Vertical land motion rates for the years 2007 to 2020 for the U.S. Atlantic coast

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Frequently anticipated questions:

What does this data set describe?

Title:
Vertical land motion rates for the years 2007 to 2020 for the U.S. Atlantic coast
Abstract:
This dataset contains rates of land subsidence and uplift derived from Sentinel-1A/B (2015-2020) and ALOS (2007-2011) synthetic aperture radar (SAR) satellites, at approximately 50-75 m resolution and mm-level precision for the U.S. Atlantic coast except for the states of North and South Carolina. The data consist of vertical land motion (VLM) rates and the 1-sigma error in land motion rates and are available as csv files. Similar vertical land motion rates for North Carolina and South Carolina are available from Barnard and others, 2023, at https://doi.org/10.5066/P9W91314.
Supplemental_Information:
This data release was funded by the USGS Coastal and Marine Hazards and Resources Program. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    Shirzaei, Manoocher, Ohenhen, Leonard O., and Engelstad, Anita, 20230315, Vertical land motion rates for the years 2007 to 2020 for the U.S. Atlantic coast: data release DOI: 10.5066/P9BQQTCI, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Barnard, Patrick L., Befus, Kevin, Danielson, Jeffrey J., Engelstad, Anita C., Erikson, Li H., Foxgrover, Amy C., Hayden, Maya K., Hoover, Daniel J., Leijnse, Tim, Massey, Chris, McCall, Robert, Nadal-Caraballo, Norberto, Nederhoff, Kees, Ohenhen, Leonard O., O’Neill, Andrea C., Parker, Kai A., Shirzaei, Manoocher, Su, Xin, Thomas, Jennifer A., Ormondt, Maarten van, Vitousek, Sean F., Vos, Kilian, and Yawn, Madison C., 2023, Future coastal hazards along the U.S. Atlantic coast: data release DOI: 10.5066/P9BQQTCI, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.989635
    East_Bounding_Coordinate: -69.998028
    North_Bounding_Coordinate: 42.999999
    South_Bounding_Coordinate: 25.494837
  3. What does it look like?
    VerticalLandMotion_US_Atlantic.png (JPEG)
    Map showing the area for which vertical land motion (VLM) data are available.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 01-Jan-2007
    Ending_Date: 20-Dec-2020
    Currentness_Reference:
    collection years of SAR data used to determine vertical land motion
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: comma-delimited text
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Point (11881475)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is International GNSS Service 14.
      The ellipsoid used is GRS 1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    VerticalLandMotion_Rate_STATE.csv
    vertical land motion rates where STATE in the file name refers to the state for which data are available (Source: Producer defined)
    Longitude(deg)
    Longitudinal values for the dataset expressed in degrees (Source: USGS)
    Range of values
    Minimum:-81.989635
    Maximum:-69.998028
    Units:Decimal Degrees
    Resolution:0.000001
    Latitude(deg)
    Latitudinal values for the dataset expressed in degrees (Source: USGS)
    Range of values
    Minimum:25.494837
    Maximum:42.999999
    Units:Decimal Degrees
    Resolution:0.000001
    VLMrate(cm/yr)
    Rate of vertical change in land mass, negative values indicate land subsidence and positive values indicate uplift. (Source: USGS)
    Range of values
    Minimum:-1.268
    Maximum:0.819
    Units:cm per year
    Resolution:0.001
    VLMerror(cm/yr)
    1-sigma error of VLM rates (Source: USGS)
    Range of values
    Minimum:0
    Maximum:2.249
    Units:cm per year
    Resolution:0.001
    Entity_and_Attribute_Overview: The first line of the csv file is a header line.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Manoocher Shirzaei
    • Leonard O. Ohenhen
    • Anita Engelstad
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

These data are intended for policy makers, resource managers, science researchers, students, and the general public. These data can be used with geographic information systems or other software to identify and assess possible areas of vulnerability. These data are not intended to be used for navigation.

How was the data set created?

  1. From what previous works were the data drawn?
    Sentinel-1A/B (source 1 of 2)
    Agency, European Space, 2021, Sentinel-1 SAR: Copernicus, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: Source was used to build a seamless map of vertical land motion
    ALOS (source 2 of 2)
    Japan Aerospace Exploration Agency (JAXA), 2021, ALOS PALSAR: OpenTopography, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: Source was used to build a seamless map of vertical land motion
  2. How were the data generated, processed, and modified?
    Date: 15-Jan-2022 (process 1 of 3)
    Previously existing Synthetic Aperature Radar (SAR) data were mined to generate a seamless map of vertical land motion (VLM) for an up to approximately 100-km-wide strip along the U.S. Atlantic coastline. Multiple overlapping frames from ALOS and Sentinel-1A/B satellites were used to cover the entire area. We applied multitemporal InSAR processing to each frame in each viewing geometry to generate an extensive set of high-quality interferograms considering the spatial and temporal baselines. A Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) of 1-arcsecond (~30 m) spatial resolution (Farr and others, 2007) and precise satellite orbital information was used to calculate and remove the effect of topographic phase and flat earth correction (Franceschetti and others, 1999). The wavelet-based InSAR, an advanced multi-temporal SAR interferometric algorithm, was applied to reduce environmental artifacts and constrain temporally variable deformation fields (Shirzaei, 2013; Shirzaei and others, 2017). A suite of wavelet-based filters (Shirzaei and others 2012; Shirzaei, 2013) and atmospheric models (Yu and others, 2017, Yu and others, 2018) was used to reduce the residual topography-correlated component of the atmospheric phase delay and spatially uncorrelated DEM error. The set of corrected interferograms was inverted using a reweighted least-squares estimation to generate deformation time-series and velocities along the line-of-sight (LOS) direction. This operation was applied to all frames separately, then the data in the overlap zones of adjacent frames were used to perform an affine transformation and to obtain a seamless map of LOS displacement velocities for each viewing geometry (Blackwell and others, 2020; Ojha and others, 2018). Assuming a constant long-term velocity, a least-squares joint inversion was applied to combine the LOS velocity fields with observations of horizontal displacement velocity at the location of GNSS stations, which allowed for resolving the 3D deformation field at high resolution and mm/yr-level precision (Blackwell and others, 2020; Ojha and others, 2018). To translate estimated vertical deformation rates into a continental framework, vertical velocities of GNSS stations, calculated in IGS14 were used (Blewitt and others, 2013), followed by an affine transformation (including a shift, a scale factor, and two rotations) to transfer the estimated VLM into IGS14 (Blackwell and others, 2020; Ojha and others, 2018). Person who carried out this activity:
    Manoocher Shirzaei
    Virginia Tech
    Geophysicist
    925 Prices Fork Road
    Blacksburg, VA

    510-333-9305 (voice)
    shirzaei@vt.edu
    Data sources used in this process:
    • ALOS satellites
    • Sentinel-1A/B satellites
    Date: 16-May-2023 (process 2 of 3)
    Edits were made to correct spelling in author name. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (scochran@usgs.gov)
    Date: 29-Mar-2024 (process 3 of 3)
    Edits were made to correct the Time Period of Content ending date from 20211231 to 20201220 and the associated date range in the title from “Vertical land motion rates for the years 2007 to 2021…” to “Vertical land motion rates for the years 2007 to 2020…”. Within the abstract section, the date range of the Sentinel-1A/B data was corrected from 2014-2021 to 2015-2020, the resolution was updated from “~75 m” to “approximately 50-75 m”, and the term “uplift” added to clarify that the VLM data include both rates of land subsidence and uplift. The above date corrections were also applied within the lineage section, to the ending date of the Sentinel-1A/B source data and to the beginning date of the ALOS data. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (afoxgrover@usgs.gov )
  3. What similar or related data should the user be aware of?
    Barnard, Patrick L., Befus, Kevin, Danielson, Jeffrey J., Engelstad, Anita C., Erikson, Li H., Foxgrover, Amy C., Hardy, Matthew W., Hoover, Daniel J., Leijnse, Tim, Massey, Chris, McCall, Robert, Nadal-Caraballo, Norberto C., Nederhoff, Kees, Ohenhen, Leonard, O’Neill, Andrea C., Parker, Kai A., Shirzaei, Manoocher, Su, Xin, Thomas, Jennifer A., Ormondt, Maarten van, Vitousek, Sean F., Vos, Kilian, and Yawn, Madison C., 2023, Future coastal hazards along the U.S. North and South Carolina coasts.

    Other_Citation_Details:
    Barnard, P.L., Befus, K., Danielson, J.J., Engelstad, A.C., Erikson, L.H., Foxgrover, A.C., Hardy, M.W., Hoover, D.J., Leijnse, T., Massey, C., McCall, R., Nadal-Caraballo, N.C., Nederhoff, K., Ohenhen, L., O’Neill, A.C., Parker, K.A., Shirzaei, M., Su, X., Thomas, J.A., van Ormondt, M., Vitousek, S.F., Vos, K., and Yawn, M.C., 2023 Future coastal hazards along the U.S. North and South Carolina coasts: U.S. Geological Survey data release, https://doi.org/10.5066/P9W91314
    Blackwell, E., Shirzaei, M., Ojha, C., and Werth, S., 2020, Tracking California’s sinking coast from space: Implications for relative sea-level rise.

    Other_Citation_Details:
    Blackwell, E., Shirzaei, M., Ojha, C., and Werth, S., 2020, Tracking California’s sinking coast from space: Implications for relative sea-level rise: Science Advances, vol. 6, p. eaba4551, https://doi.org/10.1126/sciadv.aba4551.
    Blewitt, G., Creemer, C., Hammond, W.C., and Goldfarb, J.M., 2013, Terrestrial reference frame NA12 for crustal deformation studies in North America.

    Other_Citation_Details:
    Blewitt, G., Kreemer, C., Hammond, W.C., and Goldfarb, J.M., 2013, Terrestrial reference frame NA12 for crustal deformation studies in North America: Journal of Geodynamics, vol. 72, p. 11-24, https://doi.org/10.1016/j.jog.2013.08.004.
    Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D., 2007, The shuttle radar topography mission.

    Other_Citation_Details:
    Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M. Oskin, M., Burbank, D., and Alsdorf, D., 2007, The shuttle radar topography mission: Reviews of Geophysics, vol. 45, https://doi.org/10.1029/2005rg000183.
    Franceschetti, G., and Lanari, R., 1999, Synthetic aperture radar processing.

    Other_Citation_Details:
    Franceschetti G., and Lanari, R., 1999, Synthetic aperture radar processing: CRC Press, 324 p.
    Ojha, C., Shirzaei, M., Werth, S., Argus, D.F., and Farr, T.G., 2018, Sustained groundwater loss in California’s Central Valley exacerbated by intense drought periods.

    Other_Citation_Details:
    Ojha, C., Shirzaei, M., Werth, S., Argus, D.F., and Farr, T.G., 2018, Sustained groundwater loss in California's Central Valley exacerbated by intense drought periods: Water resources research, vol. 54, p. 4449-4460, https://doi.org/10.1029/2017WR022250.
    Shirzaei, M., and Bürgmann, R., 2012, Topography correlated atmospheric delay correction in radar interferometry using wavelet transforms.

    Other_Citation_Details:
    Shirzaei M., and Bürgmann, R., 2012, Topography correlated atmospheric delay correction in radar interferometry using wavelet transforms: Geophysical Research Letters, vol. 39, https://doi.org/10.1029/2011GL049971.
    Shirzaei, M., 2013, A wavelet-based multitemporal DInSAR algorithm for monitoring ground surface motion.

    Other_Citation_Details:
    Shirzaei, M., 2013, A wavelet-based multitemporal DInSAR algorithm for monitoring ground surface motion, (in English): Ieee Geoscience and Remote Sensing Letters, vol. 10, p. 456-460, https://doi.org/10.1109/Lgrs.2012.2208935.
    Shirzaei, M., Bürgmann, R., and Fielding, E.J., 2017, Applicability of Sentinel-1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area.

    Other_Citation_Details:
    Shirzaei, M., Bürgmann, R., and Fielding, E.J., 2017, Applicability of Sentinel-1 Terrain Observation by Progressive Scans multitemporal interferometry for monitoring slow ground motions in the San Francisco Bay Area: Geophysical Research Letters, vol. 44, p. 2733-2742, https://doi.org/10.1002/2017GL072663.
    Werner, C., Wegmüller, U., Strozzi, T., and Wiesmann, A., 2000, Gamma SAR and interferometric processing software.

    Other_Citation_Details:
    Werner, C., Wegmüller, U., Strozzi, T., and Wiesmann, A., 2000, Gamma SAR and interferometric processing software: Proceedings of the ers-envisat symposium, Gothenburg, Sweden, v. 1620.
    Yu, C., Penna, N.T., and Li, Z., 2017, Generation of real-time mode high-resolution water vapor fields from GPS observations.

    Other_Citation_Details:
    Yu, C., Penna, N.T., and Li, Z., 2017, Generation of real‐time mode high‐resolution water vapor fields from GPS observations: Journal of Geophysical Research: Atmospheres, vol. 122, p. 2008-2025, https://doi.org/10.1002/2016JD025753.
    Yu, C., Li, Z., Penna, N.T., and Crippa, P., 2018, Generic atmospheric correction model for Interferometric Synthetic Aperture Radar observations.

    Other_Citation_Details:
    Yu, C., Li, Z., Penna, N.T., and Crippa, P., 2018, Generic atmospheric correction model for Interferometric Synthetic Aperture Radar observations: Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2017JB015305.

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    A formal accuracy assessment using an independent set of GNSS vertical was conducted. Results are within the expected range of 1 mm/yr standard deviation.
  2. How accurate are the geographic locations?
    A formal accuracy assessment of the horizontal positional information in the data set has not been conducted.
  3. How accurate are the heights or depths?
    Data accuracy is assumed to be close to 1mm/yr.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
  5. How consistent are the relationships among the observations, including topology?
    Data fall within expected/reasonable ranges.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_Constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey as the originator of the dataset and in products derived from these data.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - CMGDS
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov
  2. What's the catalog number I need to order this data set? These data are available in csv format in 11 zip files named VerticalLandMotion_Rate_STATE.zip where STATE refers to the state (CT - Connecticut, DE - Delaware, FL - Florida, GA - Georgia, MA – Massachusetts, MD - Maryland, NH – New Hampshire, NJ - New Jersey, NY - New York, RI - Rhode Island, and VA - Virginia) for which data are available.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: Zip file containing csv with VLM data for Connecticut in format comma-delimited text Size: 21.8
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Delaware in format comma-delimited text Size: 8.8
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Florida in format comma-delimited text Size: 43.6
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Georgia in format comma-delimited text Size: 27.7
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Massachusetts in format comma-delimited text Size: 33.3
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Maryland in format comma-delimited text Size: 25.1
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for New Hampshire in format comma-delimited text Size: 2.5
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for New Jersey in format comma-delimited text Size: 41.8
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for New York in format comma-delimited text Size: 21.5
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Rhode Island in format comma-delimited text Size: 3.3
      Network links: https://doi.org/10.5066/P9BQQTCI
      Data format: Zip file containing csv with VLM data for Virginia in format comma-delimited text Size: 11.7
      Network links: https://doi.org/10.5066/P9BQQTCI
    • Cost to order the data: None.

  5. What hardware or software do I need in order to use the data set?
    The csv data can be viewed with any text reading software.

Who wrote the metadata?

Dates:
Last modified: 29-Mar-2024
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/CMGDS_DR_tool/DR_P9BQQTCI/VerticalLandMotion_Rate_metadata.faq.html>
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