the tile, e.g., tile N20W030 has lower left corner at 20°N and 30°W, covering area of 20-21°N and 30-29°W. The absolute vertical accuracy for SRTM heights has been found to be ~9 m (90 % confidence) or better (Rodriguez et al. 2005).

Geodetic information: The SRTM GL1 DEMs are vertically referenced to the EGM96 geoid and horizontally referenced to the WGS84 (World Geodetic System 1984).

Further notes: The SRTM DEM represents bare ground elevations only where vegetation cover and buildings are absent. Over most areas, the DEM elevations reside between the bare ground (terrain) and top of canopies (surface), so are technically a mixture of terrain and surface models. Few artefacts, e.g., pits or spikes may still be present in the data set.

Data access: The homepage of SRTM mission is http://www2.jpl.nasa.gov/srtm/. SRTM v3.0 datasets can be searched in MEASURES webpage and acquired freely from USGS website (http://earthexplorer.usgs.gov/) and USGS data pool (http://e4ftl01.cr.usgs.gov/SRTM/).References:Farr, T.G., E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, P. Rosen, L. Roth, D. Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, 2007, The Shuttle Radar Topography Mission. Reviews of Geophysics, volume 45, RG2004, doi:10.1029/2005RG000183.NASA, The Shuttle Radar Topography Mission (SRTM) Collection User Guide. Available on https://lpdaac.usgs.gov/sites/default/files/public/measures/docs/NASA_SRTM_V3.pdfRodriguez, E., C.S. Morris, J.E. Belz, E.C. Chapin, J.M. Martin, W. Daffer, S.Hensley, 2005, An assessment of the SRTM topographic products, Technical Report JPL D-31639, Jet Propulsion Laboratory, Pasadena, California, 143 pp. available on http://www2.jpl.nasa.gov/srtm/SRTM_D31639.pdf

Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature v3" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2019, and the bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was removed from each satellite pixel. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2019) to each satellite was removed from all scenes from the corresponding satellite. The errors were determined by using a K-fold cross-validation to minimize error at each satellite pixel. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=Rhode Island Data Discovery/United States Geological Survey publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: http://www.coast.noaa.gov/slr This metadata record describes the digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available data known to exist at the time of DEM creation that met project specifications, for mainland Virginia, this includes portions of the following counties: Alexandria, Arlington, Charles City, Chesapeake, Essex, Fairfax, Falls Church, Franklin City, Fredericksburg City, Gloucester, Hampton, Isle of Wight, James City, King and Queen, King George, King William, Lancaster, Mathews, Middlesex, New Kent, Newport News, Norfolk, Northumberland, Poquoson City, Portsmouth, Prince George, Prince William, Richmond, Southampton, Stafford, Suffolk, Surry, Sussex, Virginia Beach, Westmoreland, Williamsburg, and York. This DEM also includes the District of Columbia. The DEM is derived from the following lidar: 1. New Kent, Charles City, Prince George Counties 2012 FEMA Middle Counties VA Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/Middle_Counties/Metadata/Project_Report/Dewberry_ProjectReport_MiddleCounties.pdf Additional coverage provided by the Virginia Base Map Program (VBMP). This data is a digital terrain model initially generated by the Center for Geospatial Technology (CGIT) for the Virginia Geographic Information Network (VGIN) using the mass points and break lines from 2002 VBMP aerial photography. 2. King William County 2011 FEMA King William County VA Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/KingWilliamCo/KingWilliam_Metadata/Dewberry_ProjectReport_KingWilliam.pdf Additional coverage provided by the Virginia Base Map Program (VBMP). This data is a digital terrain model initially generated by the Center for Geospatial Technology (CGIT) for the Virginia Geographic Information Network (VGIN) using the mass points and break lines from 2002 VBMP aerial photography. 3. Hampton and Portsmouth Cities 2011 FEMA Virginia Southern Cities Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/SouthernCities/SouthernCities_Metadata/Dewberry_ProjectReport_SouthernCities.pdf 4. Franklin City and Southampton County 2011 FEMA Virginia Counties South Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/VA_Counties_South/SouthernCo_Metadata/Dewberry_ProjectReport_Southampton.pdf 5. Fredericksburg City and Essex, King George, Prince William, Richmond, Stafford, Westmoreland Counties 2011 FEMA Virginia Counties North Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/VA_Counties_North/NorthernCo_Metadata/Dewberry_ProjectReport_NorthernCounties.pdf 6. Northumberland, Middlesex, Lancaster, King and Queen, Gloucester, Mathews, James City, Williamsburg, Surry, Isle of Wight, Suffolk Counties 2010/2011 USGS Eleven County Coastal VA Lidar This data may be downloaded from the William and Mary Center for Geospatial Analysis: http://www.wm.edu/as/cga/VALIDAR/ The project report for this data may be accessed at: http://gisfiles.wm.edu/files/lidar/a11county/Metadata/PROJECT_REPORT/Final%20Project%20Report%20for%20USGS%20Virginia%20LiDAR_01312011.pdf Additional coverage for Surry and King and Queen counties provided by the Virginia Base Map Program (VBMP). This data is a digital terrain model initially generated by the Center for Geospatial Technology (CGIT) for the Virginia Geographic Information Network (VGIN) using the mass points and break lines from 2002 VBMP aerial photography. 7. Alexandria, Arlington, and Falls Church Counties 2008 NGA Capital Region Lidar The lidar data is not publicly available, the data was provided by the State of Virginia as bare earth DEMs. 8. Fairfax County 2008 NGA Capital Region Lidar The lidar data is not publicly available, the data was provided by the State of Virginia as bare earth DEMs. 2012 FEMA Virginia Lidar This data may be downloaded from USGS EarthExplorer at: http://earthexplorer.usgs.gov/ 9. York, Poquoson City, Newport News, Norfolk, Chesapeake, Virginia Beach, and Sussex Counties Data provided by the Virginia Base Map Program (VBMP). This data is a digital terrain model initially generated by the Center for Geospatial Technology (CGIT) for the Virginia Geographic Information Network (VGIN) using the mass points and break lines from 2002 VBMP aerial photography. 2010 US Army Corps of Engineers (USACE) Lidar This data may be downloaded at: http://www.coast.noaa.gov/dataviewer/index.html?action=advsearch&qType=in&qFld=ID&qVal=1132 The metadata for this data may be accessed at: http://coast.noaa.gov/dataviewer/webfiles/metadata/usace2010_va_template.html 10.District of Columbia Washington, DC and Environs, 2008, 1/9 Arc second National Elevation Dataset (NED) This data may be downloaded at: http://viewer.nationalmap.gov/viewer/ Hydrographic breaklines were delineated from LiDAR intensity imagery generated from the LiDAR datasets. The DEM is hydro flattened such that water elevations are less than or equal to 0 meters. The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 10 meters.

Mass-wasting events that displace water, whether they initiate from underwater sources (submarine landslides) or subaerial sources (subaerial-to-submarine landslides), have the potential to cause tsunami waves that can pose a significant threat to human life and infrastructure in coastal areas (for example towns, cruise ships, bridges, oil platforms, and communication lines). Sheltered inlets and narrow bays can be locations of especially high risk as they often have higher human populations, and the effects of water displacement from moving sediment can be amplified as compared to the effects from similarly sized mass movements in open water. In landscapes undergoing deglaciation, such as the fjords and mountain slopes adjacent to tidewater glaciers found in Southeast Alaska, glacial retreat and permafrost decay can destabilize rock slopes and increase landslide potential. Establishing and maintaining inventories of subaerial and submarine landslides in such environments is critical for identifying the magnitude and frequency of past events, as well as for assessing areas that may be susceptible to failures in the future. To maintain landslide inventories, multi-temporal surveys are needed. High-resolution digital elevation models (DEM) and aerial imagery can be used to establish and maintain subaerial landslide inventories, but repeat bathymetric surveys to detect submarine landslides are generally less available than their terrestrial counterparts. However, existing bathymetry can be used to establish a spatial inventory of landslides on the seafloor to provide a baseline for understanding the magnitude of past events and for locating areas of high submarine landslide susceptibility. These data can then be used to address how future failures and the tsunamis that they could trigger could impact surrounding areas. Here, we present an inventory of mapped landslide features in Glacier Bay, Alaska that includes landslide source areas, deposits, and scarps. This data release contains geographic information system (GIS) polygons and polylines for these mapped features; the underlying digital elevation model (DEM) raster compiled from available bathymetry from the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS); a slope map created from the compiled DEM; ¬and a derivative topographic openness map used to help identify the landslide features. Bathymetric DEMs used in the compilation cover 1012.5 sq. km, which represents approximately 80% of the total area of Glacier Bay. The DEMs were collected in 2001 and 2009 for the southern and northern parts of the bay, respectively. To minimize resolution bias and maximize mapping consistency while maintaining visual fidelity, we re-sampled all the original bathymetry (resolution ranging from 1 to 16 m) to 5 m, which represents the minimum resolution for the majority of mapped areas; the lower resolution areas generally covered deeper and flatter portions of the bay where fewer landslides were present. For mapping, we used a topographic openness map (Yokoyama and others, 2002) in combination with a traditional slope map (see Red Relief Image Map in Chiba and others, 2008), which allows for good discernment of subtle concavities and convexities in the bathymetry and is well-suited for identifying landslide scars and deposits. We classified mapped landslides based on their source area type and used two primary classification categories of “slide” and “debris flow”. We used a third category, “mixed”, to classify landslides that showed evidence of both types of source area contributing to the deposit. For each landslide classified as slide or mixed, we mapped the source area and deposit as separate polygons. For landslides classified as debris flow, we mapped only deposits. Since debris flow source areas are subaerial drainage basins, delineating them should be part of larger future subaerial landslide mapping efforts in Glacier Bay National Park and Preserve. Similarly, for mixed landslides, we delineated source areas as the slide contribution area and not the larger debris-flow drainage basin component. For any source areas (for mixed and slide polygons) or deposits that included a subaerial portion, we used 2012 5-m IFSAR data, and Landsat and DigitalGlobe imagery to map subaerial parts of the polygons. IFSAR and Landsat data are available from Earth Explorer (https://earthexplorer.usgs.gov/) and DigitalGlobe imagery is available from DigitalGlobe (https://www.digitalglobe.com/). These data and images are not included in this data release. Thirty-five of the forty-four slide and mixed features initiated as subaerial landslides. However, in all cases, we only mapped landslides if we could identify a submarine deposit. For example, we did not map the subaerial Tidal Inlet landslide (Wieczorek and others, 2007) because we could not identify a submarine deposit associated with it. Additionally, we did not map subaerial and submarine deposits that appeared to be deposited by water-dominated flows (e.g., alluvial fans and fan deltas), or large submarine fans that likely resulted from turbidite flows, such as the one at the junction of Queen Inlet and Glacier Bay. Because we could not observe mapped submarine landslides in the field, we assigned a level of moderate (77 landslides) or high (31 landslides) confidence based on our certainty that the mapped features represented actual slope failures. We omitted low confidence landslides from the map. In total, we mapped 108 landslides, with 22, 64, and 22 classified as slide, debris flow, and mixed, respectively. The total area (source and deposit) for slide and mixed type landslides ranged from 0.026 to 2.35 sq. km. Debris-flow deposits ranged from 0.012 to 0.61 sq. km. Finally, we mapped a total of 7,097 individual landslide scarps where we could not identify any clear associated deposits, and where the distance between lateral flanks was approximately 50 m or more. Though we did our best to map only arcuate-shaped scarps typically formed by landslides (that is, single-mass failures), as opposed to geomorphic features formed by gradual glacial or submarine-current-related erosion (for example, submarine canyon walls), we acknowledge that some mapped scarps may have been formed by processes other than landsliding. Thus, for purposes of landslide susceptibility mapping, these scarp data are intended to be used in conjunction with other data, such as slope angle, geologic substrate, or geomorphic units. Ultimately, the full dataset is meant to serve as a qualitative component to inform future submarine and subaerial landslide susceptibility assessments in Glacier Bay National Park and Preserve. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References used: Chiba, T., Kaneta, S., and Suzuki, Y., 2008, Red relief image map: new visualization method for three dimensional data: The international archives of the photogrammetry, remote sensing and spatial information sciences, v. 37, no. B2, p. 1071–1076. Wieczorek, G.F., Geist, E.L., Motyka, R.J., Jakob, M., 2007, Hazard assessment of the tidal inlet landslide and potential subsequent tsunami, Glacier Bay National Park, Alaska: Landslides, v. 4 p. 205–215. Yokoyama, R., Shirasawa, M., and Pike, R.J., 2002, Visualizing topography by openness: a new application of image processing to digital elevation models: Photogrammetric engineering and remote sensing, v. 68, no. 3, p. 257–266.

UPDATE: Data no longer available from this page. All non-working links have been removed (19/7/21)

Users must follow instructions below from NASA to access data:

SRTM data are also available globally at 1 arc second resolution (SRTMGL1.003) through the Data Pool (https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/) or from EarthExplorer where it is listed as NASA SRTM3 SRTMGL1. Please sign in with NASA Earthdata Login Credentials to download data from the NASA LP DAAC Collections. These datasets require login on both NASA Earthdata and USGS EarthExplorer systems to access data. After you create your account, you will also need to “authorize” the LP DAAC Data Pool application. On the Profile page in your Earthdata account you will need to select My Applications. On that page make sure the LP DAAC Data Pool is listed. If it isn't then select Authorize More Applications. In the dialog box type in LP DAAC Data Pool and click Search For Applications. Select Approve when presented with the lpdaac_datapool. Keep everything checked but you can uncheck the Yes, I would like to be notified box. Select Authorize and the LP DAAC Data Pool should be added to your Approved Applications. You might benefit from using the AppEEARS tool. ·
o AppEEARS landing page: https://lpdaacsvc.cr.usgs.gov/appeears/

·
o The users will need and https://urs.earthdata.nasa.gov/?_ga=2.148606453.334533939.1615325167-1213876668.1613754504. Click or tap if you trust this link.">Earthdata Login

·
o Getting started instructions can be found here: https://lpdaacsvc.cr.usgs.gov/appeears/help

Previously available here: Digital Elevation Model of Ireland, from NASA's Shuttle Radar Topography Mission (SRTM), sampled at 3 arc second intervals in latitude & longitude (about every 90m) in heightmap (.HGT) format.''Latitudes & longitudes are referenced to WGS84, heights are in meters referenced to the WGS84/EGM96 geoid.'' Please see the linked pdf files for further documentation.''A QGIS project for the hgt files is also attached.

We provide salt marsh delineations for Barnegat Bay, New Jersey, by means of object-based image analysis of high-resolution aerial imagery and digital elevation models. We performed trends analyses of salt marsh extent from 1995 to 2015 and estimated drivers of marsh area change. We found that in 1995, 8,830 ± 390 ha were covered with marsh vegetation, while in 2015 only 8,180 ± 380 ha of salt marsh habitat remained., Data for the classification of coastal land-cover of Barnegat Bay were downloaded from the USGS Earth Resources Observation and Science (EROS) Center via the USGS Earth Explorer website. The spatial data comprised high-resolution orthoimages acquired in April 1995 (color-infrared, 3 bands, 1 m resolution) and April 2015 (4 bands, red, green, blue, near-infrared, 0.3 m resolution), as well as a bathymetric model from 2000 (10 m resolution) and a digital elevation model (DEM) from 2015 (1 m resolution). Digital elevation models were clipped to the area of interest and resampled to 1-m cell size in ArcMap 10.2.2 (Esri, West Redlands, CA, USA). Aerial imagery orthomosaics were clipped to the same extent and fused with the DEMs to generate a five-band raster file for 2015 (red, green, blue, near-infrared, elevation; 1-m cell size) and a four-band raster file for 1995 (red and near-infrared combined in band 1, 1-m cell size). These raster files were imported into eCognition Developer 9 (Trimb..., Files are provided as raster in .tiff format (8 bit). Classes are defined as follows: 1995.tif, 2015.tif: 1-Open Water; 2-Upland; 3-Salt Marsh; 4-Unvegetated Trend.tif: 1-Stable non-marsh; 2-Stable marsh; 3-Marsh gain; 4-Marsh loss  Ground-control points are provided in .csv format. Classes are defined as follows: Control_points.csv: M-Marsh; U-Unvegetated

We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, which was not available at the time of this study. However the R functions and scripts hosted on O'Connell's github will allow end-users to repeat our calculations for any Landsat 8 pixel and will also provide the ability to customize the atmospheric correction algorithms and calibrate the resulting land surface temperature estimation to ground-truth information.

The data here are large wood data collected in the Summer of 2022 in the Frying Pan River Basin in the Sawatch Mountains of Central Colorado. There are six datasets included or referenced here. The first is general geomorphic and watershed characteristics of the stream reaches surveyed. The second is data from field reports to the CAIC. The third is topographic data for the studied avalanche pathways. The fourth is summary data of the wood volumes within each surveyed reach. The fifth is the unprocessed raw data for all wood jams and individual pieces surveyed. The sixth is a table of literature-derived annual recruitment rates for mechanisms common to mountain streams. Data may also be accessed via the Dryad data repository as linked in the data accessibility statement. Raw data relate several wood jam and individual piece properties, including length, width, and depth of the former, and length and diameter of the latter. Data also indicate several other wood characteristics, such as piece orientation, stability and decay class, and the presence of a rootwad. Finally, data include information about the geomorphic impact of each surveyed piece and jam. Data were collected to examine research questions related to in-stream wood load volumes supplied by snow avalanches and the resultant geomorphic impacts. Methods Raw data were collected in the field by two trained observers. Wood loads were measured in the field using a census approach for all wood jams and individual wood pieces within the bankfull channel. Jams were identified as accumulations with three or more contiguous pieces; jam volume was quantified by measuring the length, width, and height of a rectangular prism fit to the dimensions of the jam and visually estimating porosity. Porosity was consistently estimated by two independent observers to minimize systematic bias. For individual wood pieces, diameter and length were measured and then used to compute volume via the formula of a cylinder. All measurements were made for wood at least partially contained within the bankfull channel, which was visually estimated in the field using topography (e.g., slope breaks). For wood pieces or jams that extended laterally beyond the bankfull dimensions, those portions outside were excluded from measurements. Measured wood loads were normalized by stream surface area (in ha) for comparisons between reaches with varied bankfull widths and lengths. These data have not been processed other than the above related volume calculations, which were then summarized across sites and watersheds (which have yielded the wood_volume_summary.csv data also presented here). Data regarding topographic and vegetation characteristics of the studied avalanche pathways were obtained via freely available remote datasets. These include a 1-m DEM for the study area available from USGS EarthExplorer (https://earthexplorer.usgs.gov/) and vegetation data from the LANDFIRE program (https://landfire.gov/). Canopy cover comes from the Existing Vegetation Cover raster in the LANDFIRE 2016 dataset. Planform curvature and slope angle were dervied from a 1-m DEM using the Curvature and Slope functions in ArcGIS Pro Version 2.8. Median was calculated for each pathway area using the Zonal Statistics function in ArcGIS Pro. Avalanche report data was gathered from Colorado Avalanche Information Center field reports (https://avalanche.state.co.us/observations/view-field-reports), restricting the "area" field to Sawatch and Aspen and the dates from 2019-03-01 to 2019-03-15. Literature data were gathered using a publication database/search engine (scholar.google.com) to find relevant sources via keyword sources. Data were then processed using the information in each publication to determine rates in units of m3/ha/yr. NAs in all datasets mean that the measure in question was inapplicable to the parameter under consideration. An open-source R code is provided to re-create data processing and figure creation.

1971 San Fernando earthquake displacement maps and digital elevation models, produced using aerial photographs taken in 1969 and 1972. Images were acquired from the United States Geological Survey's Center for Earth Resources Observation and Science (EROS; http://earthexplorer.usgs.gov).

1969-1972-EW.tif - east-west displacement map

1969-1972-NS.tif - north-south displacement map

1969-1972-vertical.tif - vertical displacement map

1969-DEM.tif - digital elevation model produced from images acquired in the San Fernando Valley in 1969

1972-DEM.tif - digital elevation model produced from images acquired in the San Fernando Valley in 1972