Coastal Features Extracted from Landsat Satellite Imagery, Delaware Bay, New Jersey to Shinnecock Bay, New York, 2008-2022

Frequently anticipated questions:

• What does this data set describe?
  1. How might this data set be cited?
  2. What geographic area does the data set cover?
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
  5. What is the general form of this data set?
  6. How does the data set represent geographic features?
  7. How does the data set describe geographic features?
• Who produced the data set?
  1. Who are the originators of the data set?
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
• Why was the data set created?
• How was the data set created?
  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
  3. What similar or related data should the user be aware of?
• How reliable are the data; what problems remain in the data set?
  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
  5. How consistent are the relationships among the data, including topology?
• How can someone get a copy of the data set?
  1. Are there legal restrictions on access or use of the data?
  2. Who distributes the data?
  3. What's the catalog number I need to order this data set?
  4. What legal disclaimers am I supposed to read?
  5. How can I download or order the data?
• Who wrote the metadata?

What does this data set describe?

1. How might this data set be cited?
   Bernier, Julie C., and Nick, Sydney K., 20240307, Coastal Features Extracted from Landsat Satellite Imagery, Delaware Bay, New Jersey to Shinnecock Bay, New York, 2008-2022:.

   This is part of the following larger work.
   Bernier, Julie C., Nick, Sydney K., and Miselis, Jennifer L., 20240307, Coastal Land-Cover and Feature Datasets Derived from Landsat Satellite Imagery, Delaware Bay, New Jersey to Shinnecock Bay, New York: U.S. Geological Survey data release doi:10.5066/P13HX6Y8, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

   Online Links:

   • https://doi.org/10.5066/P13HX6Y8

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -75.051314
   East_Bounding_Coordinate: -72.410569
   North_Bounding_Coordinate: 40.907352
   South_Bounding_Coordinate: 38.925725
   

3. What does it look like?
4. Does the data set describe conditions during a particular time period?

   Beginning_Date: 16-Apr-2008

   Ending_Date: 26-Nov-2022

   Currentness_Reference:

   Ground condition

5. What is the general form of this data set?

   Geospatial_Data_Presentation_Form: tabular 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: 18
      Transverse_Mercator:

      Scale_Factor_at_Central_Meridian: 1.0
      Longitude_of_Central_Meridian: -75.0
      Latitude_of_Projection_Origin: 0
      False_Easting: 500000.0
      False_Northing: 0.0
      

      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 30
      Ordinates (y-coordinates) are specified to the nearest 30
      Planar coordinates are specified in Meters
      The horizontal datum used is D WGS 1984.
      The ellipsoid used is WGS 1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.25722.
      
7. How does the data set describe geographic features?

   nfwf_features.zip

   Zip archive containing vector shoreline (nfwf_1332_ny_shrln.shp, nfwf_1332_nj_shrln.shp, nfwf_1333_shrln.shp, nfwf_1433_shrln.shp) and sand (nfwf_1332_ny_sandext.shp, nfwf_1332_nj_sandext.shp, nfwf_1333_sandext.shp, nfwf_1433_sandext.shp) feature extents corresponding to each of 143 thematic land-cover raster datasets in Esri shapefile (.shp) format. (Source: USGS)

   FID

   Internal feature number (Source: Esri) Sequential unique whole numbers that are automatically generated

   Shape*

   Feature geometry (Source: Esri) Coordinates defining the features

   Image_Date

   Image acquisition date (Source: USGS) Source image acquisition date written as YYYMMDD (4-digit year, 2-digit month, 2-digit day)

   Source

   Image source (Source: USGS)

   Value	Definition
   Landsat5	Source imagery is Landsat 5 TM
   Landsat8	Source imagery is Landsat 8 OLI

Who produced the data set?

1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
   • Julie C. Bernier
   • Sydney K. Nick
2. Who also contributed to the data set?
   U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center
3. To whom should users address questions about the data?
   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

Why was the data set created?

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: 2023 (process 1 of 5)

   The regional AOI, which was derived from the National Oceanic and Atmospheric Administration (NOAA) US Coastal Zone Management Act Boundary, was subset into four scene-specific AOIs for processing and analysis: WRS-2 path 13 row 32 New York (nfwf_1332_ny), WRS-2 path 13 row 32 New Jersey (nfwf_1332_nj), WRS-2 path 13 row 33 (nfwf_1333), and WRS-2 path 14 row 33 (nfwf_1433). Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

   Date: 2023 (process 2 of 5)

   For each image acquisition date, Collection-2, Level-2 surface reflectance (SR; reflective bands), surface temperature (ST; thermal infrared [TIR] bands), and SR-derived NDVI images were downloaded from the EROS ESPA On Demand Interface (https://espa.cr.usgs.gov/). Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

   Date: 2023 (process 3 of 5)

   All images were batch-processed using Spatial Model Editor in ERDAS IMAGINE. The SR and ST bands were stacked to create 7- (TM) or 8- (OLI) band multispectral images and clipped to the scene-specific AOI. From these composite images, two additional spectral indices, mNDWI (Xu, 2006) and NBLI (Li and others, 2017), were calculated. Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

   Date: 2023 (process 4 of 5)

   Land-cover classification was modified from the workflow described by Bernier and others (2021) using multilevel (2 thresholds per image) automatic thresholding of spectral indices and applying a rule-based classification scheme. First, Otsu's method (Otsu, 1979) for automatic histogram thresholding was applied to mNDWI images to create a binary "land"-water raster for each image acquisition date. Second, water area was masked from NBLI and NDVI images, and Otsu's method was applied to the masked images to create binary bare earth-"unclassed" and vegetated-"unclassed" rasters, respectively. The following rule-based classification was then applied, where T1 and T2 represent the first and second multi-Otsu thresholds, respectively, for each spectral index: If mNDWI > T2 then class (1) = water If NBLI > T2 then class (3) = bare earth (sand) If T1 < NBLI < T2 and UVVR = NoData then class (12) = unclassed If (T1 < NBLI < T2 and NDVI > T1 and UVVR > 0) or (NBLI < T1 and NDVI < T1) then class (4) = vegetated If NBLI < T1 and NDVI < T1 then class (11) = unclassed Where UVVR is the 2014-2018 composite Unvegetated to Vegetated Ratio for the U.S. Atlantic Coast (Couvillion and others, 2021) subset to the regional AOI. Finally, the binary land-cover images were converted to thematic rasters, merged, and single-pixel "clumps" were removed using a 3x3 majority filter to create a final land-cover raster dataset. The resulting land-cover rasters use the naming convention YYYYMMDD_lt05_AOI_lcr_ce.img (Landsat 5) or YYYYMMDD_lc08_AOI_lcr_ce.img (Landsat 8), where YYYYMMDD denotes the image-acquisition date (4-digit year, 2-digit month, 2-digit day) and AOI denotes the scene-specific AOI. All steps were batch-processed using the Image Processing toolbox in MATLAB (Otsu thresholding and binary image creation) or Spatial Model Editor in ERDAS IMAGINE (spectral index masking and generation of classed land-cover rasters). Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

   Date: 2024 (process 5 of 5)

   Vector sea- and back-barrier shorelines (representing the boundary between intertidal and open-water areas) and the landward sand extents were extracted by contouring the mNDWI and masked NBLI images using the calculated Otsu thresholds. Vectors representing estuarine marsh mainland or island shorelines are not included except along the Reservation at Shinnecock Neck on Shinnecock Bay, which is a NFWF restoration site. The resulting shapefiles (one per image acquisition date) were merged into 2 shapefiles (one each for shoreline and sand features) for each scene-specific AOI. The resulting sand (sandext) and shoreline (shrln) vector shapefiles use the naming convention nfwf_AOI_shrln.shp or nfwf_AOI_sandext.shp, where AOI denotes the scene-specific AOI. Contouring was batch-processed using Python 3 Jupyter Notebooks in ArcGIS Pro. Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Julie C. Bernier

   Geologist

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   jbernier@usgs.gov

   Data sources produced in this process:

   • nfwf_AOI_shrln.shp
   • nfwf_AOI_sandext.shp

3. What similar or related data should the user be aware of?
   Bernier, Julie C., Miselis, Jennifer L., and Plant, Nathaniel G., 20210921, Satellite-Derived Barrier Response and Recovery Following Natural and Anthropogenic Perturbations, Northern Chandeleur Islands, Louisiana: Remote Sensing 13(18), 3778; Special Issue "New Insights into Ecosystem Monitoring Using Geospatial Techniques".

   Online Links:

   • https://doi.org/10.3390/rs13183779

   Xu, Hanqiu, 20070222, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery: International Journal of Remote Sensing Volume 27, Issue 14.

   Online Links:

   • https://doi.org/10.1080/01431160600589179

   Other_Citation_Details: Pages 3025-3033
   

   Li, Hui, Wang, Cuizhen, Zhong, Cheng, Su, Aijun, Xiong, Chengren, Wang, Jinge, and Liu, Junqi, 20170307, Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index: Remote Sensing 9(3), 249.

   Online Links:

   • https://doi.org/10.3390/rs9030249

   Otsu, Nobuyuki, 197901, A Threshold Selection Method from Gray-Level Histograms: IEEE Transactions on Systems, Man and Cybernetics Volume 9, Issue 1.

   Online Links:

   • https://doi.org/10.1109/TSMC.1979.4310076

   Other_Citation_Details: Pages 62-66
   

   Couvillion, Brady R., Ganju, Neil K., and Defne, Zafer, 20210205, An Unvegetated to Vegetated Ratio (UVVR) for Coastal Wetlands of the Conterminous United States (2014-2018): U.S. Geological Survey data release doi:10.5066/P97DQXZP, U.S. Geological Survey, Woods Hole, MA.

   Online Links:

   • https://doi.org/10.5066/P97DQXZP

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

1. How well have the observations been checked?
   Due to the large spatial extent of this analysis and a lack of ground-truth datasets with similar temporal resolution, regional-scale classification accuracy was not quantitatively assessed. Using a similar methodology and workflow, Bernier and others (2021) reported classification accuracies (>=70%) that were comparable to accuracies reported for the National Land Cover Database (NLCD). Visual comparison of the classed land-cover rasters derived in this study with available NLCD data showed good agreement, except in some densely populated (Ocean City Beach on Pecks Beach Island, Atlantic City on Absecon Island, and Beach Haven and Long Beach on Long Beach Island, NJ) or modified (frequent beach scraping at Wildwoods on 5 Mile Beach, NJ) locations, where built (urban) areas landward of the beach were misclassed as sand. In these locations, vector sand contours that included significant built areas were excluded from the final dataset.
2. How accurate are the geographic locations?
   Geodetic accuracy of Landsat data products depend on the accuracy of the ground control points and the resolution of the digital elevation model (DEM) used. All Level-2 science products are derived from Level-1 Tier-1 Precision and Terrain (L1TP) corrected data and meet pre-defined image-to-image georegistration tolerances of <= 12-meter (m) radial root mean square error (RMSE). The positional accuracy of the satellite-derived features was not systematically evaluated in this study; however, recent analyses of satellite-derived shoreline (SDS) positions extracted using similar methods to those presented here report offsets of 1/3 to 1/2-pixel (10 to 15 m) seaward of measured in-situ shoreline positions. Compared with methods that extract shoreline position from precise elevation measurements (for example, , light detection and ranging [lidar] or global positioning system [GPS] surveys), SDS positions are not based on a vertical datum (for example, mean sea level or mean high water); instead, SDS represent instantaneous waterlines at time of image acquisition and may include intertidal areas.
3. How accurate are the heights or depths?
   
4. Where are the gaps in the data? What is missing?
   54 Landsat TM and 95 OLI images (Worldwide Reference System 2 [WRS-2] path 13 row 32, WRS-2 path 13 row 33, and WRS-2 path 14 row 33) acquired between April 2008 and November 2022 that were cloud- and snow- or ice-free over the regional area of interest (AOI) were analyzed.
5. How consistent are the relationships among the observations, including topology?
   For each image-acquisition date, Landsat Collection 2, Level-2 science products were downloaded from the USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface (https://espa.cr.usgs.gov/).

How can someone get a copy of the data set?

1. Who distributes the data set? (Distributor 1 of 1)
   
   U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center

   Attn: USGS SPCMSC Data Management

   600 4th Street South

   St. Petersburg, FL
   

   USA

   727-502-8000 (voice)

   gs-g-spcmsc_data_inquiries@usgs.gov
2. What's the catalog number I need to order this data set? nfwf_1332_ny_shrln.shp, nfwf_1332_nj_shrln.shp, nfwf_1333_shrln.shp, nfwf_1433_shrln.shp, nfwf_1332_ny_sandext.shp, nfwf_1332_nj_sandext.shp, nfwf_1333_sandext.shp, nfwf_1433_sandext.shp
3. What legal disclaimers am I supposed to read?
   This publication was prepared by an agency of the United States Government. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, 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 imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.
4. How can I download or order the data?
   • Availability in digital form:

     Data format:	Download files contain Esri vector sand- and shoreline shapefiles (.shp) in format Shapefile
     Network links:	https://coastal.er.usgs.gov/data-release/doi-P13HX6Y8/data/nfwf_features.zip

   • Cost to order the data: None

5. What hardware or software do I need in order to use the data set?
   Vector datasets were created using Esri ArcGIS Pro version 3.2.1 and can be opened using Esri ArcGIS version 10.0 or higher or Esri ArcGIS Pro version 3.1 or higher; these data may also be viewed using free Google Earth Pro or QGIS software.

Who wrote the metadata?

Dates:

Last modified: 07-Mar-2024

Metadata author:

U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center

Attn: USGS SPCMSC Data Management

600 4th Street South

St. Petersburg, FL

USA

727-502-8000 (voice)

gs-g-spcmsc_data_inquiries@usgs.gov

Metadata standard:

Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)