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!

Monitoring Fires From Space

Monitoring fires from space has significant advantages when compared to on-ground activity. Not only are wider areas easier to monitor, but there are obvious safety benefits too. The different ways this can be done have been highlighted through a number of reports over the last few weeks.

VIIRS Image from 25 April 2017, of the Yucatán Peninsula showing where thermal bands have picked-up increased temperatures. Data Courtesy of NASA, NASA image by Jeff Schmaltz, LANCE/EOSDIS Rapid Response.

Firstly, NASA have released images from different instruments, on different satellites, that illustrate two ways of how satellites can monitor fires.

Acquired on the 25 April 2017, an image from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite showed widespread fire activity across the Yucatán Peninsula in South America. The image to the right is a natural colour image and each of the red dots represents a point where the instrument’s thermal band detected temperatures higher than normal.

False colour image of the West Mims fire on Florida/Georgia boundary acquired by MODIS on 02 May 2017. Data courtesy of NASA. NASA image by Jeff Schmaltz, LANCE/EOSDIS Rapid Response.

Compare this to a wildfire on Florida-Georgia border acquired from NASA’s Aqua satellite on the 02 May 2017 using the Moderate Resolution Imaging Spectroradiometer (MODIS). On the natural colour image the fires could only be seen as smoke plumes, but on the left is the false colour image which combines infrared, near-infrared and green wavelengths. The burnt areas can be clearly seen in brown, whilst the fire itself is shown as orange.

This week it was reported that the Punjab Remote Sensing Centre in India, has been combining remote sensing, geographical information systems and Global Positioning System (GPS) data to identify the burning of crop stubble in fields; it appears that the MODIS fire products are part of contributing the satellite data. During April, 788 illegal field fires were identified through this technique and with the GPS data the authorities have been able to identify, and fine, 226 farmers for undertaking this practice.

Imaged by Sentinel-2, burnt areas, shown in shades of red and purple, in the Marantaceae forests in the north of the Republic of Congo.
Data courtesy of Copernicus/ESA. Contains modified Copernicus Sentinel data (2016), processed by ESA.

Finally, a report at the end of April from the European Space Agency described how images from Sentinel-1 and Senintel-2 have been combined to assess the amount of forest that was burnt last year in the Republic of Congo in Africa – the majority of which was in Marantaceae forests. As this area has frequent cloud cover, the optical images from Sentinel-2 were combined with the Synthetic Aperture Radar (SAR) images from Sentinel-1 that are unaffected by the weather to offer an enhanced solution.

Sentinel-1 and Sentinel-2 data detect and monitor forest fires at a finer temporal and spatial resolution than previously possible, namely 10 days and 10 m, although the temporal resolution will increase to 5 days later this year when Sentinel-2B becomes fully operational.  Through this work, it was estimated that 36 000 hectares of forest were burnt in 2016.

Given the danger presented by forest fires and wildfires, greater monitoring from space should improve fire identification and emergency responses which should potentially help save lives. This is another example of the societal benefit of satellite remote sensing.

Monitoring ocean acidification from space

Enhanced pseudo-true colour composite of the United Kingdom showing coccolithophore blooms in light blue. Image acquired by MODIS-Aqua on 24th May 2016. Data courtesy of NASA.

Enhanced pseudo-true colour composite of the United Kingdom showing coccolithophore blooms in light blue. Image acquired by MODIS-Aqua on 24th May 2016. Data courtesy of NASA.

What is ocean acidification?
Since the industrial revolution the oceans have absorbed approximately 50% of the CO2 produced by human activities (The Royal Society, 2005). Scientists previously saw this oceanic absorption as advantageous, however ocean observations in recent decades have shown it has caused a profound change in the ocean chemistry – resulting in ocean acidification (OA); as CO2 dissolves into the oceans it forms carbonic acid, lowering the pH and moving the oceans into a more acidic state. According to the National Oceanic Atmospheric Administration (NOAA) ocean pH has already decreased by about 30% and some studies suggest that if no changes are made, by 2100, ocean pH will decrease by 150%.

Impacts of OA
It’s anticipated OA will impact many marine species. For example, it’s expected it will have a harmful effect on some calcifying species such as corals, oysters, crustaceans, and calcareous plankton e.g. coccolithophores.

OA can significantly reduce the ability of reef-building corals to produce their skeletons and can cause the dissolution of oyster’s and crustacean’s protective shells, making them more susceptible to predation and death. This in turn would affect the entire food web, the wider environment and would have many socio-economic impacts.

Calcifying phytoplankton, such as coccolithophores, are thought to be especially vulnerable to OA. They are the most abundant type of calcifying phytoplankton in the ocean, and are important for the global biogeochemical cycling of carbon and are the base of many marine food webs. It’s projected that OA may disrupt the formation and/or dissolution of coccolithophores, calcium carbonate (CaCO3) shells, impacting future populations. Thus, changes in their abundance due to OA could have far-reaching effects.

Unlike other phytoplankton, coccolithophores are highly effective light scatterers relative to their surroundings due to their production of highly reflective calcium carbonate plates. This allows them to be easily seen on satellite imagery. The figure at the top of this page shows multiple coccolithophore blooms, in light blue, off the coast of the United Kingdom on 24th March 2016.

Current OA monitoring methods
Presently, the monitoring of OA and its effects are predominantly carried out by in situ observations from ships and moorings using buoys and wave gliders for example. Although vital, in situ data is notoriously spatially sparse as it is difficult to take measurements in certain areas of the world, especially in hostile regions (e.g. Polar Oceans). On their own they do not provide a comprehensive and cost-effective way to monitor OA globally. Consequently, this has driven the development of satellite-based sensors.

How can OA be monitored from space?
Although it is difficult to directly monitor changes in ocean pH using remote sensing, satellites can measure sea surface temperature and salinity (SST & SSS) and surface chlorophyll-a, from which ocean pH can be estimated using empirical relationships derived from in situ data. Although surface measurements may not be representative of deeper biological processes, surface observations are important for OA because the change in pH occurs at the surface first.

In 2015 researchers at the University of Exeter, UK became the first scientists to use remote sensing to develop a worldwide map of the ocean’s acidity using satellite imagery from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite that was launched in 2009 and NASA’s Aquarius satellite that was launched in 2011; both are still currently in operation. Thermal mounted sensors on the satellites measure the SST while the microwave sensors measure SSS; there are also microwave SST sensors, but they have a coarse spatial resolution.

Future Opportunities – The Copernicus Program
The European Union’s Copernicus Programme is in the process of launching a series of satellites, known as Sentinel satellites, which will improve understanding of large scale global dynamics and climate change. Of all the Sentinel satellite types, Sentinels 2 and 3 are most appropriate for assessment of the marine carbonate system. The Sentinel-3 satellite was launched in February this year andwill be mainly focussing on ocean measurements, including SST, ocean colour and chlorophyll-a.

Overall, OA is a relatively new field of research, with most of the studies being conducted over the last decade. It’s certain that remote sensing will have an exciting and important role to play in the future monitoring of this issue and its effects on the marine environment.

Blog written by Charlie Leaman, BSc, University of Bath during work placement at Pixalytics.

Reprocessing Data Challenges of Producing A Time Series

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

Being able to look back at how our planet has evolved over time, is one of the greatest assets of satellite remote sensing. With Landsat, you have a forty year archive to examine changes in land use and land cover. For in situ (ground based) monitoring, this is something that’s only available for a few locations, and you’ll only have data for the location you’re measuring. Landsat’s continuous archive is an amazing resource, and it is hoped that the European Union’s Copernicus programme will develop another comprehensive archive. So with all of this data, producing a time series analysis is easy isn’t it?

Well, it’s not quite that simple. There are the basic issues of different missions having different sensors, and so you need to know whether you’re comparing like with like. Although data continuity has been a strong element of Landsat, the sensors on Landsat 8 are very different to those on Landsat 1. Couple this with various positional, projection and datum corrections, and you have lots of things to think about to produce an accurate time series. However, once you’ve sorted all of these out and you’ve got your data downloaded, then everything is great isn’t it?

Well, not necessarily; you’ve still got to consider data archive reprocessing. The Space Agencies, who maintain this data, regularly reprocess satellite datasets. This means that the data you downloaded two years ago, isn’t necessarily the same data that could be downloaded today.

We faced this issue recently as NASA completed the reprocessing of the MODIS Aqua data, which began in 2014. The data from the MODIS Aqua satellite has been reprocessed seven times, whilst its twin, Terra, has been reprocessed three times.

Reprocessing the data can include changes to some, or all, of the following:

  • Update of the instrument calibration, to take account of current knowledge about sensor degradation and radiometric performance.
  • Appyling new knowledge, in terms of atmospheric correction and/or derived product algorithms.
  • Changes to parallel datasets that are used as inputs to the processing; for example, the meteorological conditions are used to aid the atmospheric correction.

Occasionally, they also change the output file format the data is provided in; and this is what has caught us out. The MODIS output file format has changed from HDF4 to NetCDF4 with the reason being that NetCDF is a more efficient, sustainable, extendable and interoperable data file format. A change we’ve known about for a long time, as it resulted from community input, but until you get the new files you can’t check and update your software.

We tend to use a lot of Open Source software, enabling our clients to carry on working with remote sensing products without having to invest in expensive software. The challenge is that it takes software provider time to catch up with the format changes. Hence, the software is unable to load the new files or the data is incorrectly read e.g., comes in upside down. Sometimes large changes, mean you may have to alter your approach and/or software.

Reprocessing is important, as it improves the overall quality of the data, but you do need to keep on top what is happening with the data to ensure that you are comparing like with like when you analyse a time series.

Ocean Colour Cubes

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

It’s an exciting time to be in ocean colour! A couple of weeks ago we highlighted the new US partnership using ocean colour as an early warning system for harmful freshwater algae blooms, and last week a new ocean colour CubeSat development was announced.

Ocean colour is something very close to our heart; it was the basis of Sam’s PhD and a field of research she is highly active in today. When Sam began studying her PhD, Coastal Zone Color Scanner (CZCS) was the main source of satellite ocean colour data, until it was superseded by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that became the focus of her role at Plymouth Marine Laboratory.

Currently, there are a number ocean colour instruments in orbit:

  • NASA’s twin MODIS instruments on the Terra and Aqua satellites
  • NOAA’s Visible Infrared Imager Radiometer Suite (VIIRS)
  • China’s Medium Resolution Spectral Imager (MERSI), Chinese Ocean Colour and Temperature Scanner (COCTS) and Coastal Zone Imager (CZI) onboard several satellites
  • South Korea’s Geostationary Ocean Color Imager (GOCI)
  • India’s Ocean Colour Monitor on-board Oceansat-2

Despite having these instruments in orbit, there is very limited global ocean colour data available for research applications. This is because the Chinese data is not easily accessible outside China, Oceansat-2 data isn’t of sufficient quality for climate research and GOCI is a geostationary satellite so the data is only for a limited geographical area focussed on South Korea. With MODIS, the Terra satellite has limited ocean colour applications due to issues with its mirror and hence calibration; and recently the calibration on Aqua has also become unstable due to its age. Therefore, the ocean colour community is just left with VIIRS; and the data from this instrument has only been recently proved.

With limited good quality ocean colour data, there is significant concern over the potential loss of continuity in this valuable dataset. The next planned instrument to provide a global dataset will be OLCI onboard ESA’s Sentinel 3A, due to be launched in November 2015; with everyone having their fingers crossed that MODIS will hang on until then.

Launching a satellite takes time and money, and satellites carrying ocean colour sensors have generally been big, for example, Sentinel 3A weighs 1250 kg and MODIS 228.7 kg. This is why the project was announced last week to build two Ocean Colour CubeSats is so exciting; they are planned to weigh only 4 kg which reduces both the expense and the launch lead time.

The project, called SOCON (Sustained Ocean Observation from Nanosatellites), will see Clyde Space, from Glasgow in the UK, will build an initial two prototype SeaHawk CubeSats with HawkEye Ocean Colour Sensors, with a ground resolution of between 75 m and 150 m per pixel to be launched in early 2017. The project consortium includes the University of North Carolina, NASA’s Goddard Space Flight Centre, Hawk Institute for Space Sciences and Cloudland Instruments. The eventual aim is to have constellations of CubeSats providing a global view of both ocean and inland waters.

There are a number of other planned ocean colour satellite launches in the next ten years including following on missions such as Oceansat-3, two missions from China, GOCI 2, and a second VIIRS mission.

With new missions, new data applications and miniaturised technology, we could be entering a purple patch for ocean colour data – although purple in ocean colour usually represents a Chlorophyll-a concentration of around 0.01 mg/m3 on the standard SeaWiFS colour palette as shown on the image at the top of the page.

We’re truly excited and looking forward to research, products and services this golden age may offer.