There were topological errors (overlaps) in NWI polygons that accounted for a total area less than 1 acre and were therefore not corrected for this analysis and the data were used 'as is'. Small polygons (less than 1 acre) were removed from the managed coastal wetland vector dataset before assigning NWI and C-CAP attributes, however, once NWI and C-CAP values were assigned, smaller polygons were created and are in the final dataset. In the area of Supawna Meadows National Wildlife Refuge and Abbotts Meadows Wildlife Management Area (NWI project R05Y10P05), there are mapping errors in NWI that were not corrected for this dataset. Those areas should be noted and not used in further analyses.
A visual comparison of polygons and Esri world imagery was performed at randomly selected sites to check for consistency. Polygons that were clearly misidentified due to resolution or alignment errors between NWI and C-CAP were manually removed, although some misidentified polygons may remain in the dataset.
The managed coastal wetland polygonal dataset covers Delaware, Delaware Inland Bays, the Delaware Bay area of New Jersey, and the Delaware River areas of Pennsylvania and New Jersey as far north as Trenton, NJ.
Sliver polygons of open salt water along the land/water boundary may also be included in some instances as a result of geoprocessing. Additionally, there are areas within polygons that have holes of varying sizes, and this is due to those areas being above HAT elevation or classified as estuarine or tidal wetland.
Errors within the NWI data were retained (e.g., Project R05Y10P05).
Topological errors in NWI polygons (overlaps) were retained but were negligible in calculations.
Small area (less than 1 acre) polygons were created as a result of overlaying NWI and C-CAP attributes, therefore polygons smaller than the 4046.8m2 (1 acre) minimum size precision limitation stated the logical consistency report are in the final dataset.
C-CAP is a raster with 30m resolution and therefore there may be some misclassified areas if the cell center coincided with an the edge of a land cover class in the source imagery used during creation. Additionally, the cell shape (30mx 30m square) was retained when converted to a vector and may not be coincident with NWI shape edges.
The 1m resolution elevation dataset was resampled to a 5m resolution, using nearest neighbor resampling and therefore may be different than field measurements.
All raster outputs were snapped to the interpolated surface raster and have no misalignment in the data that weren't inherent to the source data.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Danielson, J.
Originator: Tyler, D.
Publication_Date: 20160501
Title:
Topobathymetric Model for Chesapeake Bay Region - Delaware and New Jersey 1859 to 2015
Edition: First
Geospatial_Data_Presentation_Form: raster digital dataset
Publication_Information:
Publication_Place: Reston, VA
Publisher: U.S. Geological Survey
Other_Citation_Details:
Region based Coastal National Elevation Database (CoNED) data were downloaded for the area of interest in October 2022. CoNED is a 1m resolution topobathymetric dataset for the coastal region. CoNED uses a variety of data sources to create this seamless elevation data, including LiDAR. Data were downloaded as Chesapeake Bay NJ_DE_Topobathy, NAD 1983 UTM Zone 18N with the North American Datum of 1988 (NAVD88). Download date was 01/29/2020.
Online_Linkage: https://topotools.cr.usgs.gov/topobathy_viewer/
Type_of_Source_Media: Digital
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 18590101
Ending_Date: 20150131
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: USGS CoNED
Source_Contribution: Elevation dataset
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Fish and Wildlife Service
Publication_Date: 20210412
Title: National Wetland Inventory
Geospatial_Data_Presentation_Form: vector digital dataset (polygon)
Publication_Information:
Publication_Place: Madison, WI
Publisher: U.S. Fish and Wildlife Service
Other_Citation_Details:
Downloaded wetlands data for Delaware, New Jersey, and Pennsylvania in shapefile format from the NWI download page. Projection was NAD 1983 Albers. Download date was 04/12/2021.
Online_Linkage: https://www.fws.gov/wetlands/data/Data-Download.html
Type_of_Source_Media: Digital
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 2017
Single_Date/Time:
Calendar_Date: 2007
Single_Date/Time:
Calendar_Date: 1980
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NWI
Source_Contribution: Wetlands dataset
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Oceanographic and Atmospheric Administration
Publication_Date: 20220329
Title:
NOAA Tides and Currents Highest Astronomical Tide referenced to NAVD88, derived from NOAA Tides And Currents, 1983-2001 epoch
Geospatial_Data_Presentation_Form: tabular digital data
Other_Citation_Details:
R Studio (Version 2022.07.7+576) was used to pull metadata records for each station on 03/29/2022.
Online_Linkage: https://tidesandcurrents.noaa.gov/
Type_of_Source_Media: Digital
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1983
Ending_Date: 2001
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: NOAA HAT
Source_Contribution: Tidal datums
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Oceanographic and Atmospheric Administration
Publication_Date: 20200301
Title: 2016 Coastal Change Analysis Program Land Cover
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details: Downloaded 6/14/2021
Online_Linkage: https://www.fisheries.noaa.gov/inport/item/48336
Type_of_Source_Media: Digital
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2016
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: CCAP
Source_Contribution: Land cover
Process_Step:
Process_Description:
This process step and all subsequent process steps were performed by the same person, Sydney Nick, in ArcGIS Pro (ver. 3.0.2), unless otherwise stated. For simple operations, the name of the geoprocessing tool used is given in capital letters; for complex operations, the name of geoprocessing tool used is given in capital letters and any critical parameters used are specified in the step. The input and output file names are provided in [square brackets] when necessary. All steps were performed with the map projection set to NAD_1983_Albers and inputs and outputs were stored as feature classes within a file geodatabase unless otherwise noted.
This process step creates a spatial domain, converts Highest Astronomical Tide (HAT) data to NAVD88, interpolates a surface, and prepares CoNED elevation data.
1. Create an area of interest polygon [aoi] that encompasses the Delaware Bay region that can be used to eliminate excess data.
2. Create a HAT surface:
a) Download NOAA tide gauge data for gauges with both NAVD88 and HAT tidal datums. Identify the tidal gauges that have both NAVD88 and HAT values that encompass the entirety of the Delaware Bay region.
b) Subtract the NAVD88 value from the HAT value for the 31 gauges within our Delaware Bay spatial domain ([aoi]).
c) PROJECT tide gauge xy coordinates to NAD_1983_Albers [de_bay_tides.shp].
d) Using the GEOSTATISTICAL WIZARD KRIGING tool and [de_bay_tides.shp], perform a simple kriging prediction, normal score transformation, covariance variogram, and set anisotropy as enabled. Use the defaults for all other variables. EXPORT resulting surface at a 5m resolution and NAD83 spatial reference [HAT_surface].
3. Create an elevation dataset:
a) Use PROJECT RASTER to project the CoNED raster to NAD_1983_Albers [njde_proj].
b) RESAMPLE [njde_proj] to a 5m resolution using nearest neighbor resampling techniques [njde_5m]. The HAT surface was used as a snap raster to eliminate any cell misalignment during overlay.
Source_Used_Citation_Abbreviation: NOAA HAT
Source_Used_Citation_Abbreviation: USGS CoNED
Process_Date: 2022
Source_Produced_Citation_Abbreviation: njde_5m
Source_Produced_Citation_Abbreviation: aoi
Source_Produced_Citation_Abbreviation: HAT_surface
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Sydney K Nick
Contact_Organization: U.S. Geological Survey, Northeast Region
Contact_Position: Interdisciplinary Geographer/Geologist
Contact_Address:
Address_Type: mailing address
Address: 384 Woods Hole Road
City: Woods Hole
State_or_Province: MA
Postal_Code: 02543
Country: US
Contact_Voice_Telephone: 508-548-8700x2343
Contact_Facsimile_Telephone: 508-457-2310
Contact_Electronic_Mail_Address: snick@usgs.gov
Process_Step:
Process_Description:
This process step creates the Coastal Change Analysis Program (C-CAP) raster and polygon and National Wetlands Inventory (NWI) feature classes within the spatial domain.
a) Use PROJECT RASTER to project the C-CAP raster to NAD_1983_Albers, using the HAT surface raster as a snap raster in the environment settings to produce [ccap_snap].
b) Eliminate excess data beyond our spatial domain using EXTRACT BY MASK (input raster: ccap_snap; input raster or feature mask data: [aoi]; extraction area: Inside) to create [ccap_aoi].
c) Create a polygon feature class of estuarine wetland areas as defined by C-CAP ([ccap_est_poly]) by using EXTRACT BY ATTRIBUTES (input raster: [ccap_aoi]; VALUE = 16 (estuarine forested wetland) OR VALUE = 17 (estuarine scrub shrub wetland) OR VALUE=18 (estuarine emergent wetland)) and perform RASTER TO POLYGON on the output.
d) Convert C-CAP raster [ccap_aoi] to vector using RASTER TO POLYGON (input raster: [ccap_aoi]; field: Value; output polygon features: [ccap_poly], simplify polygons: FALSE, create multipart features: FALSE) to create a feature class of all C-CAP areas [ccap_poly].
e) CLIP NWI polygons to aoi [nwi].
f) Create an additional field in the [nwi] attribute table with the name "ATTRIBUTE_original" by using the CALCULATE FIELD (input table: [nwi]; field name: ATTRIBUTE_orginal; expression type: Python 3; Expression: ATTRIBUTE_original = !WETLAND_TYPE!).
g) Because oligohaline in inconsistently used throughout the dataset, remove the oligohaline modifier by using CALCULATE FIELD (input table: [nwi]; field name: ATTRIBUTE_new; expression type: Python 3; Expression: NWI_TYPE=replace(!ATTRIBUTE!). code block:
import re
def replace(val):
return re.sub ('6', '', val). This provides the user with the original NWI attribute if oligohaline modifier is desired.
h) Create a feature class of the salt marshes as defined by NWI. SELECT BY ATTRIBUTE (ATTRIBUTE LIKE "E2EM%") from [nwi] EXPORT features [saltmarsh]).
I) This step creates a polygonal feature class containing estuarine, marine, and fresh tidal, excluding those polygons with modifiers indicating they have been or are managed. To create [nwi_tidal] use the following sequence of three SQL statements on [nwi]:
Create new selection: WETLAND_TYPE = 'Estuarine and Marine Deepwater'
OR WETLAND_TYPE = 'Estuarine and Marine Wetland';
add to current selection: (ATTRIBUTE LIKE '%L%' AND ATTRIBUTE NOT LIKE 'L%' AND ATTRIBUTE NOT LIKE '%ML%')
OR
(ATTRIBUTE LIKE '%M%' AND ATTRIBUTE NOT LIKE '%EM%')
OR
ATTRIBUTE LIKE '%N%'
OR
(ATTRIBUTE LIKE '%P%' AND ATTRIBUTE NOT LIKE 'P%')
OR
ATTRIBUTE LIKE '%Q%'
OR
(ATTRIBUTE LIKE '%R%' AND ATTRIBUTE NOT LIKE '%RB%' AND ATTRIBUTE NOT LIKE '%RS%' AND ATTRIBUTE NOT LIKE 'R%')
OR
(ATTRIBUTE LIKE '%S%' AND ATTRIBUTE NOT LIKE '%SS%' AND ATTRIBUTE NOT LIKE '%US%' AND ATTRIBUTE NOT LIKE '%SB%')
OR
ATTRIBUTE LIKE '%T%'
OR
ATTRIBUTE LIKE '%V%';
remove from current selection:
ATTRIBUTE LIKE '%b%'
OR ATTRIBUTE LIKE '%d%'
OR ATTRIBUTE LIKE '%f%'
OR ATTRIBUTE LIKE '%h%'
OR ATTRIBUTE LIKE '%m%'
OR ATTRIBUTE LIKE '%r'
OR ATTRIBUTE LIKE '%s%'
OR ATTRIBUTE LIKE '%x%'
EXPORT FEATURES.
Source_Used_Citation_Abbreviation: NWI
Source_Used_Citation_Abbreviation: CCAP
Source_Used_Citation_Abbreviation: aoi
Process_Date: 2022
Source_Produced_Citation_Abbreviation: ccap_snap
Source_Produced_Citation_Abbreviation: ccap_aoi
Source_Produced_Citation_Abbreviation: ccap_est_poly
Source_Produced_Citation_Abbreviation: ccap_poly
Source_Produced_Citation_Abbreviation: nwi
Source_Produced_Citation_Abbreviation: saltmarsh
Source_Produced_Citation_Abbreviation: nwi_tidal
Process_Step:
Process_Description:
This process step, and the next two process steps, create a vegetation adjusted raster from [njde_5m]. This adjustment is to account for documented lidar inaccuracies in densely vegetated marshes, resulting in higher than ground elevations.
a) RASTER CALCULATOR with the expression: Con("njde_5m" <= "HAT_surface",1) to create [con1].
b) RASTER TO POLYGON (input raster: [con1]; field: Value, output polygon features: [con2]; simplify polygons: FALSE; create multipart features: FALSE).
c) PAIRWISE ERASE (input features: [saltmarsh]; erase features: [con2]; output features: [misses]).
d) POLYGON TO RASTER (input features: [misses]; value field: OBJECTID; output raster dataset: [missras]; cell assignment type: cell center; priority field: NONE; cell size: [HAT_surface]; environment snap raster: [HAT_surface].
i) RASTER TO POINT [missras].
ii) EXTRACT MULTI VALUES TO POINTS (fieldmap follows this format input raster: output field name. [HAT_surface]: hat; njde_5m: coned).
g) SELECT LAYER BY ATTRIBUTE "coned IS NOT NULL" and export table to csv using TABLE TO TABLE [misspts.csv].
Source_Used_Citation_Abbreviation: njde_5m
Source_Used_Citation_Abbreviation: HAT_surface
Source_Used_Citation_Abbreviation: saltmarsh
Process_Date: 2022
Source_Produced_Citation_Abbreviation: misspts.csv
Process_Step:
Process_Description:
This process step uses R Studio (Version 2022.07.7+576) to identify the 75th percentile value between the CoNED elevation and HAT surface.
a) Read [misspts.csv] into the RStudio environment with the read.csv function.
b) Add a column and calculate the difference between CoNED and HAT using the syntax coned-hat.
c) Calculate the 0.75 quantile of the differences between CoNED and HAT and use the resulting number in the following process step. For this data product, the adjustment factor was 0.25m.
Source_Used_Citation_Abbreviation: misspts.csv
Process_Date: 2022
Source_Produced_Citation_Abbreviation: 0.25 CoNED adjustment factor
Process_Step:
Process_Description:
This process step uses the 75th quantile calculated in the previous step to adjust the CoNED elevation dataset in areas where NWI and C-CAP have mapped estuarine emergent wetlands.
a) POLYGON TO RASTER [saltmarsh] using [HAT_surface] as both cell size and snap raster [sm_ras].
b) EXTRACT BY ATTRIBUTES [ccap_aoi] with the query statement: "where VALUE is equal to 18" to create [ccap_est].
c) MOSAIC TO NEW RASTER [ccap_est] and [sm_ras] to create [area2corr].
d) TIMES [area2corr] with a constant value of 0 to create [area2corr_0].
e) Add the vegetation adjustment factor (0.25 as the constant value) to [area2corr_0] using the PLUS tool and specifying in the processing environments both cell size and snap raster are set to [HAT_surface] to create [area2corr_25].
f) Using MINUS, subtract [area2corr_25] from [njde_5m] to create [correction].
g) Create the vegetation adjusted CoNED raster [coned_corr] by using MOSAIC TO NEW RASTER and listing the input rasters in the following order: [njde_5m], [correction] with the following parameters: spatial reference: [njde_5m]; pixel type: 32 bit float; number of bands: 1; mosaic operator: last.
Source_Used_Citation_Abbreviation: saltmarsh
Source_Used_Citation_Abbreviation: HAT_surface
Source_Used_Citation_Abbreviation: ccap_aoi
Source_Used_Citation_Abbreviation: ccap_est_poly
Process_Date: 2022
Source_Produced_Citation_Abbreviation: coned_corr
Process_Step:
Process_Description:
This process step uses the vegetation corrected CoNED [coned_corr] to create the tidal domain from which to identify managed tidal wetlands.
a) Use RASTER CALCULATOR to create the tidal domain raster [Tidal], which will only include areas with elevations that are at or below the interpolated HAT surface. To do so, use the map algebra expression: Con( "coned_corr"<= "HAT_surface", "coned_corr"). This will result in a vegetation adjusted CoNED raster only in the areas that meet the expression criteria (elevation values maintained within the identified tidal domain, which can be used for further analyses).
b) Use RASTER CALCULATOR to create the tidal domain raster [Tidal_simple] with the map algebra expression: Con( "coned_corr"<= "HAT_surface", 1). This will result in a simplified, binary 1/NoData raster of areas that have elevations at or below the interpolated HAT surface.
c) Vectorize the tidal domain [Tidal_simple] using RASTER TO POLYGON (simplify features: FALSE; create multipart features: FALSE; [tidal_poly].
Source_Used_Citation_Abbreviation: coned_corr
Source_Used_Citation_Abbreviation: HAT_surface
Process_Date: 2023
Source_Produced_Citation_Abbreviation: tidal_poly
Process_Step:
Process_Description:
This step uses the tidal domain [tidal_poly], [nwi_tidal], and [ccap_est_poly] to identify areas at tidal wetland elevations that are either mapped as non-tidal or managed tidal wetlands and assigns them both NWI (as ATTRIBUTE in the attribute table) and C-CAP attributes (as gridcode).
a) ERASE (input features: [tidal_poly]; erase features: [nwi_tidal]) to create [tidal_nwi_erase].
b) ERASE (input features: [tidal_nwi_erase]; erase features: [ccap_est_poly]) to create [mod].
c) MULTIPART TO SINGLEPART [mod] to create [mod_single], Eliminate features smaller than our 1acre minimum size requirement by using SELECT BY ATTRIBUTE Shape_Area greater than or equal to 4046.8 (1 acre), EXPORT FEATURES to create [mod_sizemin].
d) Using [mod_sizemin] as the input, [nwi] as the identity features, run IDENTITY to create [modnwi]
e) SELECT BY ATTRIBUTE from [modnwi] ATTRIBUTE LIKE '%E1UBLx%' And Shape_Area > 500000 to eliminate the large human-made canals..
f) Using [modnwi] as the input and [ccap_poly] as the identity features, run IDENTITY a second time to create [mod_wetlands]. The column named gridcode corresponds to C-CAP classifications.
g) An additional field was created to maintain the original NWI WETLAND_TYPE field for users who may wish to use it in further analyses. This new field can be used for examining NWI wetland type versus areas not mapped by NWI and can used to provide C-CAP categories in subsequent steps via a table join from a csv of C-CAP labels. CALCULATE FIELD (input table: [mod_wetlands]; field name: NWI_TYPE; expression type: Python 3; Expression: NWI_TYPE=typ(!WETLAND_TYPE!). code block:
def typ(p1):
if len(p1)<2:
return("Undesignated Potential Wetland")
else:
return(p1)
h) Create and calculate geometry for fields acres and hectares.
i)Project [mod_wetlands] to WGS84 using the WGS_1984_(ITRF00)_To_NAD_1983 geographic transformation.
j) Keep only the following fields: FID, Shape, Orig_FID acres, ATTRIBUTE, WETLAND_TYPE, NWI_TYPE, gridcode_1, CCAP_Category, Hectares, Shape_Length, Shape_Area, before exporting to a shapefile [managed_wetlands]. Note that the fields will be shortened to the following when exported to a shapefile: FID, Shape, ORIG_FID_Acres, ATTRIBUTE, WETLAND_TY, ATTRIBUTE_, NWI_TYPE, gridcode_1, Hectare, Shape_Leng, Shape_Area.
Source_Used_Citation_Abbreviation: tidal_poly
Source_Used_Citation_Abbreviation: nwi_tidal
Source_Used_Citation_Abbreviation: ccap_est_poly
Source_Used_Citation_Abbreviation: nwi
Source_Used_Citation_Abbreviation: ccap_poly
Process_Date: 2023
Source_Produced_Citation_Abbreviation: managed_wetlands
