Mapping Urban Footprint for Indian locations
Updated: Jul 1, 2021
It's been a while since I last analyzed satellite imagery (Mauritius Oil Spill, Aug 2020). This time we'll focus on mapping the spatial extent of built-up areas i.e. deriving the urban footprint, in a couple of Indian cities.
To share the context, not only does India have 3 cities in the Top 10 world's fastest growing cities list currently, but also it is predicted to occupy all the 10 spots in the world's fastest growing cities list between now and 2035 !
While economic activity, productivity and growth is fuelled by urban centers, unchecked urbanization is risky too, for obvious ecological reasons among others.
So how does imagery from earth observation satellites help us to monitor urbanization?
As you may already know, satellites capture the energy reflected from the earth's surface, be it from an external light source such as the Sun or from the radio waves emitted by the instrument onboard the satellite itself. The former method captures visible light in the form of optical imagery while the latter captures energy not visible to human eye (microwaves, radiowaves etc.) in the form of radar dataset / imagery. Now, radar has several advantages over optical imagery which makes it much better placed to identify and observe the earth's surface and ongoing phenomenon.
For mapping urban footprint in particular, radar has properties which make it suitable to distinguish impervious, man-made structures (these reflect a strong energy signal / back-scatter as well as have high coherence values) from natural landscapes (agriculture, vegetation, water bodies etc. as these have comparatively less back-scatter and / or coherence values).
Below, I've analysed Synthetic Radar Aperture (SAR) imagery over New Delhi to map its urban footprint. Let's have a look.
(The sliders below are best viewed on a PC.)
The image above is a RGB composite model of radar imagery bands over a cross-section of New Delhi in October 2020 (Slider of the complete study area can be seen here). Yellow color represents urban built-up areas whereas green represents non-urban areas (such as natural vegetation, agriculture and barren lands). To derive the RGB model, I laid two recent radar imagery on top of each other and studied the combined properties of the pixels. Pixels of urban areas would remain the same in both the imagery datasets (due to strong backscatter as well as coherence values) whereas corresponding pixels of non-built up areas would have a small yet significant change in the coherence values (for eg., wind causes minor distortions in vegetation orientation thereby resulting is less coherence) which enables us to distinguish it from urban areas and attribute a different color to it.
(Note: Each pixel has a spatial resolution of 5 m by 20 m. i..e. 100 sq. m.)
In the slider above, we have extracted only the yellow pixels i.e. only the urban built-up areas and attributed white color to it (binary: 1) whereas all the remaining pixels were attributed black color (binary: 0). Again, the slider above is just a subset of the study area to aid in visual representation.
As you can imagine, we can also compare urban footprint over multiple periods of time to understand how the urban landscape has changed i.e. perform temporal analysis. The above image slider compares the entire study area over two time periods nearly five years apart - Oct 2020 and January 2016.
Can you spot some deviations in the yellow pixels, in particular? It can be difficult to observe as the spatial extent comprises the entire city and beyond, however, you would be still able to spot minor deviations. Notice that a yellow spot emerges slightly north-west of Ghaziabad in the 2020 image. Let's inspect it closely...
Upon observing that precise spot on Google Earth's basemap, I have been able to ascertain that the cluster of new yellow pixels point to the newly built WUPPTCL Ataur Power Sub-station.
The accuracy of satellite imagery is remarkable!
Want to test other points for yourself? Download the KMZ output files here and open it using Google Earth on your PC.
Malappuram was recently ranked as the fastest growing city in the world (2015 -2020) in terms of population (44% increase). Let's see what Urban Footprint Mapping of this region has to suggest.
You can download the RGB and Urban Footprint files from here.
The increase in white pixels (urban areas) in the image on the right is visually discernible. Let's find out by how much -
2016 has close to 17,000 white pixels...
2020 has nearly 20,000 white pixels. An increase of 3000 pixels multiplied by 100 sq.m per pixel size gives us 300,000 sq. m. or 0.3 sq. km increase in urban area. This isn't a low figure. It is equivalent to addition of 21+ Eden Gardens cricket stadiums (14,200 sq.m each) in terms of built-up area in a space of just 4 years.
This isn't a low figure. It is equivalent to addition of 21+ Eden Gardens cricket stadiums (14,200 sq.m each) in terms of built-up area in a space of just 4 years. Therefore, we can roughly conclude that a 44% growth in population (5 years) is met by an increase of 18% increase in built-up / urban area (4 years).
It may not be completely evident from the imagery above - the images are not the direct output captured by satellites, rather, they have been derived / calculated using the various bands within the imagery datasets. The methodology of calculation is often quite complex and backed by years of scientific research. Modern technology makes it much easier to perform the calculations, however, processing the imagery datasets still requires considerable computing power.
The underlying principle is relatively easy to grasp, though. Each pixel within the imagery dataset gives us plenty of data-points about the properties of the surface captured (amplitude, phase, orientation, height etc.). However, the true beauty lies in how we play around with these available data-points to unearth new insights. For example, if we were to amplify certain properties and repress other properties, that gives us an interesting insight; if we overlay one pixel of the same area captured in a particular time period onto the corresponding pixel of the same area in another time period, the comparison unearths a whole new world of insight, and so on.
Essentially, just like a calculator, we can perform several functions, apply formulas and do complex calculations on the imagery datasets to extract more and more insights from just a tiny little pixel!
Isn't this fascinating?
You can read more of my satellite imagery analysis from the links here - 1)Forest Fires in Uttarakhand, 2) Amphan Cyclone, 3) Land Subsidence in Kolkata, 4) Urban Heat Index (UHI) in Mumbai, 5) Ship Detection in Gulf of Kutch and 6) Tropospheric NO2 levels (pre and post lock-down in India).
Feel share your comments, queries or suggestions. Which area would you like to study?
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Regards,
Arpit
Much Thanks to EO College for the training material. Analysis has been done on ESAs SNAP platform using Sentinel 1 SAR imagery.