These data were automated to provide an accurate high-resolution historical shoreline of Salem, Massachusetts suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attr...

Geodetic control points. Publication data for read only apps.

Background: In support of NASA’s Earth Surface and Interior (ESI) focus area, the Terrestrial Reference Frame (TRF) is the foundation for virtually all airborne, space-based, and ground-based Earth observations. This frame is developed by combining the observations from Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), the Global Navigation Satellite System (GNSS), and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) stations, and is realized as an international standard through the International Terrestrial Reference Frame (ITRF). Combining the different measurement techniques is only possible with accurate knowledge of the relative measurement reference points between co-located systems, and doing so is essential to take full advantage of the strengths of each technique. Currently, the ITRF is limited in accuracy by systematic errors in tying together the contributions from the different geodetic techniques. Since standard ground-based surveys providing ties between geodetic techniques have reached the limit of their capabilities, a new approach is required that extends technique ties into space.

Major goals: We propose to develop a new space flight instrument and verify a measurement concept that enables the determination of systematic errors between the VLBI, GNSS, and SLR independent measurement techniques by extending surveying techniques out to spaceflight assets. The proposed instrument, functioning as a GNSS L-to-X-band transponder, establishes frequency compatibility between VLBI and GNSS, thereby facilitating a direct space-based geodetic tie between these two radio-based techniques in post-processing. Separate laser retro-reflectors flown concurrently with GRITSS would provide additional connection to the SLR network. Because the measurement concept by which the VLBI/GNSS systematic errors will be determined only requires one VLBI station to observe the space vehicle, the space vehicle may be in Low-Earth Orbit. This is advantageous as it opens up the possibility of using inexpensive CubeSats or other small satellites, making it possible to implement a cost-effective constellation of spacecraft to provide better global coverage and further improve the accuracy of the geodetic site ties.

Specific tasks:

1) Develop modified version of an existing space flight GNSS receiver to support GNSS relaying functionality Procure commercial space flight UHF (low data rate), S, and X-band (high data rate) digital data transmitters.

2) Design and fabricate a fit-form-function instrument capable of being implemented within a Cubesat/Small Satellite.

3) Bench test breadboard concept of GNSS-to-VLBI transponder with GNSS synthetic signal generators and space vehicle GNSS receivers.

4) Bench test breadboard concept of GNSS-to-VLBI transponder with GNSS synthetic signal generators and space vehicle GNSS receivers.

5) On sight ground test of GNSS-to-VLBI transponder at Goddard Geophysical Astronomical Observatory.

6) Airborne flight test evaluation of GNSS-to-VLBI transponder at Goddard Geophysical Astronomical Observatory.

These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Leica Chiroptera 4X system. The data were acquired from 20210729 - 20220814. The data includes topobathy data in an LAS 1.4 format file classified as unclassified (1), ground (2), noise (7), water surface (topographic sensor) (9), high noise (18), bathymetric po...

This data set contains 4-band ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired on 20220814 with an Applanix Digital Sensor System (DSS). The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

The geodetic Global Navigation Satellite System (GNSS) receiver has been proven to retrieve snow depth using the phase change rate of the signal-to-noise ratio (SNR) observations. Snow density can be related to the permittivity of snow and is theoretically sensitive to the amplitude of the GNSS reflected signal. However, retrieving snow density using the SNR observations is challenging due to the difficulty in extracting the reflected amplitude since it hides in the interference waveform and changes with the satellite elevation angle. Overcoming this issue by taking an indirect path, this study proposes a novel GNSS Signal Amplitude Ratio Model (GSARM) that relates the corrected amplitude ratio (α) to the snow permittivity and the resulting snow density. First, the model extracts the instantaneous amplitude from SNR observations to derive an initial amplitude ratio (α0). Then, it uses a step-wise strategy to correct systematic errors from antenna gain and random errors from soil moisture in α0 to achieve the finalized corrected α. The GSARM-derived dry snow density is compared with three other data sources, i.e., the PBO-H2O, the ERA5-Land, and the in-situ measurements over two GNSS sites for six consecutive years. The overall mean RMSD (RMSPD) values of snow density for GSARM compared to PBO-H2O, ERA5-Land, and in-situ measurements are 0.036 g/cm³ (22.08%), 0.040 g/cm³ (21.43%), and 0.032 g/cm³ (23.05%), respectively. The corresponding MAD (MAPD) values are 0.029 g/cm³ (21.90%), 0.035 g/cm³ (18.46%), and 0.025 g/cm³ (22.87%), respectively. The findings of this study first prove the feasibility of using geodetic GNSS receivers for snow density retrieval. It also provides supportive information for extending the added-value applications of traditional geodetic GNSS sites and for developing new observation patterns.

This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired on 20200905 with an Applanix Digital Sensor System (DSS). The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEMs range from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

This data set contains 4-band ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired from20220815 - 20220819 with an Leica RCD30 RGBN 80 MP camera. The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2016 National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey (NGS) Massachusetts lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired on 20151108 with an Applanix Digital Sensor System (DSS). The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

These data provide an accurate high-resolution shoreline compiled from imagery of Port of New Bedford, MA . 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 Attrib...

This data set contains ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired on 20200905 with an Applanix Digital Sensor System (DSS). The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

This data set contains 4-band ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. The source imagery was acquired from 20220826 - 20220828 with an Leica RCD30 RGBN 80 MP camera. The original images were acquired at a higher resolution to support the final ortho-rectified mosaic.

This dataset includes: 1. teleseismic broadband waveform, 2. regional broadband waveforms 3. InSAR/SAR 4. 3D surface deformation data for the 2021 Mw7.4 Maduo earthquake in China

These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Riegl VQ820G system. The data were acquired from 20151104 - 20151109. The data includes topobathy data in an LAS 1.2 format file classified as unclassified (1), ground (2), noise (7), bathymetric point (24), sensor noise refracted (26), topobathy water surface (27), and International Hydrographic Organization (IHO) S-57 object (30) in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. This data set may also include lidar intensity values and encoded RGB image values. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division Title: Chief, Remote Sensing Division Phone: 301-713-2663

These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Riegl VQ880G system. The data were acquired from 20151109 - 20151123. The data includes topobathy data in an LAS 1.2 format file classified as unclassified (1), ground (2), noise (7), water column (25), bathymetric point (26), topobathy water surface (27), submerged object (29), and International Hydrographic Organization (IHO) S-57 object (30) in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. This data set may also include lidar intensity values and encoded RGB image values. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division Phone: 301-713-2663

This dataset includes predictions of bedrock motion due to glacial isostatic adjustment from a range of published forward and inverse models. The predictions are only for the geodetic GPS sites considered within AAS4318:
Site(4-character ID), longitude (decimal degrees), latitude (decimal degrees)
CAS1 110.519729 -66.283391
DAV1 77.972630 -68.577332
BHIL 100.599007 -66.251025
CAD2 86.100628 -68.566511
CAD3 96.363668 -66.521104
CAD4 99.143786 -67.419656
CAD5 107.764024 -66.552476
CAD6 120.990964 -66.789283

The file is ascii text with each row the GIA prediction for one site. The columns are given as:
Site(4-character ID) Latitude(decimal degrees) Lon(decimal degrees) G14(mm/yr) C18(mm/yr) REGINA(mm/yr) RATES(mm/yr) ICE6G_D IJ05R2_115kmLT(mm/yr) W12_best(mm/yr)

Values are interpolated from the resolution of the grids as published using a bicubic interpolator using GMT5 grdtrack with default settings.

The reference frame of the different GIA model predictions vary, with the forward models in centre-of-mass of the solid Earth (CE) and the inverse solutions likely in centre-of-mass of the whole Earth system (CM). The original publications should be checked to confirm this.

References
G14: Gunter, B. C., Didova, O., Riva, R. E. M., Ligtenberg, S. R. M., Lenaerts, J. T. M., King, M. A., van den Broeke, M. R., and Urban, T.: Empirical estimation of present-day Antarctic glacial isostatic adjustment and ice mass change, The Cryosphere, 8, 743–760, https://doi.org/10.5194/tc-8-743-2014, 2014

C18: Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., and Blewitt, G. (2018). GIA model statistics for GRACE hydrology, cryosphere, and ocean science. Geophysical Research Letters, 45, 2203– 2212. https://doi.org/10.1002/2017GL076644

REGINA: Ingo Sasgen, Alba Martín-Español, Alexander Horvath, Volker Klemann, Elizabeth J Petrie, Bert Wouters, Martin Horwath, Roland Pail, Jonathan L Bamber, Peter J Clarke, Hannes Konrad, Mark R Drinkwater, Joint inversion estimate of regional glacial isostatic adjustment in Antarctica considering a lateral varying Earth structure (ESA STSE Project REGINA), Geophysical Journal International, Volume 211, Issue 3, December 2017, Pages 1534–1553, https://doi.org/10.1093/gji/ggx368

RATES: Martín-Español, A. , Zammit-Mangion, A. , Clarke, P. J. , Flament, T. , Helm, V. , King, M. A. , Luthcke, S. B. , Petrie, E. , Rémy, F. , Schön, N. , Wouters, B. and Bamber, J. L. (2016): Spatial and temporal Antarctic Ice Sheet mass trends, glacio-isostatic adjustment, and surface processes from a joint inversion of satellite altimeter, gravity, and GPS data , Journal of Geophysical Research: Earth Surface, 121 (2), pp. 182-200 . doi: 10.1002/2015JF003550

ICE6G_D: Peltier, W.R., Argus, D.F. and Drummond, R. (2018) Comment on "An Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model" by Purcell et al. J. Geophys. Res. Solid Earth, 123, 2019-2018, doi:10.1002/2016JB013844.

IJ05R2_115kmLT: Ivins, E. R., James, T. S., Wahr, J., O. Schrama, E. J., Landerer, F. W., and Simon, K. M. (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118, 3126– 3141, doi:10.1002/jgrb.50208.

W12_best: Whitehouse, P.L., Bentley, M.J., Milne, G.A., King, M.A. and Thomas, I.D. (2012), A new glacial isostatic adjustment model for Antarctica: calibrated and tested using observations of relative sea‐level change and present‐day uplift rates. Geophysical Journal International, 190: 1464-1482. https://doi.org/10.1111/j.1365-246X.2012.05557.x

These data were created as part of the National Oceanic and Atmospheric Administration Coastal Services Center's efforts to create an online mapping viewer depicting 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.csc.noaa.gov/slr These data depict the potential inundation of coastal areas resulting from a projected 1 to 6 feet rise in sea level above current Mean Higher High Water (MHHW) conditions. The process used to produce the data can be described as a modified bathtub approach that attempts to account for both local/regional tidal variability as well as hydrological connectivity. The process uses two source datasets to derive the final inundation rasters and polygons and accompanying low-lying polygons for each iteration of sea level rise: the Digital Elevation Model (DEM) of the area and a tidal surface model that represents spatial tidal variability. The tidal model is created using the NOAA National Geodetic Survey's VDATUM datum transformation software (http://vdatum.noaa.gov) in conjunction with spatial interpolation/extrapolation methods and represents the MHHW tidal datum in orthometric values (North American Vertical Datum of 1988). The model used to produce these data does not account for erosion, subsidence, or any future changes in an area's hydrodynamics. It is simply a method to derive data in order to visualize the potential scale, not exact location, of inundation from sea level rise

Please see http://maps.massgis.state.ma.us/czm/moris/metadata/moris_noaa_slr_combined.htm for more details.

These data were automated to provide an accurate high-resolution historical shoreline of Narragansett, Massachusetts suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The N...