During a cricket match telecast, you may have heard something like this -
the temperature in Mumbai is 32 degrees but it feels much warmer here at the Wankhede....
Lest you feel otherwise, the commentators may actually be spot on - they could be reeling from the Urban Heat Island (UHI) effect. Due to aspects such as the stadium layout which results in the obstruction of winds, and crowd noise, the temperature within the stadium could be a few notches higher than the mean recorded temperature of the city/surroundings.
Technically, Urban Heat Island refers to the condition when there is a significant difference between the actual temperature of an Urban area and the average temperature of surrounding non-Urban areas at a given point in time.
This phenomena occurs because Urban areas generate, absorb and retain more heat than its non-Urban surroundings - these can be due to the density of infrastructure, the insulating building materials used, air pollution, air conditioning & so on. Here's a useful National Geographic explainer. A high UHI is indicative of excessive Urbanization and is a harbinger of heat-related medical conditions such as dehydration and blood pressure.
As you can imagine, Urban Heat Index data is useful for Urban Planners and Architects who can use the information to chart Infrastructure Development projects sustainably as well as adopt measures to mitigate it - such as the use of eco-friendly construction materials. The information can just as well be useful to Air Conditioner manufacturers who can ramp up their retail presence in areas with high UHI.
While the methodology of deriving UHI can be accessed here, below is my final map-based output - Classification of UHI for the metropolis of Mumbai, India at a Ward-level as on 22nd May 2019.
As you can infer from the output - the maximum UHI is found to be 1.59 °C which at first may not appear very alarming. However, do note that the temperature differential depicted in the map is averaged at Ward-level. At a more granular Pixel-level (spatial resolution of SLSTR instrument onboard Sentinel-3 is 1 kilometre in the thermal infrared channel), the maximum UHI was significantly higher - 3.00 °C. I could have depicted the UHI at a pixel level, however, it would be visually chaotic - hence, I chose to aggregate the average UHI at a more macro-level (Ward) instead.
Additionally, UHI effect is more prominent during night hours as urban areas retain the heat accumulated at day-time whereas non-urban areas are quick to dissipate that - however, S-3B Imagery dataset was not available in this preferred time period and I had to make do with a dataset acquired during the afternoon (16:10 hours).
As a result, my maximum UHI output is only a conservative estimate - I would not be far-off in assuming that the maximum UHI could reach a high of ~7 °C during the night. This is to say that if the afternoon temperature in a region comprising urban and non-urban areas is 35 °C, due to UHI effect, the maximum actual temperature would only fall to 32 °C in an urban area due to excessive heat retention from the day even if the night temperature falls to a pleasant 25 °C which would be enjoyed as-is by the non-urban area residents.
Having lived in Mumbai for a sizeable number of years, the output in Figure 1 also makes sense to me intuitively. Large swathes of the main metropolis - from Colaba to Sion (South Mumbai) have the darkest shade of UHI i.e. these have the maximum temperature differential from the non-urban surroundings. As one goes further north in the city, suburban settlements such as Vile Parle and Andheri also have a high UHI which is understandable given they are densely populated. In contrast, the region to the east of Eastern Express Highway as well as what comprises Sanjay Gandhi National Park near Borivali and adjacent areas have a comparatively lower UHI i.e. less temperature differential compared to the non-Urban areas. This can be attributed due to the fact that urban infrastructure in these zones is much lesser and less denser than the main city zones.
I must point out that because the temperature data is being averaged at a Ward level - there are some visual anomalies which should be taken into consideration. For example, the central suburbs of Powai, Vikhroli, Bhandup and Goregaon have lower UHI when compared to other similarly dense settlements such as Vile Parle and Andheri. This is because the mean UHI of the ward is being pulled down by the presence of large features with less urban density within the Ward - for example, water bodies (Powai Lake in Powai), and green zones (Aarey Colony in Goregaon).
What else did you observe from the UHI output? Can you think of new ways in which such UHI data can be useful? Let me know.
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