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GIS Monthly Maps 2020

The Teagasc 'Map of the Month' is a series of mapscreated by the spatial analysis unit. They use a number of scouces to create these maps including the Ordnance Survey of Ireland, the Central Statistics Office, and Earth observation satellites, and remote sensing technologies. In 2020 11 maps were created these can be viewed below.  (All in PDF format)  

January - Landuse Change in Co. Roscommon 1995-2017

Cartographers: Dr Stuart Green

View Map: Landuse Change in Co. Roscommon 1995-2017

The first map of 2020 provides a snapshot of changes in landuse in County Roscommon

As part of the EPA funded SOLUM project we have been looking at landuse change across the country. One output is a map of landuse change in County Roscommon. We recently reviewed original sample points from a 1995 study, both a regular grid sample and other points focused on individual
landcover classes (total of 1024 points.) looking for changes in grassland management, as well as collecting data on points that became forested since 1995. We also found one site that has become a tilled cropland since 1995, and unusually a new waterway - a marina built on the Shannon.

In total 23% of the gridded samples showed some change. In the south of the county there was little change. Across the middle of the county, south of the drumlin belts there is evidence of grasslands being more intensely managed. Across the north of the county, with peat soils being common, there is a larger percentage of new forestry and woodland. Whilst some grassland sites in this region are showing greater degrees of management it is more common for changing grassland sites to show less management now than in 1995 (whilst remembering 77% of sites haven't changed).

February - Arable farming then and now

Cartographers: Dr Jesko Zimmermann & Dr Rob O'Hara

View MapArable farming then and now

The map shows the centre point of Irish townlands with a reference to arable land in their names, and compares them to the current distribution of arable land (defined as the land at least once used for arable agriculture from 2000 to 2016). The names of places can provide valuable insights into local history and in this case can provide a proxy for past agricultural practices.

In this case we identified a number of Irish words that indicate arable practices and filtered the Irish language townland names for those containing said words. We used P. W. Joyce's book Irish Names of Places as a guideline for such references. In this analysis we limited the search to the Irish names of townlands, as the Anglicisation often obscures the Irish origins, with single anglicised forms originating from multiple words. We made an exception for Northern Ireland, where no Irish townland names were easily available. To avoid false positives we only included unambiguous anglicised terms (e.g. Cappa or Mullen in varying versions).

The current extent of cropland was derived from the Land Parcel Identification System and is displayed as the share of land under crop for at least one year between 200 and 2016 within a 2.5 x 2.5 km cell.

The map shows a number of interesting patterns. The majority of townlands names referring to arable land occur west of Ireland, while occurrence in the current extent of cropland is relatively low. This does not indicate a shift in the extent of cropland but rather a different naming culture in the east of Ireland which was under Norman control. The Irish 'gort' (a tilled field) being the most common reference occurring evenly in Connacht and Munster. It should, however, be note that the term ‘gort’ is subject to some ambiguity when looking at different sources. While P.W. Joyce translates it as ‘tilled field’, the townland register (https://www.logainm.ie/) translates ‘gort’ simply as ‘field’. ‘Ceapach’ (tilled ground) is less common but shows a similar distribution across the west. ‘Tamhnach’ occurs mainly in Connacht. Other references are very rare, except for ‘muileann’ which refers to a mill which is the most common reference in Northern Ireland. This is likely the result of excluding references with ambiguous anglicisations.

March - Rural Isolation - Other houses within 2km of each home.

Cartographers: Dr Stuart Green

View Map: March 2020 Map of the Month

As we get used to the idea of social distancing this month’s map reminds us that in many parts of the country physical distance is an issue. The Map is intended to show rural isolation, using as an indicator a 2km circle around every home in the country (our new personal geographic boundary). In this 2km boundary we counted the number of other houses (and did this for every house in the country).

A 2km radius circle is quite a bit area- 1200Ha, and within cities there are 1000's of residences in this area but in parts of rural Ireland the number of dwellings can get very small and the map highlights those area where each household has fewer than 20 neighbours. In a city or town, even when staying at home, there are neighbours next door, people walking past the window- someone to talk too over the garden fence. In these remote areas, being at home means real isolation.

These areas are remote, not only from neighbouring houses but all kinds of services and therefore pose particular issues adhering to the new social distancing/cocooning rules.


April - Covid - 19: Distribution of Populations with Higher Risks  

Cartographer: Dr David Meredith

View map: Covid-19: Distribution of Populations with Higher Risks


With the ongoing success of measures to reduce the spread of the Covid-19 virus, attention is now being given to re-opening businesses, schools and social services. At the same time, the World Health Organisation has highlighted that this virus will be with us for some time. Studies of previous pandemics suggest that we may face a number of ‘waves’ of infection. It is apparent from current trends that older age groups, people with poor health or who are already ill and those living in some types of communal establishments are particularly susceptible to Covid-19. This highlights one of the key challenges of managing future waves of infection, i.e. how to limit the spread within the overall population and, particularly, to these vulnerable populations. Whilst it is to be expected that strict controls in place in some communal residential settings will be effective in helping to protect their residents, the challenge of restricting spread in the general community will persist for some time to come.


We show the result of an analysis that assessed the proportion of the population of each Small Area that was over 64 years of age, unable to work due to sickness or disability, reported their health status to be less than ‘Good’ and  persons living in communal accommodation on the night of the Census (2016). For each indicator, we ranked Small Areas i.e. from 1 (lowest indicator value) to 18,641 (highest). We then combined these ranks to give an overall score. The classification of Small Areas into the different categories is based on a decile distribution, i.e. the 10% of Small Areas with the lowest scores are shown in Yellow whilst the 90%+ category shows the 10% of areas with the highest scores.

The map points to the concentration of populations that are particularly vulnerable to Covid-19 and highlights those areas that could be severely impacted by an infection if it were to spread within their populations. The broad pattern depicted on the map highlights rural areas outside of the major commuter zones and towns or inner city areas as recording higher vulnerability scores. This reflects the distribution of the population over 65 years of age, and concentrations of those unable to work and those with poor health.


The data used in this analysis are produced by the Central Statistics Office from the Census of Population (2016). The statistical data are drawn from the Small Area Population Statistics and are available here: SAPS2016_SA2017.csv.The spatial data are also available through the CSO here: https://data.gov.ie/dataset/small-areas-generalised-20m-osi-national-statistical-boundaries-2015.

There are 18,641 Small Areas and the CSO publishes summary data from the Census of Population for each of these. These indicators used in this analysis were selected as they, very broadly, reflect the profile of particularly vulnerable groups to Covid-19. No attempt was made to weight the relative importance of the indicators. A key limitation of this analysis is that it does not attempt to estimate the risk of infection by weighting the vulnerability score for each area by taking into consideration the population density of each area or the level of interaction between areas. Analysis is on-going to take these issues into consideration.


This analysis is an extension of Teagasc’ Rural Health Research programme undertaken by Teagasc with additional support from the Department of Agriculture, Food and the Marine. The focus of this programme is on supporting improved farm safety and the wellbeing of farmers and farm families.  

May - Distribution of Cultivated Peats

Cartographer: Dr Stuart Green

View map: Distribution of Cultivated Peats

In this months map we look at the distribution of cultivated peat soils. Whilst peat-lands which include raised bogs and blanket bogs and fens,  cover upwards of a fifth of the country, their use can be limited because they are generally very wet in the un-drained state. The extraction of peat for fuel, either mechanically or by hand is a principal use of these soils but this is drawing to a close as the climate impacts of draining and burning peats are now clear. In their natural state peats are natural sinks of carbon and are the largest reservoir of stored carbon in the Irish landscape. The remaining bogs are seen as vital parts of Irish biodiversity and so are no longer planted with forestry which has traditionally been a significant land use of peat soils. The blanket bogs cover much of the commonage areas in the country and are thus used for (and indeed maintained by) rough grazing of sheep.

However not all peat soils are bogs, a significant portion of peat soils have been used and adapted over the decades through drainage and fertilization for grass (and to a much smaller extent cereal) production. These grasslands over peats are no longer bogs in an environmental sense but they are primarily organic soils and would revert back to a natural vegetative state were it not for continuous management through drainage, grazing and fertilizing/liming.

Irish research has shown that this sort of management of organic soils, especially drainage, turns these natural stores of carbon into emitters of carbon so in order to get the correct accounts of emissions and sequestrations of land use it’s vital that we know where these cultivated organic peat soils are.

To map the distribution we used the peat soils in Teagasc Indicative Soil map, along with field boundaries mapped in the OSI National Digital Map database and parcel information in LPIS. This allowed us to identify fields, currently farmed, that overlay peat soils. The image here gives an example of 3 bare fields with a mix of soils with the darker peat soils to the right.

We excluded non-farmed bogs, commonage areas, forested areas and worked bogs (peats extracted for fuel).

To get a sense of how heavily managed and thus how productive the fields are we looked at satellite imagery for 2018 (a drought year, but one in which peat soils and poorly drained mineral soils performed well because of their moisture content). Using a method of analysis called vegetation indexes we can use the satellites to score fields between 0 and 1, with a higher score meaning more biomass production. We banded the fields into 3 bins; Low, Medium and High based on these indexes to give a crude indication of the level of production.

Whilst the analysis is done at field scale, the map is presented at 1km square scale- each 1km cell is coded for the percentage of cultivated peats in the cell and whether they are mainly high, medium or low production. We have omitted cells with less than 30% cultivated peats to make the distribution clearer and not to give a false impression of the true areal extent of cultivated peats.

This analysis suggests approximately 6% of the country or 420,000Ha is made up of cultivated peats across a wide range of farming intensities (though predominately low intensity farming).

June - Drought stress following low rainfall levels in late spring 2020

Cartographer: Dr Jesko Zimmermann

View map: Drought stress following low rainfall levels in late spring 2020

Drought has a severe impact on Irish agriculture as Irish farming systems are reliant upon regular rainfall. The dry summer of 2018 (in combination with a cold spring) for example has led to a severe shortage in animal feed due to reduced grass growth and the depletion of fodder stocks.

It is therefore not surprising that the current dry spell is causing concern within the farming community. Since early March 2020 parts of the country have seen very little rainfall with rainfall rates up to 90 % lower than the 1980 to 2010 reference period. This is impacting grass growth as well as other farming enterprises such as horticulture. As in 2018, the impact of the drought is not equally distributed across Ireland with many factors impacting the severity of the drought. These include the soil type, where heavy soils retain water much longer under dry conditions, while lighter sandy soils will dry quicker. Furthermore, weather patterns are not equal with some parts of the country (such as the south west) showing more normal rainfall levels. Finally, management also determines how much an area is impacted by low rainfall levels. Irrigation will offset the impacts, while drainage may worsen the drought effects.

In this map we use a proxy for drought stress, specifically the Normalised Difference Moisture Index (NDMI), to highlight the spatial variability in severity of the drought. The NDMI can be derived from multispectral satellites (in this case data the NASA MODIS sensor) and uses the reflectance from the near infrared and the short-wave infrared bands to estimate plant moisture content. A lower value indicates a lower plant moisture content and therefore potential drought stress. For easier interpretation, we related the average NDMI from May 2020 to the average May NDMI for the previous ten years (2009 to 2019). A negative value means the current NDMI was lower (less water in plants), and a positive values mean more water in plants.

Overall the NDMI was marginally lower in 2020. The spatial distribution, however, is of more interest showing a large area in the east and north-east, as well as several pockets in the west with drier conditions than usual. The analysis also shows areas with higher than average NDMI which are mainly situated in the upland areas and generally the south-west. The general south-west/north east gradient reflects the overall rainfall patterns in the last months. The pockets of drier and wetter conditions are likely linked to soil conditions (wetter areas occur especially in uplands and peaty areas, while drier areas in the west such as in Co. Limerick coincide with well drained soils.)

An animation of the underlying data can be found on the Earth Observation blog (https://earthobservation.wordpress.com/2020/06/11/ndmi-in-the-past-ten-years/). It shows the NDMI averages for each May from 2009 to 2020. 


July - An indicative history of green cover since the 1980's

Cartographer: Dr Stuart Green

View Map: An indicative history of green cover since the 1980's

The management history of any parcel of land is an important piece of information. Knowing the history can help us understand issues around greenhouse gas accounting, bio-diversity, soil health and many more.

This month we present an interim output from a 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 of whether vegetation is present or not.

Because of cloud cover each field is only imaged perhaps 4 times a year, so the analysis can only give an indication of how often the parcel has had low vegetation cover.

The archive used had 317 Images over 35 years up to 2019, each parcel had on average 210 observations. Each image was used to populate the Prime2 vegetation polygons (from 2017), with the average NDVI* value for each pixel in the image. The archive is quality controlled for atmospheric and cloud issues, so only good quality pixels are used. A cut-off value for bare fields was established, referenced against total image means. The number of time the reference value was met was counted for each field and this is the number of times the field has appeared bare in our satellite record.

The dark green fields in the map have been covered with vegetation almost permanently in this record, with fewer than 2 low observations. They would potentially be sites of botanical interest as they may less intensely managed than other grasslands in the area. The pink fields have been apparently bare in more than 10% of the observations. These fields are mostly tillage but some are intensely managed grassland. A few of the pink areas around Limerick city are due building in the city recently and some may be capturing a record of flood events. The rest of the fields have a range of counts of being bare- we chose to only highlight the two extremes in the map.

This is a partial, interim output- the full data set, with complete time-signatures of bare soil should be available at the end of the year.

*NDVI or Normalised Difference Vegetation Index, is a long established method to monitor vegetation cover from satellites. It relies on the fact that vegetation deeply absorbs red light but strongly reflects the very near infra-red (a wavelength slightly longer than visible red light). The ratio of the two values is the vegetation index and the higher the number, up to 1, the greater the amount of vegetation.



August - Relative sectorial income from agriculture in Electoral Districts

Cartographer: Dr Jesko Zimmermann and Dr Stuart Green

View Map: Relative sectorial income from agriculture in Electoral Districts

The Agri-Food sector is an important sector in the Irish economy. According to the Department of Agriculture, Food and the Marine it accounted for 7.5 % of the ModifiedGross National Income, 7.7 % of employment, and 10 % of total exports (all values for 2018). The national median share of income from Agriculture, Forestry and Fishing was reported at 7.6 % for 2016 (CSO). However, the share of the sector on incomes varies spatially.

The Central Statistics Office (CSO) published spatially explicit data on incomes from 2016 which we use to gain a better understanding of the spatial distribution of farmer incomes, and the importance of the agricultural sector in specific regions. Specifically we use two datasets:

  • Median income by detailed occupation per county. From this dataset we extracted the median income of farmers. Even though the agricultural sector includes other occupations than farmers, it was not possible to summarise incomes across all occupations as this would require knowledge of the number of people employed in each occupation. The table below compares the median farm income with the highest and lowest incomes in each county.
  • Proportion of earned income sectors by Electoral Division. From this we extracted the Agriculture, Forestry and Fishery income proportions and ranked it compared to the other sectors described (rank 1: largest share, rank 7: smallest share). The other sectors are; Construction; Financial, Real Estate, Administrative and Services; ICT, Scientific and Recreation; Industry; Public Service, Education and Health; and Wholesale, Transport and Accommodation.

While the these two datasets are not fully comparable due to their differing spatial scales and different groups of which the incomes/share of income were calculated they still provide an interesting overview of the distribution of incomes, and the importance of agriculture in Ireland.

As the map shows, median income follows a West-East and North-South slope, with the east and the south showing the highest median farmer incomes. This distribution is common in agriculture as intensive farming is more prevalent in the south and east while the north and west are generally more extensively farmed. Waterford has the highest median farm income in Leitrim the lowest.

The share of the Agriculture, Forestry and Fishery sectors shows a more complex distribution. The highest ranking areas show up in the midlands, as well as in parts of the south and west. This indicates that the suitability of land for more intensive farming does not always drive the importance of agriculture compared to other sectors, and that the economic importance of agriculture in an area does not necessarily show in farmers’ incomes. 

The table below shows the median farmers' income in each county compared to the lowest and highst median incomes, and respective occupations. 


Median Farm salary

Highest Earner

Lowest Earner



Medical practitioners


Sports and leisure assistants




Medical practitioners


Animal care services occupations n.e.c.




Chief executives




Cork City


Information technology and telecommunications directors


Care escorts


Cork County


Information technology and telecommunications directors


School midday and crossing patrol occupations




Medical practitioners


Elected officers and representatives


Dublin City


Civil and public service Assistant Secretary and above and senior officials


School midday and crossing patrol occupations


Dún Laoghaire-Rathdown


Civil and public service Assistant Secretary and above and senior officials


School midday and crossing patrol occupations




Aircraft pilots and flight engineers (incl. Air traffic controllers)


School midday and crossing patrol occupations


South Dublin


Chief executives


School midday and crossing patrol occupations


Galway City


Dental practitioners


School midday and crossing patrol occupations


Galway County


Chief executives


Teaching assistants




Chief executives


Vehicle valeters and cleaners




Chief executives


Teaching assistants




Medical practitioners


Cleaners and domestics




Medical practitioners


Animal care services occupations n.e.c.




Medical practitioners






Medical practitioners


School midday and crossing patrol occupations




Medical practitioners


Other elementary services occupations n.e.c.




Medical practitioners


School midday and crossing patrol occupations




Medical practitioners


Teaching assistants




Chief executives


School midday and crossing patrol occupations




Medical practitioners


Gardeners and landscape gardeners




Medical practitioners


Cleaners and domestics




Medical practitioners






Dental practitioners




South Dublin


Chief executives


School midday and crossing patrol occupations




Medical practitioners


Teaching assistants




Medical practitioners


Taxi and cab drivers and chauffeurs




Dental practitioners


Cleaners and domestics




Medical practitioners


Sewing machinists




Medical practitioners


School midday and crossing patrol occupations



September - The Islands of Ireland

Cartographer: Dr Stuart Green

View map: The Islands of Ireland

Here we have created a map of the coastal and inland islands of the island of Ireland. We've chosen to show the islands without the "mainland",an imaginary Ireland Archipelago.

Most of the 'islands' would be rightly called islets or skerries being in many cases just rock so we only map islands with an area greater the 0.1Ha but that still leaves 6800 islands mapped (the full database has 32,000 "islands"). The coast of Ireland is 1,450 km long, but the combined coastline of all the islands in the database is, 4,800km.

  • The largest Island is Achill in Co. Mayo.
  • The Island furthest from the sea is Big Island in Lough Sweedy, in county Westmeath.
  • The largest inland Island is Inishmore in the Fermanagh Lakelands.

Map of the Month October

Bioeconomy Ireland Week Map Series

In October we did not produce a single monthly map but a series of maps as part of the Bioeconomy Ireland week. These maps were created by the Department of Agrifood Business and Spatial Analsyis in collaboration with Department of Agriculture, Food and Marine (DAFM), IT Tralee, University College Dublin, Trinity College Dublin, Bord Iascaigh Mhara, the Irish Bioeconomy Foundation, and BiOrbic. 

November - A peak through the clouds: Seeing Ireland with Radar

Cartographer: Dr Jesko Zimmermann

View map: A peak through the clouds: Seeing Ireland with Radar

Satellite remote sensing can be distinguished in two main groups. Active and passive remote sensing. Passive remote sensing records the radiation (e.g. visible light and infrared) reflected from a body. All optical satellites work on that principle, recording a determined range of the electromagnetic spectrum, usually the reflection of the incoming solar radiation. Looking at the distinct bands (i.e. the blue, green, red, or infrared) of the spectrum can tell us a lot about the Earth’s surface. Absorption of red and reflection of infrared radiation tells us about plant health for example. In the recent years, a large amount of optical imagery from various satellites has become available. Especially NASA and ESA have undertaken massive efforts to make their imagery available to the general public, leading to a surge in remote sensing research.  

The problem with optical images is, that the radiation does not penetrate cloud cover. Especially in Ireland this leads to serious limitations in the use of remote sensing imagery. Beside looking at single images, observing changes over time can tell us a lot about the land. The changes over the year can distinguish coniferous from broad leave forest. They can identify tilled fields and tell us about biomass production in grasslands. To assess these changes comprehensive time series of data are required. If only a few cloud free acquisitions are available, time series can be rendered less useful to extract all information on the land.

One way around this is the use of active radar satellites. Active satellites do not rely on reflected radiation but send their own microwave signal and record the reflection. Microwaves have a much longer wavelength than visible light and can easily penetrate clouds, while requiring little energy to emit. The process is called RAdio Detection and Ranging (RADAR). Therefore using them can allow us to investigate the Earth’s surface even on cloudy days.

ESA’s Sentinel 1 mission uses a process called Synthetic Aperture Radar (SAR) to record the Earth’s surface with a revisit rate of every 6 days allowing for comprehensive time series with no obstruction from could. This month’s map shows a composite of all images acquired and published for November 2020 so far showing a full coverage radar image. It also highlights one of the main issues of radar imagery. Unlike optical images, radar only records back scatter which is mainly determined by the surface roughness and its electrical properties. This means the information provided by radar is limited compared to multi- or hyperspectral optical images.