Big Data From Space

Last week I attended the 2017 Conference on Big Data from Space (BiDS’17) that was held in Toulouse, France. The conference was co-organised by the European Space Agency (ESA), the Joint Research Centre (JRC) of the European Commission (EC), and the European Union Satellite Centre (SatCen). It aimed to bring together people from multiple disciplines to stimulate the exploitation Earth Observation (EO) data collected in space.

The event started on Tuesday morning with keynotes from the various co-organising space organisations. Personally, I found the talk by Andreas Veispak, from the European Commission’s (EC) DG GROW department which is responsible for EU policy on the internal market, industry, entrepreneurship and SMEs, particularly interesting. Andreas has a key involvement in the Copernicus and Galileo programmes and described the Copernicus missions as the first building block for creating an ecosystem, which has positioned Europe as a global EO power through its “full, free and open” data policy.

The current Sentinel satellite missions will provide data continuity until at least 2035 with huge amounts of data generated, e.g., when all the Sentinel satellite missions are operational over 10 petabytes of data per year will be produced. Sentinel data has already been a huge success with current users exceeding what was expected by a factor of 10 or 20 and every product has been downloaded at least 10 times. Now, the key challenge is to support these users by providing useful information alongside the data.

The ESA presentation by Nicolaus Hanowski continued the user focus by highlighting that there are currently over 100 000 registered Copernicus data hub users. Nicolaus went on to describe that within ESA success is now being measured by use of the data for societal needs, e.g., the sustainable development goals, rather than just the production of scientific data. Therefore, one of the current aims is reduce the need for downloading by having a mutualised underpinning structure, i.e. the Copernicus Data and Information Access Services (DIAS) that will become operational in the second quarter of 2018, which will allow users to run their computer code on the data without the need for downloading. The hope is that this will allow users to focus on what they can do with the data, rather than worrying around storing it!

Charles Macmillan from JRC described their EO Data and Processing Platform (JEODPP) which is a front end based around the Jupyter Notebook that allows users to ask questions using visualisations and narrative text, instead of just though direct programming. He also noted that increasingly the data needed for policy and decision making is held by private organisations rather than government bodies.

The Tuesday afternoon was busy as I chaired the session on Information Generation at Scale. We had around 100 people who heard some great talks on varied subjects such as mass processing of Sentinel & Landsat data for mapping human settlements, 35 years of AVHRR data and large scale flood frequency maps using SAR data.

‘Application Of Earth Observation To A Ugandan Drought And Flood Mitigation Service’ poster

I presented a poster at the Wednesday evening session, titled “Application Of Earth Observation To A Ugandan Drought And Flood Mitigation Service”. We’re part of a consortium working on this project which is funded via the UK Space Agency’s International Partnership Programme. It’s focus is on providing underpinning infrastructure for the Ugandan government so that end users, such as farmers, can benefit from more timely and accurate information – delivered through a combination of EO, modelling and ground-based measurements.

It was interesting to hear Grega Milcinski from Sinergise discuss a similar approach to users from the lessons they learnt from building the Sentinel Hub. They separated the needs of science, business and end users. They’ve chosen not to target end users due to the challenges surrounding the localisation and customisation requirements of developing apps for end users around the world. Instead they’ve focussed on meeting the processing needs of scientific and business users to give them a solid foundation upon which they can then build end user applications. It was quite thought provoking to hear this, as we’re hoping to move towards targeting these end users in the near future!

There were some key technology themes that came of the presentations at the conference:

  • Jupyter notebooks were popular for frontend visualisation and data analytics, so users just need to know some basic python to handle large and complex datasets.
  • Making use of cloud computing using tools such as Docker and Apache Spark for running multiple instances of code with integrated parallel processing.
  • Raw data and processing on the fly: for both large datasets within browsers and by having the metadata stored so you can quickly query before committing to processing.
  • Analysis ready data in data cubes, i.e. the data has been processed to a level where remote sensing expertise isn’t so critical.

It was a great thought provoking conference. If you’d like to get more detail on what was presented then a book of extended abstracts is available here. The next event is planned for 19-21 February 2019 in Munich, Germany and I’d highly recommend it!

Three Exciting Ways to Protect Forests With Remote Sensing

Forests cover one third of the Earth’s land mass and are home to more than 80% of the terrestrial species of animals, plants and insects. However, 13 million hectares of forest are destroyed each year. The United Nations International Day of Forests took place recently, on 21st March, to raise awareness of this vital resource.

Three remote sensing applications to help protect forests caught our eye recently:

Two scans show the difference between infected, on the right, and uninfected, on the left, patches of forest. Image Courtesy of University of Leiceste

Identifying Diseased Trees
In the March issue of Remote Sensing, researchers from the University of Leicester, (Barnes et al, 2017), published a paper entitled ‘Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands’. It describes how the researchers were able to identify individual trees affected by larch tree disease, also known as phytophthora ramorum.

This fungus-like disease can cause extensive damage, including the death, and diseased trees can be identified by defoliation and dieback. Airborne LiDAR surveys were undertaken by the company Bluesky at an average altitude of 1500 m, with a scan frequency of 66 Hz that gave a sensor range precision within 8 mm and elevation accuracy around 3–10 cm.

Remote sensing has been used to monitor forests for many years, but using it to identify individual trees is uncommon. The researchers in this project were able to successfully identify larch canopies partially or wholly defoliated by the disease in greater than 70% of cases. Whilst further development of the methodology will be needed, it is hoped that this will offer forest owners a better way of identifying diseased trees and enable them to respond more effectively to such outbreaks.

Monitoring Trees From Space
An interesting counterpoint to work of Barnes et al (2017) was published by the journal Forestry last month. The paper ‘Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications‘ written by Fassnacht et al (2017).

It describes work undertaken to compare the results of very high resolution optical satellite data with that of airborne LiDAR and hyperspectral data to provide support for forestry management. The team used WorldView-2 images, of a temperate mixed forest in Germany, with a 2m pixel size, alongside a LiDAR DTM with a 1 m pixel size. This data was then used to estimate tree species, forest stand density and biomass.

They found  good results for both forest stand density and biomass compared to other methods, and although the tree classification work did achieve over eighty percent, this was less than achieved by hyperspectral data over the same site; although differentiation of broadleaved and coniferous trees was almost perfect.

This work shows that whilst further work is needed, optical data has the potential to offer a number of benefits for forestry management.

Monitoring Illegal Logging
Through the International Partnership Programme the UK Space Agency is funding a consortium, led by Stevenson Astrosat Ltd, who will be using Earth Observation (EO) data to monitor, and reduce, illegal logging in Guatemala.

The issue has significant environmental and socioeconomic impacts to the country through deforestation and change of land use. The Guatemalan government have made significant efforts to combat the problem, however the area to be monitored is vast. This project will provide a centralised system using EO satellite data and Global Navigation Satellite Systems (GNSS) technology accessed via mobile phones or tablets to enable Guatemala’s National Institute of Forestry (INAB) to better track land management and identify cases of illegal logging.

The protection of our forests is critical to the future of the planet, and it’s clear that satellite remote sensing can play a much greater role in that work.

Supporting Uganda’s Farmers

Map of Uganda showing vegetation productivity. Underlying data is the MODIS 2014 NPP Product, MOD17 – Zhoa et al. (2005).

Uganda is a landlocked country of just over 240,000 square kilometres. Agriculture is a key element of the country’s economy and was responsible for 23% of gross domestic product in 2011 and almost half the country’s exports the following year. According to the Food & Agriculture Organisation of the United Nations, 80% of the population relies on farming for its livelihood.

It has an equatorial climate, with regional variations, although recent recurrent dry spells have impacted on crop and livestock productivity. Pixalytics is delighted to be part of a consortium led by the RHEA Group, working with the Ugandan Ministry of Water and Environment and local NGOs to develop a Drought and Flood Mitigation Service (DFMS) to give practical information to help local communities respond to the effects of climate change.

Using computer models populated with satellite, meteorological, water resources and ground based data an innovative Environment Early Warning Platform will be developed to provide Ugandan farmers, via local NGO organisations, with forecasts throughout the growing seasons to enable them to take actions to maximise their crop yield.

Pixalytics, along with fellow consortium member, Environment Systems, are responsible for the Earth Observation data in the project. We’ll be looking at variety of optical and radar data to provide information about flood and drought conditions alongside crops and their growing conditions.

The project should benefit local communities by:

  • Improving the ability to forecast and mitigate droughts and floods on a local actionable scale.
  • Allowing NGOs to target resources saving time, money and lives.
  • Allowing farmers to improve their lives and better protect their livestock and crops.

Alongside ourselves, and RHEA Group, our consortium includes Environment Systems, Databasix, AA International, AgriTechTalk International, HR Wallingford, UK Met Office, Mercy Corps, and Oxford Policy Management. We will also work with international partners, including the Uganda Government Ministries, Kakira Sugar Company, and the NGO Green Dreams/iCOW. The first of a number of visits to Uganda took place last week, where we had the opportunity to make lots of local contacts and meet some of those whom we hope to benefit from this work.

This work is part of the UK Space Agency’s International Partnership Programme and ours is one of 21 projects chosen to provide solutions to local issues in counties across Africa, Asia, Central and South America.

This is a really exciting project to be involved with, and we’re looking forward to providing useful information to local farmers to allow them to take real and meaningful action to enhance the productivity, and protection, of their livestock and crops.