Projected Seafloor Elevation Change and Relative Sea Level Rise Along the Florida Reef Tract from Miami to Boca Chica Key 25, 50, 75, and 100 Years from 2016

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Frequently anticipated questions:

What does this data set describe?

Title:
Projected Seafloor Elevation Change and Relative Sea Level Rise Along the Florida Reef Tract from Miami to Boca Chica Key 25, 50, 75, and 100 Years from 2016
Abstract:
The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes along the Florida Reef Tract (FRT) from Miami to Boca Chica Key, Florida. Changes in seafloor elevation were calculated from the 1930s to 2016 using digitized hydrographic sheet sounding data and light detection and ranging (lidar)-derived digital elevation models (DEMs) acquired by the National Oceanic and Atmospheric Administration (NOAA) in 2016 and 2017. Most of the elevation data from the 2016/2017 time period was collected during 2016, and, as an abbreviated naming convention, this time period was referred to as 2016. An elevation change analysis between the 1930s and 2016 data was performed to quantify and map historical impacts to seafloor elevation and to determine elevation-change statistics for 15 habitat types found within the study area along the FRT. Annual elevation-change rates were calculated for each elevation-change data point. Seafloor elevation-change along the FRT was projected 25, 50, 75 and 100 years from 2016 using these historical annual rates of elevation change. Water depth was projected 25, 50, 75 and 100 years from 2016 using historical rates of annual elevation change plus 2016 local sea level rise (SLR) data from NOAA. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.
Supplemental_Information:
The 2016 lidar data were collected by the NOAA National Geodetic Survey (NGS) Remote Sensing Division using a Riegl VQ-820-G System. The lidar data are an ancillary product of NOAA's Coastal Mapping Program (CMP), created through a wider Integrated Ocean and Coastal Mapping initiative to increase support for multiple uses of the data. Hydrographic survey data were retrieved from NOAA using the Bathymetric Data Viewer (NOAA, 2014). Relative sea level trend data were retrieved from NOAA Tides and Currents portal via two tidal stations, Station 8723970 at Vaca Key (NOAA, 2020a) and Station 8724580 at Key West (NOAA, 2020b). An average of the documented rates between the two stations for 2016 was used for relative sea level rise (RSLR) calculations in this release.
  1. How might this data set be cited?
    Yates, Kimberly K., and Fehr, Zachery W., 20220125, Projected Seafloor Elevation Change and Relative Sea Level Rise Along the Florida Reef Tract from Miami to Boca Chica Key 25, 50, 75, and 100 Years from 2016:.

    This is part of the following larger work.

    Yates, Kimberly K., and Fehr, Zachery W., 20220125, Projected Seafloor Elevation Change and Relative Sea Level Rise Along the Florida Reef Tract from Miami to Boca Chica Key 25, 50, 75, and 100 Years from 2016: U.S. Geological Survey data release doi:10.5066/P9B97Z7F, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.9480352
    East_Bounding_Coordinate: -80.0873958
    North_Bounding_Coordinate: 25.7965009
    South_Bounding_Coordinate: 24.4428661
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 1934
    Ending_Date: 2116
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Tabular, vector, and raster digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set.
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 17
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -81.0
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.6096
      Ordinates (y-coordinates) are specified to the nearest 0.6096
      Planar coordinates are specified in METERS
      The horizontal datum used is North American Datum of 1983 National Spatial Reference System (2007).
      The ellipsoid used is GRS_1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988 (NAVD88) GEOID12B
      Altitude_Resolution: 0.2
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    The detailed attribute descriptions for the elevation change data, projected elevation change analysis, and habitat definitions are provided in the included data dictionaries (DataDictionary_Shapefile.pdf, DataDictionary_HabitatTypes.pdf, and DataDictionary_ElevationChangeAnalysis_FRT.pdf). These metadata are not complete without these files.
    Entity_and_Attribute_Detail_Citation:
    The entity and attribute information were generated by the individual and/or agency identified as the originator of the dataset. Please review the rest of the metadata record for additional details and information.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Kimberly K. Yates
    • Zachery W. Fehr
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    Kimberly K. Yates
    Southeast Region: ST. PETE COASTAL & MARINE SC
    Research Oceanographer
    600 4Th Street South
    St. Petersburg, FL
    United States

    727-502-8059 (voice)
    kyates@usgs.gov

Why was the data set created?

These data were used to determine projected elevation-change and water depth values along the Florida Reef Tract from Miami to Boca Chica Key and for fifteen habitat types located within the study area.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 2020 (process 1 of 11)
    Step 1: Seafloor elevation and volume change analyses were performed using methods from Yates and others (2017). The original Digital Elevation Models (DEMs) were acquired from NOAA’s DigitalCoast website (https://coast.noaa.gov/digitalcoast/) using the Data Access Viewer (DAV) tool. The elevation search option (https://coast.noaa.gov/dataviewer/#/lidar/search/) was used to download four topobathymetric datasets, specifically the 2016 NOAA NGS Topobathy Lidar: Florida Keys Outer Reef Blocks 01, 02, 03, and 2017 NOAA NGS Topobathy Lidar: Florida Keys Outer Reef Block 04; collectively referred to as the 2016 lidar data in this release (NOAA, 2017a-c, 2018). The four DEMs were created in September 2017 and correspond to the North American Datum of 1983 (NAD83) National Spatial Reference System (NSRS2007) horizontal datum, and North American Vertical Datum of 1988 (NAVD88) GEOID12B vertical datum. The data were downloaded in 2020 from the DAV with the following specifications; Projection: Universal Transverse Mercator (UTM), Zone: Zone 17 Range 084W-078W, Horizontal Datum: NAD83, Horizontal Units: Meters, Vertical Datum: NAVD88, Vertical Units: Meters, File Format: TIFF 32-bit Float, Bin Method: TIN, Bin Size: 1.0, Bin Units: Meters, Data Classification: Bathymetric Lidar Points, Data Returns: Any Points, Ancillary Data: No Ancillary Data and Geoid Name: GEOID12B. Specifying a raster as the output product yields a DEM, even though lidar point cloud data were added to the cart. Using the DAV to create a DEM with the desired projection and datum options from the point data introduces less uncertainty than transforming the pre-made DEM with NOAA's Vertical Datum Transformation tool, VDatum (https://vdatum.noaa.gov/).
    Date: 2020 (process 2 of 11)
    Step 2: The 2016 lidar data (blocks 01-04) were merged into a single tagged image file format (TIFF) raster using the “Mosaic To New Raster (Data Management)” tool in ArcMap 10.7. Blocks were merged by prioritizing the most recently collected block in areas of overlap. This was accomplished by utilizing the following parameters: blocks 01-04 were used as input raster files and were loaded in numerical order; Output Location was set to a personal workspace; Raster Dataset Name with Extension: 2016_Data_Mosaic_Last.tif; Pixel Type (optional): 32_BIT_FLOAT; Number of Bands: 1; and Mosaic Operator (optional); LAST. All other parameters were left as default. A footprint of the original 2016 lidar data was then created by opening the DEM in ArcMap. The “Reclassify (Spatial Analyst)” tool was used to replace all old values in the reclassification table with ‘1’ in the new values column and keeping ‘No Data’ value as ‘No Data.’ The resulting TIFF was converted into a footprint using the “Raster to Polygon (Conversion)” tool to convert the raster file into a polygon shapefile. The output shapefile was named 2016_original_lidar_footprint.shp.
    Date: 2020 (process 3 of 11)
    Step 3: The DEM was then edited for removal of reduced quality data. Using Global Mapper 20.1, each DEM was visually inspected using the methods of Yates and others (2017) to identify areas characterized by coarse interpolation relative to surrounding areas and appearing as small groups of large triangles created during the development of each DEM. Polygons were manually drawn to encompass areas for removal using the “Digitizer” tool and the ‘Create Area/Polygon Features’ function. By default, these polygons are generated in the ‘User Created Features’ layer within the Control Center. The ‘User Created Features’ layer was then exported as a polygon shapefile. The 2016 data has four polygon shapefiles (Block01_2016lidar_RemovedPolygons.shp, Block02_2016lidar_RemovedPolygons.shp, Block03_2016lidar_RemovedPolygons.shp, and Block04_2016lidar_RemovedPolygons.shp). The “Erase (Analysis)” tool in ArcMap was used to remove the areas of reduced quality data from the 2016_original_lidar_footprint.shp shapefile creating the 2016_edited_lidar_footprint.shp shapefile. Using the “Clip (Data Management)” tool, areas of reduced quality data were removed from the 2016 lidar DEM by clipping the DEM to the 2016_edited_lidar_footprint.shp shapefile, creating the 2016_edited_lidar_FRT.tif file. Files used in this analysis are available in the FRT_LidarFiles.zip download file.
    Date: 2020 (process 4 of 11)
    Step 4: The original, digitized 1930s H-sheet bathymetry sounding data (XYZ formatted files) were downloaded from the NOAA Bathymetric Data Viewer (NOAA, 2014) at, https://www.ncei.noaa.gov/maps/bathymetry/. For this study, all available H-sheet data acquired from 1934-1939 and located on the seaward side of the Florida Keys (extending from Biscayne Bay to Boca Chica Key were downloaded and inspected for erroneous data points. Individual data points derived from H-sheet data were compared to digitized map data to identify and remove double entries, typographical, and other errors during conversion of hand-written to digitized data. Using VDatum version 3.9 (https://vdatum.noaa.gov/), a publicly available software package from NOAA, the H-sheet’s vertical datum was transformed from mean lower low water (MLLW) to NAVD88 (GEOID12B), and the horizontal datum was transformed from NAD83 (1986) geographic coordinates to UTM Zone 17N NAD83 (NSRS2007). The H-sheet data were also converted from soundings in meters (m) to height (m) during the transformation process using VDatum, converting original positive sounding data values into negative data to represent historical elevation. For more information on downloading, preparing, and using H-sheet data, see Yates and others (2017). One variation from the methodologies established in Yates and others (2017) concerns the application of RSLR corrections and is documented in detail in Step 5. All digitized H-sheet XYZ files were merged together into the FRT_HistoricalPoints.shp shapefile using the “Merge (Data management)” tool. Hydrographic survey numbers (Survey) for each point can be found in the attribute table of the FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.shp shapefile, which is included in the FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.zip download file.
    Date: 2020 (process 5 of 11)
    Step 5: The FRT_HistoricalPoints.shp shapefile elevation values were corrected for RSLR by applying (SLR) rate estimates collected by NOAA at the Key West and Vaca Key, Florida tide stations. The RSLR correction was calculated by adding a field to the attribute table of the FRT_HistoricalPoints.shp shapefile using the Field Calculator in ArcMap 10.7 and the expression HistE_SLRc = [Historical] - ((2016 - [Year]) * 0.00302), where 0.00302 is the mean annual rate of sea level rise in meters computed from the tide stations, [Historical] are original sounding data reprojected to elevation data, and [Year] is the attribute value containing the hydrographic survey collection year associated with each point. RSLR corrections for this study were made to the data within the attribute table of the shapefile. In previous studies, sea level rise corrections were made to the source XYZ sounding data before VDatum transformations (Step 4). Calculations for RSLR have been modified to reflect correct mathematical signage in terms of elevation and to prevent the need to modify source XYZ data. For additional information on applying SLR corrections to sounding data, see Yates and others (2017).
    Date: 2020 (process 6 of 11)
    Step 6: The historical points shapefile was clipped to the extent of the merged DEM using the "Clip (Analysis)" tool by specifying the FRT_HistoricalPoints.shp shapefile as the 'Input Features' and the 2016_edited_lidar_footprint.shp shapefile as the 'Clip Features', creating the 1934_1939_2016_lidar_extent_HistoricalPoints.shp shapefile.
    Date: 2020 (process 7 of 11)
    Step 7: Values from the merged DEM were extracted at the location of the historical points using the "Extract Values to Points (Spatial Analyst)" tool by specifying the 1934_1939_2016_lidar_extent_HistoricalPoints.shp shapefile as the 'Input Point Features' and the 2016_edited_lidar_FRT.tif file as the 'Input Raster', creating the FRT_SeafloorElevationChangePoints.shp shapefile. The elevation difference (Diff_m) between the historical sounding data (HistE_SLRc) and modern lidar data (2016_elev) were calculated by adding a field to the attribute table of the FRT_SeafloorElevationChangePoints.shp shapefile using the Field Calculator and the expression Diff_m = [2016_elev] – [HistE_SLRc], where [2016_elev] is the extracted lidar elevation data and HistE_SLRC is the RSLR corrected historic sounding elevation data. Elevation change rates (Rate) were determined at each point by creating a new attribute in the FRT_SeafloorElevationChangePoints.shp shapefile and using the Field Calculator with the expression Rate = [Diff_m] / (2016 - [Year]).
    Date: 2020 (process 8 of 11)
    Step 8: Additional fields were added to the attribute table to represent projected elevations (N_e) based on elevation change rates, projected elevation change (N_e_diff) based on N_e elevations, projected water depth (N_WDepth) after accounting for RSLR and projected elevation change, and projected relative sea level rise (N_RSLR) based on NOAA SLR rates and projected elevation change rates, where N represents the number of years projected from the collection date of the most recent bathymetry used in the analysis: 25_e, 50_e, 75_e, 100_e, 25_e_diff, 50_e_diff, 75_e_diff, 100_e_diff, 25_WDepth, 50_WDepth, 75_WDepth, 100_WDepth, 25_RSLR, 50_RSLR, 75_RSLR, and 100_RSLR. The Field Calculator was used to populate values for each of these attributes using the following equations: N_e = [2016_elev]+(N*[Rate]), N_e_diff = [N_e]-[2016_elev], N_WDepth = Abs([2016_elev]+(N*[Rate]))+(N*0.00302), N_RSLR = (N*0.00302)–[N_e_diff]. The shapefile was then exported as FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.shp. Further information on the attributes and their definitions can be found in the DataDictionary_Shapefile.pdf file in the FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.zip download file.
    Date: 2020 (process 9 of 11)
    Step 9: The original Unified Florida Reef Tract Map version 2.0 shapefile was downloaded from http://ocean.floridamarine.org/IntegratedReefMap/UnifiedReefTract.htm (FWRI, 2016). Using ArcMap, the original habitat shapefile was modified using the "Clip (Analysis)" tool to clip the habitat shapefile to the extent of the merged DEM by specifying the habitat shapefile as the 'Input Features' and the 2016_edited_lidar_FRT_footprint.shp shapefile as the 'Clip Features', creating the FRT_HabitatClip.shp shapefile. Using the "Select by Attribute" tool, 15 individual habitat shapefiles were created from FRT_HabitatClip.shp using the "Select by Attribute" tool to select one ClassLv2 habitat and exporting it as a separate shapefile. Additionally, a polygon was created by using an editing session to encompass all point locations that do not fall within the bounds of the original Unified Florida Reef Tract Map Version 2.0 shapefile. Within the newly created shapefile, an attribute was added named ClassLv2. All data points included in the ClassLv2 attribute were assigned the value, "Unclassified", prior to saving and closing the editing session. This polygon was then merged with the FRT_HabitatClip.shp shapefile using the Merge (Data Management) tool to create the FRT_HabitatClip_UnclassifiedAdded.shp shapefile included in the FRT_HabitatClip.zip download file.
    Date: 2020 (process 10 of 11)
    Step 10: Elevation-change statistics were determined by habitat type using the XYZ points from the FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.shp shapefile. The "Select Layer by Location (Data Management)" tool was used to extract points within or on the boundary of a specific habitat type by using the following parameters: Input Feature Layer: FRT_ProjectedElevationChangePoints_25yr_50yr_75yr_and_100yr_from_2016.shp shapefile; Relationship: INTERSECT; Selecting Features: Habitat shapefile; Search Distance: left blank; and Selection type: NEW_SELECTION. The Seafloor Elevation Change Analysis Tool (SECAT) was created to automate the process, since these steps had to be repeated for 15 habitat types under 5 different scenarios: one between historical hydrographic data and contemporary lidar data, and one for each N_e_diff attribute. Elevation change statistics were compiled by habitat type into the following comma-separated values (CSV) files: FRT_Historical.csv, FRT_25e.csv, FRT_50e.csv, FRT_75e.csv, and FRT_100e.csv and are provided in FRT_ProjectedSECAT.zip. For more information on SECAT and its capabilities see Zieg and Zawada (2021).
    Date: 2020 (process 11 of 11)
    Step 11: Maps were rendered for each projected change attribute calculated in Step 8. Maps appended in ‘N_e_diff' depict projected elevation change at each point N years into the future. Projected RSLR maps that consider projected elevation change analyses were developed using separate symbology palettes and were appended ‘N_RSLR,’ respectively. All maps are available within the FRT_ProjectionMaps.zip download file.
  3. What similar or related data should the user be aware of?
    Yates, Kimberly K., Zawada, David G., Smiley, Nathan A., and Tiling-Range, Ginger, 20170420, Divergence of seafloor elevation and sea level rise in coral reef ecosystems: Biogeosciences, Munich, Germany.

    Online Links:

    National Oceanic and Atmospheric Administration Office for Coastal Management, 20170914, 2016 NOAA NGS Topobathy Lidar DEM: Florida Keys Outer Reef Block 01: NOAA, Charleston, South Carolina.

    Online Links:

    Other_Citation_Details: 2017a
    National Oceanic and Atmospheric Administration Office for Coastal Management, 20170614, 2016 NOAA NGS Topobathy Lidar DEM: Florida Keys Outer Reef Block 02: NOAA, Charleston, South Carolina.

    Online Links:

    Other_Citation_Details: 2017b
    National Oceanic and Atmospheric Administration Office for Coastal Management, 20170712, 2016 NOAA NGS Topobathy Lidar DEM: Florida Keys Outer Reef Block 03: NOAA, Charleston, South Carolina.

    Online Links:

    Other_Citation_Details: 2017c
    National Oceanic and Atmospheric Administration Office for Coastal Management, 20180219, 2017 NOAA NGS Topobathy Lidar DEM: Florida Keys Outer Reef Block 04: NOAA, Charleston, South Carolina.

    Online Links:

    Zieg, Jonathan A., and Zawada, David G., 20210211, Seafloor Elevation Change Analysis Tool: U.S. Geological Survey, St. Petersburg, Florida.

    Online Links:

    NOAA National Centers for Environmental Information (NCEI), and NOAA National Ocean Service (NOS), 20140114, NOS Hydrographic Survey Data (Dynamic Map Service): NOAA, Boulder, Colorado.

    Online Links:

    NOAA Tides & Currents, 20200801, Vaca Key, Florida Bay, FL - Station ID: 8723970: NOAA, Washington, D.C..

    Online Links:

    Other_Citation_Details: 2020a
    NOAA Tides & Currents, 20200801, Key West, FL - Station ID: 8724580: NOAA, Washington, D.C..

    Online Links:

    Other_Citation_Details: 2020b
    Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute (FWRI), 20160901, Unified Florida Coral Reef Tract Map v2.0: FWRI, St. Petersburg, Florida.

    Online Links:

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

  1. How well have the observations been checked?
    Digital elevation maps were visually inspected by cross-checking them with other contemporary, overlapping datasets such as satellite and airborne imagery and/or other digital elevation maps in Esri ArcGIS for identification of anomalous elevations or data inconsistencies. Anomalous elevations were removed using methods described in Process Steps 3 and 4. The most recently collected data was used for elevation-change analyses where lidar data blocks overlapped, as described in Process Step 2.
  2. How accurate are the geographic locations?
    For the 2016 lidar used to produce the point dataset, the elevation positions were obtained using post-processed kinematic global positioning system (KGPS) methods. The horizontal accuracy of the data is better than plus or minus 1.0 meter (m); Quantitative Value: 1.0 m.
  3. How accurate are the heights or depths?
    Lidar data positions were obtained using post-processed KGPS methods. Data used to validate the lidar were collected with static GPS observational equipment and compared against the published data. The vertical accuracy of the data is better than plus or minus 0.15 m; Quantitative Value: 0.15 m.
  4. Where are the gaps in the data? What is missing?
    This dataset is considered complete for the information presented, as described in the abstract section. Users are advised to read the rest of the metadata record and Yates and others (2017) for additional details.
  5. How consistent are the relationships among the observations, including topology?
    Data cover the area specified for this project, without any known issues.

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:
Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. The U.S. Geological Survey requests to be acknowledged as originator of these data in future products or derivative research.
  1. Who distributes the data set? (Distributor 1 of 1)
    Kimberly K. Yates
    Southeast Region: ST. PETE COASTAL & MARINE SC
    Research Oceanographer
    600 4Th Street South
    St. Petersburg, FL
    United States

    727-502-8059 (voice)
    kyates@usgs.gov
  2. What's the catalog number I need to order this data set?
  3. What legal disclaimers am I supposed to read?
    Although these data have been processed successfully on a computer system at 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. The USGS shall not be held liable for improper or incorrect use of the data described or contained herein. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 25-Jan-2022
Metadata author:
Kimberly K. Yates
Southeast Region: ST. PETE COASTAL & MARINE SC
Research Oceanographer
600 4th Street South
St. Petersburg, FL
United States

727-502-8059 (voice)
kyates@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/FRT_Projected_Elevation_Change_25yr_50yr_75yr_and_100yr_from_2016_Metadata.faq.html>
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