Locusts & Monkeys

Soil moisture data from the SMOS satellite and the MODIS instrument acquired between July and October 2016 were used by isardSAT and CIRAD to create this map showing areas with favourable locust swarming conditions (in red) during the November 2016 outbreak. Data courtesy of ESA. Copyright : CIRAD, SMELLS consortium.

Spatial resolution is a key characteristic in remote sensing, as we’ve previously discussed. Often the view is that you need an object to be significantly larger than the resolution to be able to see it on an image. However, this is not always the case as often satellites can identify indicators of objects that are much smaller.

We’ve previously written about satellites identifying phytoplankton in algal blooms, and recently two interesting reports have described how satellites are being used to determine the presence of locusts and monkeys!

Locusts

Desert locusts are a type of grasshopper, and whilst individually they are harmless as a swarm they can cause huge damage to populations in their paths. Between 2003 and 2005 a swarm in West Africa affected eight million people, with reported losses of 100% for cereals, 90% for legumes and 85% for pasture.

Swarms occur when certain conditions are present; namely a drought, followed by rain and vegetation growth. ESA and the UN Food and Agriculture Organization (FAO) have being working together to determine if data from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to forecast these conditions. SMOS carries a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) instrument – a 2D interferometric L-band radiometer with 69 antenna receivers distributed on a Y-shaped deployable antenna array. It observes the ‘brightness temperature’ of the Earth, which indicates the radiation emitted from planet’s surface. It has a temporal resolution of three days and a spatial resolution of around 50 km.

By combining the SMOS soil moisture observations with data from NASA’s MODIS instrument, the team were able to downscale SMOS to 1km spatial resolution and then use this data to create maps. This approach then predicted favourable locust swarming conditions approximately 70 days ahead of the November 2016 outbreak in Mauritania, giving the potential for an early warning system.

This is interesting for us as we’re currently using soil moisture data in a project to provide an early warning system for droughts and floods.

Monkeys

Earlier this month the paper, ‘Connecting Earth Observation to High-Throughput Biodiversity Data’, was published in the journal Nature Ecology and Evolution. It describes the work of scientists from the Universities of Leicester and East Anglia who have used satellite data to help identify monkey populations that have declined through hunting.

The team have used a variety of technologies and techniques to pull together indicators of monkey distribution, including:

  • Earth observation data to map roads and human settlements.
  • Automated recordings of animal sounds to determine what species are in the area.
  • Mosquitos have been caught and analysed to determine what they have been feeding on.

Combining these various datasets provides a huge amount of information, and can be used to identify areas where monkey populations are vulnerable.

These projects demonstrate an interesting capability of satellites, which is not always recognised and understood. By using satellites to monitor certain aspects of the planet, the data can be used to infer things happening on a much smaller scale than individual pixels.

Ocean Colour Partnership Blooms

Landsat 8 Natural Colour image of Algal Blooms in Lake Erie acquired on 01 August 2014. Image Courtesy of NASA/USGS.

Landsat 8 Natural Colour image of Algal Blooms in Lake Erie acquired on 01 August 2014. Image Courtesy of NASA/USGS.

Last week NASA, NOAA, USGS and the US Environmental Protection Agency announced a $3.6 million partnership to use satellite data as an early warning system for harmful freshwater algae blooms.

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. Shellfish filter large quantities of water and can concentrate the algae in their tissues, allowing it to enter the marine food chain and potentially causing a risk to human consumption. Blooms can also contaminate drinking water. For example, last August over 40,000 people were banned from drinking water in Toledo, Ohio, after an algal bloom in Lake Erie.

The partnership will use the satellite remote sensing technique of ocean colour as the basis for the early warning system.  Ocean colour isn’t a new technique, it has been recorded as early as the 1600s when Henry Hudson noted in his ship’s log that a sea pestered with ice had a black-blue colour.

Phytoplankton within algae blooms are microscopic, some only 1,000th of a millimetre in size, and so it’s not possible to see individual organisms from space. Phytoplankton contain a photosynthetic pigment visible with the human eye, and in sufficient quantities this material can be measured from space. As the phytoplankton concentration increases the reflectance in the blue waveband decreases, whilst the reflectance in the green waveband increases slightly. Therefore, a ratio of blue to green reflectance can be used to derive quantitative estimates of the concentration of phytoplankton.

The US agency partnership is the first step in a five-year project to create a reliable and standard method for identifying blooms in US freshwater lakes and reservoirs for the specific phytoplankton species, cyanobacteria. To detect blooms it will be necessary to study local environments to understand the factors that influence the initiation and evolution of a bloom.

It won’t be easy to create this methodology as inland waters, unlike open oceans, have a variety of other organic and inorganic materials suspended in the water through land surface run-off, which will also have a reflectance signal. Hence, it will be necessary to ensure that other types of suspended particulate matter are excluded from the prediction methodology.

It’s an exciting development in our specialist area of ocean colour. We wish them luck and we’ll be looking forward to their research findings in the coming years.