Have you read the top Pixalytics blogs of 2016?

Artist's rendition of a satellite - paulfleet/123RF Stock Photo

Artist’s rendition of a satellite – paulfleet/123RF Stock Photo

As this is the final blog of the year we’d like to take a look back over the past fifty-two weeks and see which blog’s captured people’s attention, and conversely which did not!

It turns out that seven of the ten most widely viewed blogs of the last year weren’t even written in 2016. Four were written in 2015, and three were written in 2014! The other obvious trend is the interest in the number of satellites in space, which can be seen by the titles of six of the ten most widely read blogs:

We’ve also found these blogs quoted by a variety of other web pages, and the occasional report. It’s always interesting to see where we’re quoted!

The other most read blogs of the year were:

Whilst only three of 2016’s blogs made our top ten, this is partly understandable as they have less time to attract the interest of readers and Google. However, looking at most read blogs of 2016 shows an interest in the growth of the Earth Observation market, Brexit, different types of data and Playboy!

We’ve now completed three years of weekly blogs, and the views on our website have grown steadily. This year has seen a significant increase in viewed pages, which is something we’re delighted to see.

We like our blog to be of interest to our colleagues in remote sensing and Earth observation, although we also touch on issues of interest to the wide space, and small business, communities.

At Pixalytics we believe strongly in education and training in both science and remote sensing, together with supporting early career scientists. As such we have a number of students and scientists working with us during the year, and we always like them to write a blog. Something they’re not always keen on at the start! This year we’ve had pieces on:

Writing a blog each week can be hard work, as Wednesday mornings always seem to come around very quickly. However, we think this work adds value to our business and makes a small contribution to explaining the industry in which we work.

Thanks for reading this year, and we hope we can catch your interest again next year.

We’d like to wish everyone a Happy New Year, and a very successful 2017!

Remote Sensing: Learning, Learned & Rewritten

Image of Yemen acquired by Sentinel-2 in August 2015. Data courtesy of ESA.

Image of Yemen acquired by Sentinel-2 in August 2015. Data courtesy of ESA.

This blog post is about what I did and what thoughts came to mind on my three-month long ERASMUS+ internship at Pixalytics which began in July and ends this week.

During my first week at Pixalytics, after being introduced to the Plymouth Science Park buildings and the office, my first task was to get a basic understanding of what remote sensing is actually about. With the help of Sam and Andy’s book, Practical Handbook of Remote Sensing, that was pretty straightforward.

As the words suggest, remote sensing is the acquisition of data and information on an object without the need of being on the site. It is then possible to perform a variety of analysis and processing on this data to better understand and study physical, chemical and biological phenomena that affect the environment.

Examples of programming languages: C, Python & IDL

Examples of programming languages: C, Python & IDL

I soon realized that quite a lot of programming was involved in the analysis of satellite data. In my point of view, though, some of the scripts, written in IDL (Interactive Data Language), were not as fast and efficient as they could be, sometimes not at all. With that in mind, I decided to rewrite one of the scripts, turning it into a C program. This allowed me to get a deeper understanding of satellite datasets formats (e.g. HDF, Hierarchical Data Format) and improve my overall knowledge of remote sensing.

While IDL, a historic highly scientific language for remote sensing, provides a quick way of writing code, it has a number of glaring downsides. Poor memory management and complete lack of strictness often lead to scripts that will easily break. Also, it’s quite easy to write not-so-pretty and confusing spaghetti code, i.e., twisted and tangled code.

Writing C code, on the other hand, can get overly complicated and tedious for some tasks that would require just a few lines in IDL. While it gives the programmer almost full control of what’s going on, some times it’s just not worth the time and effort.

Instead, I chose to rewrite the scripts in Python which I found to be quite a good compromise. Indentation can sometimes be a bit annoying, and coming from other languages the syntax might seem unusual, but its great community and the large availability of modules to achieve your goals in just a few lines really make up for it.

It was soon time to switch to a bigger and more complex task, which has been, to this day, what I would call my “main task” during my time at Pixalytics: building an automated online processing website. The website aspect was relatively easy with a combination of the usual HTML, Javascript, PHP and CSS, it was rewriting and integrated the remote sensing scripts that was difficult. Finally all of those little, and sometimes not quite so little, scripts and programs were available from a convenient web interface, bringing much satisfaction and pride for all those hours of heavy thinking and brainstorming. Hopefully, you will read more about this development in the future from Pixalytics, as it will form the back-end of their product suite to be launched in the near future.

During my internship there was also time for events inside the Science Park such as the Hog Roast, and events outside as well when I participated at the South-West England QGIS User Group meeting in Dartmoor National Park. While it is not exactly about remote sensing, but more on the Geographic Information System (GIS) topic it made me realize how much I had learned on remote sensing in my short time at Pixalytics, I was able to exchange my opinions and points of view with other people that were keen on the subject.

A side project I’ve been working on in my final weeks was looking at the world to find stunning, interesting (and possibly both) places on Earth to make postcards from – such as one at the top of the blog. At times, programming and scientific research reads can get challenging and/or frustrating, and it’s so relaxing to just look at and enjoy the beauty of our planet.

It is something that anyone can do as it takes little knowledge about remote sensing. Free satellite imagery is available through a variety of sources; what I found to be quite easy to access and use was imagery from USGS/NASA Landsat-8 and ESA Sentinel-2. It is definitely something I would recommend.

Finally, I want to say “thank you” to Sam and Andy, without whom I would have never had the opportunity to get the most out of this experience, in a field in which I’ve always been interested into, but had never had the chance to actually get my hands on.

Blog written by Davide Mainas on an ERASMUS+ internship with Pixalytics via the Tellus Group.

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.