It's been a while since I've written on Remote Sensing - the last post was on Mauritius (MV Wakashio) Oil Spill in August 2020. In this post, I have mapped the spatial extent of Built-up Areas i.e. technically known as Urban Footprint, for a couple of Indian cities.
As a matter of context, not only does India have 3 spots in the Top 10 fastest growing cities of the world currently, but also it is expected to occupy all the 10 spots in the list henceforth until 2035!
While urban centers are hotbeds of economic activity, productivity, innovation & growth, unchecked urbanization is fraught with risk, for obvious reasons.
So how does the Imagery captured by Earth Observation satellites help to monitor Urbanization?
As you may already know, the Receiver instrument onboard such Satellites capture the energy reflected from the Earth's surface (the source of illumination being either a passive light source such as the Sun or an active one such the Transmitter instrument onboard the satellite which emits Radio waves). The data captured using the former method can be processed to create Optical Imagery (made up of Natural colors + some bands of Ultraviolet & Infrared) while the latter method captures data which can be processed to create Radar Imagery (made up of Radio wave reflectances not visible to the human eye).
Radar Imagery has certain unique properties and is preferred over Optical Imagery in several Remote Sensing Workflows. For Urban Footprint mapping workflow as well, Radar Remote Sensing is the go-to option as it is able to identify man-made structures very effectively as objects such as buildings, bridges and other infrastructure are impervious to Radio waves and strongly reflect them back to the Satellite (known technically as Backscatter). Additionally, the Coherence of reflectance for Radio waves is high too - a property which is enormously useful if one intends to compare Imagery across time periods - it allows for pixel-to-pixel comparison. Besides, the backscatter of Radio waves from natural landscapes such as agriculture, vegetation and water bodies etc. is considerably lower - which allows for effective delineation from from man-made objects and landscapes.
In the depiction below, I've displayed the extracted Urban Footprint from Sentinel-1 SAR Imagery (Synthetic Aperture Radar) over a subsection of New Delhi -
The Slider is best viewed on PC
The image on the left view of the Slider is a RGB composite of Radar Imagery over a cross-section of New Delhi captured during October 2020 (The slider of the entire study area can be viewed here).
The yellow spots denote Urban Built-up areas whereas the green ones indicate non-Urban areas such as natural vegetation, agriculture, barren land, and so on. To extract the RGB composite, I bunched two Radar Imagery datasets from different time periods and studied the properties of the overlay. The reflectance values of the pixels which contain Urban areas (man-made) would largely remain similar in both the datasets (due to strong Coherence) whereas the reflectance values of the pixels of non-Built Up areas (natural landscapes) would be noticeably dissimilar due to low Coherence - the presence of wind, for example, would cause distortions in the orientation of vegetation thereby resulting is different reflectance values across the two time periods. This property involving Coherence is what enables us to distinguish Urban areas from natural landscapes and makes capturing the Urban Footprint of a Study Area possible.
In the depiction of another cross-section of New Delhi above, I have displayed only the the Urban Built-up area pixels (white in color). The values of all the remaining pixels have been assigned as Null (black in color).
In the Slider depiction above, I am comparing the Urban Footprint of the entire study area in and around New Delhi over the two Time periods (Temporal Analysis) five years apart - October 2020 and January 2016 - to understand how the Urban landscape has evolved.
Can you spot some areas where there is a change in the Urban landscape in the recent image?
Notice the yellow spot that emerges slightly north-west of Ghaziabad in the image from 2020 above. Upon closely inspecting that same spot using Historical Imagery tool on Google Earth, this is what I found -
The cluster of new yellow pixels in 2020 point to the newly built WUPPTCL Ataur Power Substation. Remote Sensing is remarkable!
Want to inspect some of the other pixel deviations yourself? Download the KMZ files here - you can visualize it using Google Earth on your PC.
Malappuram was ranked as the fastest growing city in the world for the timespan 2015-2020 due to an exponential growth in population (44%). Since Growth of Population would imply Growth of Urban Infrastructure, I felt compelled to extract and compare its Urban Footprint over two time periods four years apart. The result is depicted below-
You can download the RGB and Urban Footprint files of the Malappuram study from here.
As anticipated, the increase in white pixels (Urban areas) in the recent output from October 2020 is quite evident. As we know the area covered by an individual pixel (its Spatial resolution is 5 m by 20 m. i.e. 100 sq. m), it is therefore possible to compute the exact area of new Urban infrastructure added over the selected four-year time period -
The image from 2016 has ~17,000 white pixels
The image from 2020 has ~20,000 white pixels. An increase of 3000 pixels represents an addition of 300,000 sq. m or 0.3 sq. km of Urban Built-up area - equivalent to 21+ Eden Gardens cricket stadiums! Thus, a 44% growth in Population (over 5 years) is met by an increase of 18% Urban Built-up area (over 4 years).
Processing the SAR Imagery Datasets, which are large in size (~1 GB per image), requires considerable computing resources. I realize that I have not shown you the method of extracting Urban Footprint in a video tutorial format. Here's where I learnt it from. Each pixel within the Imagery dataset contains plenty of information such as Amplitude, Phase, Orientation and Height and the beauty lies in manipulating the available data points to unearth insights that matter. All this from processing just a tiny pixel! Isn't it fascinating?
Explore the workflows on Remote Sensing that I've written on this professional blog here.
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Regards,
Much Thanks to EO College for the Training. The Analysis has been done on ESA's SNAP platform using Sentinel 1 SAR imagery