Cities in the U.S. are getting hotter, and that is causing significant health risks, especially to minorities, the elderly, and impoverished. There is significant spatial variation in temperature across a city due to changes in the landscape (elevation, tree cover, development, etc). NOAA has been engaged in a nationwide effort with CAPA Strategies to use a combination of Sentinel-2 satellite data along with temperature readings recorded from car- and bike-mounted sensors to generate detailed maps of the urban areas most impacted by heat. These measurements have been combined into single raster layers for morning, afternoon, and evening temperatures. As of 2020, 27 cities (26 in the U.S) have been mapped; a total of 50 cities will be mapped by the end of 2021. This layer shows the census tract (neighborhood) averages for those temperatures, along with additional information calculated for each neighborhood including:Temperature anomaly (neighborhood temperature compared to the citywide average based on the CAPA data)Impervious surfaceTree coverDemographicsTotal populationPopulation <5Population >65MinorityMedian incomePovertyCombining these different types of information can help planners identify areas at risk and help to develop mitigation and resilience plans to improve urban living conditions. More information about the campaign can be found in this Story Map by NOAA.

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. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 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.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.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): 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 Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne 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.

The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as feature services.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as feature services.

Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.

UrbAlytics is an experimental sub-project of the H2020-funded project AI4Copernicus that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners Latitudo 40 and LAND Research Lab®, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation.

The dataset

This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of Milan and Naples, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions. For each city, the dataset contains the following layers for the analysis period 2018-2022.

HEATWAVE POTENTIAL RISK (HPR)

Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.

MICROCLIMATIC PERFORMANCE INDEX (MPI)

The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance.

PARK COOL ISLANDS (PCI)

Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. The layer distinguishes between major and minor Park Cool Islands. Major PCI includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer. Minor PCI includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us giovanni.giacco@latitudo40.com, giulia.castellazzi@landsrl.com

The raster dataset of urban heat island modelling shows the fine-scale (100m pixel size) temperature differences (in degrees Celsius °C) across 100 European cities, depending on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation.

In the framework of the Copernicus European Health contract for the Copernicus Climate Change Service (C3S), VITO provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints). From this data set, the average urban heat island intensity is mapped for the summer season (JJA), which is the standard way of working in the scientific literature (e.g. Dosio, 2016). The UHI is calculated by subtracting the rural (non-water) spatial P10 temperature value from the average temperature map.

The 100 European cities for the urban simulations were selected based on user requirements within the health community.

Mayor Greg Fischer formed the Louisville Metro Office of Sustainability in 2012 with a mission of promoting environmental conservation, the health, wellness and prosperity of our citizens, and embedding sustainability into the culture of the Louisville community. Creating a culture of sustainability will be achieved through broad-based education and awareness efforts as well as implementation of projects and initiatives to influence behavior change.Data Dictionary: NEIGHBORHOOD - The neighborhood in Louisville.TOTAL NEW GREEN ROOFS - The number of green roofs installed. Each green roof is assumed to be 10,000 square feet.TOTAL GRASS PLANTED - The amount of bare dirt land planted with grass or other greenery, measured in hectares.TOTAL TREES PLANTED - The number of new trees planted.TOTAL COOL PAVING - Cool paving is pavement material engineered to exhibit a higher reflectivity than conventional pavement. Cool paving can be porous, made of a light colored material, or both. Cool paving is measured in hectares.TOTAL NEW COOL ROOFS - The number of cool roofs installed. Each cool roof is assumed to be 10,000 square feet. A cool roof can be steep-sloped or low-sloped or flat. A cool roof is define as a roof with a top-level material certified by ENERGY STAR or rated by the Cool Roof Rating Council as "cool." More information is available at https://louisvilleky.gov/government/sustainability/incentives#1Contact: sustainability@louisvilleky.gov

Polygons representing heat islands on the ground surface. A heat island is defined as the difference in temperatures observed between two surrounding environments at the same time. The different temperature differences are mainly explained by the type of soil layout such as the vegetation cover, the impermeability of the materials and the thermal properties of the materials. This difference can reach more than 12°C. The 2020-2030 Montreal Climate Plan aims, among other things, to improve planning and regulatory tools in urban planning. Montréal has thus committed to updating the climate change vulnerability analysis, including the heat island map, carried out as part of the 2015-2020 Agglomération de Montréal Climate Change Adaptation Plan and to integrating it into the next urban and mobility plan. The urban heat island maps were produced in collaboration with the Department of Geography of the University of Quebec in Montreal (UQAM). The data can also be viewed on the interactive heat island map.This third party metadata element was translated using an automated translation tool (Amazon Translate).

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Heat Index and Afternoon Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Evening Heat Index Observations in Floating-Point (°F)

Heat islands are urban or developed areas that experience higher than average temperatures due to heat retained or even amplified by the built-out urban environment.The data displays the difference in surface temperature from the regional average.2017 Urban Heat Index as derived from LandSat Data.

Heat stress maps for Berlin representing
* the average number of heatwave days
(1986 – 2005 | 2026 – 2045 | 2081 – 2100)
per statistical unit or per grid

* The Urban Heat Island effect at 11pm per year
(1986 - 2005) per statistical unit or per grid

The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.

Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:
* Average number of heat wave days per year
* Urban Heat Island effect at 11pm per year

Scenario: Base scenario (situation LULC today)

This scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 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 scene 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.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 Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/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 Pete.Aniello@tpl.org with feedback.

Land surface temperature (LST) maps, and urban heat island (UHI) maps, for Australian urban centres, calculated over summer 2015/16. Generated as part of an investigation into changes in urban greenspace. Lineage: Land surface temperatures were calculated using data from the Landsat 8 thermal infrared sensor (TIRS) band 10. Each image was processed using the generalised single channel method of Jiménez-Muñoz et al. (2003, 2009). The required atmospheric parameters were obtained from publicly available observations by the Australian Government Bureau of Meteorology (BOM). The required land surface emissivity (LSE) values were estimated using the NDVI approach (Sobrino & Raissouni 2000). As many overpasses as possible during the summer of 2015/16 were processed, and the results averaged to obtain an estimate of typical summer LST. Urban Heat Island (UHI) was estimated by subtracting from the LST images an estimate of non-urban baseline temperature. This baseline was estimated by a first-order fit to the temperature of native vegetation within and around each urban centre.

The data presented on this page concern the 2013-2014 mapping of temperature differences, the classification maps of these temperature differences (i.e. urban heat and freshness islands) and the map of the urban heat island intensity index. These different maps are detailed below: - The mapping of Temperature differences in °C represents the temperature difference in the city compared to a nearby forest. It was produced at the scale of the Quebec ecumene (2016 census, 167,764 km2). This mapping, provided on a grid with a spatial resolution of 15 m, was carried out with a predictive machine learning model built on Landsat-8 satellite data provided by the United States Geological Survey (USGS) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. Islands of urban heat and freshness (ICFU) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) was conducted for * population centers from the 2021 census * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which is added a 2 km buffer zone. It thus covers all major urban centers, i.e. 14,072 km2. The method for categorizing ICFUs is the ranking of predicted temperature differences for each population center into 9 levels. Classes 8 and 9 are considered Urban Heat Islands and classes 1, 2, and 3 as Urban Freshness Islands. The interval values for each class and population center are shown in the production metadata file. Since surface temperatures were analyzed at the Quebec ecumene scale, but the classification intervals were calculated for each population center individually, the differences in temperature grouped into the different classes vary from region to region. Thus, there are differences observed in the predicted temperature differences between North and South Quebec and according to urban realities. For example, a temperature difference of 2°C may be present in class 1 (cooler) in a population center located in southern Quebec, but may be present in class 9 (very hot) in a population center in northern Quebec. It is therefore important to interpret the identification of heat islands in relation to the relative temperature difference data produced at the Quebec ecumene scale. - The Urban Heat Island Intensity Index (SUHII) map represents the Surface Urban Heat Island Intensity Index (SUHII) represents the Surface Urban Heat Island Intensity Index (SUHII) map represents the Surface Urban Heat Island Intensity Index (SUHII) map. This index is calculated for each * dissemination island * (ID) of Statistics Canada included in the * 2021 census population centers * (CTRPOP) * () * (CTRPOP). It highlights areas with higher heat island intensity, by calculating a weighted average from temperature difference classes, giving more weight to the hottest classes. This weight is proportional to the class number (e.g. a class 9 surface is 9 times more important in the index than the same area with a class 1). These maps as well as those of * 2020-2022 * are used for the * Analysis of change between the mapping of heat/freshness islands 2013-2014 and 2020-2022 . For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (version complete). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.This third party metadata element was translated using an automated translation tool (Amazon Translate).

For an urban heat island map during a warm summer see this dataset. This map shows the results of an ‘UrbClim’ simulation for the mean temperature at midnight during the summer of 2011 (May to September) at a resolution of 250m. This summer has been selected as a typical summer for a West-European city. On average, the night-time temperature is approximately 4°C higher in the city centre. During some hot nights, even larger effects are observed. UrbClim is an urban climate model designed to model and study the urban heat island effect (UHI) at a spatial resolution of a few hundred meters. This project was carried out by VITO as part of an EU-funded RAMSES programme on the urban impacts of climate change. For more information about the model, please see the Technical Information and Interpretation report. Please direct any enquiries to Hans Hooyberghs (hans.hooyberghs@vito.be) or Koen De Ridder (koen.deridder@vito.be).

The data presented on this page concern the 2013-2014 mapping of temperature differences, the classification maps of these temperature differences (i.e. urban heat and freshness islands) and the map of the urban heat island intensity index. These different maps are detailed below: - The mapping of Temperature differences in °C represents the temperature difference in the city compared to a nearby forest. It was produced at the scale of the Quebec ecumene (2016 census, 167,764 km2). This mapping, provided on a grid with a spatial resolution of 15 m, was carried out with a predictive machine learning model built on Landsat-8 satellite data provided by the *United States Geological Survey (USGS) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) _ was conducted for * population centers from the 2021 census * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which is added a 2 km buffer zone. It thus covers all major urban centers, i.e. 14,072 km2. The method for categorizing ICFUs is the ranking of predicted temperature differences for each population center into 9 levels. Classes 8 and 9 are considered Urban Heat Islands and classes 1, 2, and 3 as Urban Freshness Islands. The interval values for each class and population center are shown in the production metadata file. Since surface temperatures were analyzed at the Quebec ecumene scale, but the classification intervals were calculated for each population center individually, the differences in temperature grouped into the different classes vary from region to region. Thus, there are differences observed in the predicted temperature differences between North and South Quebec and according to urban realities. For example, a temperature difference of 2°C may be present in class 1 (cooler) in a population center located in southern Quebec, but may be present in class 9 (very hot) in a population center in northern Quebec. It is therefore important to interpret the identification of heat islands in relation to the relative temperature difference data produced at the Quebec ecumene scale. - The _Urban Heat Island Intensity Index (SUHII) _ map _ represents the Surface Urban Heat Island Intensity Index (SUHII) _ represents the Surface Urban Heat Island Intensity Index (SUHII) map _ represents the Surface Urban Heat Island Intensity Index (SUHII) _ map. This index is calculated for each * dissemination island * (ID) of Statistics Canada included in the * 2021 census population centers * (CTRPOP) * () * (CTRPOP). It highlights areas with higher heat island intensity, by calculating a weighted average from temperature difference classes, giving more weight to the hottest classes. This weight is proportional to the class number (e.g. a class 9 surface is 9 times more important in the index than the same area with a class 1). These maps as well as those of * 2020-2022 * are used for the * Analysis of change between the mapping of heat/freshness islands 2013-2014 and 2020-2022 *. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (version complete). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Heat stress maps for the city of Almada representing
* the average number of heatwave days
(1986 – 2005 | 2026 – 2045 | 2081 – 2100)
per statistical unit or per grid

* The Urban Heat Island effect at 11pm per year
(1986 - 2005) per statistical unit or per grid

The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.

Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:
* Average number of heat wave days per year
* Urban Heat Island effect at 11pm per year

This dataset mainly includes six folders. They are as follows: 1. Cover image folder in the *.JPG format; 2. Haikou bright temperature difference image folder in the *.JPG format from 2008 to 2015; 3. Heat island intensity map folder in the *. JPG format during 2008 to 2018; 4. Haikou city location map folder in the *. PNG format; 5. Temperature inversion code folder, which contains Landsat5(LST5) and Landsat 8(LST8) temperature inversion code data in the *. pro format; 6. Process folder, which contains data on the surface temperature inversion process in the *. VSD format.

For an urban heat island map during an average summer see this dataset. A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. This urban heat island map was produced using LondUM, a specific set-up of the Met Office Unified Model version 6.1 for London. It uses the Met Office Reading Surface Exchange Scheme (MORUSES), as well as urban morphology data derived from Virtual London. The model was run from May until September 2006 and December 2006. This map shows average surface temperatures over the summer period of 2006 at a 1km by 1km resolution. To find out more about LondUM, see the University of Reading’s website. The hourly outputs from LondUM have been aggregated and mapped by Jonathon Taylor, UCL Institute for Environmental Design and Engineering. Variables include: WSAVGMAX= the average of the maximum daily temperatures across the summer period (May 26th-August 31st) WSAVG=the average temperature across the summer period WSAVGMIN = the average minimum daily temperature across the summer period HWAVGMAX= the average of the maximum daily temperatures across the 2006 heatwave (July 16th-19th) HWAVG=the average temperature across the across the 2006 heatwave HWAVGMIN = the average minimum daily temperature across 2006 heatwave period The maps are also available as one combined PDF. The gif below maps the temperatures across London during the four-day period of 16-19th July, which was considered a heatwave. If you make use of the LondUM data, please use the following citation to acknowledge the data and reference the publication below for model description: LondUM (2011). Model data generated by Sylvia I. Bohnenstengel (), Department of Meteorology, University of Reading and data retrieved from http://www.met.reading.ac.uk/~sws07sib/home/LondUM.html. () Now at Metoffice@Reading, Email: sylvia.bohnenstengel@metoffice.gov.uk Bohnenstengel SI, Evans S, Clark P and Belcher SeE (2011) Simulations of the London Urban Heat island. Quarterly journal of the Royal Meteorological Society, 137(659). pp. 1625-1640. ISSN 1477-870X doi 10.1002/qj.855. LondUM data (2013).