Supporting Soil Fertility From Space

Sentinel-2 pseudo-true colour composite from 2016 with a Kompsat-3 Normalized Difference Vegetation Index (NDVI) product from 2015 inset. Sentinel data courtesy of ESA/Copernicus.

Last Tuesday I was at the academic launch event for the Tru-Nject project at Cranfield University. Despite the event’s title, it was in fact an end of project meeting. Pixalytics has been involved in the project since July 2015, when we agreed to source and process high resolution satellite Earth Observation (EO) imagery for them.

The Tru-Nject project is funded via Innovate UK. It’s official title is ‘Tru-Nject: Proximal soil sensing based variable rate application of subsurface fertiliser injection in vegetable/ combinable crops’. The focus is on modelling soil fertility within fields, to enable fertiliser to be applied in varying amounts using point-source injection technology which reduces the nitrogen loss to the atmosphere when compared with spreading fertiliser on the soil surface.

To do this the project created soil fertility maps from a combination of EO products, physical sampling and proximal soil sensing – where approximately 15 000 georeferenced hyperspectral spectra are collected using an instrument connected to a tractor. These fertility maps are then interpreted by an agronomist, who decides on the relative application of fertiliser.

Initial results have shown that applying increased fertiliser to areas of low fertility improves overall yield when compared to applying an equal amount of fertiliser everywhere, or applying more fertiliser to high yield areas.

Pixalytics involvement in the work focussed on acquiring and processing, historical, and new, sub 5 metre optical satellite imagery for two fields, near Hull and York. We have primarily acquired data from the Kompsat satellites operated by the Korea Aerospace Research Institute (KARI), supplemented with WorldView data from DigitalGlobe. Once we’d acquired the imagery, we processed it to:

  • remove the effects of the atmosphere, termed atmospheric correction, and then
  • converted them to maps of vegetation greenness

The new imagery needed to coincide with a particular stage of crop growth, which meant the satellite data acquisition period was narrow. This led to a pleasant surprise for Dave George, Tru-Nject Project Manager, who said, “I never believed I’d get to tell a satellite what to do.’ To ensure that we collected data on specific days we did task the Kompsat satellites each year.

Whilst we were quite successful with the tasking the combination of this being the UK, and the fact that the fields were relatively small, meant that some of the images were partly affected by cloud. Where this occurred we gap-filled with Copernicus Sentinel-2 data, it has coarser spatial resolution (15m), but more regular acquisitions.

In addition, we also needed to undertake vicarious adjustment to ensure that we produced consistent products over time whilst the data came from different sensors with different specifications. As we cannot go to the satellite to measure its calibration, vicarious adjustment is a technique which uses ground measurements and algorithms to not only cross-calibrate the data, but also adjusts for errors in the atmospheric correction.

An example of the work is at the top, which shows a Sentinel-2 pseudo-true colour composite from 2016 with a Kompsat-3 Normalized Difference Vegetation Index (NDVI) product from 2015 inset. The greener the NDVI product the more green the vegetation is, although the two datasets were collected in different years so the planting within the field varies.

We’ve really enjoyed working with Stockbridge Technology Centre Ltd (STC), Manterra Ltd, and Cranfield University, who were the partners in the project. Up until last week all the work was done via telephone and email, and so it was great to finally meet them in-person, hear about the successful project and discuss ideas for the future.

Identifying Urban Sprawl in Plymouth

Map showing urban sprawl over last 25 years in the areas surrounding Plymouth

Map showing urban sprawl over last 25 years in the areas surrounding Plymouth

Nowadays you can answer a wide range of environmental questions yourself using only open source software and free remote sensing satellite data. You do not need to be a researcher and by acquiring a few skills you can the analysis of complex problems at your fingertips. It is amazing.

I’ve been based at Pixalytics in Plymouth, over the last few months, on an ERAMUS+ placement and decided to use Plymouth to look at one of the most problematic environmental issues for planners: Urban Sprawl. It is well known phenomenon within cities, but it can’t be easily seen from ground level – you need to look at it from space.

The pressure of continued population growth, the need for more living space, commercial and economic developments, means that central urban areas tend to expand into low-density, monofunctional and usually car-dependent communities with a high negative ecological impact on fauna and flora associated with massive loss in natural habitats and agricultural areas. This change in how land is used around cities is known urban sprawl.

As a city Plymouth suffered a lot of destruction in World War Two, and there was a lot of building within the city in the 1950s and 1960s. Therefore, I decided to see if Plymouth has suffered from urban sprawl over the last twenty-five years, using open source software and data. The two questions I want to answer are:

  1. Is Plymouth affected by urban sprawl? and
  2. If it is, what are Plymouth’s urban sprawl trends?

1) Is Plymouth affected by urban sprawl?
To answer this question I used the QGIS software to analysis Landsat data from both 1990 and 2015, together with OpenStreetMap data for natural areas for a 15 kilometre area starting from Plymouth’s City Centre.

I then performed a Landscape Evolution analysis, as described in Chapter 9 of the Practical Handbook of Remote Sensing, written by Samantha and Andrew Lavender from Pixalytics. Firstly, I overlaid natural areas onto the map of Plymouth, then added the built up areas from 2015 shown in red and finally added the 1990 built-up areas in grey.

Detailed map showing the key urban sprawl around Plymouth over last 25 years

Detailed map showing the key urban sprawl around Plymouth over last 25 years

The map, which has an accuracy of 80 – 85%, shows you, no major urban development occurred in the city of Plymouth and its surroundings in the last 25 years – this is of course about to change the development of the new town of Sherford on the outskirts of the city.

However, as you can see in the zoomed in version of the map on the right, there is a noticeable urban development visible in the north west of the city and a second in Saltash in Cornwall on the east of the map. The built up area in the 15km area around Plymouth increased by around 15% over the 25 year period. The next question is what are the trends of this sprawl.

2) What are Plymouth urban sprawl trends?
A large amount of research tries to categorize urban sprawl in various types:

  • Compact growth which infill existing urban developments, also known as smart growth, and mainly occurs in planning permitted areas
  • Linear development along main roads
  • Isolated developments into agricultural or wildlife areas in proximity with major roads.

These last two have a bad reputation and are often associated with negative impacts on environment.

Various driving forces are behind these growth types, creating different patterns for cities worldwide. For example, rapid economic development under a liberal planning policy drives population growth in a city which then is expands and incorporates villages located in near or remote proximity over time. This is fragmented approach, and results in a strong land loss.

But this is not the case for Plymouth which in the last 25 years showed a stable development in the extend permitted by planning policies with a predominant infill and compact expansion, a smart growth approach that other cities could take as an example.

These conclusions can be taken following only a few simple steps- taking advantage of free open source software and free data, without extensive experience or training.
This is a proven example of how you can make your own maps at home without investing too much time and money.

This is the end my internship with Pixalytics, and it has been one of my best experiences.

Blog written by Catalin Cimpianu, ERASMUS+ Placement at Pixalytics.