Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

This 3D model of Mount Saint Helens shows the topography using wood-textured contours set at 50m vertical spacing, with the darker wood grain color indicating the major contours at 1000, 1500, 2000, and 2500 meters above sea level. The state of the mountain before the eruption of May 13, 1980 is shown with thinner contours, allowing you to see the volume of rock that was ejected via the lateral blast.The process to create the contours uses CityEngine and ArcGIS Pro for data processing, symbolization, and publishing. The steps:Create a rectangular AOI polygon and use the Clip Raster tool on your local terrain raster. A 30m DEM was used for before, 10m for after.Run the Contour tool on the clipped raster, using the polygon output option - 50m was used for this scene.Run the Smooth Polygon tool on the contours. For Mount St. Helens, I used the PAEK algorithm, with a 200m smoothing tolerance. Depending on the resolution of the elevation raster and the extent of the AOI, a larger or smaller value may be needed. Write a CityEngine rule (see below) that extrudes and textures each contour polygon to create a stair-stepped 3D contour map. Provide multiple wood texture options with parameters for: grain size, grain rotation, extrusion height (to account for different contour depths if values other than 100m are used), and a hook for the rule to read the ContourMax attribute that is created by the Contour tool. Export CityEngine rule as a Rule Package (*.rpk).Add some extra features for context - a wooden planter box to hide some of the edges of the model, and water bodies.Apply the CityEngine-authored RPK to the contour polygons in ArcGIS Pro as a procedural fill symbol, adjust parameters for desired look & feel.Run Layer 3D to Feature Class tool to convert the procedural fill to multipatch features. Share Web SceneRather than create a more complicated CityEngine rule that applied textures for light/dark wood colors for minor/major contours, I just created a complete light- and dark-wood version of the mountain (and one with just the water), then shuffled them together.Depending on where this methodology is applied, you may want to clip out other areas - for example, glaciers, roads, or rivers. Or add annotation by inlaying a small north arrow in the corner of the map. I like the challenge of representing any feature in this scene in terms of wood colors and grains - some extruded, some recessed, some inlaid flat.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted depth to 100 degrees C.

The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in “pred” are the predicted mean for that resource, and files ending in “err” are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource.

Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report.

The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource.

A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource.

Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model.

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcPro 3.1.0 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.LUID - Unique ID number for each polygon in the final dataset, not consistent between yearly datasets.Landuse - A general land cover classification differentiating how the land is used.Agriculture: Land managed for crop or livestock purposes.Other: A broad classification of wildland.Riparian/Wetland: Wildland influenced by a high water table, often close to surface water.Urban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop.Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot.None: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian area.Acres - Calculated acreage of the polygon.State - State where the polygons are found.Basin - The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes.SubArea - The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop.LABEL - A shorthand descriptive label for each crop description and irrigation type.Class_Name - The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description).OldLanduse - Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.Field_Check - Indicates the year the polygon was last field checked. *New for 2019SURV_YEAR - Indicates which year/growing season the data represents.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcMap 10.X with NAIP or Google imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.2018 marked the first year a comparison could be made using the CDL methodology. The comparison between 2017 and 2018 showed a large change in agricultural land use to other land use. It was determined this shift was due to crop land being allowed to sit fallow for a season and did not represent a shift away from agricultural land. The following code amended the data:*************************************************************************************************************************************####On 02/07/2020 this dataset was amended with the following R script to better reflect agricultural land changes:require(arcgisbinding)arc.check_product()####Bring in layersLU17<-arc.open("Path to data")LU17<-arc.select(LU17)#####Amend dataLU17$Landuse[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Agricultural"LU17$CropGroup[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Fallow/Idle"LU17$IRR_Method[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Dry Crop"arc.write("Path to data", LU17)*************************************************************************************************************************************LUID -Unique ID number for each polygon in the final dataset, matches object.Landuse - Land use type, similar to land cover and represents our own categories of how the land is used.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Methods.Acres - Calculated acreage of the polygon.State - Spatial intersection identifying the State where the polygons are found.County - Spatial intersection identifying the County where the polygons are found.Basin - Spatial intersection identifying the Basin where the polygons are found. Basins, or Utah Hydrologic Basins are large watersheds created by DWRe.SubArea - Spatial intersection identifying the Subarea where the polygons are found. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates specific cover and use types.LABEL - Old shorthand descriptive label for each crop and irrigation type or land use type.Class_Name - Zonal Statistics majority value derived from the USDA CDL Cropscape raster layer, may differ from final crop determination.OldLanduse - This is the old short code found under landuse in past datasets and is kept to maintain connectivity with historical data.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR - Indicates which year/growing season the data represents. Is useful when comparing to past layers.

For a file geodatabase (.gdb) Click Here (includes files used to create data).For the final report, full documentation, and metadata Click Here.Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 2.6.3 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.AttributesLanduse– A general land cover classification differentiating how the land is usedAgriculture: Land managed for crop or livestock purposesOther: A broad classification of wildlandRiparian/Wetland: Wildland influenced by a high water table, often close to surface waterUrban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the cropDry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plotNone: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian areaAcres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR– Indicates which year/growing season the data represents.

Notice: this is not the latest Heat Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. The Heat Anomalies is also reclassified into a Heat Severity raster also published on this site. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Full Range Heat Anomalies - USA 2022Full Range Heat Anomalies - USA 2021Full Range Heat Anomalies - USA 2020Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

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. 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 3.0 national 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

Reason for SelectionHuman development along shorelines, along with jetties, groins, seawalls, revetments, and other structures, provide a measure of overall habitat alteration and shoreline condition. Human infrastructure along shorelines generally stabilizes barrier islands, impeding natural beach migration and barrier island rollover processes. Groins, seawalls, jetties, and revetments have resulted in narrowing of beaches, or greater beach loss, compared to unstructured beaches (Dugan et al. 2008, Hall and Pilkey 1991, Mohanty et al. 2012, Pilkey and Wright III 1988). Jetties also alter sand transport and may result in downdrift erosion (Bruun 1995). Hardened structures landward of tidal wetlands can cause “coastal squeeze” by accelerating erosion during storms and preventing inland migration in response to sea-level rise (Gittman 2015). In addition, hardened shorelines, particularly seawalls, generally support lower levels of biodiversity (Gittman et al. 2015, 2016). Studies funded by NOAA’s National Centers for Coastal Ocean Science found that shoreline hardening has a negative impact on amounts of submerged aquatic vegetation (SAV) and on fish density and egg-laying (NOAA NCOS 2015, 2013).Input DataNational Oceanic and Atmospheric Administration (NOAA) Environmental Sensitivity Index (ESI) National Shoreline dataset: This dataset classifies shorelines into types of natural habitat and hardened structures to identify coastal resources that are at risk if an oil spill occurs nearby.Spatial designations of the Coastal Barrier Resources System (CBRS), as a result of the Coastal Barrier Resources Act (1982) and subsequent amendments (last updated 3-15-2019), accessed 1-31-2022 The CBRS was used to set boundaries of relatively undeveloped coastal areas that are more difficult to develop. The Act incentivizes the conservation of mostly intact coastal barrier landforms—such as barrier islands, beaches and dunes, fringing mangroves, and others—to protect valuable natural resources and minimize the loss of human life and property due to hurricanes. It restricts federal expenditures that encourage development in these areas, such as flood insurance. Base Blueprint 2022 extentSoutheast Blueprint 2023 extentMapping StepsConvert the CBRS polygons to a 30 m raster. Partially armored and natural shoreline pixels will receive a bump in their score if they fall within a CBRS polygon.From the ESI data, extract saline estuarine shorelines, where LINE field is not ‘H’ and ENVIR field is ‘E’. The GENERALIZED_ESI_TYPE field contains 1-3 shoreline types, listed landward to seaward. If the field contains only ‘1: Armored’, consider it fully armored and assign it the lowest value (0) in the indicator.If GENERALIZED_ESI_TYPE contains multiple shoreline types and any of them are armored, consider it partially armored. Assign these pixels a value of 1 (if they are outside the CBRS) or 2 (if they are inside the CBRS) in the indicator.Consider all other GENERALIZED_ESI_TYPE fields as natural shoreline. Assign them values of 3 (if they are outside the CBRS) or 4 (if they are inside the CBRS) in the indicator.Convert the entire ESI linework to a 30 m raster.Clip to the spatial extent of Base Blueprint 2022.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 valuesIndicator values are assigned as follows:4 = Natural and harder to develop3 = Natural2 = Partially armored and harder to develop 1 = Partially armored0 = ArmoredKnown IssuesThis indicator overestimates shoreline condition in areas near hardened structures. It only predicts low values in places that are currently hardened and not in nearby areas that can also be impacted by that hardening.This indicator overestimates shoreline condition in areas with active beach renourishment. Beach renourishment negatively affects some beach and dune species but is not captured by this indicator.This indicator does not take into account protected natural areas (e.g., National Wildlife Refuges such as St. Marks in Florida) that are unlikely to be developed but are not within the CBRS.In areas where source data is outdated, this indicator sometimes overestimates shoreline condition because it does not capture more recent shoreline alteration (such as the addition of new residential docks and boat slips along the Texas coast). This dataset is a compilation of individual state inputs that vary in age, ranging back to 1996. Some states, like Florida and Texas, maintain their own ESI data separate from the national NOAA ESI, but differences in data structure and formatting made it difficult to combine those with the national ESI data this year. We hope to incorporate more recent state data in future Blueprint updates.Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedBruun, P., 1995. The development of downdrift erosion. J. Coast. Res., 1242-1257. [https://www.jstor.org/stable/4298427]. Dugan, J.E., Hubbard, D.M., Rodil, I.F., Revell, D.L., Schroeter, S., 2008. Ecological effects of coastal armoring on sandy beaches. Marine Ecology 29, 160-170. [https://onlinelibrary.wiley.com/doi/full/10.1111/j.1439-0485.2008.00231.x]. Gittman, R.K., Fodrie, F.J., Popowich, A.M., Keller, D.A., Bruno, J.F., Currin, C.A., Peterson, C.H. and Piehler, M.F. (2015), Engineering away our natural defenses: an analysis of shoreline hardening in the US. Frontiers in Ecology and the Environment, 13: 301-307. [https://doi.org/10.1890/150065]. Gittman, Rachel K., Steven B. Scyphers, Carter S. Smith, Isabelle P. Neylan, Jonathan H. Grabowski, Ecological Consequences of Shoreline Hardening: A Meta-Analysis, BioScience, Volume 66, Issue 9, 01 September 2016, Pages 763-773. [https://doi.org/10.1093/biosci/biw091]. Hall, M.J., Pilkey, O.H., 1991. Effects of hard stabilization on dry beach width for New Jersey. J. Coast. Res., 771-785. [https://journals.flvc.org/jcr/article/view/78532/75937]. Mohanty, P.K., Patra, S.K., Bramha, S., Seth, B., Pradhan, U., Behera, B., Mishra, P., Panda, U.S., 2012. Impact of groins on beach morphology: a case study near Gopalpur Port, east coast of India. J. Coast. Res. 28, 132-142. [https://bioone.org/journals/journal-of-coastal-research/volume-28/issue-1/JCOASTRES-D-10-00045.1/Impact-of-Groins-on-Beach-Morphology--A-Case-Study/10.2112/JCOASTRES-D-10-00045.1.full]. National Oceanic and Atmospheric Administration Office of Response and Restoration, Seattle, Washington. May 2017. National Environmental Sensitivity Index Shoreline: GULF/ATLANTIC ESI: ESI Shoreline Types – Lines. [https://response.restoration.noaa.gov/esi_download]. National Oceanic and Atmospheric Administration National Centers for Coastal Ocean Science. August 10, 2015. Hardened Shorelines Make it Hard for Submerged Aquatic Vegetation. [https://coastalscience.noaa.gov/news/hardened-shorelines-make-hard-submerged-aquatic-vegetation/]. National Oceanic and Atmospheric Administration National Centers for Coastal Ocean Science. January 23, 2013. Project Finds Fish Prefer Natural Shorelines. [https://coastalscience.noaa.gov/news/riprap-project-finds-fish-prefer-natural-shorelines-human-response-to-sea-level-rise-at-issue/]. U.S. Fish and Wildlife Service. March 2019. John H. Chafee Coastal Barrier Resources System Approximate Polygons Vector Digital Dataset. Falls Church, VA. Accessed January 31, 2022. [https://www.fws.gov/program/coastal-barrier-resources-act/maps-and-data]. Pilkey, O.H., Wright III, H.L., 1988. Seawalls versus beaches. J. Coast. Res., 41-64. [https://www.jstor.org/stable/25735351]. Rice, T.M., 2012a. Inventory of Habitat Modifications to Tidal Inlets in the Continental U.S. Coastal Migration and Wintering Range of the Piping Plover (Charadrius melodus). Appendix 1b, in Comprehensive Conservation Strategy for the Piping Plover (Charadrius melodus) in its Coastal Migration and Wintering Range in the Continental United States. U.S. Fish and Wildlife Service, East Lansing, Michigan.

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

Reason for SelectionImpervious cover is easy to monitor and model and is widely used and understood by diverse partners. It is also strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. Impervious surface affects not only aquatic habitats and biodiversity, but also human communities. High levels of impervious surface cause more frequent flooding by increasing the volume of stormwater runoff, reduce the amount of available drinking water by preventing groundwater recharge, and pollute waterways where people swim and fish (Chesapeake 2023, USGS 2018, EPA 2018). The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major, negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surface threshold (i.e., 5% impervious) has been documented to impact Piedmont fish tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae) and estuarine species blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus). While most of these species do not occur in Puerto Rico and the U.S. Virgin Islands, we kept these thresholds in the Caribbean for consistency with the continental version of the indicator.Input DataSoutheast Blueprint 2023 subregions: Caribbean Southeast Blueprint 2023 extent2012 National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) land cover files for the U.S. Virgin Islands (St. Thomas, St. John, and St. Croix are provided as separate rasters) accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products2010 NOAA C-CAP land cover files for Puerto Rico, accessed 11-10-2022; learn more about C-CAP high resolution land cover and change productsNational Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the dataCatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key terms NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)Mapping StepsNHDPlus HR catchments are currently only available for the islands of Puerto Rico, Vieques, Culebra, St. Croix, St. John, and St. Thomas. Because the catchments don’t cover many of the smaller islands, use CUSP to add islands larger than 900 sq m (the area of a 30 m pixel). Start by converting CUSP shoreline lines to polygons.Dissolve interior waterbodies on islands to represent each island with only one polygon.To eliminate alignment issues between the CUSP and catchment polygons, remove most island areas that overlap with or are near (<10 m from) the NHDPlus HR catchments, ensuring that all of Culebra is retained.The original NHDPlus HR catchment data was missing coverage of a small area on the west coast of Puerto Rico (just east of Parcelas Aguas Claras). Create an additional catchment polygon for this missing area so that the indicator covers the entire island of Puerto Rico.The missing area is essentially outlined by extremely thin catchment polygons. To fill the gap, make a new rectangular feature class covering the missing area, then union it together with the original NHDPlus HR catchments. From that output, select the newly created polygon that fills in the hole. The resulting polygon is a multipart feature, so use the explode tool to separate out just the missing catchment. Export it as a shapefile.Union together the missing catchment with the other NHDPlus HR catchments and use that combined output as the catchment layer for the rest of the mapping steps.Remove islands created from the CUSP dataset that are less than 900 sq m.Merge the remaining CUSP islands with the NHDPlus catchments to create a single set of polygons in which to calculate average permeable surface.Convert the C-CAP land cover rasters for Puerto Rico (2 m resolution) and the U.S. Virgin Islands (separate downloads for St. Thomas, St. John, and St. Croix with 2.4 m resolution) from .img format to .tif using the Copy Raster function.For each individual C-CAP layer, use the ArcPy Conditional function to make a binary raster assigning the impervious class a value of 100 (representing fully impervious) and all other classes a value of 0 (representing fully permeable). This mimics the data format of the 2019 National Land Cover Database used in the continental Southeast permeable surface indicator, which provides a continuous impervious surface value ranging from 0 to 100.Using the ArcPy Mosaic to New Raster function, mosaic all 4 rasters into 1 raster. Reproject to match the Blueprint projection and the 2 m cell size of the original Puerto Rico C-CAP data.Calculate the average percent of impervious surface for each NHDPlus catchment or CUSP island using the ArcPy Spatial Analyst Zonal Statistics “MEAN” function, assigning the average impervious surface value to each catchment or island.Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast Blueprint indicators (where high values indicate better ecological condition).Reclassify the above raster into 4 classes, seen in the final indicator values below.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 valuesIndicator values are assigned as follows:4 = >95% of catchment or small island permeable (likely high water quality and supporting most sensitive aquatic species)3 = >90-95% of catchment or small island permeable (likely declining water quality and supporting most aquatic species)2 = >70-90% of catchment or small island permeable (likely degraded water quality and not supporting many aquatic species)1 = ≤70% of catchment or small island permeable (likely degraded instream flow, water quality, and aquatic species communities)Known IssuesThis indicator may not account for differences in permeability between different types of soils and land uses.The C-CAP impervious layer used in this indicator contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permeable surface in some catchments.C-CAP dates from 2010 for Puerto Rico and 2012 for the U.S. Virgin Islands. As a result, this indicator likely overestimates permeable surface values in areas that have been developed since the data was collected. C-CAP landcover is not available for some islands over 900 sq m. While these islands exceeded the size threshold for inclusion in this indicator, they are therefore scored as NoData. This indicator only covers areas where C-CAP landcover is present, and either NHDPlus HR catchments or islands over 900 sq m that were generated using CUSP data are also present. NHDPlus HR contains multiple catchments that are very small. The reduced size of these catchments may result in exaggerating their values in the indicator. Other Things to Keep in MindThe impervious surface in the C-CAP data has impervious surface as one class in the landcover, which differs from the 2019 NLCD percent developed impervious layer used in the continental Southeast version of the permeable surface indicator. NLCD 2019 is served up as a continuous raster ranging from 0-100% impervious.We used the Caribbean island size and extent layer for this indicator and not others because landcover data was available for small islands that were not covered by catchments, which otherwise would have been excluded. This was not the case for other indicators. For example, while we use catchments in natural landcover in floodplains, the floodplains and flowlines did not occur on small islands, anyway, so we did not leave any data out by using the catchments only and not supplementing with the islands layer.Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedChesapeake Bay Program. 2023. Stormwater Runoff. Accessed September 7, 2023. [https://www.chesapeakebay.net/issues/threats-to-the-bay/stormwater-runoff]. Environmental Protection Agency. EnviroAtlas. Data Fact Sheet. January 2018. Percent of Stream and Shoreline with 15% or More Impervious Cover within 30 Meters. Accessed September 7, 2023. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/Percstreamw15percentimperviousin30meters.pdf]. Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User’s guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019– 1096, 66 p., [https://doi.org/10.3133/ofr20191096]. National Oceanic and Atmospheric Administration, Office for Coastal Management. “C-CAP Land Cover

Reason for SelectionProtected 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 DataSoutheast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee EasementPuerto 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 censusOpenStreetMap data “multipolygons” layer, accessed 3-14-2023A 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 3-8-2023U.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 StepsMost 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 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 <10 acre urban park1 = <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.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 MindThis 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 because the landcover data available in the Caribbean does not assess percent impervious in a comparable way.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint

For a file geodatabase (.gdb) Click Here (includes files used to create data).For the final report, full documentation, and metadata Click Here.Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 3.2.1 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.AttributesLanduse– A general land cover classification differentiating how the land is usedAgriculture: Land managed for crop or livestock purposesOther: A broad classification of wildlandRiparian/Wetland: Wildland influenced by a high water table, often close to surface waterUrban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the cropDry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plotNone: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian areaAcres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR– Indicates which year/growing season the data represents.

2023 marks the seventh year of using the CDL Method for the whole state of Utah. This method utilizes the Cropland Data Layer from the USDA which provides acreage estimates for major commodities.Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 3.2.1 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.AttributesLanduse– A general land cover classification differentiating how the land is usedAgriculture: Land managed for crop or livestock purposesOther: A broad classification of wildlandRiparian/Wetland: Wildland influenced by a high water table, often close to surface waterUrban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the cropDry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plotNone: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian areaAcres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR– Indicates which year/growing season the data represents.

Reason for Selection Native grasslands and savannas are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands and savannas have significantly declined from their historical extent. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland and savanna birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential habitat. Input Data

Texas Ecological Mapping Systems: statewide raster, accessed 12-2023
Oklahoma Ecological Systems Map: download the raster, accessed 12-2023
Protected Areas Database of the United States (PAD-US): PAD-US 3.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement; PAD-US 4.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement
National Land Cover Database (NLCD): 2021 Land Cover, 2021 U.S. Forest Service (USFS) Tree Canopy Cover, 2013 Land Cover, and 2013 USFS Tree Canopy Cover
2020 LANDFIRE Biophysical Settings (BPS) [LF 2.2.0]
Southeast Blueprint 2024 landscape condition indicator
Southeast Blueprint 2024 extent

Known grasslands

Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University in Oct 2020 (available on request by emailing rua_mordecai@fws.gov); this is an improved version of the Known Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO) layer
Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailing rua_mordecai@fws.gov).
Grassland polygons from the Catawba Indian Nation, provided by Aaron Baumgardner, Natural Resources Director, in July 2023 (for more information email rua_mordecai@fws.gov)
Grassland polygons from two iNaturalist projects in Texas: erwin-park-prairie-restoration-area, stella-rowan-prairie
Southeastern Grasslands Institute polygons from selected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: 
    allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades, folder-six-glades

Mapping Steps

Combine all known grasslands polygons and convert to raster, assigning them a value of 7.
From the 2021 and 2013 NLCD landcover, create rasters that only include classes likely to have grasslands and savannas. The classes included are based on NLCD classes that overlap known grassland and savanna polygons. Any class that covered >1% of known grasslands and savannas is included: 31 Barren Land, 41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, 52 Scrub/Shrub, 71 Grassland/Herbaceous, 81 Pasture/Hay.
For those 2021 and 2013 selected landcover rasters, remove forest with ≥ 60% canopy cover using NLCD USFS Tree Canopy Cover for the corresponding year. This results in potential grassland and savanna rasters for 2021 and 2013.
Make a single potential grassland and savanna raster that only includes pixels that are potential grasslands and savannas in both 2013 and 2021. This removes temporary grasslands and savannas that result from clearcuts.
From the Texas and Oklahoma ecological systems maps, extract classes that predict areas invaded by mesquite, a non-native tree that spreads aggressively in the grasslands and savannas of the Southwest and disrupts natural ecosystems through its heavy water consumption. For Oklahoma, this is VegName = 'Ruderal Mesquite Shrubland'. For Texas, this is CommonName = 'Native Invasive: Mesquite Shrubland'. Combine these and use them to remove areas that are no longer grassland and savanna due to mesquite invasion. The resulting layer represents potential grasslands.
To identify potential grasslands and savannas in natural landscapes, use values 5 and 6 from the landscape condition indicator. Assign a value of 3 to any potential grassland pixel that receives a landscape condition score of 5 or 6. Assign all other potential grassland pixels a value of 2.
To identify likely grasslands and savannas, overlay the potential grasslands and savannas raster with select polygons from PAD-US 4.0. To pull out types of protected lands that commonly manage grasslands and savannas, we used GAP status, designation type, manager name, and easement holder. We also identified a number of protected areas directly by name that had important areas of grassland and savanna but weren’t captured by the other rules.
    GAP status (GAP_sts) 1 or 2: Gap status 1 and 2 refer to areas managed for biodiversity that are not subject to extractive uses like logging and mining. GAP status 2 is technically intended to encompass areas where disturbance events are suppressed, but in practice, most protected areas in the Southeast that are actively managing grasslands and savannas are classified as GAP status 2.
    Designation type (Des_Tp) of ‘NWR’, ‘MIL’, ‘NF’, or ‘NG’ (i.e. National Wildlife Refuge, military installation, National Forest, or National Grassland)
    Manager name (Mang_Name) of ‘RWD’ (i.e. Regional Water District)
    Local manager name (Loc_Mang) of 'Ducks Unlimited (Wetlands America Trust)'
    Easement holder (EsmtHldr) of 'Tall Timbers Research Station & Land Conservancy'
    Unit name (Unit_Nm) of ‘Point Washington State Forest’, ‘Pine Log State Forest’, ‘M. C. Davis - Seven Runs Creek Conservation Easement’, ‘Nokuse Plantation Conservation Easements’, ‘Tate's Hell State Forest’, ‘Box-R Wildlife Management Area’, ‘Aucilla Wildlife Management Area’, ‘Snipe Island Unit’, ‘Big Bend Wildlife Management Area’, ‘Goethe State Forest’, ‘Amelia Wildlife Management Area’, ‘Powhatan Wildlife Management Area’, ‘Cumberland State Forest’, ‘Appomattox-Buckingham State Forest’, ‘Haw River State Park’, ‘R. Wayne Bailey - Caswell Game Land’, ‘Medoc Mountain State Park’, ‘Embro Game Land’, ‘Dupont State Forest’, ‘Hanging Rock State Park’, ‘Bladen Lakes State Forest’, ‘Whitehall Plantation Game Land’, ‘Suggs Mill Pond Game Land’, ‘Bushy Lake State Natural Area’, ‘Pondberry Bay Plant Conservation Preserve’, ‘Green Swamp Game Land’, ‘Holly Shelter Game Land’, ‘Chowan Swamp Game Land’, ‘Brookgreen Gardens’, ‘Cary State Forest’, ‘Suwannee Ridge Mitigation Park Wildlife and Environmental Area’, ‘Adams-Alapha Ag & Conservation Easement’, ‘Twin Rivers State Forest’, ‘Chattahoochee Fall Line Wildlife Management Area’, ‘Enon Plantation’, or ‘Georgia-Alabama Land Trust Easement #214’, ‘Covington Wildlife Management Area’, ’ Magnolia Branch Wildlife Reserve’, ‘Little River State Forest’, or ‘Susan Turner Plantation’, or have the local name (Loc_Nm) 'Sandhills Game Land', 'Blackwater River State Forest', 'Three Lakes Wildlife Management Area', 'Herky Huffman/Bull Creek Wildlife Management