False Colour Composites (FCC)
What do you do when you need to map large features in difficult to reach places? You use satellite imagery of course. Satellite imagery is great, it reveals insights that would otherwise go unnoticed during geological field mapping by allowing you to see Earth’s surface in new ways. At Umvoto, our geologist’s regularly use various satellite imagery to better understand and map the earth’s surface. However, at Umvoto we don’t only use Google Earth, we also prefer something more informative (with the bonus of creating art).
False colour composites (more details here) are not merely fancy looking images but have a wide range of applications, from mapping and investigating geological features, such as the Great Dyke and Vredefort Dome to assessing agricultural water use, all with the bonus of a growing set of custom scripts one can use. At Umvoto we regularly use the wonderful, and publicly free, European Space Agency Sentinel-2 imagery (see infographic below) to investigate the earth, from landuse mapping to highlighting lithological variations.
A big (data) challenge
One of the challenges of processing and analysing satellite imagery is downloading, processing, and storing of enough imagery to have adequate spatial and temporal coverage to create a product that is useful to Geologists, such as this false colour composite (FCC) of Eritrea.
Searching for and downloading imagery is a very time-consuming process, and without guarantee of being totally cloud free. Perhaps, more importantly, image processing can be just as, if not more, time consuming and (to many people) a boring task. Depending on your internet speed, it can take 2 hours to download one Sentinel-2 scene, followed by 2 hours to process and package that scene into an easily useable format – that’s 4 hours per scene. Once assembled, a single scene’s file size is approximately 650 MB (only for the raw files) with this size easily doubling during analysis and soon your hard-drive is filled.
For a relatively small country like Eritrea (smaller than the Western Cape province of South Africa) it takes 37 scenes of Sentinel-2 imagery to map it. That means, if all runs smoothly, from image search to a final FCC snapshot of Eritrea, it will take 148 hours, and 50 GB of storage space. No small task. But what if we are interested in changes and dynamic patterns and to include all Sentinel-2 images of Eritrea for the year 2020? This would equate to 3 879 images, and approximately 5 TB of storage space and we would require 15 516 hours to process the country. This would mean working for nearly 2 years, without any recess, to pull off such an analysis. More importantly this leaves us with no time for any fun, exploration, and interpterion of the imagery.
Working smarter not harder
Using GEE we can shift from dealing with the mundane task of data assembly, to following our curiosity and playing with different datasets. By focusing on asking questions and exploring data we can find solutions to the problems posed to us. Looking again at our Eritrean FCC, a product of 3 879 Sentinel-2 images, it took me a day, not years, to complete, giving me more time to analyse the image. The real question now in remote sensing analysis is no longer “how to?”, but rather “where to?”.