This provides a polygon coastline and islands layer which is based on the Topo50 products. It is a combination of the following layers:

• NZ Coastlines (Topo 1:50k)
• NZ Island Polygons (Topo 1:50k)
• NZ Auckland Is Island Polygons (Topo, 1:50k)
• NZ Campbell Is / Motu Ihupuku Island Polygons (Topo, 1:50k)
• NZ Antipodes Is Island Polygons (Topo, 1:25k)
• NZ Kermadec Is Island Polygons (Topo, 1:25k)
• NZ Bounty Is Island Polygons (Topo, 1:25k)
• NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k)
• NZ Chatham Is island polygons (Topo, 1:50k)

This topographic coastline is the line forming the boundary between the land and sea, defined by mean high water.

Islands from the NZ Island Polygons layer that lie within the NZ Coastline and Chatham Islands areas (i.e. islands in lakes, rivers and estuaries) have been removed.

The GIS workflow to create the layer is:

1. NZ Coastlines were converted from a polyline to a polygon using a polyline to polygon tool.
2. The resulting coastal polygon was then used as an input into an erase tool and run against the NZ Island Polygon layer to remove all islands lying within the NZ Mainland and Stewart Island.
3. This was then merged with the NZ Chatham Is island polygons (Topo, 1:50k) that have had the islands within the main island polygon removed, NZ Auckland Is Island Polygons (Topo, 1:50k), NZ Campbell Is / Motu Ihupuku Island, NZ Antipodes Is Island Polygons (Topo, 1:25k), NZ Kermadec Is Island Polygons (Topo, 1:25k), NZ Bounty Is Island Polygons (Topo, 1:25k) and NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k) layers using a merge tool.

For more detailed description of each layer refer to the layer urls above.

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services ArcGIS REST API

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022
Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee Easement
Puerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 
2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 census
OpenStreetMap data “multipolygons” layer, accessed 3-14-2023

A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

TNC Lands - Public Layer, accessed 3-8-2023
U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)

Mapping Steps

Most mapping steps were completed using QGIS (v 3.22) Graphical Modeler.
Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.
Merge the terrestrial PR and VI PAD-US layers.
Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.
Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.
Fix geometry errors in the resulting merged layer using Fix Geometry.
Intersect the resulting fixed file with the Caribbean Blueprint subregion.
Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.
Clip the Census urban area to the Caribbean Blueprint subregion.
Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.
Dissolve all the park polygons that were selected in the previous step.
Process all multipart polygons to single parts (“explode”) again.
Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.
Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.
Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.
Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.
Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.
Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. 
Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.
Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).
Export the final vector file to a shapefile and import to ArcGIS Pro.
Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = 75+ acre urban park 5 = >50 to <75 acre urban park 4 = 30 to <50 acre urban park 3 = 10 to <30 acre urban park 2 = 5 to <10 acre urban park 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues

This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. 
This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.
This indicator includes parks and beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.

Other Things to Keep in Mind

This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. 
The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast

Coastline for Antarctica created from various mapping and remote sensing sources, consisting of the following coast types: ice coastline, rock coastline, grounding line, ice shelf and front, ice rumple, and rock against ice shelf. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region.

Major changes in v7.5 include updates to ice shelf fronts in the following regions: Seal Nunataks and Scar Inlet region, the Ronne-Filchner Ice Shelf, between the Brunt Ice Shelf and Riiser-Larsen Peninsula, the Shackleton and Conger ice shelves, and Crosson, Thwaites and Pine Island. Small areas of grounding line and ice coastlines were also updated in some of these regions as needed.

Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.

Further information and useful links

Map projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.

The currency of this dataset is May 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.

For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.

A related medium resolution dataset is also published via Living Atlas, as well medium and high resolution polygon datasets.

For background information on the ADD project, please see the British Antarctic Survey ADD project page.

Lineage

Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each line has attributes detailing the source which can give the user further indications of its suitability for specific uses. Attributes also give information including 'surface' (e.g. grounding line, ice coastline, ice shelf front) and revision date. Compiled from sources ranging in time from 1990s-2022 - individual lines contain exact source dates.

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

AbstractCoastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with ‘land’, ‘ice shelf’, ‘ice tongue’ or ‘rumple’ attribute. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polygons. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics.Changes in v7.10 include updates to the coastline of Alexander Island and surrounding islands, and the ice shelf fronts of the Wilkins and Brunt ice shelves.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2024 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related high resolution dataset is also published via Living Atlas, as well medium and high resolution line datasets.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageDataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a ‘surface’ attribute with either ‘land’, ‘ice shelf’, ‘ice tongue’ or ‘rumple’. Details of when and how each line was created can be found in the attributes of the high or medium resolution polyline coastline dataset. Data sources range in time from 1990s-2024 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All polygons <0.1km² not intersecting anything else were deleted and the ‘simplify’ tool was used in ArcGIS with the ‘retain critical points’ algorithm and a smoothing tolerance of 50 m.CitationGerrish, L., Ireland, L., Fretwell, P., & Cooper, P. (2024). Medium resolution vector polygons of the Antarctic coastline (Version 7.10) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/93ac35af-9ec7-4594-9aaa-0760a2b289d5If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, 2024'

New political and administrative boundaries Shapefile of Nepal. Downloaded and republished from the Survey Department website.

A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. Please see the associated publications: 1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5. https://ieeexplore.ieee.org/abstract/document/9242297 2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313. https://github.com/csdms-contrib/fwdet 3. Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019 4. Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609 Sample products and data availability: https://sdml.ua.edu/models/fwdet/ https://sdml.ua.edu/michigan-flood-may-2020/ https://cartoscience.users.earthengine.app/view/fwdet-gee-mi https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0 GEE implementation output: fwdet_gee_brazos.tif ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016') Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27). Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney Emails: bpeter@ua.edu, sagy.cohen@ua.edu, ronan.m.lucey@nasa.gov, dsmunasinghe@crimson.ua.edu, aaraney@crimson.ua.edu Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville Last Modified: 10/08/2020 To cite this code use: Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet ------------------------------------------------------------------------------------------------------------------------- How to run this code with your flood extent GEE asset: User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options. Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name....

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

World Countries Generalized represents generalized boundaries for the countries of the world as of August 2022. The generalized political boundaries improve draw performance and effectiveness at a global or continental level. This layer is best viewed out beyond a scale of 1:5,000,000.This layer's geography was developed by Esri and sourced from Garmin International, Inc., the U.S. Central Intelligence Agency (The World Factbook), and the National Geographic Society for use as a world basemap. It is updated annually as country names or significant borders change.

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of Mexico sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf of Mexico Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of Mexico seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC) South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data from The Artificial Reefs Interactive Mapping Application (direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida (.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of Mexico), 4 (Central Gulf of Mexico), and 5 (Western Gulf of Mexico), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA) Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic; read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data and draft final report entitled Assessment of Benthic Habitats for Fisheries Management provided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster.Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = 'Florida Keys'“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s 5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = '01. mapped hard bottom area' and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value

This dataset represents Exclusive Economic Zones (EEZ) of the world. Up to now, there was no global public domain cover available.
Therefore, the Flanders Marine Institute decided to develop its own database. The database includes two global GIS-layers: one contains polylines that represent the maritime boundaries of the world countries, the other one is a polygon layer representing the Exclusive Economic Zone of countries. The database also contains digital information about treaties. Please note that the EEZ shapefile also includes the internal waters of each country.