Drought Assessment at Indirasagar Reservoir using Satellite Imagery
Updated: Jul 3
'Water is a precious resource'
As a city dweller residing for most parts in the tropical, reclaimed wetland that is Kolkata, I've always found the city to be 'water-positive'. The south-westerly winds continue to bring with it copious quantities of rainfall every monsoon, the murky Hooghly river never really ceases to appear menacingly pregnant, the vegetation is evergreen and the moisture-laden air & soil seems to perennially brimming with a distinct watery-odour.
Yet, underneath this seeming abundance lies a worrying prospect - the city's groundwater levels are receding and I've even mapped it using SAR satellite imagery before. Most residents wouldn't know that and only feel the pinch when their water lines run on empty and a tanker needs to be ordered.
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For all the misery that a Drought brings (it is the most costly weather event after a Cyclone), it is rather innocuously defined as 'drier-than-normal conditions that results in water-related problems'.
While Drought is largely attributed to natural causes such as (lack of) Precipitation and (dry) Seasonality, us humans are also playing an influential role in exacerbating it with our misuse of Earth's resources - in the form of Climate Change & Global Warming or activities such as Over Farming, Excessive Irrigation & Deforestation.
Having read National Geographic's succinct explanation as to why a drought is such a complicated phenomenon to monitor, I can draw parallels to my study on Sargassum Seaweed Infestation as well as on Volcano Deformation. Both these events often build up subtly over a period of time and it is very tricky to establish its onset, as a result.
There are three major categories of Droughts categorized as per its causes -
Meteorological Drought- When precipitation (rainfall / snowfall) is below average for an extended period of time
Hydrological Drought- When the water levels in natural or man-made storage mediums such as aquifers, lakes, reservoirs etc. fall below a threshold
Agricultural / Ecological Drought - when poor farming practises result in erosion / depleting soil conditions essentially resulting in limited / no water availability to crops
The repercussions of droughts are wide-ranging - destruction of vegetation and crops, famine, loss of biodiversity, migration, soil erosion & resulting wildfires, increased water pollution, scarcity of water for irrigation & urban consumption, lower power generation at dams etc.
Does a Drought end when it rains? - Not really. The type and continuity of rains do matter as well. The rainfall essentially needs to 'soak' the soil and replenish the groundwater for vegetation to reappear and for the drought conditions to subside.
Developing & Under-developed economies are most vulnerable to drought - as can be ascertained from the 2022 UNCCD Index's output above - largely because of their dependence on agriculture as a measure of economy.
I was taken aback to know that as much as 42% of India's land area was being affected by drought conditions as per Drought Early Warning System's 2019 assessment. The situation appears to have subsided significantly but 19% as of October 2022 isn't particularly encouraging either.
Just in the last five years alone (2016-17 to 2021-22), 35 million hectares of cropped area (farmlands) have been damaged due to drought in India. To give you a perspective of how large this figure is, it is roughly 22% of India's total arable land of 160 million hectares (as estimated in 2011). The vagaries of weather in India - too much rainfall at certain locations and too little at others - is particularly brutal.
I found some sections of this article devoted to how the state of Gujarat has combated drought to be particularly revealing. Across the world, enormous emphasis is placed on developing Satellite-based Early Warning Systems as a precautionary measure to detect Drought conditions as well as on practical solutions to combat drought such as irrigation planning, rainwater harvesting, drought-resistant crops, sustainable agri-practises and even innovative techniques such as cloud-seeding.
Drought Monitoring using Satellite Imagery: Practical Walk-through
I have used Optical Imagery from Sentinel-2 satellite mission to map and monitor (Hydrological) Drought Conditions at Indirasagar Reservoir in Madhya Pradesh, India over the last four years (2019-2022).
I chose Indirasagar Reservoir (emanating from the Indirasagar Dam) as the study area because -
a) I wanted to select a location within my country, India, as it is considerably affected by droughts
b) It is a large & important water resource - the largest reservoir in India in terms of area as well as water storage capacity. The water is extensively utilized for irrigation purposes
c) The state in which it is located in, Madhya Pradesh, is experiencing increasing mean annual temperatures making it particularly susceptible to drought conditions. It stands third in the most drought-affected states of India in 2022 as well
I've prepared an elaborate video walkthrough which will take you along the entire chain of processing steps involved in Satellite Imagery Analytics for Drought Monitoring - right from downloading the datasets to processing it on an Imagery analytics tool to mapping it using two GIS applications.
Watch it below-
Video 1: Elaborate walkthrough of performing Satellite Imagery Analytics to monitor Drought Conditions at Indirasagar Reservoir between 2019 - 2022
Prior to monitoring 8 satellite images of Indirasagar Reservoir, I had also studied 12 images of Theewaterskloof Dam's Reservoir in South Africa as well, over the same period (2019-2022). The latter was, in a way, an extension to my tutorial which monitored the drought conditions at this reservoir for five years until 2020.
Theewaterskloof stores and supplies a significant portion of the water needs of the city of Cape Town and was affected by severe drought in 2016 - 2018 with water levels estimated to have receded by as much as 89% during its peak. So, I will include a snippets from that study as well in the ensuing content.
Timeline of Imagery Used
For the Indirasagar study, I had selected two images from each year, one from a summer month (May) and the other from a monsoon month (October). For the Theewaterskloof study, I had selected three images from each year, two from the summer months (December - March) and one from the monsoon months (July - August).
It took me a while to conceptually acknowledge that drought conditions don't necessarily need to be in effect during the summers (evaporation of water), it could very well be in effect during the monsoons (less than adequate rainfall).
Maintaining consistency in terms of the timeline used is also the right way to go - not just in the selection of the month but also in terms of the selection of the day of the month. For example, in an Indian summer one can reasonably expect May 15th - 31st to be reasonably warmer that May 1st - 15th. To compare a May 28th image from a particular year to a May 2nd image from the next year wouldn't exactly be apple-to-apple comparison - the water body could be much drier come the end of the month.
Also, while satellite imagery readings are available cyclically (Earth Observation Satellites tend to have a fixed path and coverage), the tricky aspect is the presence of clouds in optical imagery - it is very important that the cloud cover in the image, especially over our area of interest (reservoir) during that particular day, is mostly cloud-free. In both Indirasagar & Theewaterskloof imagery selection, I had to be very mindful of this aspect and sometimes the timeline had to be slightly inconsistent due to the absence of sufficiently cloud-free images within the desired date-range.
The rationale is simple - cloud pixels hinder surface feature analysis because of absence of reflectance readings or inaccurate reflectance readings. Particularly for the Indirasagar analysis, I do recollect that most of the images I selected had very high cloud-free specifications - <1% cloud cover for most images, at max it was 2-3% for a couple of images I believe.
Just to clarify, we don't get images of our study area directly from the satellite imagery distribution service. Rather, we get standard-sized images over a region which cover our target area. If the study area is split across two images or is particularly large, we may need to stitch images together as well.
Also, one of the tasks during post processing entails reducing the size of the image so that only what is important for our analysis, i.e. the study area, remains - be it in terms of geographic extent or in terms of the specific bands of information within an imagery. We do so because each satellite image is large in size - the ones we've used are nearly 1 GB each. Reducing the size helps the processing to be completed quickly.
The figure above depicts the map and satellite view of a Sentinel-2 imagery product over Indirasagar Reservoir. Notice the chunk of cloud towards the south-west of the satellite view on your right. While the cloud cover percentage in the image is miniscule (<1 %), had that chunk of clouds been directly over the reservoir, I would have opted to seek a better, more cloud-free image.
Nonetheless, the image above is much desirable when compared to the one below -
Post-Processing the Raw Imagery
I was fortunate that I could use Level-2A Sentinel-2 Imagery products for my study instead of Level-1C products. This is because Level-2A products are being generated relatively recently (December 2018) and hence, were available for my entire study period from 2019 to 2022. These products are atmospherically corrected i.e. they contain Bottom of Atmosphere (BoA) reflectance values instead of Top of Atmosphere (ToA) reflectance values.
The algorithm generated BoA reflectance eliminates atmospheric noise which reaches the satellite sensor i.e. the image resembles more like how the sunlight has been reflected just off the earth's surface instead of resembling how it appears after the reflected sunlight has passed through the atmosphere.
BoA reflectance is useful because we are measuring Ground surface properties here (water extent) and not Atmospheric properties (for example, pollution). Converting Level-1C product to Level-2A product would normally entail deploying the Sen2Cor processing algorithm which is a time-consuming process. Having Level-2A directly from the imagery distribution service helped me save considerable processing time. I suspect if one were to monitor drought using the 'Evotranspiration' method i.e. measuring the water vapour content in the atmosphere, then Level-1C products from the applicable satellite service provider would have been sufficient to do the analysis.
To analyse and visualize drought monitoring from the Level-2A products, I have used three applications - SNAP, QGIS & ArcGIS Pro. SNAP imagery processing software was predominantly used for the main analysis (identifying water pixels) whereas the latter two GIS applications (Geographic Information System) helped to refine, organize and chart the extracted imagery output onto a map.
Much of what is done in the post-processing phase is elaborately explained in my video walkthrough. I'd recommend you to watch it if you are interested to see the workings involved. Read on to get a detailed summary of the steps and the final output.
Exploring the Product on SNAP
I visualized the raw satellite imagery product in different color-modes to begin with-
An optical imagery product such as the one i am using contains several spectral 'bands' of information - the manipulation of which helps us to ascertain, study, monitor or measure the characteristics of the surface which we had intended to discover.
Besides spectral bands, Atmospheric correction information such as cloud pixels is also stored in separate masks within the imagery product.
Processing on SNAP
There are three broad processing steps which I've performed on SNAP for drought monitoring purposes-
In Phase 1 (Refining), the imagery product needs to be 'Resampled' first so that all the bands have the same resolution. This is because the software is only able to run certain processing commands if there is an equivalence in resolution across all the bands used during calculation.
Thereafter, we 'Subset' the product. This process entails discarding unnecessary information such as extra geographic area from the product and extra bands of information. The former is very pertinent in Theewaterskloof processing as you'll realize from Figure 10 that the reservoir forms just a tiny portion of the total imagery product. To process the entire 1GB imagery product would be a waste of computing resources. Also, if we are sure which bands are to be used during our processing (as we are in this particular case), we can remove the undesirable bands to make the product size even lower.
Phase 2 (Analysis) is the most important step in SNAP, across all the three applications rather.
In this phase, we manipulate the bands of information to isolate the pixels which are 'water' from the imagery.
In hydrological drought monitoring, what we are essentially setting out to do is to see how the reservoir area changes over a period of time. If the reservoir shrinks by a significant portion, it is indicative of drought. Therefore, in this step, we use the band combination methods (water radiometric indices) proposed by researchers to identify the water pixels.
The four radiometric indices which we've used for this study are:
a) NDWI - Normalized Difference Water Index
b) MNDWI - Modified Normalized Water Index
c) MDWI+5 - Revised Modified Normalized Vegetation Index
d) AWEI - Automated Water Extraction Index
These indices are either newly devised by researchers or they are an advanced version of an earlier method or sometimes even a method suited during certain scenarios: eg. in a specific terrain type - urban environment or a vegetated land.
The way to input these indices on SNAP is like a formula in mathematics. For example NDWI is 'B3 band minus B8 band divided by B3 band plus B8 band'. (B3 has a Central Wavelength of 560 nanometers whilst B8 has a Central Wavelength of 842 nanometers)
As you'll infer from the figure above, the reflectance percentage of water drastically diminishes from the wavelength 600 nanometers (0.6 micrometer * 1000) and beyond while soil & green vegetation's reflectance tends to increase and move further away from that of water at this juncture. Thus, the surface reflectance properties of these materials show a clear divergence at this particular wavelength and beyond and NDWI formula exploits this peculiarity to isolate water pixels from non-water pixels. Cool, isn't it?
In Phase 3 (Extraction), we take the output of all the four radiometric indices and ask SNAP to select only those pixels which are 'water pixels across all the indices' i.e. select the best of all worlds. This helps us to be assured that all the shortlisted pixels (10m each in size) are very likely to be water.
Thereafter, we create a separate 'Water Mask' layer from our shortlisted pixel selection and export it to our computer as a GeoTIFF file. These GeoTIFFs will be inputted in QGIS application later for further processing.
SNAP is a powerful, free-to-use application. For those who may not want to refine the imagery product on a GIS platform, they can still do quite a lot of interesting analysis and comparison from within SNAP application itself. For example, we can compute the area of the water pixels in the imagery by creating a Water Mask and computing the Mask's Area.
Or, we can do some Time Series Analysis as well. Refer to the video below where we are are comparing the pixel values of the NDWI band across all the 8 images on a graph simultaneously upon hovering the cursor over a product.
Video 2: Demo of Time Series Analysis tool on SNAP
Question for your to ponder: Why is there is a clear cyclicity observed in the readings on the graph on the bottom-left of the video?
Processing on QGIS
I'd have preferred to do the ensuing processing steps, which I had performed on QGIS, on SNAP itself. Moving data from one platform to the other is a little tedious and one has to know the basics of operating different applications as well. That doesn't do away with the fact that QGIS is a very powerful GIS application - suitable to do a variety of mapping workflows. The best aspect is that it is open-source and free-to-use making it a favorite among beginners as well as professionals alike.
In QGIS, I performed data transformation and extracted water pixels simultaneously for all the images in the timeline.
By data transformation, I mean converting Raster form of imagery output into a Vector form (Vector vs Raster explained). In a Raster image, each pixel has a data value but then there are no shapes acting as containers for clusters of similar data values. Converting the imagery it into a vector format helps address our requirement of having distinct shapes in the image containing similar pixels within: a reservoir, in geospatial terms, is shaped like a polygon within which lie only water pixels.
So what we are doing in QGIS with this data transformation is to create two distinct types of shapes from the raster imagery data - that of clusters of water pixels and of clusters of non-water pixels.
Thereafter, we discard the non-water clusters because they is not useful for us in the analysis-
Notice that there are water shapes outside the reservoir area as well.
We'll proceed to refine our output and perform analysis and visualization steps next on ArcGIS Pro. This could have been done on QGIS itself, however, I am more comfortable using ArcGIS Pro to perform these type of activities.
Processing on ArcGIS Pro
Esri's ArcGIS Pro is a premium, paid application and is useful to perform a wide variety GIS mapping, location analytics and imagery analytics workflows. It is user-friendly and a trial version is also available. The software also has several dedicated extensions for performing specialized workflows specific to certain sectors and industries. For example: solar, airports, military, urban planning among others. The workflows performed on SNAP earlier can also be performed on ArcGIS Pro itself by using its Image Analyst extension.
Watching my video walkthrough would be the ideal if you'd like to know the processing steps on ArcGIS in detail as there are several minor steps which are difficult to outline in the article itself.
Explore the slider below and try to Q) guess this Processing step done on ArcGIS Pro-
Slider 1: Before - After comparison of an important processing step in ArcGIS Pro
A: We've refined the output to do away with several small water pools surrounding the reservoir from our original imagery product which we had exported from QGIS as a vector shapefile.
Why have we done so?
A: Because we do not want to overestimate the area computation of the reservoir - we'd like to restrict it to the main reservoir as much as we can.
So what's the way to do so?
A: Ideally, we can locate the shapefile for the reservoir from within ArcGIS' repository (from ArcGIS Online or from Living Atlas) or from other online sources on the Web.
Or we can create one ourselves using a handy technique, as I've done for this Indirasagar Drought Monitoring case - Because I had converted the raster product into vector format, hundreds of water shapes were created across all the eight sets of imagery. It is easy to know that the largest shape in the attribute table belongs to that of the reservoir. Therefore, using the Buffer tool I created an outline around the largest shape (from across all the eight years) and then used it as a base in the Clip tool to eliminate noise from all the eight imagery layers. The output of a single layer clipped is depicted as the image on the right in the slider above.
Besides cleaning up and organizing the data further, deriving 'Geometry' attributes (Area & Perimeter) using the Merge tool, converting Area in Sq. m to Sq. km using 'Calculate field' function on the attribute table and adding new fields such as Date & Season, ArcGIS Pro is particularly useful when it comes to 'Symbolizing' the layer.
We can visualize the output on the map in multiple ways using shapes, transparency, colors etc. In a way, that's the essence of map-making - how to present the information so that the desired message is highlighted and conveyed to to the reader.
The map output above shows the four outlines of Theewaterskloof's reservoir during one summer day from 2019 to 2022. It is a nice way to visually observe whether there was drought and if so, which portions of the reservoir receded the most.
In Theewaterskloof's case, it becomes evident that it was not affected by drought as the years went by, rather it was emerging from the drought it had faced between 2016-18 and was showing improvement in water levels almost year-on-year.
Besides the visual component, one can also analyze the map's data using dynamic charts, statistic generators and infographs-
From Indirasagar Reservoir's chart above, it becomes very clear that there was a significant decline in water levels in the period between 2021 monsoon and 2022 summer when compared to the three previous years, thereby signalling a period of drought to me.
The charts below validate and further our understanding on the drought conditions for both our study areas.
ArcGIS Pro finally helps me to combine all the useful data points into a beautiful map which conveys the main message / learning / inference which I derived from the study-
I hope you found this article to be interesting and appreciated the importance of water as a precious resource. Feel free to drop in your feedback on email@example.com or on the YouTube video's comment section.
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