GIS Monthly Maps 2018
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 2018 12 maps were created these can be viewed below. (All in PDF format)
- Average field sizes in Ireland, calculated per townland
- Grass cover for Moorepark 20th February 2018
- Probability of finding a stonewall in Ireland
- New automatic methods to map farmland using satellites
- Fragmented farmscapes: total length (km) of shared farm boundaries per hectare by townland (RoI)
- Ireland on the 24th June, 2018 as pictured from space
- Monitoring weather anomalies in the first half of 2018
- Coverage of spatial datasets held by the Teagasc Agri-Food Business and Spatial Analysis Department
- Freshwaters of Ireland
- The true colours of Irish soils
- Ireland 2016: Mapping long commutes
- Poor fodder production in 2018 compared to average
Cartographer: Dr. Jesko Zimmermann
This map uses OSI field boundary data and townland data. We calculated the average field size in townlands but we excluded those townlands that are not dominated by enclosed farm lands. These include uplands and commonage areas, raised bogs, forested areas, lakes and of course built up areas.
The map, at first glance, seems to tell a familiar story, with larger fields in the south and east, where farms are bigger and more intensive and smaller fields (in shades of green) in the north, where farms are smaller and less productive. But a closer look reveals many local details and we can see the effects of landscape, history and soil on the range of field sizes across the country and within and between neighbouring counties.
Data sets used: OSI Prime 2, EEA CORINE Land-cover 2012
© Ordnance Survey Ireland. All rights reserved. Licence number DOF 11/1
Cartographer: Richa Marwaha, Walsh Fellow
Map: Grass cover for Moorepark 20th February 2018 (pdf)
February’s Map of the Month is an output from our national grass mapping projects. This project “National farm scale estimates of grass yield using remote sensing” is developing a national model to estimate current biomass, at field scale for all grasslands in Ireland. It follows on from an earlier project that used satellite images to accurately measure grass biomass at a number of trial sites using machine learning methods.
This map of Moorepark from the 20th of February uses vegetation indices to show the relative range of grass covers between paddocks. Vegetation indices are derived from measurements of red and near infra-red light reflected from the grass in the paddocks and captured by the satellite. A value close to zero indicates no grass (bare soil), while higher values, up to 1, indicate increasing grass covers/biomass.
This map shows how new satellite data from ESA allows us to look at grass cover at paddock scale up to every 5 days. It’s this data (temporal images from Sentinel-2 and Landsat-8) that allows for mapping of biomass and observations of management.
Data sets used: ESA Sentinel 2
Cartographer: Stuart Green
March’s Map of the Month is an output from our farm boundary projects.
Knowing the location, extent and type of farm boundary is important in many areas of study; landscape characterisation, biodiversity, surface water studies and many more.
Stonewalls are common in parts of Ireland and predominate over hedgerows in areas of limestone bedrock, where loose pieces of rock are found in the soil. During enclosures in earlier centuries, as these rocks were cleared from the soil they were used to form field boundaries. In other part of the country ditches and hedges are used.
Detecting stonewalls remotely is not easy, especially as in many cases they now have hedges growing alongside, or on top of. Ground observations are needed and the European Environmental Agency carries out point and transects surveys, LUCAS, of landuse and landcover every 3 years which give +5000 field boundary observations in Ireland. If we combine these with the geology map and field boundary location map we can build a probability map, interpolating between the points, which shows the percentage likelihood that a field boundary is a stone wall and not a hedgerow.
We will be using this layer in a number of ecosystem service and landscape type projects in the coming months.
Cartographer: Shafique Matin
April's map of the month illustrates how we move from pixels in a satellite image to automatically clustering pixles into discrete "objects" and being able to label those objects, its the differnce between labelling a pixel "grass" and identifying a "Improved grassland paddock" and do this routinely and automatically.
Object-based segmentation results in a quick and accurate delineation of surface features based on the four basic geometric parameters, i.e., tone, shape, size, and compactness. It takes the advantage of incorporating spatial context and mutual relationships between different objects and reduces the image noise to group contiguous pixels with similar features into one object.
Object-oriented classification is a very useful technique to classify farmland landscape with different stages of intensification due to its ability to differentiate considering its colour, texture, and shape. It also provides the opportunity to implement a ruleset to merge hedgerows, tree shadow, etc to the associated field parcels.
In a high-resolution satellite image, an intensified farmland appears bright green in colour, with a defined rectilinear boundary with sharp edges and a smooth texture. Besides, a non-intensified (semi-natural) farmland appears brownish in colour, rough-textured and often associated with rivers and forests.
The current map shows four stages of farmland mapping using object-oriented classification on high-resolution SPOT 7 satellite image of 1.5 m resolution. The radiometrically corrected image was used for a quick multiresolution segmentation that classified the image automatically into seven unsupervised classes. Finally, samples were collected for pre-defined classes which further classified the map into intensified and non-intensified farmlands and four other land cover classes.
May - Fragmented farmscapes: total length (km) of shared farm boundaries per hectare by townland (RoI)
Cartographer: Stuart Green
When we talk of “farm fragmentation” we mean farmers working land that is not all in one parcel, but instead in parcels spread out over the local area, interwoven with parcels of land owned by other farmers.
Fragmentation is largely seen as a negative impact of farm efficiency and productivity. As farmers have to travel distances between parcels it increases operating costs. It inhibits improvement and development of land and the adoption of some new farm technologies. Because of this fragmented farm areas are often less improved with a higher occurrence of High Nature Value farmlands.
Farm landscapes can be fragmented for many reasons, history, demographic, topography of all three. In this month’s map, we’ve used a national farm boundary database created under the Teagasc lead, EPA funded Cosaint Project to measure the length of farm boundaries shared by two farmers. In more fragmented complex landscapes the length of shared boundary per hectare increases (imagine a chess board- if every square was owned by the same farmer, the boundary is the outside of the board, but if every square is owned by a different farmer then the shared boundary is the sum of the boundaries of all the squares on the board).
The Aran islands have the most fragmented farm landscape, whilst counties like Monahan, Roscommon, Galway and Mayo have a large share of fragmented farms. Upland commonage areas are shared but have no boundaries demarking the shared areas so come out as low. For the boundaries themselves it’s interesting to note hedgerows marking property boundaries are often older than internal field boundaries.
Data sets used: OSI Prime 2
© Ordnance Survey Ireland. All rights reserved. Licence number DOF 11/1
Cartographer: Stuart Green
As the forcasters are predicting a prolonged warm dry spell, June's Map of the Month is a rare cloud free image of Ireland captured by NASA's Terra Satellite on the 24th of June.
Whilst the public may enjoy a heat wave, it's a problem for farmers as the absence of rain causes grass to stop growing and hinders the developement of crops. This is particulalry worrying this year as it comes on the back of a very late spring, meaning grass growth was delayed (because the temperature was too low) and fodder stores exhausted. This dry spell means farmers could struggle to harvest enough fodder for this winter.
Cartographer: Simone Falzoi
Using meteorological indicators and satellites we can monitor the progress of the current drought. Monitoring conditions across the country shows us that, while the drought affects the whole country, its impact varies from place to place.
The drought is first seen in the rainfall record and we compare rainfall totals in a month with average values and see that while January was very wet, the following months had low rainfall. We can model the impact of reduced rainfall using one of many weather indices. The index shown in row 2 is the Standardised Precipitation Evapotranspiration Index (SPEI). SPEI looks at long term rainfall at different timescales and compares with expected demand as indicated by normal evapotranspiration – giving a single number indicating conditions. The images show that this year, up to June, every month was either drier or wetter than normal and that June was ‘severely dry’.
NASA’s MODIS satellite images can measure the impact by measuring the greenness of the country, expressed as an ‘Enhanced Vegetation Index’ - ranging from 1 being very lush green pasture to 0 being completely barren. By comparing the index each month with the average, we can see how the landscape is coping. And now we can see some regional variability with vegetation growth well below normal in the south and east (well-drained soils) in June but above normal in the north and west (poorly-drained soils).
This is because in these locations the heavy soils in the north and west are at an advantage in that they hold moisture for much longer allowing plants to take advantage of the higher temperatures. However, these soils can’t hold out continually and, as the drought progresses, we shall see growth impacted in these regions too.
August - Coverage of spatial datasets held by the Teagasc Agri-Food Business and Spatial Analysis Department
Cartographer: Jesko Zimmermann
In the past 20 years the spatial analysis team in the Department of Agri-Food Business and Spatial Analysis have created or acquired a multitude of data. Especially interesting are the spatial data. In a recent exercise metadata (a file that describes the properties and history of another file- “data about data”) for around 500000 datasets were catalogued. In this map we used the information on the extent of each dataset to map the number of datasets coincident with each Electoral District (ED) in the Republic of Ireland.
The map shows the dominance of raster imagery (pixels on an image, like a digital photograph) all over Ireland with each ED covered by between 5 and 12 times more raster images than vector datasets (a more traditional form of spatial data, where each feature, such as rivers or roads are represented by distinct objects). Particular striking features are three bands crossing Ireland in an east-west line from Clare to Wexford, Mayo to Louth and in Donegal. These lines are caused by overlaps of imagery from the NASA Landsat satellite. The inset map shows the extent of Landsat image acquisitions (tracking the orbit of the satellite) highlighting the areas of overlap.
Despite the total number of datasets catalogued, the number of datasets actually overlapping with individual EDs are much lower, ranging from 22942 (Béal an Mhuirthead , Co. Mayo) to 41218 (Rathfeigh, Co. Meath). While the number of datasets is still large it also needs to be considered that many satellite images may be cloudy and can therefore not be used for further analysis.
The map is a stylised 3D representation of the traditional ED boundary map, the height of the ED polygon represents the total number of mapped data sets coincident with the ED and the colour represents the ratio of satellite images to maps.
Cartographer: Stuart Green
September Map of the Month is an output from the EPA funded COSAINT project looking at access to watercourses by cattle. Post Doc Paul Kilgarriff, in order to make estimates of the national cost fencing of watercourses produced a new stream and river dataset from the OSI Prime2 data to have common geometry with the national farm database he also produced.
We present the Fresh Waters of Ireland in this map with no other cartographic feature- no boundaries, roads, cities or marker of any kind. It really illustrates how the freshwater rivers, streams and lakes are vital arteries of the country. To complete the map we’ve also used some open source mapping to give similar looking information in NI (though without the same geometric properties) to create the Freshwaters of Ireland.
This map really rewards clicking on the PDF and zooming in to see the detail.
Cartographer: Gabriela M. Afrasinei, Jesko Zimmermann
This map shows the true colour of 261 soil samples measured by a spectrometer “translated” into natural colours as seen by the human eye.
The Statistical Office of the European Union (Eurostat) started the Land Use/Land Cover Area Frame Survey LUCAS (https://ec.europa.eu/eurostat/web/lucas) survey in 2006 and it is based on the field assessment of land use and land cover parameters that are deemed relevant for agricultural policy. This survey was complemented with a soil sampling in 2009, with the aim to produce the first coherent pan-European physical and chemical topsoil database, which can serve as baseline information for an EU wide soil monitoring.
These topsoil samples (top 20 cm) were taken and measured as part of the 2009 LUCAS soil campaign. Within the LUCAS laboratory analysis, diffuse high resolution reflectance spectra were collected for all air-dried and sieved (2 mm) samples using the XDS Rapid Content Analyzer spectroscope (FOSS NIRSystems Inc., Laurel, MD). This measured a continuous reflectance spectrum from 400 to 2500 nm (one thousand-millionth of a metre) covering the visible (Vis) and near infrared (NIR) portion of the EMS with 0.5 nm spectral resolution, offering 4200 measured wavelengths.
To convert these spectra into colour values we could print they were subset to the visible wavelength range 400 to 700 nm needed for processing in RStudio environment, where red corresponds to the EM wavelength range of 620 to 680 nm, green - 530 to 570 nm and blue - 450 to 500 nm. The data processing was carried out by Dr Jesko Zimmermann using R package pavo 1.0. The CIE (International Commission on Illumination) colour spaces are a suite of models of human colour vision and perception. Using colour matching functions, the reflectance values of each soil sample for each range of colour were processed to obtain tristimulus values and create the visual
Cartographer: Dr David Meredith
The November Map of the Month is an output from research undertaken using the 2016 Census of Population data assessing the extent to which rural communities are integrated with or impacted by larger urban centres. This is a significant issue as most of the employment opportunities associated with contemporary economic development are concentrated in these places.
The map depicts the distribution of the population commuting more than 45 minutes to work, school or college. The 45 minute threshold has long been recognised amongst researchers internationally as significant with commutes under this limit considered 'bearable' whilst those in excess of this threshold are viewed as socially unsustainable over the longer term, i.e. these people will, if possible, change job or residential location. Whilst it is likely that these findings apply to the Irish population it should be recognised that there are a variety of issues that deter many people from doing so, e.g. the high cost of housing in larger urban centres, the lack of suitable employment opportunities in rural areas, family ties to particular localities, or perceptions regarding the relative quality of life in rural areas.
Whilst the map highlights the extensive rural areas that are influenced by the larger cities, i.e. Dublin, Cork, Limerick and Galway it also highlights the significance of places such as Letterkenny, Tralee and Castlebar. It is notable that the southeast region has a number of competing centres, i.e. Waterford City, Kilkenny, Wexford and Clonmel.
At nearly 40% Kiltullagh in Co. Galway has the highest proportion of commuters travelling for 45mins or longer.
Cartographers: Stuart Green & Jesko Zimmermann
The final map of the month for 2018 looks back at the imapct of this years extreme weather.
Satellites can look at green cover using a vegetation index that ranges from 0 (no vegetation) up to 1 - with Irish summer grass having values > 0.8.
By adding up monthly observations across the year we can get an idea of how well grass has grown throughout the season. Here we used NASA modis images produced each month in 2018 and summed them up from Feb to October. We compared them with average Feb-Oct growth for the ten years 2002-2012.
We can see that the eastern agricultural regions produced between 5 and 10% less grass than normal this year - equivalent to about 1 ton/ha less. Nearly the whole country was negatively imapcted, though the poorly drained soils in the north and west fared best. In fact the "natural" areas the uplands of the west and bogs of the midlands actually produced more biomass than ususal this year as they were less constrained by water shortages and benefitted from the extra warmth and sun light.
To make the map easier to understand the information has been averaged at ED level.