Identification of heat islands to support city planning

Identification of heat islands to support city planning

(A Geografiska Informationsbyrån and Epsilon Italia presentation)


This slidset addresses city planners and is about Urban Heat Islands and the importance of their identification in a spatial planning context. The slidset introduces the EO surface temperature maps, with their potential and their limitations.

Learning outcomes

  • Understand the importance of identifying UHIs in a spatial planning context (what are the consequences if, in a spatial planning context, the UHIs are not identified?)
  • Understand how EO surface temperature time series can support identification of UHIs
  • Understand how to interpret EO surface temperature time series to identify UHIs
  • Understand how to derive maps from EO surface temperature data, to be integrated with other relevant information related to UHI identification in a spatial planning context
  • Understand how to analyse information derived from EO surface temperature data integrated with other relevant information related to UHI identification in a spatial planning context
  • Understand the limitations of the EO surface temperature data due to the spatial resolution

What are Urban Heat Islands?

  • a difference in temperature of urban and suburban areas compared to their outlying rural surroundings
    • E.g. the annual mean air temperature of a city with one million or more people can be 1 to 3°C warmer than its surroundings,
    • Usually the difference decreases as city size decreases.


Urban Heat Island: the phenomenon


Source: M. Magoni, C. Cortinovis, «Interventi di mitigazione delle ondate di calore in contesti urbani», 2014


Urban heat islands have been recognized and described a long time ago:

“The generally higher temperatures inside towns have been noted for a long time, indeed Luke Roward (Roward, 1818), a pioneer of urban climatology though more famous for his work on cloud classification, first measured temperature differences in London as early as 1809. And yet, in spite of the numerous studies in urban temperatures, it still remains to quantify, even relatively, the heat exchange mechanisms leading to the excess heat of towns. There are obviously four contributory factors: changes in the thermal characteristics (albedo, heat conductivity and thermal capacity) of the surface following the substitution of buildings and roads for farms and fields; changes in the airflow patterns with a reduced diffusion of heat from streets and courtyards; changes in evaporation rates and heat losses and, the heat added by humans and human activities. These seem to be of differing importance in different cities so that the character of temporal variations also varies. In many cities of western Europe, heat islands are strongest in summer whilst in central and northern Europe there is, apparently, little difference between summer and winter intensities. In several Japanese cities, maximum heat island intensities occur in winter.”

(T. J. Chandler, Urban climatology - Inventory and prospect, Urban climates: proceedings of the WMO Symposium on Urban Climates and Building Climatology, WMO, 1970)

Why analyse urban heat islands?

UHIs have several impacts on environment and population, for example:

  • Increased Energy Consumption
  • Elevated Emissions of Air Pollutants and Greenhouse Gases
  • Compromised Human Health and Comfort
  • Impaired Water Quality

(Heat Island Impacts, ; 'Heat island' effect could double climate change costs for world's cities, )

The consequences of Urban Heat

Consequences of UH

Source: M. Magoni, C. Cortinovis, «Interventi di mitigazione delle ondate di calore in contesti urbani», 2014

State of the art:

Recently, the dependency of UHI on a city’s layout could be shown:

“The arrangement of a city's streets and buildings plays a crucial role in the local urban heat island effect, which causes cities to be hotter than their surroundings, researchers have found. The new finding could provide city planners and officials with new ways to influence those effects.
Some cities, such as New York and Chicago, are laid out on a precise grid, like the atoms in a crystal, while others such as Boston or London are arranged more chaotically, like the disordered atoms in a liquid or glass. The researchers found that the "crystalline" cities had a far greater buildup of heat compared to their surroundings than did the "glass- like" ones.
The study […] found these differences in city patterns, which they call "texture," was the most important determinant of a city's heat island effect.”

(Urban heat island effects depend on a city's layout,

3 causes of UHI

1) Physical/material factors

  • They depend on the materials with which the city is built
  • 2) Morphological factors

  • They depend on the shape of the city
  • 3) Anthropogenic factors

  • They depend on the functions and activities carried out in the city

  • Presence of green areas

    causes and effects

    • Evapotranspiration: combined effect of evaporation of water from the soil and plant transpiration (photosynthesis)
    • Water storage in the soil: prolongation of the cooling effect due to evaporation
    • Shading and surface protection against direct irradiation

    Issues to address

  • How can I monitor local heat islands?
  • How can I benefit from EO-data when analysing heat island effects?
  • How to interpret data?
  • How can I use this in planning situations for the mitigation of urban heat?
  • Benefits, limitations and errors

  • How to analyse urban heat islands?

    To detect UHIs it is used the land surface temperature (LST). Be aware that this is not equal to the air temperature that people will experience in that place. However, it is a good and easy-to-access indicator.

    It is possible to use data from Sentinel-3, which has thermal bands included and thus provides LST. Another alternative would be Landsat-8 which has a higher resolution (Sentinel-3: 500m, Landsat-8: 100m).

    Remote sensing

    • the acquisition of information about an object or phenomenon without making physical contact
    • used in numerous fields, including geography, land surveying and most Earth science disciplines (for example, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications.
    • repeating satellite orbits enable continuous monitoring of the object, phenomenon or area in general, i.e. gathering EO time series

    EO time series

    • Efficiently provide information on
      • Land cover change
      • Vertical and horizontal land movements
      • Sea level change
      • Ice sheet balance change
      • Temperature change over the season or different time
      • Specific events such as fires, floods, landslides, earthquakes, etc.
      • Enable global and local analysis (such as detecting and monitoring Urban Heat Islands, UHIs)

    EO time series for UHI monitoring

    • Satellite sensors can observe surface temperature using thermal sensors
    • Sensors available at different satellites

    Vegetation – UHI relationship

    • More the vegetation less the UHI effect
    • Methods on quantifying this
      • NDVI (Normalized Difference Vegetation Index)
      • Vegetation fraction
      • LAI (Leaf Area Index)
      • Other methods
        • Handheld temperature sensors
        • Aerial temperature measurements

    Vegetation – UHI relationship


    Numerical indicator showing live green vegetation

    NDVI = (NIR – RED) / (NIR + RED)

    Vegetation fraction

    Derived from spectral mixture model

    Uses LSMA to determine vegetation – at sub-pixel level

    Sometimes more accurate than NDVI


    Can be computed from NDVI

    Can be derived from field measurements (collection of leaf litterfall or destructive harvesting of leaves within a vertical column passing upward through the entire tree canopy)




    Learn more...

    Case: City of Stockholm, Sweden

    • Population ~1.0 million
    • Area 188 sq. km.
    • Summer temperatures normally 14-22 deg. celcius
    • Heatwave in 2018

    What are we going to do?

    • Screening of the city for LHI
      • Using surface temperature maps
        • Using time series
      • Make statistic measures from time series
        • Max temp
        • Mean temp
      • Locate and delineate main LHI
      • Locate cool structures
      • Describe how this can be related to planning issues


    • TIRS time-series Max temperature June Aug 2014-2020
    • Buildings, residential areas
    • Green infrastructure
    • Pre-schools

    Description of the selected case

    Residential areas and people living in or close to LHI

  • Study area - South Stockholm
  • Connection to buildings and planning
  • Access to “cool structures”
  • Access to green structures
  • Schools and preschools

  • Surface heat temperature map 2014-2020, Time series.

  • Max temperatures showing warm and cool areas
  • Landsat 8 - Band 10 Thermal Infrared.
  • connection

    • 21°C - Trees provide both shade and evaporation, two important factors that explain that forests are cooler than grasslands
    • 23°C - Abundance of trees in residential areas provide cooler outside and indoor temperatures.
    • 25°C - As the number of trees and their height decreases, the temperature increases.
    • 28°C - In environments with few trees and large open and impervious surfaces, the heat increases faster.
    • 33°C - In environments with a small amount of green spaces and large open and impervious surfaces, the heat increases faster.

    Local Heat islands are delineated > 28°C


    Cool structures are delineated < 22°C Mostly water and forested areas


    Green structures - GIS layer from land cover mapping


    GIS layer with buildings


    Assessing the impact of LHI by the use of Surface temperature data and GIS-layers

      Buildings within LHI
      • No green or cool structures nearby (>100m)
      • Green or cool structures nearby (<100m)
      Buildings outside LHI
      • No green or cool structures nearby (>100m)
      • Cool and green structures nearby (<100m)
      • Green structures nearby (<100m)
      • Cool structures nearby (<100m)

    Southeast of city

    Planning takeaways: Caution - Residents in red areas can be affected by heat stress and little access to green or cool structures


    Inner city south

    Planning takeaways: Caution - Population in red areas can be affected by heat stress and little access to green or cool structures


    Southwest of city

    Planning takeaways:

  • Mainly industrial areas / workplaces
  • Caution - Population in red areas can be affected by heat stress and little access to green or cool structures
  • connection

    Situation of the preschools in Stockholm

    Location of preschools in South Stockholm


    Southeast of city

    Planning takeaways: Caution - many Preschools within or close to LHI - lack of access to green structures


    Southwest of city

    Planning takeaways: Better situation with preschools mostly outside of LHI and better access to green structures


    Planning takeaways

  • The majority of the preschools are located outside LHI, with access to green structures
  • Many preschools in the inner city are located in LHI, with little access to green structures
  • connection

    Conclusions / Recap

  • Urban heat can cause physical discomfort and health problems for its inhabitants
  • Urban heat can also deteriorate urban ecosystems and environment
  • Urban heat waves causes drought
  • Human perception of heat stress is a combination of several factors, e.g. air temperature, heat radiation from surfaces and personal conditions
  • Conclusions / Recap

  • Surface temperatures can be monitored by Sentinel-3, Landsat-8 and Aster, with different resolutions
  • In the current case we are using summer maximum temperatures from Landsat-8 during a time-series to illustrate local heat islands within the city and in the suburbs
  • The obvious correlation between dense building structures, lack of green structures and high surface temperatures is confirmed
  • Conclusions / Recap

  • Information presented here can be used on a general scale (block level) to identify areas potentially exposed to urban heat
  • Information presented in this case can be taken into consideration when planning for measures to mitigate urban heat
  • It is important to remember that local conditions like e.g. shading and cooling structures can have a large impact on perceived heat
  • Conclusions / Recap

    • Examples of planning takeaways
      • Avoid further soil sealing and densification in areas close to heat islands
      • Increase the share of green areas in certain “Hot spots” in the urban environment
      • Access to green and cool structures may be even more important for residents in areas exposed to urban heat
      • Special consideration should be given to sensitive groups such as elderly, preschool children, etc

    Limitations of data

  • Resolution of the thermal bands (Sentinel-3 500m, Landsat-8 100m)
  • Surface temperature from satellites can be seen as an indicator of urban heat, but there are more factors to be studied on a more detailed level
  • Depending on satellite revisiting times and atmospheric conditions information might be obtained on different temporal scales, reducing the efficiency

  • “One of seven priority goals in The City of Stockholm Environment programme 2020-2023, is a climate-adapted Stockholm. Working with climate adaption and urban heat issues, we need to integrate many different data sources to further increase our knowledge. Surface temperature measurements from satellite can together with data on e.g. census, land use and urban planning be an important tool for improving our basis of decision.”

    Peter Wiborn, Senior Project Manager - Ecosystem Services, Environment and health department, City of Stockholm

    Thank you!

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