Picking Up Penguins From Space

Danger Islands, off Antarctica. Landsat-8 image acquired on 7th December 2017. Image courtesy of NASA/USGS.

World Wildlife Day is the 3rd March, and so fittingly this blog is looking at how satellite imagery and remote sensing techniques were used to recently discover an unknown colony of 1.5 million Adélie penguins on the Danger Islands off the Antarctica Peninsula in the north-western Weddell Sea. Adélie penguins only live along the Antarctic coast, and they grow to a height of around 70cm and weigh between three and six kilograms.

The paper by ‘Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot by Borowicz et al was published in Scientific Reports on the 2nd March 2018. It is interestingly not only because of the discovery of unknown penguins but also because the research combines historic aerial imagery, satellite imagery, drone footage and remote sensing techniques.

The research has its roots in an earlier paper by Lynch and Schwaller from 2014, entitled ‘Mapping the Abundance and Distribution of Adélie Penguins Using Landsat-7: First Steps towards an Integrated Multi-Sensor Pipeline for Tracking Populations at the Continental Scale.’ It describes the development of an algorithm to analyse Landsat and high resolution imagery from WorldView-2 to estimate the size of penguin colonies based on the extent of the guana area. A classification approach was developed from a training dataset of 473 Landsat-7 pixels covering existing Adeline colonies, supported by over 10,000 pixels relating to features such as rock, soil and vegetation.

In the recently published paper, of which Heather Lynch was also a co-author, the team combined a range of imagery alongside some in-situ data to achieve their results. Different types of imagery were used:

  • High Resolution Imagery: Areas of guano staining on the Danger Islands were identified manually from WorldView-2 scenes.
  • Historical aerial photographs: Images taken by Falkland Islands Dependencies Aerial Survey Expedition (FIDASE) on the 31st January 1957 were digitally scanned and geo-referenced to the WorldView-2 data. They were then divided into polygons and analysed using manual classification processing using the open source QGIS software.
  • Landsat: The algorithm previously developed by Lynch and Schwaller was enhanced to work with data from Landsat-4, 7 and 8 by calculating the mean difference of similar bands from Landsat-4 and 8 compared to Landsat-7, and then adjusting based on the mean differences in each spectral band. The enhanced algorithm was then used to classify the penguin colony areas.
  • Drone data: Using a 1.2 megapixel camera flown at height of between 25 m and 45 m, captured footage was processed to produce georeferenced orthomosaics of the Danger Islands. Machine learning techniques were then applied using a deep neural network to locate and identify potential penguins. A training dataset of 160 images with 1237 penguins, followed by a validation dataset of 93 images with 673 penguins was used to teach the network. Once fully trained it analysed all the islands, and the results were validated with a number of manual counts. The scientists worked on an accuracy of plus or minus ten percent for the automated counts, although the variation with the in-situ counts was only 0.6 percent.

The outcome of this research was an identification of 751,527 pairs of previously unknown Adélie penguins on the Danger Islands. More surprisingly is that this increases the world estimates of this type of penguin by almost 50%, when it had been thought that the population had been declining for the last 40 years. The historical aerial imagery has led scientists to speculate that this new colony has remained constant for around the last 60 years in contrast to other known colonies.

This work is a great example of not only how much can be achieved with free-to-access imagery, but also how satellite imagery is helping us discover new things about our planet.

Five Learning Points For Developing An Earth Observation Product Portal

Landsat mosaic image of the Isle of Wight. Data courtesy of NASA.

This week we’re gently unveiling our Pixalytics Portal at the DATA.SPACE 2018 Conference taking place in Glasgow.

We’ve not attended DATA.SPACE before, but great feedback from some of the last years attendees convinced us to come. It’s an international conference focusing on the commercial opportunities available through the exploitation of space-enabled data and so it seemed the perfect place to demonstrate our new development.

Regular readers will know we’ve had the product portal idea for a little while, but it often went to the back of the work queue when compared to existing work, bid preparation and our other developments. Hence, six months ago we pinpointed the DATA.SPACE as our unveiling event!

On the 1st and 2nd February at Technology & Innovation Centre in Glasgow we have a stand where we’re inviting everyone to come up and have a look at the portal and give us feedback on the idea, principles and the look and feel of the portal.

We’re demonstrating five products, and we’re looking to expand this, these are:

  • Landscape Maps of the UK
  • Water Extent Mapping
  • Flood Water Mapping
  • Coastal Airborne Lidar Survey Planning Datasets
  • Open Ocean Water Quality Parameters

We’re not just attending, we’re exhibiting and Sam’s presenting!! So we’re going to have the full triumvirate conference experience. Sam is presenting in the first day’s second session titled ‘Looking at our Earth’ which starts at 11.10am. Her presentation is called ‘Growing Earth Observation By Being More Friendly.’

Developing this portal to its current state has been a really interesting journey. When we began we didn’t know why some of the larger companies haven’t cracked this already! Six months later and we’ve started to understand the challenges!

We thought it might be helpful to reveal are five top learning points for any other SME’s in our industry considering developing a portal. They are:

  1. Challenging the Digital e-commerce Process: Standard digital e-commerce systems allow customers to purchase a product and then download it immediately. The need to have an additional step of a few minutes, or even hours, to undertake data processing complicates things. It means that simple off-the-shelf plug-ins won’t work.
  2. Don’t Go for Perfection: Building a perfect portal will take time. We’ve adopted the approach of Eric Ries, author of The Startup Way, who advocates building a system for ten purchases. We’re perhaps a bit beyond that, but certainly we know that this will only be the first iteration of our portal.
  3. Linking The Moving Parts: Our portal has a web-front end, a cloud processing backend and the need to download requested data. We’ve tried to limit the amount of data and processing needed, but we can’t eliminate it entirely. This means there are a lot of moving parts to get right, and a lot of error capturing to be done!
  4. Legal & Tax issues: Sorting out the products is only one part of the process, don’t forget to do the legal and tax side as that has implications on your approach. We have learnt a lot about the specific requirements of digital services in e-commerce!
  5. Have a deadline: We chose to exhibit at DATA.SPACE to give us a deadline. We knew if we didn’t have a hard deadline we’d still be debating the products to include, and have developed none of them! The deadline has moved us really close to having a portal.

If you’re at DATA.SPACE this week, please come up and say hello. If you’ve got a few minutes to spare we’d love to get you feedback on our portal.

Earth Observation’s Flying Start to 2018

Simulated NovaSAR-S data.

Earth Observation (EO) is taking off again in 2018 with a scheduled launch of 31 satellites next Friday, 12th January, from a single rocket by the Indian Space Research Organization (ISRO). The launch will be on the Polar Satellite Launch Vehicle (PSLV-40) from the Satish Dhawan Space Centre in Sriharikota, India. ISRO has history of multiple launches, setting the world record in February 2017 with 104 satellites in one go.

The main payload next week will be Cartosat-2F, also known as Cartosat-2ER. It is the next satellite in a cartographic constellation which focuses on land observation. It carries two instruments, a high resolution multi-spectral imager and a panchromatic camera. It’s data is intended to be used in urban and rural applications, coastal land use, regulation and utility management.

At Pixalytics we’re particularly excited about the Carbonite-2 cubesat built by Surrey Satellite Technology Ltd (SSTL) which is on this launch. .

Carbonite-2 is a prototype mission to demonstrate the ability to acquire colour video images from space. It has been developed by Earth-i and SSTL, and carries an imaging system capable of delivering images with a spatial resolution of 1 m and colour video clips with a swath width of 5 km. Earth-i have already ordered five satellites from SSTL, as the first element of a constellation that will provide colour video and still imagery for the globe enabling the moving objects such as cars, ships or aircraft to be filmed. These satellites are planned for launch in 2019.

However, this isn’t the only cubesat with an EO interest on next week’s launch. In addition, there are:

  • KAUSAT 5 (Korea Aviation University Satellite) will observe the Earth using an infrared camera and measure the amount of radiation from its Low Earth Orbit (LEO).
  • Parikshit is a student satellite project from the Manipal Institute of Technology in India that carries a thermal infrared camera, using 7.5-13.5 µm wavelengths, and will be used to monitor urban heat islands, sea surface temperature and the thermal distribution of clouds around the Indian subcontinent.
  • Landmapper-BC3, a commercial satellite from Astro Digital in the USA to provide multispectral imagery at 22 m spatial resolution with a swath width of 220 km
  • ICEYE-X1 is a SAR microsatellite from the Finnish company ICEYE which is designed to provide near real-time SAR imagery using the S-Band. ICEYE is a recent start-up company who have raised $17 m in venture capital funding in the last few years. They hope to have a global imaging constellation by the end of 2020.

Amongst the remaining cubesats, there are a couple of really intriguing ones:

  • CNUSail 1 (Chungnam National University Sail) is a solar sail experiment from Chungnam National University in South Korea. It aims to successfully deploy a solar sail in LEO and then to de-orbit using the sail membrane as a drag-sail. There has been a lot of discussion around solar sails from propulsion systems through to mechanisms to clear space debris, so it will be fascinating to see the outcome.
  • IRVINE01 is the culmination of a STEM project started in 1999 in six public high schools in Irvine, California, which has given students the experience of building, testing and launching a cubesat to inspire the next generation of space scientists. This is a fantastic project!

We’re also really excited about the launch of the NovaSAR-S cubesat, which was also originally planned to be on this launch (as reflected in the first version of this blog). It is going to be launched later this year. NovaSAR-S, also built by SSTL, is of particular interested to Pixalytics as we’ve previously been involved in a project to simulate NovaSAR-S data and so we’re excited to see what the actual data looks like. NovaSAR-S is a Synthetic Aperture Radar (SAR) mission using the S-Band, which will operate in a sun-synchronous orbit at an altitude of 580 km. It has four imaging modes:

  • ScanSAR mode with a swath width of 100 km at 20 m spatial resolution.
  • Maritime mode with a swath width of > 400 km and a spatial resolution of 6 m across the track and 13.7m along the track.
  • Stripmap mode with a swath width of 15-20 km and a spatial resolution of 6 m.
  • ScanSAR wide mode with a swath width of 140km and a spatial resolution of 30 m.

The data will be used for applications including flooding, disaster monitoring, forestry, ship tracking, oil spill, land cover use and classification, crop monitoring and ice monitoring. We’ve going to keep an eye out for its launch!

This is just the start of 2018, and we hope it’s piqued your interest in EO as it’s going to be an exciting year!

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!

Earth observation satellites in space in 2017?

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

Earth Observation (EO) satellites currently account for just over a third of all the operational satellites orbiting the Earth. As we described two weeks ago, according to the Union of Concerned Scientists database there were 1 738 operational satellites at the end of August 2017, and 620 of these have a main purpose of either EO or Earth Science.

This represents a massive 66% increase in the number of EO satellites from our 2016 update, and the percentage of overall active satellites is also up from one quarter. These figures demonstrate, once again, that EO is a growing industry.

What do Earth observation satellites do?
Looking more closely at what EO satellites actually do demonstrates that despite increases in satellite numbers in almost all categories, it’s clearly growth in optical imaging which is the behind this significant increase. The purposes of active EO satellites in 2017 are:

  • Optical Imaging: 327 satellites representing a 98% increase on last year
  • Radar imaging: 45 satellites, a 32% increase on last year
  • Infrared imaging: 7 satellites, no change to last year
  • Meteorology: 64 satellites, a 73% increase on last year
  • Earth Science: 60 satellites, a 13% increase on last year
  • Electronic intelligence: 50 satellites, a 6% increase on last year
  • 16 satellites with other purposes, a 133% increase on last year
  • 51 satellites simply list EO as their purpose, a 100% increase on last year

Who controls Earth observation satellites?
Despite the huge increase in EO satellites, the number of countries who control them has not seen the same growth. This year there are 39 different countries listed with EO satellites, an increase of only 15% on last year. In addition, there are satellites run by multinational agencies such as the European Space Agency (ESA).

The USA leads the way controlling over half the EO satellites, although this is largely due to Planet who account for 30% on their own! Following USA is China with 14.4%, and then come India, Japan and Russia who each have over 3%.

The USA is followed by China with about 20%, and Japan and Russia come next with around 5% each. The UK is only listed as controller on 4 satellites all related to the DMC constellation, although we are also involved in the ESA satellites.

Size of Earth observation satellites
It’s interesting to look out the size breakdown of these satellites which shows the development of the small satellite. For this breakdown, we’ve classed satellites into four groups:

  • Large satellites with a launch mass of over 500kg
  • Small satellites with a launch mass between 100 and 500 kg.
  • Microsats with a launch mass between 10 and 100 kg.
  • Nanosats/Cubesats with a launch mass below 10 kg.

For the current active EO satellites there are:

  • 186 large satellites equating to 30.00%
  • 74 small satellites equating to 7.26%
  • 100 microsats equating to 16.13%
  • 215 Nanosats/Cubesats equating to 34.68%
  • The remaining 45 satellites do not have a launch mass specified.

Who uses the Earth observation satellites?

There has also been significant movement in the breakdown of EO satellites users since 2016. The influence of small commercial satellites undertaking optical imaging is again apparent. In 2017 the main users for EO were:

  • Commercial users with 44.68% of satellites (up from 21% in 2016)
  • Government users with 30.81% (down from 44% in 2016)
  • Military users with 19.35% (down from 30% in 2016)
  • Civil users with 5.16% (approximately the same as in 2016)

It should be noted that some of these satellites have multiple users.

Orbits of Earth observation satellites
In terms of altitude, unsurprisingly the vast majority, 92.25%, of EO satellites are in low earth orbits, 6.45% are in geostationary orbits and 1.3% are in an elliptical orbits.
There is a much greater variation in type of orbits:

  • 415 in a sun-synchronous orbit
  • 125 in a non-polar inclined orbit
  • 17 in a polar orbit
  • 8 in an equatorial orbit
  • 5 in an elliptical orbit
  • 5 in a Molniya orbit (highly eccentric elliptical orbits of approximately 12 hours)
  • 45 satellites do not have a type of orbit listed

Few interesting facts about active Earth observation satellites

  • Oldest active EO satellite is the Brazilian SCD-1 Meteorology/Earth Science satellite.
  • Valentine’s Day (14th February) 2017 saw Planet launch its Flock 3P meaning that 88 active EO satellites were launched on that day.
  • Most popular launch site is Satish Dhawan Space Centre operated by Indian Space Research Organisation (ISRO) who have put 169 into space.
  • ISRO’s Polar Satellite Launch Vehicle is also the most popular launch vehicle with 114 satellites.
  • The EO satellite furthest away from the Earth is the USA’s Electronic Intelligence satellite Trumpet 3 which has an apogee of 38 740 km.

What’s next?
It’s not clear whether the rapid growth in the number of EO satellites will continue into 2018. Planet, one of the key drivers, announced earlier this month that they had successfully completed their objective to image the globe’s entire landmass every day – which is a massive achievement!

That’s not say that Planet won’t push on further with new ideas and technologies, and other companies may move into that space too. China launched a number of EO satellites last weekend and there are already a number of interesting satellites planned for launch between now and the middle of 2018 including, Cartosat-2ER, NovaSAR-S, GOES-S and Sentinel-3B to name a few. .

One thing is for certain, there is a lot collected EO data out there, and it is increasing by the day!

3 Ways Earth Observation is Tackling Food Security

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

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

One of the key global challenges is food security. A number of reports issued last week, coinciding with World Food Day on the 16th October, demonstrated how Earth Observation (EO) could play a key part in tackling this.

Climate change is a key threat to food security. The implications were highlighted by the U.S. Geological Survey (USGS) report who described potential changes to suitable farmland for rainfed crops. Rainfed farming accounts for approximately 75 percent of global croplands, and it’s predicated that these locations will change in the coming years. Increased farmland will be available in North America, western Asia, eastern Asia and South America, whilst there will be a decline in Europe and the southern Great Plains of the US.

The work undertaken by USGS focussed on looking at the impact of temperature extremes and the associated changes in seasonality of soil moisture conditions. The author of the study, John Bradford said “Our results indicate the interaction of soil moisture and temperature extremes provides a powerful yet simple framework for understanding the conditions that define suitability for rainfed agriculture in drylands.” Soil moisture is a product that Pixalytics is currently working on, and its intriguing to see that this measurement could be used to monitor climate change.

Given that this issue may require farmers to change crops, work by India’s Union Ministry of Agriculture to use remote sensing data to identify areas best suited for growing different crops is interesting. The Coordinated Horticulture Assessment and Management using geoinformatics (CHAMAN) project has used data collected by satellites, including the Cartosat Series and RESOURCESAT-1, to map 185 districts in relation to the best conditions for growing bananas, mangos, citrus fruits, potatoes, onions, tomatoes and chilli peppers.

The results for eight states in the north east of the country will be presented in January, with the remainder a few months later, identifying the best crop for each district. Given that India is already the second largest producer of fruit and vegetables in the world, this is a fascinating strategic development to their agriculture industry.

The third report was the announcement of a project between the University of Queensland and the Chinese Academy of Sciences which hopes to improve the accuracy of crop yield predictions. EO data with an improved spatial, and temporal, resolution is being used alongside biophysical information to try to predict crop yield at a field scale in advance of the harvest. It is hoped that this project will produce an operational product through this holistic approach.

These are some examples of the way in which EO data is changing the way we look at agriculture, and potential help provide improved global food security in the future.

Flip-Sides of Soil Moisture

Soil Moisture changes between 19th and 25th August around Houston, Texas due to rainfall from Hurricane Harvey. Courtesy of NASA Earth Observatory image by Joshua Stevens, using soil moisture data courtesy of JPL and the SMAP science team.

Soil moisture is an interesting measurement as it can be used to monitor two diametrically opposed conditions, namely floods and droughts. This was highlighted last week by maps produced from satellite data for the USA and Italy respectively. These caught our attention because soil moisture gets discussed on a daily basis in the office, due to its involvement in a project we’re working on in Uganda.

Soil moisture can have a variety of meanings depending on the context. For this blog we’re using soil moisture to describe the amount of water held in spaces between the soil in the top few centimetres of the ground. Data is collected by radar satellites which measure microwaves reflected or emitted by the Earth’s surface. The intensity of the signal depends on the amount of water in the soil, enabling a soil moisture content to be calculated.

You can’t have failed to notice the devastating floods that have occurred recently in South Asia – particularly India, Nepal and Bangladesh – and in the USA. The South Asia floods were caused by monsoon rains, whilst the floods in Texas emanated from Hurricane Harvey.

Soil moisture measurements can be used to show the change in soil saturation. NASA Earth Observatory produced the map at the top of the blogs shows the change in soil moisture between the 19th and 25th August around Houston, Texas. The data is based on measurements acquired by the Soil Moisture Active Passive (SMAP) satellite, which uses a radiometer to measure soil moisture in the top 5 centimetres of the ground with a spatial resolution of around 9 km. On the map itself the size of each of the hexagons shows how much the level of soil moisture changed and the colour represents how saturated the soil is.

These readings have identified that soil moisture levels got as high as 60% in the immediate aftermath of the rainfall, partly due to the ferocity of the rain, which prevented the water from seeping down into the soil and so it instead remained at the surface.

Soil moisture in Italy during early August 2017. The data were compiled by ESA’s Soil Moisture CCI project. Data couresy of ESA. Copyright: C3S/ECMWF/TU Wien/VanderSat/EODC/AWST/Soil Moisture CCI

By contrast, Italy has been suffering a summer of drought and hot days. This year parts of the country have not seen rain for months and the temperature has regularly topped one hundred degrees Fahrenheit – Rome, which has seventy percent less rainfall than normal, is planning to reduce water pressure at night for conservation efforts.

This has obviously caused an impact on the ground, and again a soil moisture map has been produced which demonstrates this. This time the data was come from the ESA’s Soil Moisture Climate Change Initiative project using soil moisture data from a variety of satellite instruments. The dataset was developed by the Vienna University of Technology with the Dutch company VanderSat B.V.

The map shows the soil moisture levels in Italy from the early part of last month, with the more red the areas, the lower the soil moisture content.

Soil moisture is a fascinating measurement that can provide insights into ground conditions whether the rain is falling a little or a lot.

It plays an important role in the development of weather patterns and the production of precipitation, and is crucial to understanding both the water and carbon cycles that impact our weather and climate.

Algae Starting To Bloom

Algal Blooms in Lake Erie, around Monroe, acquired by Sentinel-2 on 3rd August 2017. Data Courtesy of ESA/Copernicus.

Algae have been making the headlines in the last few weeks, which is definitely a rarely used phrase!

Firstly, the Lake Erie freshwater algal bloom has begun in the western end of the lake near Toledo. This is something that is becoming an almost annual event and last year it interrupted the water supply for a few days for around 400,000 residents in the local area.

An algae bloom refers to a high concentration of micro algae, known as phytoplankton, in a body of water. Blooms can grow quickly in nutrient rich waters and potentially have toxic effects. Although a lot of algae is harmless, the toxic varieties can cause rashes, nausea or skin irritation if you were to swim in it, it can also contaminate drinking water and can enter the food chain through shellfish as they filter large quantities of water.

Lake Erie is fourth largest of the great lakes on the US/Canadian border by surface area, measuring around 25,700 square km, although it’s also the shallowest and at 484 cubic km has the smallest water volume. Due to its southern position it is the warmest of the great lakes, something which may be factor in creation of nutrient rich waters. The National Oceanic and Atmospheric Administration produce both an annual forecast and a twice weekly Harmful Algal Bloom Bulletin during the bloom season which lasts until late September. The forecast reflects the expected biomass of the bloom, but not its toxicity, and this year’s forecast was 7.5 on a scale to 10, the largest recent blooms in 2011 and 2015 both hit the top of the scale. Interestingly, this year NOAA will start incorporating Sentinel-3 data into the programme.

Western end of Lake Erie acquired by Sentinel-2 on 3rd August 2017. Data

Despite the phytoplankton within algae blooms being only 1,000th of a millimetre in size, the large numbers enable them to be seen from space. The image to the left is a Sentinel-2 image, acquired on the 3rd August, of the western side of the lake where you can see the green swirls of the algal bloom, although there are also interesting aircraft contrails visible in the image. The image at the start of the top of the blog is zoomed in to the city of Monroe and the Detroit River flow into the lake and the algal bloom is more prominent.

Landsat 8 acquired this image of the northwest coast of Norway on the 23rd July 2017,. Image courtesy of NASA/NASA Earth Observatory.

It’s not just Lake Erie where algal blooms have been spotted recently:

  • The Chautauqua Lake and Findley Lake, which are both just south of Lake Erie, have reported algal blooms this month.
  • NASA’s Landsat 8 satellite captured the image on the right, a bloom off the northwest coast of Norway on the 23rd July. It is noted that blooms at this latitude are in part due to the sunlight of long summer days.
  • The MODIS instrument onboard NASA’s Aqua satellite acquired the stunning image below of the Caspian Sea on the 3rd August.

Image of the Caspian Sea, acquired on 3rd August 2017, by MODIS on NASA’s Aqua satellite. Image Courtesy of NASA/NASA Earth Observatory.

Finally as reported by the BBC, an article in Nature this week proposes that it was a takeover by ocean algae 650 million years ago which essentially kick started life on Earth as we know it.

So remember, they may be small, but algae can pack a punch!

Silver Anniversary for Ocean Altimetry Space Mission

Artist rendering of Jason-3 satellite over the Amazon.
Image Courtesy NASA/JPL-Caltech.

August 10th 1992 marked the launch of the TOPEX/Poseidon satellite, the first major oceanographic focussed mission. Twenty five years, and three successor satellites, later the dataset begun by TOPEX/Poseidon is going strong providing sea surface height measurements.

TOPEX/Poseidon was a joint mission between NASA and France’s CNES space agency, with the aim of mapping ocean surface topography to improve our understanding of ocean currents and global climate forecasting. It measured ninety five percent of the world’s ice free oceans within each ten day revisit cycle. The satellite carried two instruments: a single-frequency Ku-band solid-state altimeter and a dual-frequency C- and Ku-band altimeter sending out pulses at 13.6 GHz and 5.3 GHz respectively. The two bands were selected due to atmospheric sensitivity, as the difference between them provides estimates of the ionospheric delay caused by the charged particles in the upper atmosphere that can delay the returned signal. The altimeter sends radio pulses towards the earth and measures the characteristics of the returned echo.

When TOPEX/Poseidon altimetry data is combined with other information from the satellite, it was able to calculate sea surface heights to an accuracy of 4.2 cm. In addition, the strength and shape of the return signal also allow the determination of wave height and wind speed. Despite TOPEX/Poseidon being planned as a three year mission, it was actually active for thirteen years, until January 2006.

The value in the sea level height measurements resulted in a succeeding mission, Jason-1, launched on December 7th 2001. It was put into a co-ordinated orbit with TOPEX/Poseidon and they both took measurements for three years, which allowed both increased data frequency and the opportunity for cross calibration of the instruments. Jason-1 carried a CNES Poseidon-2 Altimeter using the same C- and Ku-bands, and following the same methodology it had the ability to measure sea-surface height to an improved accuracy of 3.3 cm. It made observations for 12 years, and was also overlapped by its successor Jason-2.

Jason-2 was launched on the 20 June 2008. This satellite carried a CNES Poseidon-3 Altimeter with C- and Ku-bands with the intention of measuring sea height to within 2.5cm. With Jason-2, National Oceanic and Atmospheric Administration (NOAA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) took over the management of the data. The satellite is still active, however due to suspected radiation damage its orbit was lowered by 27 km, enabling it to produce an improved, high-resolution estimate of Earth’s average sea surface height, which in turn will help improve the quality of maps of the ocean floor.

Following the established pattern, Jason-3 was launched on the 17th January 2016. It’s carrying a Poseidon-3B radar altimeter, again using the same C and Ku bands and on a ten day revisit cycle.

Together these missions have provided a 25 year dataset on sea surface height, which has been used for applications such as:

  • El Niño and La Niña forecasting
  • Extreme weather forecasting for hurricanes, floods and droughts
  • Ocean circulation modelling for seasons and how this affects climate through by moving heat around the globe
  • Tidal forecasting and showing how this energy plays an important role in mixing water within the oceans
  • Measurement of inland water levels – at Pixalytics we have a product that we have used to measure river levels in the Congo and is part of the work we are doing on our International Partnership Programme work in Uganda.

In the future, the dataset will be taken forward by the Jason Continuity of Service (Jason-CS) on the Sentinel-6 ocean mission which is expected to be launched in 2020.

Overall, altimetry data from this series of missions is a fantastic resource for operational oceanography and inland water applications, and we look forward to its next twenty five years!

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.