CORINE Landcover 2012

Ireland Final Report

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GIO Land Monitoring 2011 – 2013 in the framework of

regulation (EU) No 911/2010

Pan-EU Component

Grant Agreement 3541/B2012/R0-GIO/EEA.55110

Final Report Ireland

Acknowledgements: The project team would like to thank our colleagues in the following

organisations for providing data, expert advice and feedback to the project:

Department of Agriculture, Food and the Marine (DAFM) The Forest Service (part of DAFM)

Coillte National Parks and Wildlife Service

Ordnance Survey Ireland Teagasc

EPA European Environment Agency

Authors:

Kevin Lydon (Compass Informatics) and Gavin Smith (EPA)

November, 2014

ENVIRONMENTAL PROTECTION AGENCY An Ghníomhaireacht um Chaomhnú Comhshaoil

PO Box 3000, Johnstown Castle, Co.Wexford, Ireland Telephone: +353 53 916 0600 Fax: +353 53 916 0699

Email: info@epa.ie Website: www.epa.ie

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Contents Executive Summary .......................................................................................................................... 3

List of Abbreviations ......................................................................................................................... 4

CORINE Classification nomenclature and legend* ........................................................................... 5

1. Background ................................................................................................................................... 6

1.1 Previous CLC projects in Ireland ......................................................................................... 6

1.2 CLC 2012 in Ireland ............................................................................................................. 6

2. Data sources ................................................................................................................................. 7

2.1 Raster data ............................................................................................................................. 7

2.1.1 Image 2006 ...................................................................................................................... 7

2.1.2 Image 2012 ...................................................................................................................... 7

2.1.3 Additional Raster based imagery used ............................................................................ 8

2.2 Vector data ............................................................................................................................. 8

2.2.1 LPIS – The Land Parcel Information System .................................................................... 8

2.2.2 Forestry Data ................................................................................................................... 9

2.2.3 Water bodies data ......................................................................................................... 10

2.2.4 CLC 2006 ........................................................................................................................ 10

3. CORINE Production Methodology .............................................................................................. 11

3.1 CLC 2012 Semi-automated Methodology ............................................................................ 11

3.2 CORINE 2012 Production Process......................................................................................... 11

3.2.1 Pre-production .............................................................................................................. 11

3.2.2 CORINE 2012 Full Production Stage .............................................................................. 16

3.2.2.2 GIS based Integration of outputs from automated processes ................................... 19

3.2.3 Post-Production Stage ................................................................................................... 23

4. Project management and Organisation ..................................................................................... 24

4.1 Work Schedule & Milestones ............................................................................................... 24

4.2 Training and Verification ...................................................................................................... 26

4.2.1 CORINE Technical Team training visit ............................................................................ 26

4.2.2 Remote Verifications of Completed Datasets ............................................................... 26

5. Results & Discussion ................................................................................................................... 31

5.1Results ................................................................................................................................... 31

5.1.1 CLC 2006 Revision .......................................................................................................... 33

5.1.2 CLC Changes 2006 – 2012 .............................................................................................. 35

5.1.3 CLC 2012 ........................................................................................................................ 38

5.2 Discussion of results ............................................................................................................. 40

5.2.1 Revision of CLC 2006 ..................................................................................................... 40

5.2.2 Discussion of change mapping results .......................................................................... 49

5.2.3 CLC 2012 Landcover dataset ......................................................................................... 52

6 Conclusion ................................................................................................................................... 53

7 References ................................................................................................................................... 54

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Executive Summary One of the first initiatives of its kind, the CORINE (Co-ORdinated INformation on the Environment) landcover dataseries was devised as a means of compiling geo-spatial environmental information in a standardised and comparable manner across the European continent. Through the initial 1990 baseline dataset and subsequent change updates, CORINE has become a key data source for informing environmental and planning policy on a national and European level. CORINE is co-ordinated and part-financed on a European level by the European Environment Agency under the Copernicus (formerly GMES) land monitoring programme. In Ireland, the Environmental Protection Agency has been co-ordinating the project since 2000 under its role as the Irish National Focal Point (NFP) and National Reference Centre (NRC) for EIONET and GMES Land monitoring activities. In 2011, the EPA undertook an internal review of the CLC mapping process prior to committing to produce CLC 2012. There were concerns based on the experiences of previous projects that the dataset was not providing value for money in terms of the quality of the dataset and the resources involved in producing it. The internal review proposed a new management structure for the project and devised a new semi-automated production methodology which would take advantage of the improved availability of in-situ datasets, software and expertise to produce CLC 2012 in a more efficient manner whilst also producing a dataset of improved quality. The new production methodology was centered on revising the current CORINE 2006 dataset using in-situ national landuse vector datasets, in particular the Land Parcel Information System (LPIS). The vector data which covered agricultural areas, forestry, water bodies, semi-natural areas and coastlines provided high-resolution, direct-source spatial information on landcover and landuse in Ireland and was used alongside satellite imagery obtained from the European Space Agency to revise the existing 2006 dataset and to identify changes within the 2006-2012 reference period. Once completed, the revised 2006 landcover dataset and the changes were spatially integrated to produce the new CORINE 2012 25 ha landcover dataset. The CORINE 2012 project for Ireland then produced the following three datasets as its deliverables:

1) A revised 25ha CORINE 2006 dataset (CLC 2006_revised) 2) The 2006 - 2012 5ha Change dataset (CLCCh 2006-2012) 3) The Updated CLC 2012 25 ha dataset (CLC 2012)

Under the revision process the amount of arable land nationally was revised downwards from 7.32% to 5.13% of the total national area (% na). The dominant pasture class was revised upwards from 50.53% na to 54.37% na and artificial surfaces were also increased to 2.49% na. Wetlands and forestry were both revised downwards to 15.79% na and 9.26% na respectively. Cyclical changes between arable and pasture on agricultural land and between mature and transitional forest on forested land dominated the 2006 - 2012 change statistics, together accounting for 76% of all change by area (38.45% and 37.63% respectively). Afforestation (planting new forestry on non-forested land) accounted for the biggest landcover ‘type’ change with afforestation on agricultural and peatland together accounting for 12.73% of all change. There was minimal new construction identified but significant completion of existing construction sites, in particular the finishing of the national motorway network which began prior to 2006 and accounting for 2.38% of all changes. The CLC 2012 dataset, produced by integrating the changes mapped in the CLCCh 2012 dataset with the revised CLC 2006 dataset shows that agriculture is still by far the dominant landcover group in the country in 2012, covering 68.13% of the national area. This has reduced slightly from 68.25% in 2006, largely due to afforestation on agriculture land which saw the national coverage of forestry rise from 9.26% to 9.32%. Artificial surfaces also made a slight gain of 0.04% bringing the amount of the country covered by artificial surfaces to 2.49%. Wetlands saw the second biggest loss in percentage of national area (0.04%), mainly through afforestation on peatlands. Wetlands now account for 15.75% of the national area (peatlands account for 14.54% na) whilst water bodies account for 2.14%.

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List of Abbreviations

CLC Corine LandCover

CLC 2006 The 2006 CORINE 25ha landcover dataset

CLC 2006 revised The revised 2006 CORINE 25ha landcover dataset

CLCCh 2006-2012 The 2006-2012 5ha landcover change dataset

CLC 2012 The 2012 CORINE 25ha landcover dataset

COPERNICUS

The European Earth Observation Programme (formerly GMES)

CORINE

Co-ORdinated INformation on the Environment

Coverage 1

Spring imagery set

Coverage 2

Summer image set

CTT

CORINE Technical implementation Team

DRV

Direct Revision

EEA

European Environment Agency

EPA Environment Protection Agency

EIONET

European Environment Information and Observation Network

EO

Earth Observation

EOLI

Earth Observation Link - ESA portal for accessing EO imagery

ESA

European Space Agency

ETB Eastern working block

FS

Forestry Service

GMES

Global Monitoring for Environment and Security

Ha Hectare

Image 2006

Satellite Image set covering reference period of 2006

Image 2012

Satellite Image set covering reference period of 2012

LPIS

Land Parcel Information System

% NA Percentage of the total national area

NPWS

National Parks and Wildlife Service

MMU Minimum mapping unit

MDB Midlands working block

INT

National CORINE implementation Team

OSI

Ordnance Survey Ireland

PCP(s)

Potential Change Polygon(s)

PI

Photographic Interpretation

RC

Real Change

RV

Revision

RVRC

Revision & Real Change

TG 2006

The CORINE 2006 Technical Guidelines

TGA 2012

The 2012 amendment to the TG 2006 document

WB(s) Working Block(s)

WTB Western working block

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CORINE Classification nomenclature and legend*

*A full description of individual classes can be found at: http://sia.eionet.europa.eu/CLC2000/classes

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1. Background

The CORINE (Co-ORdinated INformation on the Environment) dataseries was established in by the European Community (EC) with the first iteration of the dataseries released in 1990. One of the first initiatives of its kind, CORINE was devised as a means of compiling geo-spatial environmental information in a standardised and comparable manner across the European continent. The first dataset in 1990 provided a baseline of the geographical distribution of natural and built environments across Europe. Through this baseline and subsequent updating of changes, CORINE has become a key data source for informing environmental and planning policy on a national and European level. The CORINE programme originally included the then 12 member states of the EC and the programme has expanded over the years to now include 37 countries across the wider European continent. CORINE is now co-ordinated and part-financed by the European Environment Agency under the Copernicus (formerly GMES) land monitoring programme.

1.1 Previous CLC projects in Ireland

Ireland has participated in all three previous releases of the CORINE Land Cover inventory (CLC).The 1990 CORINE project (CLC 1990) for Ireland was undertaken as a co-operative cross-border initiative, covering the whole island of Ireland. The Ordnance Survey of Ireland and the Ordnance Survey of Northern Ireland jointly coordinated the project which began in 1992 and produced the CLC1990 dataset for both the Republic of Ireland and Northern Ireland (O’Sullivan et al. 1990). The second iteration of CORINE was based on the reference year of 2000 (CLC 2000) and was managed by the Environmental Protection Agency (EPA) which was had taken on the role of the Irish National Focal Point (NFP) and National Reference Centre (NRC) for EIONET and GMES Land monitoring activities. The 2000 programme saw the addition of the 5ha CORINE Change database (CLCCh) which greatly complimented the dataseries. In the 2000 programme, the decision was taken in Ireland to map grassland and peatland classes to the higher Level 6 thematic detail. This was done as it was sent that CORINE Level 3 did not sufficiently describe these classes which are critical to understanding Irish landcover. The CORINE 2006 programme (CLC 2006) was again co-ordinated by the EPA. During this exercise significant revision of the CLC2000 dataset was undertaken aswell as the validation of the 2006 Soil Sealing high resolution layer.

1.2 CLC 2012 in Ireland

In 2011, the EPA undertook an internal review of the CLC mapping process prior to committing to produce CLC 2012. There were concerns based on the experiences of previous projects that the dataset was not providing value for money in terms of the quality of the dataset and the resources involved in producing it. The internal review proposed a new management structure for the project and also devised a new semi-automated production methodology which would take advantage of the improved availability of in-situ datasets, software and expertise. These changes to the CORINE project aimed to produce CLC 2012 in a more efficient manner whilst also producing a dataset of improved quality.

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2. Data sources

The data used in this project is described below in terms of raster and vector spatial data.

2.1 Raster data

The raster data used in this project consisted of two separate sets of multi-spectral satellite imagery from the two reference periods of 2006 and 2012. The data was supplied under license agreement by the European Space Agency (ESA). The 2006 imagery (Image 2006) had been stored locally on the EPA’s spatial data network since the CLC 2006 project. Newly acquired 2012 imagery (Image 2012) was downloaded from the ESA’s data repository application ‘Eolisa’. For each reference period, two ‘coverages’ were acquired – one in spring before the growth season and another during the summer growth season – referred to as Coverage 01 and Coverage 02 respectively. However, this was the ideal scenario, in reality cloud coverage provided a major obstacle to the availability of imagery.

2.1.1 Image 2006

There was relatively good imagery availability for Image 2006. Coverage 01 consisted of a combination of 20m SPOT4, SPOT5 and Indian Resource Satellite imagery (IRS-P6-LISSIII). Coverage 01 covered approximately 80% of the national area as can be seen in figure 2.1. There was a near full complement for Coverage 02 which consisted of IRS imagery, with two SPOT image scenes added to fill cloud gaps. Figure 2.2 shows the spatial distribution of Coverage 02 imagery.

Figure 2.1 Image 2006 – Coverage 01 Figure 2.2 Image 2006 – Coverage 02

2.1.2 Image 2012

There was only one usable coverage in the Image 2012 data set. A second coverage was supplied but this was too inconsistent in terms of image type, geographical coverage and date of acquisition to be used in the semi-automated work flow programme. However, due to the utilisation of vector data in the CLC 2012 project this was not a limiting factor and the decision was made to use just one image coverage in 2012 in conjunction with the ancillary vector data. The 2012 imagery then consisted of two sets of IRS 30m imagery with images within the same set captured on the same respective date. This greatly aided the ability to use these images in the semi-automated production process. The spatial distribution of Image 2012 can be seen in figure 2.3. The eastern set of images consisted of two IRS images acquired on the 27/05/2012. The western group of three images was acquired on the 29/04/2011. Due to the late arrival of the summer growing season in both years, the images can be considered to be ‘spring’ imagery as tree foliage etc. was not in full bloom by the end of May. The

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two image sets gave approximately 85tional coverage. Ortho-photography and web-based Bing maps were used to fill the data gaps in the image set.

Figure 2.3 Image 2012

2.1.3 Additional Raster based imagery used

In addition to Image 2006 and Image 2012, the 2005 Ortho-photograph from Ordnance Survey Ireland (OSi) were used as an additional reference layer for the 2006 period and the 2012 Bing maps were used as a reference layer for the 2012 reference period. Bing maps have very good high resolution (2m) imagery of Ireland acquired in 2012 which made them very useful as an independent reference layer throughout the production process.

2.2 Vector data

As will be explained in section 3, the 2012 CORINE landcover project (CLC 2012) for Ireland has been produced using an innovative production process as opposed to the standard CORINE photo-interpretation (PI) methodology. As part of this new approach, a wide range of previously unavailable high resolution vector datasets will be utilised, enabling us to map the landcover classes of CORINE to a higher degree of certainty and accuracy, in particular, the agricultural and forestry classes.

2.2.1 LPIS – The Land Parcel Information System

The Land Parcel Information System (LPIS) is, like CORINE, a pan-European database organised at a national level. It is funded by the European Commission’s Directorate General for Agriculture and Rural Development and is co-ordinated in Ireland by the Department of Agriculture, Food and the Marine (DAFM). It was introduced as a means of managing the allocation of single farm payments and other farming grants. The geo-spatial vector database stores the outline of all registered farm holdings in the country. Each year an annual LPIS dataset is produced from this database which contains individual polygons for all farm holdings against which a grant claim has been made in the reference year, e.g. LPIS 2012. The dataset holds valuable information from a land monitoring perspective as it contains a first-hand account of what the land use/management on the farm holding has been in for the relevant year. An example of the LPIS 2011 dataset is shown in figure 2.4

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below. As a result of improved data sharing agreements between the two government agencies, the DAFM granted the EPA with access to LPIS 2011 in mid-2012 for use in the CLC 2012 project.

Figure 2.4 LPIS and Forest Service data covering the Wexford harbour North slob lands and the NPWS Raven nature reserve.

2.2.2 Forestry Data

As there is no one single dataset for forestry in Ireland, a combination of different data sources was used for forested areas. The data was primarily obtained from the Forest Service in DAFM with an additional dataset being obtained from Coillte.

2.2.2.1 Forest Service Data

Two datasets were obtained from the Forest Service for this project which contained the most up to date forestry data available from their database:*

1) Forestry07 – This dataset is a combination of the FIPS98 dataset plus planting since 1998 to end of 2007. It includes public grant-aided and non-grant aided private forestry’s.

2) Premiums 08-12 – This additional dataset includes all afforestation (planting) from 2008 – 2012. It provides a very valuable data source of afforestation for the CLC change dataset as it is difficult to identify new areas of afforestation using satellite imagery.

*Specific details of the forestry datasets produced by the Forest Service can be found at the following location: http://www.agriculture.gov.ie/forestservice/forestservicegeneralinformation/abouttheforestservice/forestcoverdatasets/

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2.2.2.2 Coillte data

Coillte is a commercial, semi-state forestry company which owns over 445,000ha of land in Ireland (approx. 7%). Coillte maintains a Land Use Type (LUT) GIS database of all its land which contains the associated land usage with planting status for all its land parcels. Although some of Coillte forestry parcels are also present within the FS data above, the overlap is not consistent and so the Coillte data was used as an addition to the Forest Service data.

2.2.3 Water bodies data

Under its obligations to the Water Framework Directive, the EPA has produced and disseminates a number of datasets pertaining to water bodies, river networks and coastlines.* These were incorporated into the project to refine the waterbodies and coastal land boundaries within CORINE.

2.2.4 CLC 2006

The 2006 CORINE dataset was a core data source for the CLC 2012 project. The dataset was used as the initial dataset from which the revised CLC 2006 was produced. The version used was received centrally from the EEA prior to the project start-up and was taken from the seamless pan-European EEA CORINE layer so as to ensure compatibility across the border with Northern Ireland which is part of the UK CORINE dataset.

*EPA water bodies data can be accessed at: http://gis.epa.ie/

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3. CORINE Production Methodology

3.1 CLC 2012 Semi-automated Methodology

In a significant departure from previous CORINE projects, the Irish CLC 2012 inventory was not produced using the standard Photo-Interpretation (PI) method where satellite imagery is visually inspected by an interpreter to identify areas of change or revision. Instead, a semi-automated approach was implemented in which GIS and object-oriented remote sensing techniques were used to produce the three deliverables of the project which are 1) a revised 25 ha CLC 2006 dataset, 2) the 5 ha CORINE 2006-2012 change dataset (CLCCh 2012) and 3) the 25 ha CLC 2012 dataset.

The aim of taking a different methodological approach was two-fold; firstly to reduce the large amount of work and time involved in the PI approach and secondly, it was felt that the quality and accuracy of the CLC database could be greatly enhanced by using ancillary datasets and techniques which were not available during the CLC 2006 inventory programme.

During the 2011 EPA internal CLC review, a pilot study investigated different options for producing CORINE including alternative approaches adopted previously in other countries such as Finland, Sweden and the UK. The pilot trialled different techniques and developed a methodology which was best suited to the specific datasets and software available to the Irish project team. Although the new methodology was a bespoke model developed uniquely for Ireland, it adhered to the spatial parameters set out in the CLC 2006 Technical Guidelines (TG2006) and the CLC 2012 Addendum to the 2006 Technical Guidelines (TGA2012), i.e. the finished datasets should have a minimum polygon width of 100m and a minimum area of 5ha and 25ha for change polygons and landcover polygons respectively.

As this is a novel methodology applied for the first time, certain adjustments and refinements were made during the production process, particularly following feedback from the CORINE Technical Implementation team during the verification stages. These adjustments are detailed in section 4.

3.2 CORINE 2012 Production Process

The CLC 2012 production process for Ireland was broken into three work packages: 1. Pre-production – Data acquisition, generalisation and organisation of working units. 2. Data production – Data revision and change mapping using automated spectral analysis and

manual photo-interpretation 3. Post-production – Data assembly and error checking

3.2.1 Pre-production

There was a pre-production phase which took place in Q4 of 2012, immediately preceding the full production phase which began in January 2013. This stage involved gathering, cleaning and generalising the data in order to make it ready to use in the full production stage.

Data acquisition

The first task was to acquire all the raster and vector data to be used in the production process (see section 2). All raster satellite imagery was acquired directly from ESA. The two main ancillary vector datasets (LPIS & Forestry) were supplied by DAFM with additional dataset obtained from Coillte, National Parks and Wildlife Service (NPWS), Ordnance Survey Ireland (OSi) and the EPA. Permission was sought from the four external agencies to access these datasets during 2012 in advance of the project with delivery of all data complete by Q4 2012, prior to full-production.

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Vector Data Generalisation

All ancillary vector datasets were obtained as ESRI shapefiles, originally derived from hand-digitisation of parcel boundaries therefore having a very high spatial resolution. Their attribute information was also highly detailed containing information on specific land use, crop type, tree species, etc. This level of detail was too detailed for direct comparison and integration with CORINE and so the data needed to be generalised, both spatially and thematically, to make them ‘CORINE compatible’. The generalisation processes described below are depicted in figures 3.1 – 3.4

LPIS

The polygons in the LPIS dataset are attributed with a text field called ‘Crop_description’ which details what is the present agricultural management (landuse) in that holding. As there is no standardisation of the crop description entries, the text field is essentially a transcription of what the farm holder has entered on their single farm payment application form. Due to this, there is a high degree of variation in this attribute field with over 1,000 unique crop description entries. The first step of generalisation was then to categorise these unique entries to the CORINE classification schema. Singular entries such as ‘permanent pasture’ or ‘grassland’ would directly translate to the CLC ‘Pastures’ class (code 231), whereas holdings with arable crops would typically have a combination of entries such as ‘winter wheat, oilseed rape and barley’ which would fit directly with the definition of the CLC Arable land class (211). Apart from pure-type entries; there are a large amount of mixed-type parcels, where the crop description field would have a number of entries of both pasture and arable landuse types. Such entries were categorised to the ‘Complex Cultivation Patterns’ class (242) unless there was an overall majority of one type of agriculture listed. Crop descriptions with a significant amount of semi-natural habitats such as scrub, forestry or wet grassland listed were classified to the ‘Agricultural areas with significant areas of natural vegetation’ class (243). The LPIS dataset would also contain entries such as ‘bog’ or ‘forestry’ in the crop description field. As reporting of such landcover types is not mandatory and as a result very inconsistent, it was not included in the generalised dataset.

Forestry datasets

Generalising the forestry datasets was a complex process as the main data source from the Forest Service (FS) was itself an amalgamation of several historical forestry datasets. Added to that, the Coillte dataset partially overlapped the FS dataset. After an initial assessment of both datasets it was decided to prioritise the FS dataset over the Coillte dataset as it contained more comprehensive and detailed forestry coverage nationally. Any Coillte forest parcels that overlapped the FS dataset were removed with the remainder appended to the FS dataset to produce a combined forestry dataset. All forestry parcels were then categorised to the four main CLC forestry categories of Broadleaved forestry (311); Coniferous forestry (312); Mixed forestry (313) and Scrub/transitional woodland (324) based on the relative abundance of each type of forestry as ascertained from the species type entries. The FS dataset also provided a planting date which was used to determine if a forestry strand was mature or immature, i.e. 312 or 324 respectively. A rule of ten years was set as the age at which an immature forest strand matured into mature coniferous woodland. It was accepted by the project team that this would not be true in all cases but in order to categorise the data for automated processing a rule had to be applied to separate the species data into mature and immature woodland. After both LPIS and the forestry datasets were classified to the CORINE nomenclature, both datasets were then spatially generalised together into a 5ha minimum mapping unit (mmu) which was compatible with the 5ha scale of the CORINE change dataset. ArcGIS spatial generalisation tools were used to merge neighbouring LPIS parcels with the same CLC code together. Any polygons less than 5ha were dissolved into the most suitable neighbouring polygon which was >5ha in area and then any holes, slivers or other topological errors were removed. The output from this was a national scale, 5ha CLC-compatible, agri-forestry data layer which covered 72% of the national land area. It was given the working title ‘DAFM5ha’ as it was mainly derived from DAFM data.

Figure 3.1 LPIS dataset of Wexford Harbour North Slob (pre-generalisation). Arable areas in orange, pasture in green.

Figure 3.2 The generalisation process produces a ‘CORINE compatible’ 5ha dataset, classified to the CORINE classification

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Figure3.3 The forestry data is also generalised and added to the generalised LPIS dataset to create the 5ha DAFM dataset.

Figure 3.4 Completed national scale DAFM dataset: DAFM_5ha_2012

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Potential Change Polygon (PCP) Production

The final step of data pre-production was the creation of a ‘Potential Change Polygon’ (PCP) dataset. This was produced by creating a spatial union between the DAFM 5ha dataset and the existing CLC 2006 dataset. Where the two datasets did not agree on the CLC classification a polygon was created. Figures 3.6 - 3.8 demonstrate this process. These new polygons make up the ‘Potential change Polygon (PCP)’ dataset which had a total of 176,268 individual PCPs which were subsequently spectrally analysed and sorted into either revision (RV) or real change (RC) as will be described in section 3.2.2.

Organisation of Working Units

In previous CLC projects, the country was sub-divided into a grid system of working units, measuring approximately 50km x 50km in area. This was suited to the PI approach but is an in-efficient means of implementing our semi-automated methodology. Instead ‘working blocks’ (WBs) were created which were aligned to the swath path of the IRS sensor as IRS imagery was the main image type used in the project. The WBs were aligned to the edge of the image swath of this sensor and the country was divided based on the best fit of Image 2006 and Image 2012. This resulted in the country being organised into three main WBs - Eastern (ETB), Midlands (MDB) and the Western block (WTB). All interpretation processes which followed were organised into these three WBs. Undertaking the automated work in these large block units maximised the workflow efficiency and minimised the amount of algorithm adjustment and data fragmentation needed. A subsection of the MDB was used to trial the methodology and for the initial verification by the CORINE technical team. After adjustment and refinement the methodology was then rolled out iteratively on the three large WBs.

Figure 3.5 Layout of Working Blocks for CLC 2012 production

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3.2.2 CORINE 2012 Full Production Stage

The CORINE 2012 (CLC 2012) project officially commenced in January 2013 after the pre-production stage. The main stages of production were:

1. Analysis of the Potential Change Polygon (PCP) dataset using object-oriented image

analysis to produce revisions and changes.

2. Integration of revisions identified in automated stage into existing CLC 2006 database.

3. Creation of preliminary 2006-2012 change dataset from PCPs identified as change.

4. Manual photo-interpretation of areas not covered by PCP dataset to identify revision

and changes in these areas.

5. Visual inspection of finished working units to check for errors and omissions.

6. Checking of completed CLC 2006 revised and CLCCh 2012 datasets using Intercheck

software to check for thematic and topological errors followed.

7. Construction of the CLC 2012 dataset using the Arc Toolset supplied by the EEA.

8. Final checking of three deliverable datasets – CLC 2006 revised; CLCCh 2012 and CLC

2012 using the online QC tool (http://clcqc.gisat.cz)

3.2.2.1 Automated analysis of the Potential Change Polygons (PCP) dataset

This was a key stage in the semi-automated methodology as it produced the majority of revisions and changes within the project. Firstly the PCP dataset was analysed to spectrally sort all polygons into either change or revision. After that, any other non-PCP areas which were suitable candidates for spectral identification of change or revision were analysed.

Object-oriented image analysis

All remote sensing based interpretation was done in the object-oriented software platform eCognition Developer 8.64. Within eCognition, the potential change polygon (PCP) dataset was used to segment the 2006 Image, resulting in a set of image objects which represented the original PCPs. For these PCP-based objects, the 2012 code (PCPCode2012) is known as it is derived from the ancillary data. As the PCP represents areas where the ancillary data disagrees with the existing CLC 2006 dataset, it follows that there must have either been a change on the ground between 2006 and 2012 or the existing 2006 dataset is incorrect. Using this logic, the 2012 code of all PCPs was compared against the spectral values from Image 2006 to decide if they were revision or change. There were four sub-types of revision and change - revision (RV), real change (RC), revised change (RVRC) or direct revision (DRV) as described below in table 3.1

Revision (RV), Real Change (RC), Revised Change (RVRC) and Direct Revision (DRV) derived from the Potential Change Polygon (PCP) dataset

RV

Where PCPCode2012 corresponds to the spectral values of Image 2006 we can assume that no change has taken place on the ground and therefore the PCP represents a correction of the original dataset, i.e. a revision (RV). For example, a field is currently classified as 211 (Arable) in CORINE 2006 and as 231 (Pasture) in LPIS 2012. The field is automatically taken to be pasture in 2012 and is then checked against Image 2006 to see if it was also pasture in 2006. The field appears to be pasture in 2006 according to the imagery and so the PCP polygon is categorised as revision (RV). The 2006 dataset is revised accordingly with the revision carried through to the 2012 dataset. The large 211-231 PCP in the bottom left of fig 3.4 is an example of this (e.g. '01' in figure).

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RC

Where PCPCode2012 does not correspond to the spectral values of the 2006 image we can assume that change has taken place on the ground from code 2006 to PCPCode2012. For example, a field is currently classified as 211 (Arable) in CORINE 2006 but 231 (Pasture) in LPIS 2012. The field is automatically taken to be pasture in 2012 and is also checked against Image 2006 to see if it was also pasture in 2006. The field appears to be arable in the 2006 image and so the PCP polygon is categorised as real change (RC.) with the RC object added to the 5ha change dataset. The large 211-231 PCP in the top centre of fig 3.4 is an example of this (e.g. '02' in figure). For all RC polygons the 2006 code had to be verified to see if it correct – see ‘RVRC’.

RVRC

Where the CLC 2006 code in a RC polygon is incorrect (does not match Image 2006). All RC polygons must have their 2006 code verified because the current code may be incorrect due to inaccuracies in the original dataset and/or the different resolutions between the main landcover (25ha) and change (5ha) datasets. Therefore, when a PCP is identified as RC, the 2006 code is re- assessed to see if it is a true reflection of the imagery. If the 2006 code doesn’t match the spectral values of the 2006 image then the RC polygon is classed as revised real change (RVRC). For example, a 10 ha field was farmed with arable crops (211) in 2006. As it was <25 ha in area it was generalised to pasture (231) in the 25 ha CORINE 2006 dataset. Sometime between 2006 and 2012 the same field was planted with forestry (324). A RC polygon is formed for the area and we know from the ancillary data that the new 2012 code is ‘324’ (transitional woodland). A direct usage of the current 2006 code would have resulted in the incorrect change code of ‘231-324’ being applied. To ensure the correct 2006 code is used, the RC polygon is double-checked against Image2006 to make sure the true code is listed in the change polygon. In this example the change polygon was verified to actually be 211-324 and not 231-324. Following on from this, if we know that the 2006 code is incorrect we can also use RVRC polygons to potentially revise the original dataset. The RVRC polygon is then also included in the RV dataset so that in the example above the parcel of land can be revised to arable land if it’s particular geo-spatial situation allows it, i.e. if the RVRC polygon neighbour an existing 211 polygon >25ha.

DRV

Where a PCP gives an implausible potential change value. This is where the proposed change is not feasible. An example would be '312-231' (coniferous forest to pasture). Under current forestry policy, it is highly unlikely to have a scenario where a mature coniferous forest is clearfelled and converted back into pasture within a 6 year timeframe. Such instances of implausible change were labelled as Direct Revision (DRV). Another example would be 112-231 (Discontinuous Urban fabric to Pasture).

Table 3.1 Explanation of RV, RC, RVRC and DRV polygons, derived from the PCP dataset.

Figure 3.6 Existing CLC 2006 dataset (Image 2006)

Figure 3.8 PCPs created where the existing CLC 2006 and DAFM 5 ha datasets do not agree on CLC code

Figure 3.7 DAFM 5ha dataset of same sample area (Image 2006)

Figure 3.9 Revised CLC 2006 with real change polygons identified during PCP analysis (Image 2012)

E.g. 01

E.g. 02

E.g. 01

E.g. 02

Spectral analysis of non-PCP areas

The PCP analysis covered approximately 74% of the national area. The remaining areas needed to be spectrally checked for any changes or revision. Automated analysis based solely on spectral properties was suitable for homogenous and spectrally distinct landcover classes such as Continuous urban fabric (111), Discontinuous urban fabric (112), agricultural and forestry areas not covered by ancillary data, semi-natural areas and peatlands (412). Heterogeneous classes which do not have a consistent or unique spectral signature such as Complex cultivation patterns (242) needed more manual assessment. The process for non-PCPs is similar to that of the PCP areas in that image objects are created within eCognition and then separated into RC, RV and RVRC. The first step is to segment non PCP areas to the existing CLC 2006 dataset. The resulting objects are then further sub-segmented based on their spectral properties of Image 2006. Any sub-segments which do not spectrally correspond to the existing CLC 2006 classification are deemed to be revision.

3.2.2.2 GIS based Integration of outputs from automated processes

Following the creation of the different revision and change polygons in the spectral analysis stage, all changes and revisions (RC, RV, RVRC and DRV), were imported into ArcGIS for spatial integration with the existing CLC database to create the revised CLC 2006 and the CLCCh 2012 database.

Integration of revision and change polygons to existing CLC boundaries

The first step was to incorporate all three revision types – DRVs, RVs and RVRCs into the existing 2006 CLC dataset.

Spatial Integration of Revisions

All RVs were spatially integrated into the existing CLC database using a number of cartographic refinement steps performed using standard ArcGIS tools to remove any holes, kinks, spurs or other irregular shapes in their outlines. Revision polygons within 100m of each other were merged together and all features were then snapped to the existing CLC 2006 at a spatial tolerance of 100 metres. A ‘union’ was then created between the two datasets with ‘gap polygons’ retained to pick out any holes or gaps between the revision polygons and the original CLC database. Any gaps <5 ha were eliminated into the new polygons so that the outline of the revision polygons were now spatially co-incident to the existing CLC database. The key to these processes is maintaining a very high spatial resolution (.0001 mm) and a low spatial tolerance (.0002mm) within the ArcGIS tool environments settings.

Dissolve dataset to revised code boundaries

The final stage of reconfiguring the existing CLC dataset to the outlines of the revision polygons involved assigning a ‘New_Code’ field to the unified revision and CLC dataset produced above. This was then populated by the revised CLC code where present and where absent the original CLC code was used. The dataset was then dissolved by the New_Code field resulting in the dataset adopting the boundaries of the revised polygons over the original CLC 2006 outlines.

Figure 3.10 Non-PCP based RC polygons exported from eCognition. Red circles indicate areas of revision polygons to be snapped to CLC outline (in black)

Figure 3.12 A ‘union’ is then created between the RC polygons and the CLC dataset, retaining all gaps (highlighted).

Figure3.11 The RV polygons are snapped to the CLC (black) boundaries

Figure 3.13 Sub 5ha gaps are ‘eliminated’ into the RV polygons, making the RV polygons spatially compatible with the CLC boundaries (in red)

Thematic Generalisation

After this initial dissolve, there were still a large amount of sub 25ha revision polygons which were left behind, i.e. they did not have a neighbouring polygon with the same code which they could merge into. A straightforward generalisation of these isolated revision polygons would result in the loss of important landcover information. To try and minimise this loss, a thematic generalisation was favoured over a straightforward spatial generalisation. In this, an isolated sub 25ha revision polygon was merged into the neighbouring polygon which it was most thematically compatible with, not most geometrically co-incident with. In the example in figures 3.7 -3.8 overleaf, a series of sub 25ha polygons are neighbouring each other. Using a hierarchical thematic generalisation process the Broadleaved forest (311) polygon and Pasture (231) polygons are generalised into the Agriculture with areas of natural vegetation (243) polygons. A straight forward geometric generalisation would have resulted in the 231 and 243 polygons been generalised into the 211 Arable polygon and the 311 polygon being generalised into the background 231 pasture polygon, thus resulting in a loss of information.

Figure 3.14 The 311, 243 and 231 polygons are all below 25ha and need to be generalised.

Figure 3.15 The 311 and 231 polygons are merged into the 243 polygon to create a >25ha 243 polygon which is still thematically accurate

The output from these series of processes was the first deliverable of the project – the revised CLC 2006 25ha dataset. The scale of the revision was quite significant and necessitated a virtual re-construction of the existing CLC 2006 dataset. The final step of the semi-automated process was then to integrate the change polygons to the new revised CLC 2006 dataset.

Integrating CLC Changes with the CLC 2006 revised dataset

The change polygons consisted of the RC and the RVRC polygons. Due to provisions taken up to this point, the majority of change polygons dataset should automatically sit seamlessly onto the new revised CLC dataset. However a geometric checking procedure was done to remove any holes, gaps, overlap etc. that may exist between the revised and change datasets. This process is similar to the steps taken to integrate the revised dataset. All invalid change polygons were deleted, the remaining polygons were snapped to the outline of the new revised dataset and then a union was created between the change polygons and the revised dataset. All gaps, slivers were removed to result in all change polygons fitting seamlessly onto the outline of the new CLC 2006 revised dataset.

3.2.2.3 Manual photo-interpretation and verification stage

At this stage all automated changes and revisions were completed and integrated into the CLC database resulting in a near complete version of the CLC 2006 revised and CLCCh 2012 datasets. To complete the production process two tasks were remaining:

1) Manual photo-interpretation of areas not covered by automated processes or imagery. 2) Verification of the outputs from the automated processes

Areas which were not covered by ancillary vector data and/or satellite imagery needed to be manually photo-interpreted. Along with that, verification of other areas was deemed preferable due to the novelty of the approach, the variation in availability of the different data sources and the widespread revisions undertaken. The most efficient and thorough means of undertaking the two tasks was to work on them together in a combined photo–interpretation / verification stage. Each large working block (WB) was ‘diced’ so that any large polygons, in particular the expansive 231 (Pasture) polygons, were split into a multi-part polygon based on the number of vertices. Each resulting ‘grid’ varied in size but provided the ideal spatial reference on which to base the photo-interpretation and manual verification check, see Figure 3.18 below. Each grid was checked iteratively in a manual process. Any omitted or incorrect revisions or changes were modified accordingly using the editing tools in ArcMap. The general geometric quality of the data was also assessed with any irregular shapes rectified. Although this was a time consuming stage it ensured that all areas were thoroughly checked and verified.

Figure 3.18 Midland Working Block diced into multipart polygons (grids) for manual interpretation & verification stage.

The dicing approach had the advantage in that it avoided widespread clipping, merging, etc. of polygons as the multipart polygon could be simply dissolved back into itself at the end of the process. It also dramatically improved the performance of the software when drawing, refreshing and editing the dataset. After all grids were checked the working block was now complete and the diced polygons were reassembled. This was done iteratively for the three main working blocks after which a final geometric check of both the revised and change layer was done.

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3.2.3 Post-Production Stage

Validation of finished Working Blocks

After each working block was completed, they were individually checked using the Intercheck software provided by the EEA. This stand-alone software package was designed specifically for the purpose of checking completed CLC revision and change databases for technical, thematic and geometric errors. Each working block was processed as a whole through the software package, any errors found were corrected either by the software itself or manually. This process was repeated until the database was verified as error free. After the three Working blocks were passed as error free, the full national scale databases was assembled by joining the three blocks together and manually inspecting the join line between the blocks. An overlap of 2km was originally included to facilitate this re-joining process, helping to minimise any omissions or errors along the WB borderlines. The full national scale CLC 2006 revised and CLCCh 2012 datasets were now complete and ready to be integrated to produce the CLC 2012 dataset.

Creating CLC 2012

To process of creating the final CLC 2012 dataset involved updating the revised CLC 2006 landcover dataset with the changes mapped in the 2006 - 2012 change dataset (CLCCh 2012). As discussed before the difference in spatial resolutions between the two datasets means that a direct intersection of the two datasets will leave many invalid sub 25ha polygons in the resulting dataset. In an attempt to minimise the loss of information due to this generalisation process, the EEA developed an ArcGIS generalisation toolset which uses a lookup table to direct the generalisation tool to dissolve sub 25ha polygons into the neighbouring polygons which is most similar to its landcover type. Each CLC code has a pairing value against all other CLC codes and the generalisation tool is run in a step by step process, to produce an intelligent thematic generalisation, instead of a simple spatial generalisation. The CLC 2012 dataset for Ireland was then produced using this toolset which used the CLC 2006 revised dataset and the CLCCh 2012 dataset as its two inputs, producing the output of the CLC 2012 dataset. The three final deliverables were now ready to be uploaded to the CLC online Quality checking (CLCQC) tool and then submitted to the EEA. The CLCQC tool is a web-bases application which runs several topological, geometrical and thematic checks to ensure the final datasets are valid before uploading to the EEA’s Central Data Repository (CDR). After using the CLCQC tool the three datasets were loaded into a personal geodatabase – clc12_IE.mdb and loaded into the EIONET CDR along with associated metadata files.

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4. Project management and Organisation

As stated in the introduction the EPA carried out an internal review of the CORINE exercise before agreeing to undertake the 2012 update. The review proposed to undertake the CLC 2012 programme but with significant changes to both project management and data production to introduce cost efficiencies and improve the finished dataset. The new methodological approach, described in detail in Section 3, was the first step to introduce work efficiencies by reducing the amount of manual work involved. The second step was to re-organise the project management structure.

Changes to CLC Project implementation and management

The main change to the management of the project was that the CLC 2012 project was produced and directly managed on-site by the EPA. This was seen as a more cost-effective approach than putting the project out to tender for external production. Severe financial constraints which existed in Ireland at the time dictated that the project would have to be streamlined in terms of staff resources allocated to the task. A condensed production team was adopted with a remote sensing consultant based on-site at the EPA headquarters in Johnstown Castle, Wexford undertaking the technical work within the GIS and Informatics Unit of the EPA’s Office of Environmental Assessment. The project was directly managed by core EPA Management.

Project team: Technical production and co-ordination: Kevin Lydon – Contractor (Compass Informatics) Project management: Gavin Smith, EPA Data Dissemination: Melanie Mageean - Contractor (Compass Informatics)

4.1 Work Schedule & Milestones

The project was initially due to begin in mid-2012 but due to delays in acquiring Imagery the wider CORINE programme was not launched across the participating countries until January 2013. The Irish project used some of this intervening time to undertake pre-production preparation of the data which was detailed in section 3. The Irish national CORINE2012 project then began officially in January 2013 and was scheduled to run until June 2014. It was originally scheduled to undertake the verification of the GMES High Resolution Layers (HRLs) for Ireland during this timescale. However delivery of the datasets to Ireland for verification was also delayed, due to cloud related imagery acquisition problems, until May 2014. At this late stage, all available resources were prioritised to finish up the production on the CLC 2012 project and so work on the HRLs had to be postponed. An extension of two months was therefore offered by the EEA to give time to undertake the verification work on the HRLs. The full CLC 2012 project including verification work on the high resolution layers was then completed in August 2014, with a total project time of 20 months (CLC -18months, HRL 2 months). The revised work schedule is given in Table 4.1

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Table 4.1 CLC 2012 Work programme schedule

Project Milestones

Project pre-production & planning: Q4 2012

Full commencement of CLC 2012 project: January 2013

CLC technical team training visit: March 2013

1st Verification: June 2013

Interim Verification March 2014

2nd Verification: May 2014

Completion of technical production for CLC012: June 2014

Final Quality checking August 2014

Final CLC report Q3 2014

Verification of High Resolution Layers: August 2014

Completion of CORINE 2012 project for Ireland: August 2014

Table 4.1 – Work Schedule Task Q4

2012 Q1 2013

Q2 2013

Q3 2013

Q4 2013

Q1 2014

Q2 2014

Q3 2014

Q4 2014

Pre-project planning and production

Project setup

CLC 2006 revision

CLC 2012

Change mapping

Verification by CLC

technical team

Quality Assurance of final datasets

Project

Management & QC

Final Report

Validation of HRLs

Data dissemenation

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4.2 Training and Verification

As there was a new production team and methodology, the CLC technical team liaised closely with the Irish national team to assist with the implementation of the new production process for CORINE. This included a training visit to the national team and a series of verifications to ensure the new methodology was producing a dataseries which was compliant with the CLC technical specifications.

4.2.1 CORINE Technical Team training visit

The CORINE technical implementation team (CTT) visited the Irish national team (INT) in the early stages of the project (March 2013) to review the new methodology and provide technical advice and assistance to the team. The CTT examined the first sample unit produced in detail to check for errors. The sample working unit was centred over South Tipperary/West Waterford covering the Commeragh and Knockmealdown mountains and extending from Dungarvan in the south to Thurles in the North of the Midlands WB. After examination, the CTT found a number of technical issues and gave suggestions on how to refine the methodology in the training visit report. It was agreed to schedule the first remote verification for a couple of months after the visit, giving the national team time to re-adjust the production methodology. The training visit report highlighted a number of issues which the national team were to address:

1) Forest boundaries and growth was mapped well due to the use of the ancillary data but clearfelling was not well mapped, with a significant number of omitted forest clearcuts found.

2) Arable/pasture land rotation. Instances of revision incorrectly mapped as change in these two inter-dynamic classes were found. It was proposed by the CTT to only map as change if there is clear evidence to support the decision. Otherwise, map as revision.

3) Abandonment of pasture. An unusually high number Pasture to Agriculture with areas of natural vegetation (231- 243) changes were found. It is more likely that these changes are in fact revision. It was advised to re-examine the process of determining the 243 category directly from LPIS.

4) Urban sprawl. A low number of urban sprawl changes i.e. Pasture to Discontinuous urban fabric (231-112) were mapped. It was advised to focus on areas directly outside existing urban centres to pick up instances of urban sprawl.

5) Technical changes. The definition of technical changes was miss-applied by the INT in the first unit as the methodology incorrectly retained sub 25ha sections of a polygon which were left after part of the polygon has changed. It was advised to delete such polygons from the change database as they were unnecessary.

4.2.2 Remote Verifications of Completed Datasets

4.2.2.1 First Remote Verification

Following on from the training visit, the 1st remote verification of the Irish CLC 2012 results took place in two phases (Jul 2013 & Feb 2014) where two separate working units (WUs) were verified.

Verification 1 WU01

Phase one of the first verification was a re-check of the same sample unit that the CTT inspected during their training visit in March 2013 – working unit 01 (WU01). Both the revision and change dataset were conditionally accepted, meaning that some mistakes were found but these were relatively easy to correct.

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Issues arising from verification of WU01 CLC 2006_revised

1. The verification found instances where landcover boundaries often had positional mistakes

>100m. Examples include the boundary between agricultural land and a watercourse and also

along the coastline.

2. Several examples of omitted arable land parcels were found in the 2006 layer

3. The complex cultivation code 242 is sometimes erroneously used instead of arable or pasture.

4. In forestry classes the boundaries were good but the largest problem is the improper

separation of Coniferous (312) and transitional (324) forestry. Often large 312 and 324

polygons are not detailed enough with sub units of 324 within large 312 parcels or vice-versa.

To address these issues the INT revised the methodology by:

1. Using the EPA water vector database to refine the boundaries of water bodies and coastline.

This was done by producing a generalised 5ha water feature dataset similar to the DAFM5ha

dataset and intersecting it with the existing CLC 2006 database to improve the boundaries of

water features and coastlines.

2. Where possible summer imagery was used to identify any arable land not picked up during

the PCP revision phase.

3. Use of the Complex cultivation patterns (242) class will be minimised. Both pasture and

arable landuse surfaces can be highly dynamic in Ireland due to inter-annual rotation patterns,

multiple silage cutting and pasture re-seeding patterns. Analysis of these areas based solely on

spectral variation would lead to mis-interpretation of pure pasture or arable areas as the

CORINE 242 class. True mapping of the 242 class is therefore quite difficult and leads to the

omission of pure pasture or arable classes from the dataset. It was attempted to address this

problem by using crop variation in LPIS entries as a proxy for complex cultivation. However

due to the unknown relatively proportions of different crop in a mixed LPIS Crop description

entry, this proved to be an unreliable approach. The CTT has advised to use 242 only when

necessary and so use of this class will be minimised.

4. The mapping of forestry areas proved to be one of the more difficult aspects as firstly it

required wide scale revision and secondly because of the inconsistency of the vector data. The

boundaries of the vector forest parcels proved to be highly accurate but internally within the

larger forest unit’s boundaries between mature coniferous (312) and transitional forestry (324)

parcels were not well mapped. To address these problems the reliance on the 10 year

maturation date from year of planting to discriminate between mature and immature forestry

parcels was dropped and all forestry parcels were sub-segmented and spectrally assessed to

identify smaller areas (5ha mmu) of 312 or 324 within the larger forest structure. The 5ha

units will be used as part of a refined change mapping process and then merged to form the

25ha dataset.

Issues arising from verification of WU01 CLCCh 2012

1. In some cases non-changed areas > 5 ha were not cut from Pasture to arable (231-211) and 211-231 change polygons. Apart from that 231-211 and 211-231 changes were mapped successfully.

2. Changes between pure agriculture classes and complex cultivation (e.g. 231-242) and vice-versa (e.g. 242-231) were found to be inaccurate. Coding according to pure changes was proposed by the CTT (i.e. 231-211 or 211-231).

3. Forest growth (324-312) was the largest change by area. Significant overestimation of change area has been found due to the inclusion of non-changed parts with area >5 ha. Also non-changed areas were mapped as change particularly in changes with small (<25 ha) area. Several omitted forest growths areas have also been found.

4. Clear-cutting of forestry (312-324) was successfully mapped. Sometimes non-changed areas > 5 ha were not cut. Several omitted forest clear-cuts have also been found.

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5. Afforestation (on arable land, pasture & peatland): 231-324 change type represented the largest number of change polygons. Many of these changes (very young plantations) would not have been possible to identify by pure visual photo-interpretation method. This demonstrated the benefits of using in-situ data.

To address these issues the INT further revised the methodology by:

1. Adding an extra step in the change checking procedure to remove areas of non-change >5ha which were inadvertently included in the change polygon.

2. As with the revision dataset, mapping of 242 related changes is very problematic due to the highly variable spectral nature of pure agriculture classes in Ireland. Where possible the mapping of pure changes will be used instead of the complex cultivation code.

3. Forestry related changes area proving complex aswell as forestry revisions. As described above forestry in the revised 2006 dataset will be sub-segmented to 5ha mmu to identify sub-forestry units which will then be checked against Image 2012 for change. This will ensure the correct mapping of forest type, growth and clearfelling.

Summary of WU01 verification results

WU01 was conditionally accepted pending corrections to the WU and refinement to the methodology. The INT acknowledged that there were a number of issues with the production process that needed to be addressed before continuing any further. As this is a new and unique approach it was inevitable that some amount of refinement would be needed after initial testing. It is also noted however that a large amount of the problems encountered arise from the incorrect mapping of previous datasets. Although it was known that a significant amount of revision would be needed on the existing CLC 2006, the degree of revision required on the existing dataset is on a much greater scale than anticipated. The INT will now check all classes for necessary revision. This will add a significant amount of work onto the project but it is necessary in order to ensure the validity of the new 2012 dataset. Forested areas proved problematic as they were highly dynamic and inconsistently mapped. The ancillary vector data whilst giving good boundaries was less reliable internally within the forest parcel. All forest areas will require spectral verification down to the 5ha scale to facilitate correct change mapping. An example of this added step is shown in Figures 4.1-4.2. The mapping of afforestation was very successful. High resolution planting data from the FS enabled us to map these changes which would not be recognised via satellite imagery. Following the verification it was decided not to use the Complex cultivation patterns class (242) and Agriculture with areas of natural vegetation class (243) derived from LPIS due to their incompatibility with the CORINE definition of these classes. The holdings based spatial breakdown of LIPS makes it impossible to determine which parcels within a farm holding polygon contain which landuse in a mixed holding. The inclusion of semi-natural habitats in the crop description was taken as a proxy for natural vegetation and such holdings were categorised as 243. However due to the inability to determine the relative degree of complexity or presence of natural vegetation using this method the process of using the 242 and 243 categories on the LPIS derived dataset was discontinued. Due to the need for further refinements, it was agreed to submit the CTT with a second sample unit for verification under the first verification stage.

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Figure 4.3 Location of the four verification areas

Verification 1B, WU02

The next sample unit for verification, Working Unit 02 (WU02) was also part of the central midlands working block. This WB was the large central section of the country and included approx. 45% of the national land area. WU02 was a sample section of this covering 10% of the working block (4.5% of the national area). WU02 covered parts of counties Westmeath, Roscommon and Longford, including sections of Lough Ree, Lough Derg, the Shannon Callows and the extensive raised peat bog complexes in the areas. The main towns in the area were Athlone, Tullamore and Longford. The area then included a good mix of artificial, agricultural, peatland and water classes. Both the revision and change layer of WU02 was fully accepted by the CTT with no major mistakes found. The CTT reported that there was a significant improvement in the dataset compared with the first series of verifications. They reported that the datasets had no technical mistakes (i.e. polygons with an area below the MMU, invalid codes, neighbour polygons with the same code, narrow polygons, etc.).

Revision of WU02 CLC 2006

Significant improvement was achieved in his working unit with only small inaccuracies found. Less positional inaccuracies were found in this working unit and only a few omitted arable land (211) polygons were found with size around 25 ha. Some imprecisions in classification of forestry were found, but to a much lesser extent than previously. Considering the amount of revision needed on the forested areas, this was seen as a positive result. Some of the 243 areas found were too large or simply non-relevant and it was suggested to minimise these areas by separating parcels of pure land cover (most often 231) from larger 243 polygons.

Mapping of changes WU02 CLC 2006-2012

The mapping of changes was also found to have improved significantly. Urban and industrial sprawl were mostly mapped properly. Pasture to arable land changes and vice-versa were improved with

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only a few cases where the 2006 code was not true. The mapping of forest growth and clearfelling was mapped properly with significant improvements following on from the revised approach to interpreting the forestry vector data.

Summary of WU02 verification results

The INT was satisfied that the multiple refinements made to the production process appeared to have improved the resulting datasets significantly. Due to the coarse scale of the dataset and the large-scale revision of all major classes that is being undertaken, there will inevitably be some minor errors in the dataset. However systematic errors such as the miss-mapping of forested areas have been eliminated by the refinement of the production process.

4.2.2.2 Second remote verification

For the second verification two units were requested, one covering the Dublin city region (WU03) and another centred on Co. Donegal (WU04).

Verification of WU03

WU03 covered the greater Dublin region including parts of counties Meath, Wicklow and Kildare. Both the revision and change datasets of WU03 were fully accepted by the CTT with no major mistakes found. Some small inaccuracies were found in the artificial classes including a certain amount of overestimation of the area of construction sites. The Dublin area has the highest concentration of artificial surfaces and construction sites in the country. Added to this is the complication of abandoned construction sites due to the collapse of the construction industry leading to re-growth of vegetation on some construction sites, which alters the spectral characteristics of the site considerably. Apart from the construction sites most classes were satisfactorily mapped apart from individual errors or omissions found, e.g. a large 321 polygon was miss-mapped as 211. The WU03 change dataset was well mapped considering the highly dynamic nature of the WU. Urban sprawl was a dominating class which was mapped well and the abandonment of construction sites was well accounted for by non-change at these sites. Arable-pasture rotation was a dominate change type and this was mapped well. In some cases forest growth was overestimated. It can be difficult to precisely separate mature from immature forestry in an automated process when there is a high degree of variability in growth patterns. Any individual errors found by the INT were corrected.

Verification of WU04

WU04 covered central and North West Donegal. The revision layer of WU04 was conditionally accepted meaning that some errors were found, this was mainly where patches of cultivated land and forestry within larger peatland complexes were not mapped. In some cases the areas of beaches were exaggerated. The change dataset of WU04 was fully accepted by the CTT. Most of the changes were found to be correctly mapped apart from some of the clearfelling polygons which were false changes.

Summary of Second Remote Verification

Following on from the second remote verification, the INT was requested to correct the errors found in the WU03 & WU04 and to check for the following issues on the rest of the national datasets.

a) Over mapped areas of construction and associated changes. b) Missing parcels of cultivated land and forestry within large peatland complexes. c) Areas of non-change incorrectly included within large change polygons >50ha.

As these were specific problems it was relatively straightforward to perform a targeted search for the errors and fix where possible. This was done manually after receiving feedback from the CTT.

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5. Results & Discussion

5.1Results

The statistical outputs from the three deliverable datasets - CLC 2006_revised; CLCCh 2012 and CLC 2012 - are summarised and discussed in this chapter with reference to the respective dataset.

CLC 2006 existing CLC 2006 revised

Code Description Count Area_ha % Count Area_ha %

111 Continuous Urban Fabric 34 2831 0.04

35 3095 0.04

112 Discontinuous Urban Fabric 756 108011 1.52

788 109577 1.54

121 Industrial or Commercial units 127 9737 0.14

165 12020 0.17

122 Road and Rail networks 30 4213 0.06

33 4699 0.07

123 Port areas 14 1041 0.01

16 1049 0.01

124 Airports 13 2492 0.03

12 2392 0.03

131 Mineral extraction sites 188 8851 0.12

189 8989 0.13

132 Dump sites 12 678 0.01

24 1524 0.02

133 Construction sites 45 2340 0.03

69 3351 0.05

141 Green Urban areas 40 2742 0.04

57 3566 0.05

142 Sport and leisure facilities 309 19378 0.27

329 21884 0.31

211 Non-Irrigated arable land 4061 536638 7.53

2509 364572 5.13

222 Fruit trees and berry plantations 0 0 0

6 288 0.00

231 Pastures 1869 3598888 50.53

1257 3865434 54.37

242 Complex cultivation patterns 1378 147034 2.06

586 63243 0.89

243

Land principally occupied by agriculture with areas of natural

vegetation 2398 446505 6.27

2497 502624 7.07

311 Broad-leaved forest 465 29292 0.41

434 29066 0.41

312 Coniferous forest 2276 230203 3.23

2473 278127 3.91

313 Mixed forest 299 30065 0.42

836 70265 0.99

321 Natural grassland 457 89582 1.26

215 45256 0.64

322 Moors and heathland 147 55362 0.78

291 95699 1.35

324 Transitional woodland-shrub 4196 419156 5.89

2783 280521 3.95

331 Beaches, dunes and sand plains 99 10905 0.15

113 12033 0.17

332 Bare rock 34 14531 0.20

43 15871 0.22

333 Sparsely vegetated areas 132 20877 0.29

206 54923 0.77

411 Inland Marshes 223 16389 0.23

215 19271 0.27

412 Peat Bogs 1992 1094436 15.37

1807 1036649 14.58

421 Salt Marshes 56 4839 0.07

64 5206 0.07

423 Intertidal flats 188 53561 0.75

195 48588 0.68

511 Water courses 27 8119 0.11

217 7582 0.11

512 Water bodies 544 119236 1.67

495 113838 1.60

521 Coastal lagoons 10 821 0.01

17 1903 0.03

522 Estuaries 36 33513 0.47

50 26781 0.38

523 Sea and Ocean 1 N/A N/A

1 N/A N/A

Total

22455 7122267 100 18809 7109887 100

Table 5.1 Table showing landcover statistics of CLC 2006 revised in comparison to the existing CLC 2006 dataset

Figure 5.1 National scale maps of the existing CLC 2006 (LHS) and the revised CLC 2006 (RHS)

5.1.1 CLC 2006 Revision

The results of the revision process showed significant revision in three to four key classes with moderate revision in most other classes. The results are described below by their Level 1 classes. The full description of individual class codes is given in table 5.1. All percentage figures are given in terms of a class’ percentage of the national land area (% na)

Artificial Surfaces 1xx

Artificial Surfaces were revised upwards by 9,832 ha or 0.16% na. Most revision was upward in nature with the largest revision by class taking place in classes of 142 (.04% na), 121 (.03% na) and 112 (0.02% na). The only artificial class to be revised downwards in area was the 124 class which had a reduction in area of 100 ha.

Agricultural Areas 2xx

Agricultural areas were revised upwards by 67, 096 ha or 1.42% na in total. There were significant increases in the 231 class 266,546 ha / 3.84% na and a 56,119 ha / 0.8% na increase in the 243 class. These upward revisions in agriculture were offset by reductions in the 211 class of 172,066 ha / 2.4% na and a decrease of 83,791 ha/ 1.17% na in the 242 class. A new agriculture class (222) which was not mapped previously was added to the revised dataset. Six instances of this class were found with an overall area of 288 ha. There was a significant reduction in the total number of polygons in all agricultural classes, particularly classes 242, 211 and 231 (57.47%, 38.22% and 32.74% respectively). This equated to a reduction of 2,851 (29%) in the total number of agricultural polygons.

Forestry and Semi-natural Areas 3xx

The CLC Level 3xx group has two distinct sub-groups - forestry and semi-natural areas. In forestry, the 312 and 313 classes were revised upwards by 47,924 ha / 0.68% na and 40,200 ha / 0.57% na respectively, whereas the area of the 324 class was reduced by 138,635 ha / 1.94% na. This gives a net reduction of 34, 818ha or 0.69% na in all forested areas, revising the area of forested land nationally down to 657,979 ha or 9.26% na. The semi-natural classes were also significantly revised in certain classes with the 322 and 333 classes increased by 40,200 ha / 0.57% na and 34,046 ha / 0.48% na respectively whilst the 321 class was reduced by 44, 326 ha or 0.62% na. In total, the area of semi-natural landcover was reduced by 32,525 ha or 0.01% na.

Wetlands 4xx

Overall, the 4xx water bodies group was revised downwards by 11, 585ha or 0.15% na. This reflects the statistics for the dominant class 412, which was itself revised downwards by 57,787 ha or 0.79%. There was a slight increase in the area of the 411 class by 2,882 ha or 0.04% na.

Water bodies 5xx

For analysis purposes the 523 class is not included from the main 5xx group as it skews the analysis of the terrestrial components of the group. The main revision in this group was in the 512, and 522 classes which were revised downwards by 5,398 ha 0.07%, and 6,732 ha / 0.09% respectively. An arbitrary buffer of 12km was extended from the coastline to create the 523 Sea & Ocean class.

CLC 2006 Existing CLC 2006_revised

CLC Level 1 Count Area_ha % Count Area_ha %

Artificial Surfaces (1xx) 1568 162314 2.29

1717 172146 2.45

Agricultural Areas(2xx) 9706 4729065 66.83

6855 4796161 68.25

Forest and semi-natural areas(3xx) 7840 853660 12.06

7032 798934 11.37

Wetlands (4xx) 2459 1169225 16.52

2281 1109714 15.79

Water (5xx) 617 161689 2.29

592 150104 2.14

Sea & Ocean (523) 1 N/A N/A 1 N/A N/A

Table 5.2 Landcover statistics by Level 1 grouping for CLC 2006 existing and CLCL2006_revised

34

CLCCh 2012

Change code Count Area_ha

Change code Count Area_ha

211-231 1878 33730.55

211-242 1 42.77

312-324 1500 29418.01

331-523 1 37.49

324-312 679 22598.93

411-324 3 37.17

231-211 1446 19441.82

142-142 2 34.90

231-324 930 11499.29

242-122 1 33.69

412-324 219 3656.24

131-231 2 32.59

133-112 238 3295.57

133-141 1 29.80

243-324 213 2453.69

324-412 2 28.72

242-231 62 2085.85

142-324 1 27.81

324-324 130 1789.96

423-331 1 27.17

133-122 19 1168.75

211-122 1 22.99

133-121 49 723.75

122-122 1 21.79

231-122 5 545.08

523-123 2 20.46

211-211 40 534.35

211-133 2 19.63

231-112 50 422.80

321-324 1 19.45

231-121 27 417.07

243-231 2 18.53

211-324 36 385.63

132-132 2 17.61

324-313 13 325.68

313-313 1 17.60

312-312 21 325.46

321-231 1 17.38

231-133 34 324.98

331-423 1 16.64

313-324 19 245.17

133-124 1 15.60

133-142 7 239.19

243-112 3 15.40

132-231 4 207.84

243-133 1 14.41

322-324 11 196.29

242-112 1 13.30

231-231 12 171.76

231-124 1 12.95

231-142 7 166.24

243-142 1 12.62

412-131 5 145.87

311-122 1 12.08

121-121 11 133.72

133-131 1 9.92

231-131 11 104.89

312-132 1 9.72

231-242 3 104.29

313-412 1 8.24

131-131 5 88.85

512-122 1 8.13

121-231 3 87.36

121-133 1 7.68

311-324 7 80.64

312-112 1 7.49

133-231 6 78.41

313-311 1 7.34

211-121 6 70.47

231-312 1 6.87

112-112 5 65.76

243-312 1 6.15

243-131 2 53.84

141-112 1 5.81

211-112 7 53.39

211-131 1 5.28

324-311 3 47.24

133-111 1 5.20

242-121 2 47.18

141-122 1 3.31

242-324 4 44.66

324--112 1 2.02

Table 5.3 Results from the 2006 – 2012 change dataset (CLCCh 2012) in order of total area of change (ha)

35

5.1.2 CLC Changes 2006 – 2012

A full list of landcover changes from 2006 - 2012 mapped during the change mapping process is given in table 5.3 with total number of polygons and area of change listed per individual change type. There was 138,290 ha or 1,383km2 of change in total. This is dominated by eight main change types, highlighted in red in table 5.3, which together account for 91.18% of all change. Chart 5.1 gives the percentage breakdown of these eight change types, technical changes and all other change types. The highest change occurs internally within the agricultural and forestry groups. Firstly, from arable to pasture (211-231) and also vice-versa (231-211). Secondly, from coniferous forest (312) to transitional woodland (324) and vice-versa. These two groups of change account for 76% of all change. After these, afforestation accounts for another three of the eighth biggest change types with afforestation on agriculture (211-324, 231-324 & 243-324) and peatland (412-324) accounting for a further 13% of the change. The other significant change type is the completion of construction sites (133-112) which accounts for 2% of the change. Chart 5.2 shows the gains and losses in hectares per class. From this we can clearly see that the classes 211, 231, 312 and 324 are the most dynamic classes with significant gains and losses in all four. The 324 class shows the biggest net gain of 25,063 ha or 8.82% of total area. On the other hand, the construction site (133) and peatlands (412) classes shows the biggest net losses of 5,199ha / 93% and 3,765 / 0.3% of total area. Looking at the total net gain or loss of landcover per Level 1 category in chart 5.3, it is clear that the forestry and semi-natural areas (3xx) group shows the highest net gains of 1.92% followed by artificial surfaces which saw a gain of 1.16% of area. The landcover types with the biggest losses are wetlands and agriculture which showed a net loss of 0.34% and 0.33% respectively. Figure 5.2 shows the national spatial distribution of change polygons. On an observational level it is clear that there is more change occurring in the east and south east than in the rest of the country.

Chart 5.1 Percentage breakdowns of main landcover change types

24.39% 21.27%

16.34%

14.06% 8.32% 2.64%

2.38%

1.77%

6.78%

2.04%

CORINE Landcover Change 2006 -2012

211-231

312-324

324-312

231-211

231-324

412-324

133-112

243-324

Other

Technical Change

Chart 5.2 Total losses and gains by area (ha x 100) per class

Chart 5.3 Level 1 Losses and gains by percentage of existing class area

111 112 121 122 123 124 131 132 133 141 142 211 231 242 243 311 312 313 321 322 324 411 412 423 523

Gain 5 381 125 179 20 29 320 10 367 30 418 194 362 147 0 55 226 326 0 0 480 0 37 17 37

Loss 0 0 95 0 0 0 33 208 556 9 28 343 330 222 257 93 294 261 37 196 230 37 380 27 20

0

10,000

20,000

30,000

40,000

50,000

60,000

Are

a (H

a)

Loss & Gains per Class

ArtificalSurfaces

AgriculturalAreas

Forestry andSemi-Natural

AreaWetlands Water Sea & Ocean

Gain / Loss 1.16 -0.33 1.92 -0.34 -0.01 0.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

% G

ain

/ L

oss

Percentage Loss & Gain per Level 1 Class

Figure 5.2 Map showing national scale distribution of CLC changes 2006- 2012

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5.1.3 CLC 2012

The CLC 2012 dataset is a directly derived product from the updating of the revised CLC 2006 dataset with the changes mapped in the CLCCh 2012 dataset. The landcover statistics for CLC 2012 should then reflect the results shown for the two original datasets. Table 5.4 gives the breakdown of landcover per class in the 2012 dataset alongside the revised 2006 dataset for comparison. Table 5.5 shows this table summarised by Level 1 groups.

CLC 2006_revised

CLC 2012

Code Description Count Area_ha % Count Area_ha %

111 Continuous Urban Fabric 35 3095 0.04

35 3100 0.04

112 Discontinuous Urban Fabric 788 109577 1.54 786 111914 1.57

121 Industrial or Commercial units 165 12020 0.17 175 13003 0.18

122 Road and Rail networks 33 4699 0.07

47 6457 0.09

123 Port areas 16 1049 0.01

16 1049 0.01

124 Airports 12 2392 0.03

12 2412 0.03

131 Mineral extraction sites 189 8989 0.13 195 9302 0.13

132 Dump sites 24 1524 0.02

21 1314 0.02

133 Construction sites 69 3351 0.05

9 304 0.00

141 Green Urban areas 57 3566 0.05

57 3573 0.05

142 Sport and leisure facilities 329 21884 0.31 336 22263 0.31

211 Non-Irrigated arable land 2509 364572 5.13 2491 343075 4.83

222 Fruit trees and berry plantations 6 288 0.00

6 288 0.00

231 Pastures 1257 3865434 54.37 1232 3882382 54.61

242 Complex cultivation patterns 586 63243 0.89 552 60018 0.84

243

Land principally occupied by agriculture with areas of natural vegetation 2497 502624 7.07

2493 502095 7.06

311 Broad-leaved forest 434 29066 0.41 432 29218 0.41

312 Coniferous forest 2473 278127 3.91 2562 277143 3.90

313 Mixed forest 836 70265 0.99 836 70089 0.99

321 Natural grassland 215 45256 0.64 215 45383 0.64

322 Moors and heathland 291 95699 1.35 291 95735 1.35

324 Transitional woodland-shrub 2783 280521 3.95 2856 289767 4.08

331 Beaches, dunes and sand plains 113 12033 0.17 113 12013 0.17

332 Bare rock 43 15871 0.22

43 15871 0.22

333 Sparsely vegetated areas 206 54923 0.77 206 54923 0.77

411 Inland Marshes 215 19271 0.27 216 19392 0.27

412 Peat Bogs 1807 1036649 14.58 1826 1033832 14.54

421 Salt Marshes 64 5206 0.07

64 5179 0.07

423 Intertidal flats 195 48588 0.68 194 48541 0.68

511 Water courses ) 7582 0.11

23 7613 0.11

512 Water bodies 495 113838 1.60 497 113937 1.60

521 Coastal lagoons 17 1903 0.03

17 1903 0.03

522 Estuaries 50 26781 0.38

50 26781 0.38 523 Sea and Ocean 1 4058538 N/A

1 4058555 57.08

Totals 18809 7109887 100 18904 7109870 100.00

Table 5.4 Table comparing landcover statistics from the revised CLC 2006 dataset and the final CLC 2012 dataset

39

Figure 5.3 Final CLC 2012 national scale map

CLC 2006_revised CLC 2012

CLC Group Count Area_ha %

Count Area_ha %

Artificial Surfaces 1717 172146 2.45

1689 174691 2.49

Agricultural Areas 6855 4796161 68.25

6774 4787858 68.13

Forestry and semi-natural areas 7032 798934 11.37

7192 807335 11.49

Wetlands 2281 1109714 15.79

2300 1106944 15.75

Water 592 150104 2.14

587 150234 2.14

Sea and Ocean 1 4058538 N/A 1 4160972 N/A

Table 5.5 Comparison of landcover statistics by Level 1 grouping for CLC 2006_revised and CLCL2012

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5.2 Discussion of results

5.2.1 Revision of CLC 2006

At the outset of the project, it was planned to undertake targeted revision of key agricultural and forestry classes. However as the project developed it became clear that a widescale revision of the majority of landcover classes would be necessary to improve the integrity of the 2006 dataset and to maintain this through the change mapping and 2012 update process. The revision of the original CLC 2006 dataset ended up accounting for approx. 65% of the workload of the project as a whole. As the spatial resolution of the dataset is set at 25ha and so could not be improved, it was seen that the most effective way to revise and improve the existing dataset was to address the other main identified shortcoming of the dataset – it’s spatial and thematic accuracy. Spatially, this meant improving the delineation of boundary lines between different landcover types, producing ‘cleaner’ more accurate polygon features. The thematic improvements came by way of classifying the polygons more accurately and removing polygons which are incorrectly classified. These two tasks formed the focus and objective of the revision process. The results of the revision process will be discussed below as per the Level 1 CLC grouping. Full description of the CORINE codes can be found in tables 5.1 & 5.4.

Artificial surfaces (1xx)

By comparing the statistics for the 1xx classes between the existing and revised datasets it would appear that there has not been a large degree of revision of artificial surfaces. Artificial surfaces occupy the smallest proportion of the national land surface and so it is not representative to look at the revision in terms of the percentage of national area. Taking the dominant class in this sector, 112, which translates into medium density towns or villages in an Irish context, the overall number of polygons rose by 32 (4.23%), representing a respective increase in the amount of towns or villages mapped nationally. A visual inspection of the original dataset would show that often the boundaries of existing 112 polygons were not accurately mapped and the feedback from the CLC technical team in their verification reports pointed to this error in the original dataset. The overall area of the class was only increased by 1,566 ha (.02% na) but as is depicted in Figures 5.4 the majority of revision in this class involved small-scale adjustments and refinements to existing 112 boundaries. The neighbourhood relationships tool of eCognition was used to good effect here where all areas which bordered an existing 112 polygon were isolated and spectrally checked to see if they matched the spectral characteristics of the neighbouring artificial surface class. If they did, they were assimilated into the neighbouring polygon. Figure 5.4 shows a completed example of this process whereby 112 and 121 areas on the outskirts of Limerick city which were not mapped in the existing CLC 2006 dataset were then ‘found’ by using this neighbourhood search technique to scan the outskirts of the city for any omitted artificial surface segments. Other artificial classes which were significantly revised were classes 121, 133 and 141 which saw an increase in total area mainly through more accurate delineation of artificial landcover boundaries. Independent national figures on the percentage of the country covered by artificial fabric are hard to come by as many studies reference CORINE itself. A look for proxy indicators found that the national population Census of 2011 gives the percentage of land as ‘Urban’ (calculation based on dwellings) at 2.4% which would broadly reflect the existing and revised CLC figures (2.29% and 2.45% respectively). The GMES soil sealing layer (SSL) of 2006 mapped imperviousness surfaces on a 20m pixel resolution giving the degree of sealing on a 0-100% scale. Taking areas with a sealing percentage of 30% and over the SSL give a figure of 1.3% sealing of the national land area. This would suggest that the CLC dataset is most likely overestimating the amount of artificial fabric due to its coarse resolution. On the other hand the coarse resolution of CORINE is most certainly leading

41

to the omission of many artificial areas which are below the 25ha minimum mapping unit (mmu). Both cases highlight the lack of definitive data on the geographic scale and distribution of artificial and sealed surfaces in Ireland. Whereas the statistics point to minimal revision in this group overall (+0.16% na), a visual qualitative inspection of the finished dataset shows that there has been significant improvement in mapping portions of towns of villages which were not mapped as 112 or other artificial fabric in the existing CLC 2006 dataset.

Figure 5.4 Black arrows indicating revision of agricultural land (231) to artificial land (112 & 121) east of Limerick city. Extent of original artificial polygons highlighted in blue, revised boundaries shown in red.

Agricultural Classes (2xx)

Agricultural land accounts for over 67 % of the national land area (table 5.1), making it by far the dominant landcover type in Ireland. EU and national agricultural policies and their downstream farmland management practises have a significant influence on biodiversity, water quality and carbon emissions on a local and national scale. For these reasons, it is imperative that the main agricultural landcover classes in CORINE, as the de-facto national landcover dataset, are mapped accurately and classified correctly. Bearing this in mind, the agricultural classes were targeted from the outset as classes in need of revision and validation. The utilisation of the LPIS dataset made it possible to undertake this widescale revision of the agricultural classes in CLC 2006, bringing it forward into CLC 2012 and future CORINE updates. Agriculture in Ireland is dominated by grassland pasture of varying degrees of improvement with localised regions of medium-intensity arable tillage in particular the south-eastern counties, north east Leinster and south east Munster. Separating the two main agriculture classes of pasture (231) and arable (211) is often straightforward due to the distinctive spectral reflectance properties of some tillage crops. However, there are many exceptional cases where it is difficult to confidently discern between the two classes. Although agricultural grass is quite a homogenous vegetation type, the spectral signature of a pasture field can be easily confused with arable crops at certain times of

42

the year due to land management practises. Grass-covered fields are commonly cut for silage and hay up to three times per year. These freshly cut fields can often be mistakenly classified as arable crops due to their bright spectral signature on satellite imagery. Grasslands fields are also periodically ploughed and re-seeded leading to confusion with recently sown arable parcels. Arable crops come in a variety of types, species and growth seasons, making it a highly heterogeneous landcover class in spectral terms. It is often quite clear when a field is sown with crops but in some cases the spectral properties can suggest it is grassland, an example being young winter or spring wheat in spring imagery. Routine annual and inter-annual rotation between crop types and from pasture to arable is also common practise in Ireland. All these factors combine to make it at times, difficult to confidently discern if an agricultural land parcel is either pasture or arable land. Similar to Ingmar et al. 2014 which found that the best time of year to distinguish between improved and semi-improved pasture types in Ireland is in spring, the experience of this project was that late Spring was the best time to spectrally discern between pasture and arable fields. At this time, pasture fields of varying degree of improvement have homogenous medium - range Normalised Difference Vegetation Index (NDVI) values which is an indicator of the biomass intensity of vegetation. No silage cutting has begun and it is the time of year when summer tillage crops are sown and the ploughed fields are readily identifiable in the imagery. Later on in the summer growth period, the landuse factors described above come into play to override the natural landcover oriented spectral signal of the two classes. The spring IRS imagery used in the project then provided a good ‘snapshot’ of the agricultural landscape before the major summer growth season began. This imagery was then used in conjunction with the LPIS attribute information to try and separate pasture and arable agricultural areas as accurately as possible. As a whole, the 2xx group saw a 1.42% na upward revision in area but this masks a more significant internal reconfiguration between the classes within the 2xx group as discussed below.

Revision of 231 (Pasture)

The 231 class was revised upwards by 3.84% from 50.53% na to 54.37% na. This increase in area came mostly from the 211 class which was revised downwards by 2.4% na and the 242 class which was also revised downwards by 1.17% na. There was undoubtedly a significant amount of secondary revision in and out of the 231 class as it borders all other landcover classes across the country which would have added to or taken away from the 231 class when they were revised themselves. This secondary pasture related revision (e.g. revision of coastlines, peatlands, forestry, etc.) seems to have evened itself out with the decrease in 211 and 242 roughly equating to the increase in pasture.

Revision of class 211 (Arable lands)

As referred to above the 211 arable land classes was revised downward from 7.53% na to 5.13% na. This is one of the most significant revisions of the whole dataset. Per hectare, arable land has a higher carbon emission value than pasture land and is also subject to higher degrees of fertilisation, chemical treatment, etc. Any revisions to the 211 statistics would have a significant effect on the results of studies which use CORINE as a data source to estimate ecological conditions or carbon emissions on a national or local scale. An initial inspection of the existing CLCL2006 dataset led the INT to feel that there was a general over estimation of arable lands and a definite miss-classification of pasture lands in the western counties as arable, see Figures 5.6. Arable fields, in particular contiguous arable plots >25ha are quite rare in western counties due to the climatic and soil conditions present in the west. The revision of arable plots, based on a combination of information from the LPIS dataset and spectral signatures of satellite imagery, was significant in terms of both total area and also total count of polygons. The number of arable polygons was reduced nationally by 38% from 4,061 to 2,509. A look at this on regional scale shows that the total number of arable polygons in Connaught was revised down from 600 to 35 plots (94.2% reduction). A similar analysis of the south eastern

43

region where arable land is a traditional farming activity shows that the count was only reduced by 15.9% from 1,011 to 851. Similarly in the North east (Counties Dublin, Louth Meath and Kildare) where the count was only reduced by 8.2% from 633 to 581 arable plots. This regional bias in revision and its consequent results concur with the accepted wisdom that arable farming on a scale >25ha is virtually non-existent in the West and North-Western Counties and is confined to the main tillage regions of the South East, North East, and East Munster with a narrow band of tillage land east of Ballybofey in Co. Donegal. This can be seen in Figures 5.8 & 5.9 which shows the difference in national distribution of arable plots in the existing and revised CLC 2006 datasets.

Figure 5.6 Example of incorrect classification of pasture land as arable land (highlighted) in Erris, Co. Mayo

Revision of classes 242, 243 and 222

Class 242 (Complex cultivation patterns) refers to, in an Irish context, an area with a mix of arable and pasture where one type does not exceed 25ha in area or 30% of the total polygon area. These rules are based on manual inspection and decision making by an interpreter and can be very difficult to implement in an automated remote sensing-based classification due to the spectral confusion between pasture and arable land mentioned above. It is also difficult to directly translate landuse information in LPIS into this class as LPIS does not give relative distribution of landuse in a mixed landuse farm parcel. This, added with the recommendation of the CORINE Technical Team to minimise the use of this class, led us to adopt the policy of trying where possible to revise existing 242 polygons into one of the pure agriculture type of pasture or arable. To this effect, the 242 class was significantly revised downwards from 2.06% na to 0.97% na.

Figure 5.8 National distribution of 211 Arable Figure 5.9 National Distribution of 211 Arable

polygons in the existing CLC 2006 dataset. polygons in the revised CLC 2006 dataset.

The 243 (Agriculture with significant areas of natural vegetation) class is another heterogeneous landcover class and can be difficult to map consistently across the country. In an Irish context, the class represents extensive areas of agriculture, mainly pasture land but also including arable land which, regardless of the degree of land clearance, drainage and improvement, contains a significant proportion (>25%) of natural vegetation such as scrub, forestry, wetlands, peatlands. The 243 class is an important class as it is used as a proxy by ecological users of the dataset to identify potential areas of high biodiversity and habitats for flora or fauna species. To maintain the integrity of this class for such applications, a cautionary approach was taken where land was only mapped as 243 where there was good reason to believe, obtained from satellite imagery and manual interpretation, that there was a significant amount of natural vegetation present in the land parcel. Poor quality land with a low degree of improvement was not automatically included in this class and was classified as 231 pasture if no natural vegetation other than grass was present. The 243 class had a regional variation whereby areas of better land quality in the south and east were characterised by improved pasture or arable land bordered by small ‘natural islands’ of mature broadleaved forestry and linear riparian zones along river banks. In areas of poorer land quality, particularly in the west and northwest, the 243 class was typified by extensive marginal pasture land with an irregular combination of scrub, mature woodland, wet grassland species, peat bog, dense hedgerow networks or upland semi-natural habitat types. The class as a whole was revised upwards in area from 6.27% na to 7.07% na. Whilst the increase is not significant on a national scale, it is hoped that these areas are now more accurately mapped thanks to the utilisation of the NIR and SWIR bands of satellite imagery complemented with visual verification. The 222 class (Fruit trees and berry plantations), was not mapped in the existing 2006 dataset. This class does not normally occur in plots >25ha in Ireland and can also be difficult to spectrally discern from 211 arable land but using information gathered in the LPIS dataset, this class was added to the Irish CLC dataseries with six instances of this class >25ha mapped, adding up to 288 ha in total.

Figure 5.10 National distribution of forestry parcels in CLC 2006 revised

Forestry and semi-natural classes (3xx)

The Level 1 3xx group has two main components – Forestry and semi-natural habitats.

Revision of Forestry (311,312 313 & 324)

Forestry is one of the main landcover types in Ireland and is the fastest growing in terms of national land area coverage. It is also a vital landcover type in terms of national carbon accounting and more local-scale biodiversity, environmental and ecological ‘health’. According to the 2006 National Forestry Inventory (NFI) and the 2008 State of the Environment Report, Ireland is the least forested EU country with the percentage of forested land just under 10%, well below the EU average of 30%. A significant amount of work went into the revision of forested areas and whilst the statistical values have only been marginally revised overall, significant improvements in the delineation of forest parcels has been achieved by the application of the ancillary forestry vector data. The total area under forestry has been revised down from 9.95% na to 9.26% na (708,716 ha to 657,979 ha). The Forestry07 dataset gives a national forested cover at 679,485 ha which is in between the two figures. The 2006 NFI is a good source of independent data for comparison as it was produced independently of the Forest Service vector data used in this project. Table 2.1.2 in the NFI 2006 report* gives the area of stocked forested land at 625,750 ha or 9. The revision of CLC 2006 would therefore seem to bring the dataset more in line with the NFI results and the Forest Service’s accepted figures on national forestry cover. The revision of forestry was most prominent in the 324 class which was reduced by 1.94% na. The gains in the mature classes of 312 and 313 would have accounted for some of this revision with *http://www.agriculture.gov.ie/media/migration/forestry/nationalforestinventory/nationalforestinventorypublications/4330NFIResults.pdf

46

some areas possibly being revised as 243. The increase in accuracy of the spatial boundaries of forest parcels are seen as one of the biggest success of the whole revision process. Also internal boundaries between intact forest stands and sub-parcels off clearfelling were improved using the NIR and SWIR bands in the satellite imagery. This was one disadvantage of the vector data as although it mapped forest parcels since 1998, areas of clearfelling were not updated and so they had to be identified spectrally.

Figure 5.10 Existing CLC 2006 classification of forestry parcels (red arrows indicate inaccurate boundaries)

Figure 5.11 Revised CLC 2006 dataset showing

improved forest parcel boundaries

Revision of semi-natural classes (321, 322, 331, 332 & 333)

Revision of these semi-natural classes was done mainly by objective visual interpretation. Revision was not as widespread as in the agricultural and forested classes but was significant in terms of the overall area of the individual classes involved.

321 - Natural grassland

Although grassland covers approximately 60% of the Irish land area (Bryne, 1996, CSO 2012, BEC, 2013) there are very few grassland areas in Ireland which could be characterised as being in a fully natural state. The vast majority of grassland in Ireland is managed by being improved to some degree, used for grazing or hay and silage production. Low productivity grassland which may be considered for inclusion in this class is, in an Irish context, normally used for extensive, marginal grazing by cattle or sheep and is modified to some extent by the grazing actions of livestock on the land. Land in Ireland which is not managed, grazed or altered in any way by man will naturally revert to scrub and native deciduous woodland over time (Cross, 1998), not grassland. There is however, a significant proportion of low productivity, biologically diverse agricultural grasses nationally and these are generally referred to as semi-improved grasslands. Although in some cases these may qualify under the criteria for the 321 class in CORINE, it is difficult to separate genuine semi-improved grasslands from bad-quality rough grazing land which is highly modified by grazing and would be more suited to the 231 Pasture class. The decision was made to map semi-improved grasslands as either Pasture (231) or Agriculture with areas of significant areas of natural vegetation (243) unless there was in-situ data or other information to suggest the grassland is in a near natural state. The area of natural grassland was revised downwards from 1.26% na to 0.64% na. This is a significant reduction in area (44,326 ha) and the majority of revisions took place on peatland or marginal pasture land, particularly on upland areas which were incorrectly mapped as natural grassland.

322 - Moor and Heathland

Heathland habitats in Ireland are open areas of dwarf shrubs and mosses (Fossitt, 2000) and can be typically found on peat soils and /or upland areas. Distinguishing between heathland and peatland is

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difficult to do as there is a significant amount of overlap in their definitions so that an area of land could potentially be classified as either. The main distinction is the presence of significant surface heath vegetation on top of the peat surface. This is common on intact, non-modified peat complexes. In this case the peatland would have a landcover of heathland and so would be classified as 322 Moor and Heathland in CORINE. In the revision process any peatland with significant heath vegetation detectable on its surface was re-classified from 412 (Peatland) to 322. The 322 class was consequently revised upwards from 0.78% na to 1.35% na. This typically occurred on extensive upland slopes of blanket bog.

332 & 333 Bare rock and sparsely vegetated areas

The 332 (Bare rock) and 333 (Sparsely vegetated areas) classes were revised during the visual verification stage using satellite imagery and aerial photography. A significant amount of upland areas with low levels of soil or vegetation cover were mistakenly classified as 412 peatland and so these were revised where it was evident that the amount of rock outcrop exceeded the thresholds for the 332 and 333 classes (90% and 50% respectively). A slope threshold was also used on the 412 peatland class, where upland areas with a slope >25% were assessed for re-classification. From these steps a certain amount of 412 areas were reclassified as either 332 or 333. This typically occurred in high mountains or upland areas particularly in the mountain ranges on the Iveragh and Beara peninsulas in Co.’s Cork and Kerry. Figure 5.12 below shows a large area of the Caha mountains which were revised from 412 to 333 due to the large amount of exposed bedrock on the mountains. The 332 class was revised slightly from 0.20 to 0.22 whereas the 333 class was more significantly revised from 0.29% na to 0.77% na.

Figure 5.12 Healy pass in the Caha Mountains, Co Cork

Revision of 412 Peatlands

Peatlands are a significant national landcover class in Ireland accounting for 15.37% na in the existing CLCL2006 dataset. They are significant in terms of national carbon accounting, biodiversity

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and as a source of industrial and domestic fuel. The 2006 figure for peatlands was revised downwards by 57,787 ha to 14.58% na in the revision process. As stated before some peatland was reclassified as heathland or bare rock but this masks the true extent of revision which saw a lot of re-configuration of inaccurate boundaries of peat complexes. Extensive peatland areas typically contain pockets of non-peat landcover, which were not always mapped accordingly. Added to this was widespread inclusion of significant amounts of pasture and forestry along 412 boundaries due to inaccurate separation of the 412 polygon from the surrounding area. Where possible these errors were corrected, where there was a significant amount of vegetation detected on the peat surfaces the NIR & SWIR values of the imagery was used to separate this into pasture (231), heath (322) or scrub (324). As stated above, if the slope of upland 412 areas was found to be too steep for viable peat accumulation (>25%) or there was significant amount of rock outcrop visible the areas was reclassified as 321, 322, 332 or 333.

Figure 5.13 National distribution of peatlands (412) in CLC 06 revised

Revision of Water bodies (5.1.x & 5.2.x)

Although it was not intended at the outset of the project, it was decided after the first external verification to use EPA vector data on water bodies to revise inland water bodies and the national coastline. This was done to improve the precision and accuracy of the boundaries of these classes as their alignment was found to be inaccurate by >100m in some places. The vector data was intersected with the existing CLC dataset and where the two did not agree, priority was given to the in-situ vector data. Due to the 25ha mmu and 100m width rule many smaller rivers and lakes were generalised and not included in the final dataset. The overall area of these classes was not significantly changed however, as with other classes, minor adjustments to the boundaries led to overall improvements in the dataset. Figure 5.14 below gives an example of how the boundary of a water body (Lake Boffin, Co. Leitrim) has been improved through revision based on vector data.

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The overall area of sea and ocean (class 523) was reduced by 5,059 ha which was re-distributed into other classes which bordered the coastline and were revised themselves using the coastline data.

Figure 5.14 Lough Boffin, Co. Leitrim – yellow arrows indicate examples of boundary revision

Summary of revisions

The comprehensive revision of the existing CLC 2006 dataset resulted in a widescale re-drawing and re-alignment of landcover boundaries so that they now resemble real and distinctive landcover features on the ground. The classification of these refined polygons is now backed up by the most detailed national in-situ data available and complex interpretation of satellite imagery. Through the revision there has been a marked reduction in the overall amount of individual polygons from 22,190 to 18,477 (16.73%). This may suggest a simplification of the dataset but the reality is that there was a degree of ‘over-mapping’ in the original dataset, where pasture areas (231) in particular were regularly incorrectly mapped as arable (211) or Complex cultivation patterns (242). The largest reduction in polygon count per class was in the 211, 231, 242 and 324 classes (38.22%, 32.74%, 57.47%, and 33.67% respectively). The 242 and 211 classes saw the highest amount of revision per in terms of their original area with both being revised downwards by 57% and 32% respectively. The dominant 231 pasture class was revised upwards by 7.4% of its original area with the area of artificial surfaces also increasing by 13% of its original area. Wetlands and forestry were both also revised downwards by 5% and 2% of their respective original areas.

5.2.2 Discussion of change mapping results

As mentioned in section 5.1.2, there were eight dominant change types with four main change categories.

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1) Agricultural rotation

There was a significant amount of change, both ways, between the pasture (231) and arable (211) classes. Long term change from arable to permanent pasture or vice versa does occur but what would account for most of this change would be annual or multi-year rotation of field parcels from arable to pasture or hay/silage and back to arable. CORINE maps landcover by taking a ‘snapshot’ of the two reference years (2006 and 2012), not by tracking change through the intervening years. The disadvantage of this approach is that we cannot ascertain if a perceived change between these two classes is part of a permanent shift in agriculture type or as part of a temporal rotational system. A system of tracking agricultural land parcels through each year would enable the discrimination between actual ‘permanent’ pasture fields and grass fields that are part of a rotational system.

Figure 5.15 National distribution of changes between the arable and

pasture classes in the 2006-2012 change dataset.

There was however, a significantly higher amount of change from arable to pasture (33,731ha) than from pasture to arable (19,442 ha). This may be due to a short-term shift from arable to pasture farming due to weather, crop prices, etc., but it could be further adjustment of the arable / pasture classes following the integration of the LPIS dataset into the CORINE mapping process. This should not be a factor in future CORINE iterations, assuming it will be also based on LPIS or its future equivalent. In this scenario, parcel changes can be extracted from the LPIS dataset and used directly in the CORINE change mapping process. This is another significant benefit of adopting the semi-automated approach devised.

2) Forestry - Clearfelling, growth and afforestation

Forestry is the most dynamic landcover groups, being associated with three main types of change. Clearfelling is the harvesting of mature coniferous plantations after a growth period of approximately 20 years. The land will eventually be replanted with another strand of forestry and so

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where clearfelling occurs we assume that it is a change from mature coniferous to transitional scrub (312-324). This was the largest change class by area in the forestry sector representing the harvesting of 29,419 ha of mature coniferous forestry. Forestry growth is the maturation of transitional forestry strands into mature coniferous plantations (324-312). 22,599 ha of forest were deemed to have matured in the six year period from 2006 to 2012. Initial attempts to categorise this maturation by planting date proved unreliable due to high variation in growth rates across the country and so the biomass of a forestry strand was assessed using the NIR and SWIR bands of the satellite imagery which enabled good discrimination between transitional and mature coniferous forest parcels. Afforestation is the planting of forestry on non-forested land. The majority of afforestation took place on agricultural land and particularly on pasture land where there was 930 instances and 11,499 ha of afforestation on pasture land (231-324). Taking into account afforestation on arable and other agricultural land, there has been a permanent conversion of 143 km2 of agricultural land to forestry over the six year period of the study. Along with this, 219 instances (3,656ha) of peatland were afforested. Figure 5.16 shows the national spatial distribution of new afforestation plots on peatland and agricultural areas.

Figure 5.16 National distribution of afforestation plots on agricultural areas and

peatland, 2006-2012

3) Completion of Construction Sites

The seventh largest individual change category was the completion of construction sites (133-112). 238 construction sites (3,295 ha), were converted into the Discontinuous urban fabric (112) class, whilst another 1,167 ha and 724 ha were converted into Road and rail networks (122) and Industrial or commercial units (121) respectively. The 1,167 ha converted into the 122 class represents the completion of a number of national motorways which were undertaken prior to 2006. On the other side of this there were only 38 new construction sites with a combined area of 367 ha identified. This represents the downward trend in development nationally since 2008.

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4) Other change

The rest of the change is taken up with technical change (2.04%) and other changes (6.78%). Within this there are a number of significant individual changes notably five instances (146 ha) of peatland changed to 131 (mineral extraction sites). These are either rock quarries or industrial peat extraction facilities constructed on peatbogs. There was 78 ha of construction reverted back to pasture which represents a national scale phenomenon of unfinished housing estates known as ‘ghost estates’. There were also some unlikely changes which may be considered to be unfeasible but they do occur in certain circumstances. A change of 37.49 ha of beach to sea and ocean (331-523) represents a large scale natural removal of a sand bank at Glenbeigh, Co. Kerry. A change of 20.46 ha from sea and ocean to port areas (523-123) represents the building of a harbour wall in Greystones, Co. Wicklow and a 8.13ha change of a waterbody converted to road and rail infrastructure (512-122) took place at the construction of the Limerick bypass tunnel under the Shannon where a lake was dammed and a dual carriageway constructed at the southern entrance to the tunnel.

Summary of changes

Landcover changes in Ireland from 2006 - 2012 as mapped in the CLCCh 2006- 2012 dataset are dominated by cyclical changes within the agricultural and forestry landcover types. In agriculture, annual changes of agricultural land from pasture to arable and vice-versa accounted for 38.45% of all change per area. In forestry changes from transitional woodland to mature coniferous and vice-versa accounted for 37.63% of all change per area. The changes in agriculture reflect an annual rotation from one type of landuse to another whilst the changes in forestry reflect the growth and clearfelling cycle of planted coniferous forestry. Apart from clearfelling/ re-planting cycle, forestry saw a net gain of 1.92% in area due to new afforestation of land which was previously classified as agriculture and peatland. This accounted for 12.73% of all change. The other main change type was the completion of construction sites which accounted for 2.38% of all changes. There was not so much conversion of non-artificial surfaces to artificial (1.16%) which shows the slowdown in urban ‘landtake’ as a result of the collapse of the construction sector since 2008.

5.2.3 CLC 2012 Landcover dataset

The CLCL2012 landcover dataset is a derived product from the updating of the CLC 2006 dataset with changes mapped in the CLCCh 2012 dataset. It now forms the most up to date, national scale dataset available on landcover. The wide scale revisions undertaken in the CLC 2006 dataset have been carried through to the 2012 dataset through accurate change mapping aided by ancillary vector data and remote sensing techniques. The data series is now directly aligned to and compatible with national high resolution in-situ data on agriculture, forestry and water. Updating the CORINE dataset in future will be greatly aided by this. Agriculture is still by far the dominant CORINE landcover group in the country in 2012. This has reduced slightly from 68.25% na to 68.13% na. 3xx Forestry and semi-natural areas class were the only group to have shown an increase in national area, increasing by 0.12% na to 11.49% na. This increase is driven by afforestation, highlighted by the increase in forested areas of .11% na. Artificial areas saw a slight increase of 0.04% na reflecting the slow down in the construction and development within the reference period. There was a decrease of .04% na in wetlands, driven by afforestation on peatland. A direct statistical comparison between the 2006 and 2012 datasets does not give an exact representation of all changes mapped in the change dataset due to the difference in spatial scales between the 5ha change dataset and the 25ha landcover datasets.

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6 Conclusion

As part of the CORINE 2012 update project for Ireland, an extensive revision of the CLC 2006 was undertaken. The aim of this was to address accepted shortcomings in the quality of the existing dataset. A combination of ancillary vector data and interpretation of satellite imagery was used to both revise the existing 2006 dataset and to identify changes within the 2006-2012 reference period. The highly detailed Land Parcel Information System (LPIS) was used as a direct source of landuse information for agricultural areas, vector data from the Forest Service and Coillte was used to aid the mapping of forested areas whilst national scale data on water bodies from the EPA was used to improve the mapping of water feature boundaries and the coastline. The ancillary data was generalised into a 5 ha intermediate dataset which was coded to the CORINE classification schema. This intermediate dataset was then intersected with the existing CLC 2006 dataset with areas of this disagreement being sorted into change or revision via spectral interpretation of the satellite imagery in an object-oriented remote sensing platform. Areas of the existing dataset not covered by ancillary data were assessed by spectral means for areas of mis-classification The existing CLC 2006 dataset was significantly modified through the revision process. Polygon boundaries are now based on the precise and accurate outlines of the ancillary vector dataset, whilst internally within these polygons, the classification is informed by both the ancillary attribute information and spectral properties of the 2006 and 2012 image sets. The dataset has been ‘cleaned’ of erroneous, irregular and miss-classified polygons with the overall amount of individual polygons being reduced from 22,109 to 18,477 (16.73% reduction). The agricultural classes 242 and 211 saw the highest amount of revision per area, being revised downwards from 2.06% na and 7.53% na to 0.89% na and 5.13% na respectively. The dominant 231 pasture class was revised upwards by 3.84% na bringing its national coverage to 54.51% na. The percentage of the country covered with artificial surfaces was revised upwards by 0.16% na to 2.45% na, which is an increase of 6% of its original area. Wetlands and forestry were both revised downwards and now cover 15.79% na and 9.26% na respectively. In the change mapping dataset, cyclical changes between arable and pasture in agricultural areas and between mature and transitional forest in forested areas dominated the change statistics, accounting for 38.45% and 37.63% of all changes per area. The planting of new forestry parcels on agriculture land or peatland together accounted for the biggest landcover ‘type’ change with afforestation making up 12.73% of changes per area. There was not much new construction mapped but the completion of existing construction sites, in particular the new motorway network which began prior to 2006, accounted for 2.38% of all changes. The CLC 2012 dataset was produced by integrating the changes mapped in the CLCCh 2012 dataset with the revised CLC 2006 dataset. Agriculture is still by far the dominant CORINE landcover group in the country in 2012. This has reduced slightly from 68.25% na to 68.13% na. 3xx Forestry and semi-natural areas class were the only group to have shown an increase in national area, increasing by 0.12% na to 11.49% na. This increase is driven by afforestation, highlighted by the increase in forested areas of .11% na. Artificial areas saw a slight increase in area of 0.04% whilst wetlands saw adecrease in area of 0.04% na. A significant amount of effort has been put into firstly improving the existing 2006 CORINE dataset and secondly in bringing these improvements through to the new CORINE 2012 dataset. The spatial accuracy of polygon boundaries and their thematic classification has been enhanced by a production process which integrated in-situ vector data and interpretation of satellite imagery into a semi-automated production model. CORINE is now aligned to and comparable with key national vector data on landuse and landcover. It is envisaged that future updates of CORINE will be able to utilise this alignment and automate a large degree of the change mapping process by obtaining changes derived from these in-situ datasets.

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7 References

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