Heat Severity - USA 2025

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. 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 2025. It was developed by the Trust for Public Land’s Geospatial Team, which specializes in nationwide spatial analysis to help communities and governments strategically identify, prioritize, and deliver parks, trails, and conservation plans to support their park access, public health, and climate resilience goals. To explore previous versions of the data, visit the links below: Heat Severity - USA 2024 Heat Severity - USA 2023 Heat Severity - USA 2022 Heat Severity - USA 2021 Heat Severity - USA 2020 Heat Severity - USA 2019 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. Trust for Public Land uses analyses like this to help communities identify priority areas for park creation, tree canopy expansion, and other nature-based solutions that reduce heat risk and improve public health. Learn more about TPL’s broader climate program. 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 Summary This dataset was developed using proprietary Python code developed at Trust for Public Land, running on the EarthDaily (formerly Descartes Labs) platform through their Python API. The EarthDaily 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. TPL’s Geospatial Team has more than 20 years of experience building national datasets, custom decision-support tools, and spatial analyses used by governments, nonprofits, and planners across the country. 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. Common applications include stand-alone urban heat island analysis, park and green infrastructure planning, and public health risk assessment. 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 Services The data is made available as an ArcGIS Online hosted tiled image service layer. The service does not have a processing template and can be used directly in web maps, ArcGIS Pro or QGIS. The service returns actual pixel values so you can apply your own renderer to the imagery. 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 Information Please 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 Center Dr. Ladd Keith, University of Arizona Dr. 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 Resiliency About the Trust for Public Land Geospatial Team Trust for Public Land’s planning and GIS (geographic information systems) service uses cutting-edge research, innovative mapping technologies, and groundbreaking planning practices to help communities and governments strategically identify, prioritize, and deliver parks, trails, and conservation plans. Their work spans national datasets, regional modeling, and community-scale decision support, with a focus on producing actionable, policy-relevant insights. Disclaimer/Feedback With 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 future versions of the national UHI dataset can be improved. TPL also works with partners to refine and apply geospatial data for local planning and analysis. Contact [e-mail] with feedback.