data jungle adventures

In the coming decades, urban heat islands (UHI) are predicted to become more challenging for cities, putting more stress on various social and biophysical systems, including human health, social comfort, urban infrastructure, energy demand, and water supply. One of the significant difficulties confronting our current generation of scientists and engineers is understanding how to reduce the detrimental impacts of urban heat islands while also creating sustainable cities.

Urban heat island effects are determined by the solar reflectance, thermal emissivity, and heat capacity of urban materials and their ability to reflect, emit and absorb solar radiation. Changing the thermal properties of a city’s surface materials is the cheapest way to reduce the urban heat island effect. In densely populated locations, urban trees and plants also help to reduce uncomfortably high urban thermal conditions. Examining the appropriate parameters for implementing these urban cooling mitigation measures lays the groundwork for future initiatives.

The negative impact of rapid urbanisation includes pollution, waste heat production from human activity, diseases, and many more. Vegetation is lost as cities and towns expand. Urban surfaces are paved or covered with structures, resulting in less shade and air moisture. It demonstrates that densely populated areas evaporate more water, resulting in higher surface and air temperatures. 

Here, we describe how satellite studies support urban planning and management to assess and manage the heat island effect. 

Land Surface Temperature (LST)

LST is the brightness temperature of a surface and not the actual temperature on the surface, but it has a strong relationship with both land surface and air temperature. LST can be derived from various satellite surface and airborne surveys and is commonly used to understand UHI distribution. 

Evidence for a relationship between land cover and land use is plentiful and commonly used to advise land use, master and subdivision planning to mitigate and reduce the UHI effect. For example, vegetation, water bodies, not-sealed surfaces, and certain building materials and colours reduce the reflectance and impact on air temperature.

• Urban heat island (UHI) effects are determined by the solar reflectance, thermal emissivity, and heat capacity of urban materials and their ability to reflect, emit and absorb solar radiation. Changing the thermal properties of a city’s surface materials is the cheapest way to reduce the urban heat island effect. 
• Optimising urban planning and designing green infrastructure distribution based on the relationship between urban structure and LST have reduced the effect of surface reflectance in Shenzen, Melbourne and other major metropoles.

In Shanghai, residential land contributed the most to the UHI, followed by industrial land. A case study in Beijing used satellite images to show that a 10% increase in green space led to a drop in LST. The area of expansion in the urban fabric and the proportion of each land-use type significantly influenced Beijing’s UHI. The park’s size dramatically affects LST.

Relationship between land use and LST

Air temperature modelling

Air temperature modelling aims better to understand spatiotemporal patterns and variations in air temperature. Models relying on remote sensing help improve this knowledge. Spatial interpolation of historical air temperature values or the effect of geographical variables gives us an incomplete comprehension. 

• Geographical variables: altitude, latitude, continentality, solar radiation
• Remote sensing data variables: LST, NDVI, albedo

A study on air temperature modelling for Catalonia used geographical and remote sensing data and meteorological data of ground stations. Processing geographical variables included approaching latitude as the distance of stations from the Equator, altitude as extracted from a DEM, continentality as the distance from the sea, and solar radiation from a potential radiation model. As for processing Landsat-5TM and Landsat-7ETM+ data, it included geometric correction, radiometric correction, Land Surface temperature, albedo, and cloud removal.

Surface material selection to improve urban climate

Building materials pose environmental, legal, and financial challenges for people in property management and urban planning decision-makers. One of the main challenges is the role of building surface materials in impacting urban climate. Remote sensing techniques can help identify building surface materials to understand the role of different building materials in the urban context through spectral characteristics. 

Unlike multispectral remote sensing, hyperspectral remote sensing imagery gives us access to more detailed information. An experimental study supported by the National Key Technologies R&D Program of China (2017)  investigated hyperspectral RS based on building spectrum characteristics and explored a new method to identify colour steel, clay, glazed tile, and asphalt concrete. They found that different materials have different spectral sensitivity and a significant correlation between building materials and similar chemical compositions. Such findings are valuable for urban planning since they contribute to orienting policy-making towards adopting environment-friendly regulations, especially regarding reducing the urban heat island.

Conclusion

Meteorological variables and urban parameters are the two factors influencing the occurrence, distribution, and intensity of UHIs. The key parameters are the city’s location, size and population, the density of built-up regions, the urban layout, and land use. For example, surface temperatures are often high in built-up areas characterised by impervious surfaces and industrial land with significant energy consumption.

Satellite images and remote sensing play a significant role in ecologically-oriented urban planning decisions to support the mitigation of urban heat island effects. While several publications mention the limited integration of remote sensing in urban planning, the need to put the first in service of the second imposes itself, especially in the smart city era. Built-up indices, detection of impervious surfaces, and the Land Use Land Cover (LULC) pattern change detection are remote sensing techniques that continue to improve urban planning decision-making.