Urban Heat Island (UHI) effect in Mumbai using Remote Sensing
Updated: Jun 13
During a cricket match telecast, you may have heard TV commentators utter something on these lines- "The temperature in Mumbai is 34 degrees but it feels much warmer here at the Wankhede..."
The commentator is feeling correctly, actually. He is feeling the Urban Heat Island (UHI) effect. Due to factors such as the construction material used to build the stadium, limited winds, crowd noise: the temperature within the stadium is actually a few notches higher than the average temperature of the city or its surroundings at that particular time.
Urban Heat Island (UHI) phenomena occurs when there is a significant difference between the actual temperature of an urban area and the average temperature of nearby non-urban areas at a given point in time.
UHI occurs because urban areas generate & absorb more heat as well as release less heat due to factors such as infrastructure density, insulating building materials used, population density, air pollution, less ground storage of rainfall, less Evotranspiration, & so on when compared to non-urban areas in the immediate vicinity. Here's a useful National Geographic explainer for this phenomena.
A high UHI is indicative of excessive urbanization, high pollution and high population and is a harbinger of heat-related medical conditions such as dehydration and blood pressure. As you would imagine, determining UHI is very useful for urban planners and architects who can use this output to chart future development as well as promote the use of eco-friendly construction materials. Air Conditioning Manufacturers can also use this information to good effect - by stationing its sales force and setting up its retail presence in areas with high UHI.
The Ward-level result of my UHI study for Mumbai (India) is depicted in the map-based output below:
Prepared using thermal remote sensing feature - SLSTR - Level 2 LST product - in Copernicus Sentinel-3B satellite as on 22nd May 2019, 16:10 Hrs. Much thanks to RUS Copernicus and GitHub for the training material. Nearby non-urban areas used for comparison fall just outside the Mumbai city extent.
As you would observe in the map - the maximum UHI - i.e. differential to base temperature - is found to be at 1.59 degrees Celsius which doesn't strike as alarming. However, do note that the temperature differential is aggregated at the Ward level (each polygon on the map). The original data output generated was actually computed at a pixel level (1 pixel = 20 metres). However, to depict this UHI information at a pixel level on the map would be more visually chaotic than meaningful - hence, I've aggregated the results to reflect the Mean temperature at a Ward level. Just for your information, at an individual pixel level, the maximum temperature differential to base was found to be significantly higher - a maximum of 3.0 degrees Celsius.
Besides, UHI effect is most prominent during the night hours, whereas I've use Sentinel imagery output generated during afternoon (16:10) during peak summer (May 2019) as Night observation wasn't available for the given time period. Why is UHI prominent at night? Because the non-urban areas cool down much quicker than the urban areas during the night leading to a large temperature differential reading. As a result, my output is only a conservative estimate of the real UHI. It is not difficult to imagine that the maximum real UHI for Mumbai can be as high as 7 -10 degrees Celsius. To help you understand better, imagine that the afternoon temperature is 35 degrees and you expect the night temperature to fall to a pleasant 25 degrees due to cooling-effect, but in reality the temperature only falls to 32 degrees in your urban area due to UHI - the surrounding non-urban areas are actually experiencing the natural 25 degrees temperature at the same point in time!
The output also makes sense to me from a location point-of-view as well. Large swathes of the main city area i.e. South Mumbai - from Colaba / Churchgate to Parel / Sion have the darkest shade of UHI i.e. maximum temperature differential. As one goes north, dense settlements such as Vile Parle and Andheri also have high UHI. 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 less UHI i.e. lower temperature differential, when compared to the non-urban base, due to comparatively limited urban infrastructure in these areas.
Because the data is aggregated at a Ward level - there are some visual anomalies as well, which should be taken into consideration. For example, you'd have observed that central suburbs such as Powai, Vikhroli & Bhandup have less UHI when compared to other similarly dense settlements. This is because the mean UHI could have been balanced out by the presence of large non-urban features within the Ward - for example, by water bodies (Powai Lake) & due to green areas (Aarey Colony).
What else can you infer from the output? Can you think of more ways in which UHI readings could be useful?
Intelloc Mapping Services | Mapmyops is engaged in providing mapping solutions to organizations which facilitate operations improvement, planning & monitoring workflows. These include but are not limited to Supply Chain Design Consulting, Drone Solutions, Location Analytics & GIS Applications, Site Characterization, Remote Sensing, Security & Intelligence Infrastructure, & Polluted Water Treatment. Projects can be conducted pan-India and overseas.
Several demonstrations for these workflows are documented on our website. For your business requirements, reach out to us via email - email@example.com or book a paid consultation (video meet) from the hyperlink placed at the footer of the website's landing page.