Notice: this is 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 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.

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. 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.

Reason for Selection Large areas of intact natural habitat are favorable for conservation of numerous species, including reptiles and amphibians, birds, and large mammals. The Esri Green Infrastructure data covers the entire United States and has been used in other broad-scale conservation planning efforts, so using this existing data helps align the Blueprint with other conservation efforts and reduce duplication of effort. We chose to use “Core Size (acres)” as the metric for this indicator. Other evaluation attributes included in this index, such as the default “Core Score”, were less suitable because they were calculated using inputs that are duplicative of other indicators. Input Data

2021 National Land Cover Database (NLCD)
Southeast Blueprint 2024 extent
Esri’s Intact Habitat Cores 2023, accessed 2-16-2024: Core Size (Acres); to download, select “Open in ArcGIS Desktop” and make a local copy. 

According to Esri’s data description for the 2023 intact habitat cores update: “This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2019 National Land Cover Data. Cores were derived from all “natural” landcover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons.” Mapping Steps

Convert the Esri Intact Habitat Cores 2023 polygons to a 30 m raster using the values in the “Acres” field. We used the feature layer map service as the input in the Polygon to Raster function in the code.
Reclassify the above raster into 4 classes, seen in the final indicator values below.
Use NLCD to remove zero values in deep marine areas, which are outside the scope of this terrestrial indicator. Use a conditional statement to assign NoData to any area with a pixel value >0 in the NLCD.
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 the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 3 = Large core (>10,000 acres) 2 = Medium core (>1,000-10,000 acres) 1 = Small core (>100–1,000 acres) 0 = Not a core Known Issues

The core analysis for this indicator is based on the 2019 NLCD, not the more recent 2021 NLCD. Esri has shared the scripts and input data used to create this layer, which may also help update this indicator in the future.
Even small dirt roads serve as hard boundaries for habitat cores. While this makes sense for some species, this indicator likely underestimates the effective size of the patch for some more mobile animals.
Waterbodies like reservoirs are also considered part of habitat cores, so this layer likely overestimates the effective size of the habitat core for most species.
Many intact habitat cores have a speckling of small altered areas inside of them. In some cases, like in areas of west TX with concentrated oil wells, there can be many alterations in a gridded pattern across the entire core. This indicator underestimates the cumulative impacts of interior alterations—especially when the small altered footprints are densely packed in a grid within a habitat core.

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 Cited Esri Green Infrastructure Center. Data Description: Detailed Description and Methodology for Intact Habitat Cores. PDF. Last updated June 30, 2023. [https://nation.maps.arcgis.com/home/item.html?id=047d9b05e0c842b1b126bc0767acfd5e].

Esri Green Infrastructure Center, Inc. 2023. Intact Habitat Cores (2023). [https://www.arcgis.com/home/item.html?id=b404b86a079a48049cb50272df23267a].

The SACS Tier 1 Environmental and Cultural Resources Exposure Index is a percentage-based aggregation of three Tier 1 Risk Assessment exposure indices: Environmental Index 30%, Cultural Index 40%, and Habitat Index 30%.

The SACS Tier 1 Environmental Exposure Index depicts a weighted aggregate of national GIS datasets related to administration and identification of high value environmental resources, within the SACS study area. The input datasets were identified during the USACE North Atlantic Coast Comprehensive Study (NACCS), with a few additional datasets in the Audubon Important Bird Areas, and National Estuarine Research Reserves. These data were clipped to the SACS study area and weighted consistently with the NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These data were then converted to a uniform grid based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a stretch symbology, percent clip (min -.5 max .5). This grid resolution is 30m.

The SACS Tier 1 Habitat Exposure Index depicts a weighted aggregate of national GIS datasets related to high value habitat areas, within the SACS study area. The input datasets were identified during the North Atlantic Coastal Comprehensive study, with an addition of the UNEP WCMC identified global seagrass locations. These data were clipped to the SACS study area, weighted consistently with the NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These data were then converted to a uniform gird based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a stretch symbology, percent clip (min -.5 max .5). This grid resolution is 30m.

The SACS Tier Cultural Resources Exposure Index depicts a weighted aggregated of national GIS datasets related to cultural resources within the SACS study areas. The input datasets include the National Register of Historic Places as well as the USGS Protected Areas Database– Historic or Cultural Areas. These national datasets were clipped to the SACS study area and weighted consistently with the USACE NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These vector data were then converted to a uniform grid based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a 4-class equal interval symbology to be able identify point features that have been converted to grid pixels. This grid resolution is 30m.

Input datasets, weighting, and download locations are referenced as follows:

Environmental

USFWS - https://www.fws.gov/GIS/data/national/index.html Coastal Barrier Islands under CBRA – Weight - 91 Rare, Threatened, and Endangered Species – Weight - 86 Refuges – Weight 89

TNC - https://maps.tnc.org/gis_data.html

Conservation Areas – Weight - 73

NOAA - https://coast.noaa.gov/digitalcoast/data/nerr.html National Estuarine Research Reserves – Weight - 75

Audubon- https://www.audubon.org/important-bird-areas Important Bird Areas – Weight 75

DHS City, County, State and Federal Parks > 100 Acres – Weight – 44

Habitat

UNEP WCMC 2018 (https://data.unep-wcmc.org/datasets/7) Seagrass Locations – Weight – 88

NOAA Coastal Change Analysis Program (C-CAP)

·
Estuarine Emergent Marsh – Weight - 96 Forested Wetland – Weight - 80 Scrub – Shrub Wetland – Weight - 73

USFWS – National Wetland Inventory (NWI)

·
Freshwater Forested/Shrub Wetland – Weight - 82 Riverine Wetlands – Weight - 61

NOAA – Environmental Sensitivity Index (ESI)

·
Rocky Shoreline – Weight - 31 Unconsolidated Shore – Mud, Organic, Flat – Weight - 47 Unconsolidated Shore – Sand, Gravel, Cobble – Weight – 66

Cultural Resources

NPS

·
National Register of Historic Places – Weight – 75

USGS Protected Areas Database – Historic or Cultural Areas – Weight - 75This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download