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 An indicative history of green cover since the 1980’s

Map of the Month for July from Teagasc presents an interim output from a remote sensing study looking to see how often fields are bare of vegetation, due to harvesting or re-seeding. Using the Teagasc archive of satellite imagery going back to the 1980’s we can use vegetation indices to give an indication the number of times a every field in the country has been bare. This will be a useful baseline dataset in many applications like GHG accounting, biodiversity, and landuse  studies.

https://www.teagasc.ie/rural-economy/rural-economy/spatial-analysis/gis-monthly-maps/

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EPA’s Research on Remote Sensing of Aerosols, Clouds and Wind

EPA’s report on Remote Sensing of Aerosols, Clouds and Wind at Mace Head Atmospheric Research Station is now available, focusing on identifying pressures on air quality, weather and climate but also on economy and human health, informing policy and developing solutions.

Authors: Jana Preißler and Colin O’Dowd

The power of remote sensing lies in its ability to automatically and continuously characterise parts of the atmosphere that can be far away from the sensor, e.g. at high altitudes from the ground in the case of this study. This fellowship focused on continuous high-resolution (vertical and temporal) profiling of the atmosphere over Mace Head Atmospheric Research Station using active and passive ground-based remote sensing techniques.

In addition to contributing to high-impact research studies in past years, remote sensing data were also sent to the European-scale networks Cloudnet and E-Profile for joint processing and large-scale studies. The existence of such networks underlines the importance of ground-based remote sensing of the atmosphere at a continental scale. Remote sensing at Mace Head provides a large part of the Irish contribution to those pan-European networks.

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Remote Sensing of Lightning Strikes

We’ve had a few days of summer storms and lightning strikes leading to blackouts and animal deaths. If you want see where lightning is happening here and now, then real-time lightning detection has the answer. The detectors measure Very Low Frequency radio waves and triangulate bursts to locate lightning strikes. Met Eireann feeds data (from the valentia island observatory) into a system run by UK met office and shows the results on its landing page map every 15 mins. My favorite site is lightningmaps.org which is a service run by volunteers and hobbyists across the world. You can follow lightning storms as they happen and it has audio to mimic strikes in the area you are looking at.

https://www.lightningmaps.org/?lang=en#m=oss;t=3;s=0;o=0;b=0.00;ts=0;tsc=1;

https://www.met.ie/

 

https://www.metoffice.gov.uk/public/weather/observation/map/#?map=Lightning&fcTime=1592175600&zoom=5&lon=-4.00&lat=55.01

 

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NDMI in the past ten years

In the latest Teagasc Map of the Month (https://www.teagasc.ie/rural-economy/rural-economy/spatial-analysis/gis-monthly-maps/) I used MODIS Terra surface reflectance data to calculate the Normalised Difference Moisture Index (NDMI) as a proxy for the spatial variation in the effect of the spring 2020 drought in Ireland. The specific measure I used was the difference in the average NDMI in May 2020 to a long-term May average (2009 to 2019). The map showed well the spatial variation. To better highlight the underlying data I produced an animation of the calculated NDMI averages for May of each year since 2009. Brown tones indicate lower NDMI values (drier) while blue tones indicate higher NDMI (wetter).

NDMI_Avg_May_new

The animation draws a very different picture to the map of the NDMI difference, with bogs and upland showing much lower NDMI values than the rest of the country. The reason is that NDMI is an index for leaf water content, based on the near and short-wave infrared reflectance, and in the absence of green vegetation (such as in cut bogs and sparse uplands) will be lower.

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Map of Drought Stress in May

The current Teagasc Map of the month creates a Normalized Difference Moisture Index for May form MODIS data and compares it with a decadal May average to show which parts of the country are plants under stress due to drought.

https://www.teagasc.ie/rural-economy/rural-economy/spatial-analysis/gis-monthly-maps/

 

 

 

 

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Job in JRC for EO/Soil Scientist

FG IV – Scientific Project Officer – Land and Soil
Degradation Assessment

The Soil Unit in the JRC in Italy is looking for a a project offcier (3 year contract) From the PANAGOS newsletter:

The position will support the implementation of the European Land and Soil Observatory through the devel­opment of an EU-wide land degradation and restoration assessment, reflecting the global approach adopt­ed by the IPBES. This should include a detailed European Land Degradation map, an underpinning data­base and a detailed analysis of the land restoration potential in the EU. Candidates must possess an under­graduate degree in soil science, earth sciences, environmental sciences or agricultural sciences, together with at least 5 years Job-Related experience on modelling or geostatistical analysis of soil condition or land degradation processes. A post-graduate qualification in a related field, contributions to national-continental-global land or soil degradation assessments, experience of working with large spatial datasets (including EO data) or in the implementation of land or soil restoration techniques, would be an advantage.  Dead­line: 25.5.2020  Apply: https://recruitment.jrc.ec.europa.eu/ (code 2020-IPR-D3-FGIV-014171)

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Earth Obs and Covid

Great Idea from Copernicus, a competition for new ideas looking at the impacts that COVID crisis has had on certain sectors:

European Space Agency (ESA), in coordination with the European Commission, is launching a special edition of the Custom Script Contest, focused on the support of space assets during the COVID-19 crisis, managed by Euro Data Cube group. Following a similar format, but further to looking for new algorithms, we are in the quest for ideas on how satellite data could help monitor and mitigate the situation for the upcoming months, while the world will organize to get back to business and will need to adapt from this crisis.

https://www.sentinel-hub.com/contest

This is not just “make work”- the impact of COVID is so severe and rapid that governments ability to understand what is happening can’t keep up. Remote Sensing approaches could give vital information about the performance of certain sectors in society before conventional statistical gathering (hampered themselves by the impact of Covid-19) can provide the needed information.

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Teagasc Podcast on Remote Sensing

I’m the guest in the latest episode of the Teagasc Podcast series – the Research Field presented by Sean Duke.

In Teagasc we use Earth Observation technology to help us understand the status of Irish agriculture and develop tools to allow farmers to better manage their farms. Earth Observation is a branch of remote sensing, using technology like satellites and drones to measure see what’s going on in Ireland.

The satellites we use are optical (like orbiting digital cameras) and radar (which broadcast microwaves and measure how they bounce back). We can use these satellites to characterise the landscape, telling us what type of land-cover we have. They are particularly good at mapping habitats and nature. Radar and a similar technology, lidar (which uses lasers), allow us to understand the 3D world we live in, measuring the shape of the earth and objects like hedgerows.

The optical satellites work very well in helping understand the current status of field or farm and seeing how it changes or compares to the past. For grassland production, which is so important in Ireland, regular, in some cases daily observations by satellites mean we can see the grass grow. Using these images with artificial intelligence means we very reliably measure, from space the amount of grass growing in a field and the whole farm. Satellites are also very useful in seeing variation across the country at anyone time to understand the carried impacts of droughts or floods.

Across a wider landscape we can use the historical record of satellite images (going back to the 1970’s) to see how current conditions compare to normal or we can see how some land-use, like forestry, have changed over time.

One problem with using Earth Observation in Ireland is that it’s so often cloudy. Radar satellites overcome this because they can “see” through clouds but the information they relay can be difficult to interpret and be a little limited. For Ireland and Irish agriculture drones offer the solution. Modern drone are easy to fly, easy to program and can carry many different types of sensors. Drones are used to map farms to help farm planning and design, monitor crop and grass growth (especially to detect disease within a field) and they can even see underground, using thermal cameras.

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Evaluating the Suitability of the Land Parcel Identification System for Assessing Land Use and Land Use Change-Related Greenhouse Gas Emission – EPA Research Report 309

EPA published the LPIS related report authored by our colleague Dr Jesko Zimmermann, Teagasc and Professor Jane Stout, TCD

The project: Identification of grassland management and land-use change using high resolution spatial databases,  is an assessment of the Land Parcel Identification System (LPIS), a high spatial and temporal resolution database developed as part of the European Union Common Agricultural Policy to assist farmers and authorities with agricultural subsidies, for the needs of national greenhouse gas reporting, including its potential strengths and limitations. The study demonstrated that, in general, reporting on arable crops showed both a high spatial and a high thematic resolution. With regard to grasslands, however, thematic accuracy was limited as different grassland categories were exchangeable (e.g. “grass” and “permanent grassland”). In addition, although more detailed categories existed (e.g. “rough grazing”), these were not always reported. Similarly, forestry was reported in the LPIS but reporting was not comprehensive.