1. Revising the JRC/EEA EU-level HNV Farmland methodology – expert Workshop
Agenda
Welcome and introduction (Chair: J-E Petersen)
10.15 Session 1: Review of available national data for refining rules on selection of CLC classes by environmental zone, Michael Weiss, UBA Vienna; followed by discussion (introduced by Angela Lomba, CIBIO, Portugal)
11.30 Brief coffee break
Session 2: Options for including a land use intensity dimension into the spatial representation of HNV farmland, Marta Bonato University of Amsterdam; followed by discussion (introduced by Maria Luisa Paracchini, JRC)
12.45 Lunch break
13.45 Continuation of session 2
14.30 Session 3: Comparison of current HNV ‘map’ with satellite data opportunities, Tomas Soukup, GISAT (tbc); followed by discussion (introduced by Doris Marquardt, EEA)
Brief coffee break
15.50 Summing up by organisers and next steps
16.00 End of workshop

2. Revising the JRC/EEA EU-level HNV Farmland methodology
session 1
Review of available national data for refining rules on selection of CLC classes by environmental zone
19 October 2018

3. Work description
Based on the work started in 2017, continue to improve the selection rules of CLC for HNV farmland in the different environmental zones using national experience, case studies and datasets as far as methodological approach is comparable and spatial data are available.
1. Selection of 9 country case studies with available spatial and/or statistical data on national HNV farmland.
2. Comparison of the methodological basis for defining and identifying HNV farmland in the national HNV approaches with the JRC/EEA HNV farmland methodological approach using the 3 HNV types set out in the JRC report (Parachini et al. 2008).
3. The GIS analysis comprises
the comparison of the time-period (actuality) of reference data
the comparative analysis of GIS spatial explicit data
the identification of sources of omission and commission of European wide HNV farmland in comparison to national HNV farmland or biotope mapping data, grouped according to environmental regions and CLC-classes.
The following countries provided national HNV farmland data and biodiversity data respectively (statistical tables and GIS data in original resolution):

4. Country case studies Country Data Format HNV type Resolution Cover
Environmental
Zones
Comparability
spatial
thematic
Austria
national HNV farmland data
1, (2)
1 * 1 km
Alpine South, Continental, Pannonian y
Portugal
Research case study data
1,2
Polygons, rasterized to 100 * 100m
Minho-Lima region, Melgaço municipality
Lusitanian
Netherlands
1,2,3
Atlantic North, Atlantic Central, Continental
Estonia
1, 2, 3 (EHNV, MHNV, RLHNV)
Boreal, Nemoral
partially
Italy
10 *10 km
Alpine South, Mediterranean mountains, Mediterranean North & South n
Romania
national HNV grassland dataset 1
LAU2
Carpathian Region
Alpine South, Continental
Czech Republic
national grassland biotope layer
100 * 100m
Czech Republic
Alpine South, Continental, Pannonian
Germany
national HNV farmland dataset
1,2,3 (EHNV, HNV, MHNV)
1,278 sample sites of 1 km² each
Alpine South, Continental, Pannonian, Atlantic Central, Atlantic North
Croatia
1,3
CLC polygons, rasterized to 100 * 100 m
Mediterranean North, Alpine South, Continental, Pannonian, Mediterranean mountains Y

5. Environmental zones
Austria
Portugal
Germany
Czech Republic
Nether lands
Estonia
Croatia
Sum
Boreal 1
Nemoral
Atlantik North 2
Atlantik Central
Continental 5
Lusithanian
Panonnian
(1)  3
Alpine South 4
Alpine North
Mediteranean North
Mediteranean Mountains
Not considered due to missing spatial comparability:
Italy
Romania

6. CONTINENTAL ALPINE SOUTH
CONTINENTAL
Sum
CLC Class AT DE CZ HR NL o c m
231 pasture
c;o 1 3 2
321 natural grassland
324 transit. Woodland -
211 non irrigated
m; o
242 complex pattern
243 principally occupied
No pattern can be derived for changing selection rules for Environmental zones
ALPINE SOUTH
Sum
CLC Class AT DE CZ HR o c m
231 pasture 1 2
321 natural grassland
324 transit. Woodland -
211 non irrigated
242 complex pattern
243 principally occupied 3
o=source of omission c=source of commission m=good match

7. Overall findings
The comparative analysis may be hampered by the fact that the case studies have very different MMUs. The European HNV farmland dataset is based CLC 25 ha, HNV areas are often smaller and cannot be properly represented at this scale (e.g. in the Netherlands or Portugal). Other case studies have observation scales that are much coarser than CLC (Italy – 10 km2, Romania – LAU 2).
Land outside of UAA – the JRC/EEA approach considers areas outside UAA as HNV farmland. This reduces the comparability with national approaches where the UAA is taken as reference point and is based on official agricultural data sets, such as IACS (e.g. Austria, Netherlands)
The ecological definition of HNV farmland (Type 1) differs between European and national approach: often the EU level approach sets a rather high ecological threshold, whereas countries may consider a wider or narrower range of species and ecosystems to represent HNV farmland/farming (for which there can be good reasons) >> there can be different types of HNV farmland, representing higher and lower ecological thresholds, with a wider definition adopted in some national approaches.

8. Overall findings ff
The rule that assigns automatically all CLC classes as HNV in Special Areas (IBA, PBA; CDDA, Natura 2000) should be reconsidered, because this leads to an higher estimation of HNV farmland in these areas (e.g. in Estonia).
Use of National Biodiversity Data (UK, CZ, EE, LT, SE) is problematic as they are irregularly updated. It reduces comparability at European scale and also affects the estimation of HNV farmland changes.
In comparison to national HNV farmland datasets some CLC-Classes arrive at much higher estimates in the European HNV approach. This is due to the binary consideration of CLC classes (HNV yes or no).
Neither the current European nor national data sets are necessarily ‘right’

9. Comparison of National and european hnv dataset

10. Specific findings for CLC classes
(Austria)
1) Sources of commission
CLC classes 321, 231 and 243 often arrive at higher estimation in the European approach. This may result because
a) these classes also occur outside areas that are not UAA
b) the coarse MMU of CLC.

11. Specific findings for CLC classes
(Austria)
2) Sources of omission:
CLC class 211 is not considered as HNV at the European level, but often is indicated at national level. Obviously, there can be small patches with extensive land use within this class that are not covered due to the coarse MMU of 25 ha.

12. Suggestion for improvement/way forward
A) Geometric enhancement
Application of High Resolution Layers
Exclusion of non-agricultural areas
 HRL forest, HRL imperviousness
refinement of higher estimated CLC Classes e.g. 231, 321, 243
 HRL grassland
Time series?
B) Thematic enhancement
Application of intensity mask:
Exclusion of intensively used areas
Improvement of classes with sources of omission in extensively used areas (e.g. CLC-211)

13. We want to point out
Neither the current European nor national data sets are necessarily ‘right’
The comparison only works properly when ‘like with like’ can be compared, in terms of HNV definition and spatial scale of data sets used  Case studies were not always or only partially comparable (thematically, geometrically)
Basically, the analyses have shown that the CLC classes chosen by the JRC/EEA approach can also be found in national case studies.
Nevertheless, many classes come to higher estimations according to the JRC/EEA approach than in national datasets
No general conclusions for whole Environmental Zones can be drawn
the selection of national case studies for comparison is still driven by availability of such data in EEA countries and is not a fully representative selection per environmental zone

14. Contact & Information
Michael Weiß, , Elisabeth Schwaiger, , Gebhard Banko, ,
Umweltbundesamt
Revising the JRC/EEA EU-level HNV Farmland methodology – Expert workshop Vienna 