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Layer: CSDCIOP_Structure_Points (ID:2)

View In:   Map Viewer

Name: CSDCIOP_Structure_Points

Display Field: HAT_CONTACT

Type: Feature Layer

Geometry Type: esriGeometryPoint

Description: Feature class that compare the elevations between seawall crests (extracted from available LiDAR datasets from 2006, 2010, 2013, and 2014 for York and Cumberland, and 2022 LiDAR datasets for Sagadahoc, Lincoln, Knox, Waldo, Hancock, and Washington) with published FEMA Base Flood Elevations (BFEs) from preliminary FEMA DFIRMS (Panels issued in 2018 and 2019) in coastal York, Cumberland, Sagadahoc, Lincoln, Knox, Waldo, Hancock, and Washington counties. The dataset included the development of an inventory of coastal armor structures from a range of different datasets. Final attributes include:• HAT_CONTACT (York and Cumberland counties only): whether or not the structure contacts HAT• FLD_ZONE: The FEMA flood zone type that the structures intersect with• Elev_ft: the elevation, in feet, of the wall crest from LIDAR• STATIC_BFE: the associated BFE in which the point falls; if no published value, -9999 or <Null> is used• BFE_ELEV_COMPARE: comparison between BFE and structure maximum elevation• NEAR_DIST: the nearest distance, in meters, to the flood zone, if the point is not in a flood zone• NEAR_FLD_ZONE: the nearest flood zone to the point, if not in a flood zone• NEAR_STATIC_BFE: the nearest static BFE, if point not in a flood zone)• STRUCTURE_TYPE (Sagadahoc through Washington counties): the seaward-most structure, classified as either rip-rap or bulkhead.• SEAWARD_HAB (York and Cumberland counties only): the seaward habitat• STRUCTURE1 (York and Cumberland counties only) descriptor of the seaward-most structure, if appropriate• STRUCTURE2 (York and Cumberland counties only): descriptor of the landward-most structure, if appropriate• DUNE: descriptor of the dune system the structure is located within, either 0 (sand dune), 1 (frontal dune), or 2 (back dune), as appropriateProcess steps to create the dataset included:• For York and Cumberland: LiDAR data for York and Cumberland county beach, dune, and just inland areas was downloaded from the NOAA Digital Coast Data Access Viewer. This included 2006 and newer topobathy data available from 2010 (entire coast), and selected areas from 2013 and 2014 (Wells, Scarborough, Kennebunk). LiDAR tiffs from 2006, 2010, 2013 and 2014 data (with 2013 and 2014 being the first dataset laying on top of the 2010 data) were mosaiced into one raster dataset. This raster was then mosaiced into the cascobaydem_ftNAVD raster (this is from the MEGIS bare-earth model). This covered almost all of the study area except for armor along several areas in York. Resulting in LidAR206_2010_2013_Mosaic.tif. • For Sagadahoc through Washington: Latest high-resolution 1m NOAA LiDAR data (collected in 2021 and 2022, and released in 2024) was downloaded from the NOAA Digital Coast Data Access Viewer. LiDAR tiffs were mosaiced into new raster datasets by county.• Shoreline structures from the most recent NOAA EVI LANDWARD_SHORETYPE feature class were extracted using county boundaries. This included 1B: Exposed, Solid Man-Made structures, 8B: Sheltered, Solid Man-Made Structures; 6B: Riprap, and 8C: Sheltered Riprap. This resulted in the creation of ESIL_Structure layer that represented each coastal county. Note that ESIL uses the MHW line as the feature base.• Shoreline structures from the work by Rice (2015) were extracted using the York and Cumberland county boundaries. This resulted in the creation of Cumberland_Rice_Structures and York_Rice_Structures. Note that Rice structure data was not extracted for Sagadahoc through Washington counties. • Additional feature classes for structures were created for county structures that were missed. These were called Slovinsky_York_Structures, Slovinsky_Cumberland_Structures, and Berman_Midcoast2Downeast_Structures.• GoogleEarth (for York through Washington), Vexcel, and Nearmap imagery (for Sagadahoc through Washington) were inspected while additional structures were being added to the GIS. 2012 York and Cumberland County imagery was used as the basemap for Cumberland and York counties, and 2024 Vexcel imagery was used as the basemap for Sagadahoc through Washington counties. Structures were classified as bulkhead or rip-rap. Also, whether or not the structure was in contact with the 2015 HAT was noted for York and Cumberland counties.• For York and Cumberland: MEDEP was consulted to determine which permit data (both PBR and Individual Permit, IP, data) could be used to help determine where shoreline stabilization projects may have been conducted adjacent to or on coastal bluffs. A file was received for IP data and brought into GIS (DEP_Licensing_Points). This is a point file for shoreline stabilization permits under NRPA.Clip GISVIEW.MEDEP.Permit_By_Rule_Locations to the boundaries of the study area and output DEP_PBR_Points.Join GISVIEW.sde>GISVIEW.MEDEP.PBR_ACTIVITY to the DEP_PBR_Points using the PBR_ID Field. Then, export this file as DEP_PBR_Points2. • For York and Cumberland: Using the new ACTIVITY_DESC field, select only those activities that relate to shoreline stabilization projects:  PBR_ACTIVITY ACTIVITY_DESC  02 Act. Adjacent to a Protected Natural Resource  04 Maint Repair & Replacement of Structure  08 Shoreline Stabilization• For York and Cumberland: Select by Attributes > PBR_ACTIVITY IN (‘02’, ‘04’, ‘08’) select only those activities likely to be related to shoreline stabilization, and export the selected data as a DEP_PBR_Points3. Then delete 1 and 2, and rename this final product as DEP_PBR_Points.• For York and Cumberland: Next, visually inspect the Licensing and PBR files using ArcMap 2012, 2013 imagery, along with Google Earth imagery to determine the extents of armoring along the shoreline. Using EVI and Rice data as indicators, and the LiDAR data as a proxy, manually inspect and digitize the crests (highest points) of the coastline that are armored. Classify the seaward shoreline type (beach, mudflat, channel, dune, etc.) and the armor type (wall or bulkhead). Bring in the HAT line and, using that and visual indicators, identify whether or not the armored sections are in contact with HAT. Use Google Earth at the same time as digitizing in order to help constrain areas. Merge digitized armoring into Cumberland_York_Merged.• For Sagadahoc through Washington: Previous steps were not used. Instead, Google Earth, Nearmap, and Vexcel imagery were used to determine the extents of armoring along the shoreline, while EVI data was used as an indicator. Sections of armored shoreline were manually inspected and digitized along the crests (highest points), using the LiDAR data as an elevation proxy. The armor type was classified as bulkhead or rip-rap. Digitized armoring was merged into the feature class, Midcoast2Downeast_Merged.• Using the LiDAR data as a proxy, next create a dune crest line feature class which follows along the coast, digitizing sand dunes according to their approximate highest point. This will be used to extract LiDAR data and compare with preliminary flood zone information. The line is called Dune_Crests.• Using an added tool Points Along Line, create points at 5 m spacing along each of the armored shoreline feature lines and the dune crest lines. Call the outputs PointsonLines and PointsonDunes. • For York and Cumberland: Using the Extract Multi Values to Points tool, extract LIDAR elevations to the points using the 2006_2010_2013 Mosaic first. Call this LidarPointsonLines1. Select those points which have NULL values, export as this LiDARPointsonLines2. Then rerun Extract Values to Points using just the selected data and the state MEGIS DEM. Convert RASTERVALU to feet by multiplying by 3.2808 (and rename as Elev_ft). Select by Attributes, find all NULL values, and in an edit session, delete them from LiDARPointsonLines2. Then, merge the 2 datasets and call it LidarPointsonLines.• For Sagadahoc through Washington: Using the Extract Multi Values to Points tool, extract LIDAR elevations to the points using the 2022 LiDAR mosaic. Call this new feature class LidarPointsonStructures.• Do the same above with dune lines and create LidarPointsonDunes.• Next, use the FEMA flood zone layers to intersect the points with the appropriate flood zone data. Create ….CumbFIRM, …YorkFIRM, and …Midcoast2DowneastFIRM files for the dunes and lines.• Select those points from the Dunes feature class that are within the X zone – these will NOT have an associated BFE for comparison with the Lidar data. Export the Dune Points as Cumberland_York_Dunes_XZone or Midcoast2Downeast_Dunes_XZone. Run NEAR and use the merged flood zone feature class (with only V, AE, and AO zones selected). Then, join the flood zone data to the feature class using FID (from the feature class) and OBJECTID (from the flood zone feature class). Export as Cumberland_York_Dunes_XZone_Flood or Midcoast2Downeast_XZone_Flood. Delete ancillary columns of data, leaving the original FLD_ZONE (X), Elev_ft, NEAR_DIST (distance, in m, to the nearest flood zone), FLD_ZONE_1 (the near flood zone), and the STATIC_BFE_1 (the nearest static BFE).Do the same as above, except with the Structures files, but also select those features that are within the X zone and the OPEN WATER. Export the points as Cumberland_York_Structures_XZone or Midcoast2Downeast_Structures_XZone. • Again, run the NEAR using the merged flood zone and only AE, VE, and AO zones selected. Export the file as Cumberland_York_Structures_XZone_Flood, or Midcoast2Downeast_XZone_Flood. Merge the above feature classes with the original feature classes. • Add a field BFE_ELEV_COMPARE. Select all those features whose attributes have a VE or AE flood zone and use field calculator to calculate the difference between the Elev_ft and the BFE (subtracting the STATIC_BFE from Elev_ft). Positive values mean the maximum wall value is higher than the BFE, while negative values mean the max is below the BFE. Then, select the remaining values with switch selection. Calculate the same value but use the NEAR_STATIC_BFE value instead. • Select by Attributes>FLD_ZONE=AO, and use the DEPTH value to enter into the above created fields as negative values. • Delete ancillary attribute fields, leaving those listed in the _FINAL feature classes described above the process steps section.• Finally, datasets created in phases 1 and 2 (York, Cumberland, and Sagadahoc county up to Phippsburg) were merged with datasets created in phase 3 (Sagadahoc through Washington counties). Merged feature point classes were named Maine_StructurePoints_FINAL and Maine_DunePoints_FINAL, and the merged line feature classes were named Maine_StructureLines_FINAL and Maine_DuneLines_FINAL.

Copyright Text: The Maine Geological Survey (MGS) in the Department of Agriculture, Conservation and Forestry (DACF) created this dataset in support of local, regional and state resiliency efforts. This effort was funded by the National Oceanic and Atmoshperic Adminstration (NOAA) through Section 309 funding through several different awards. Please reference MGS as the creator of this dataset as follows: Berman, J, 2025, Shoreline engineering structures and dune crests from Phippsburg to Perry, Maine Geological Survey, Department of Agriculture Conservation and Forestry, Augusta, ME. Slovinsky, P.A., 2020, Shoreline engineering structures and dune crests from Kittery to Phippsburg, including Casco Bay, Maine Geological Survey, Department of Agriculture, Conservation and Forestry, Augusta, ME.

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Supported query Formats: JSON, geoJSON, PBF

Use Standardized Queries: True

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HasZ: false

HasM: false

Has Attachments: false

Has Geometry Properties: false

HTML Popup Type: esriServerHTMLPopupTypeAsHTMLText

Object ID Field: OBJECTID

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Is Data Versioned: false

Has Contingent Values: false

Supports Rollback On Failure Parameter: true

Last Edit Date: 11/21/2025 3:09:14 PM

Schema Last Edit Date: 11/21/2025 3:09:14 PM

Data Last Edit Date: 11/21/2025 3:09:14 PM

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