Spinning Python in Green Spaces

2016 map of green spaces in Plymouth, using Sentinel-2 data courtesy of Copernicus/ESA.

2016 map of green spaces in Plymouth, using Sentinel-2 data courtesy of Copernicus/ESA.

As students, we are forever encouraged to find work experience to develop our real-life skills and enhance our CV’s. During the early period of my second year I was thinking about possible work experience for the following summer. Thanks to my University department, I was able to find the Space Placements in INdustry (SPIN) scheme. SPIN has been running for 4 years now, advertising short summer placements at host companies. These provide a basis for which students with degrees involving maths/physics/computer science can get an insight into the thriving space sector. I chose to apply to Pixalytics, and three months later they accepted my application in late March.

Fast forward a few more months and I was on the familiar train down to Plymouth in my home county of Devon. Regardless of your origin, living in a new place never fails to confuse, but with perseverance, I managed to settle in quickly. In the same way I could associate my own knowledge from my degree (such as atmospheric physics, and statistics) to the subject of remote sensing, a topic which I had not previously learnt about. Within a few days I was at work on my own projects learning more on the way.

My first task was an informal investigation into Open data that Plymouth City Council (PCC) has recently uploaded onto the web. PCC are looking for ways to create and support innovative business ideas that could potentially use open data. Given their background, Pixalytics could see the potential in developing this. I used the PCC’s green space, nature reserve and neighbourhood open data sets and found a way to calculate areas of green space in Plymouth using Landsat/Sentinel 2 satellite data to provide a comparison.

Sentinel-2 Image of Plymouth from 2016. Data courtesy of Copernicus/ESA.

Sentinel-2 Image of Plymouth from 2016. Data courtesy of Copernicus/ESA.

There were a few challenges to overcome in using the multiple PCC data sets as they had different coordinate reference systems, which needed to be consistent to be used in GIS software. For example, the Nature Reserves data set was partly in WGS84 and partly in OSGB 1936. Green space is in WGS 84 and the neighbourhood boundaries are in OSGB 1936. This meant that after importing these data sets in GIS software, they wouldn’t line up. Also, the green space data set didn’t include landmarks such as the disused Plymouth City airport, and large areas around Derriford Hospital and Ernsettle. Using GIS software I then went on to find a way to classify and calculate areas of green space within the Plymouth city boundary. The Sentinel-2 which can be seen above, has a higher spatial resolution and allowed me to include front and back gardens.

My green space map for 2016 created from Sentinel 2 data is the most accurate, and gives a total area of green space within the Plymouth neighbourhood boundary of 43 square kilometres, compared with 28 square kilometres that PCC have designated within their dataset. There are some obvious explainable differences, but it would be interesting to explore this deeper.

My second project was to write computer code for the processing and mosaicking of Landsat Imagery. Pixalytics is developing products where the user can select an area of interest from a global map, and these can cause difficult if the area crosses multiple images. My work was to make these images as continuous as possible, accounting for the differences in radiances.

I ended up developing a Python package, some of whose functions include obtaining the WRS path and row from an inputted Latitude and Longitude, correcting for the difference in radiances, and clipping and merging multiple images. There is also code that helps reduce the visual impact of clouds on individual images by using the quality band of the Landsat 8 product. This project took up most of my time, however I don’t think readers would appreciate, yet alone read a 500 line python script, so this has been left out.

I’d like to take this opportunity to thank Andrew and Samantha for giving me an insight into this niche, and potentially lucrative area of science as it has given me some direction and motivation for the last year of my degree. I hope I’ve provided some useful input to Pixalytics (even if it is just giving Samantha a very long winded Python lesson), because they certainly have done with me!

 

Blog written by:
Miles Lemmer, SPIN Summer Placement student.
BSc. Environmental Physics, University of Reading.