Assessment versions

Published (reviewed and quality assured)

• 06 Dec 2018, 05:26 PM
  - Ecosystem coverage

Rationale

Justification for indicator selection

MAIN ADVANTAGES OF THE INDICATOR

• Policy relevance: the indicator is highly relevant for the EU 2020 and global biodiversity targets. Ecosystems are components of biodiversity as defined by the Convention on Biological Diversity.
• Biodiversity relevance: the indicator has a high relevance for biodiversity because it indicates the area of available habitats and ecosystems across Europe. If an area decreases drastically it will have a negative influence on the species dependent on that habitat. In that sense this indicator is particularly important for specialist species and endemic species that are dependent on particular habitats in the ecosystem and cannot survive in other ecosystems.
• Well established methodology: the Corine land cover classes (CLC )methodology is widely accepted. The indicator is easy to understand and gives a simple and clear overview of the trends in ecosystems.
• Aggregation to different scales/levels: the CLC data can easily be aggregated at different scales according to user needs. The data unit is hectares

Scientific references

• No rationale references available

Indicator definition

Proportional and absolute change in extent and turnover of land cover categories aggregated to relate to MAES ecosystem types in Europe from 2006 to 2012. MAES ecosystem types are: (1) urban; (2) cropland; (3) grassland; (4) woodland and forest; (5) heathland and shrub; (6) sparsely vegetated land; (7) inland wetlands; (8) rivers and lakes; (9) marine inlets and transitional waters; and (10) marine.

This indicator is based on photo-interpretation of satellite imagery and gives a 'wall-to-wall' picture of the changes and dynamics in Europe with respect to ecosystems. Additional indicators can be used to further highlight trends in extent and state of each of the ecosystem types mentioned above using computations from other data sources.

Units

The area change is MAES ecosystem classes is measured in hectares (ha) or square kilometres (km²).  The change in MAES ecosystem classes coverage is measured in percentage (%).

Policy context and targets

Context description

This indicator uses photo-interpretation of satellite imagery to give a rough picture of the trend in the observed area and proportion of the major ecosystems in Europe since 1990.

Satellite imagery offers the potential to characterise land cover over very large areas in an efficient and very cost effective way. It is possible to produce land cover maps from satellite imagery based on the spectral properties of each pixel within a scene. By grouping pixels into classes with similar spectral properties and associating these classes with particular land cover types, it is possible to produce maps that delineate land cover. Land cover change is then used to indicate the trends in the extent of major ecosystems such as forests, croplands, wetlands, etc. For this indicator we use data from the Corine land cover (CLC) database (CooRdinate Information on the Environment - Corine).

The CLC data are based on 44 land cover classes that are aggregated into 10 MAES ecosystem types for the purpose of this indicator. Spectral properties allow the CLC project to distinguish between land cover classes. For example, CLC has three classes showing forest land cover: broad-leaved forest, coniferous forest and mixed forest. By aggregating the information of these three land cover classes we have information on the extent of the forest ecosystem within the limitations of the CLC data (see section on main disadvantages). The CLC data however are the best available at present to cover large areas of Europe in a harmonised way.

Targets

EU Biodiversity Strategy 2020 — headline target and Target 2

Related policy documents

• EU 2020 Biodiversity Strategy
  in the Communication: Our life insurance, our natural capital: an EU biodiversity strategy to 2020 (COM(2011) 244) the European Commission has adopted a new strategy to halt the loss of biodiversity and ecosystem services in the EU by 2020. There are six main targets, and 20 actions to help Europe reach its goal. The six targets cover: - Full implementation of EU nature legislation to protect biodiversity - Better protection for ecosystems, and more use of green infrastructure - More sustainable agriculture and forestry - Better management of fish stocks - Tighter controls on invasive alien species - A bigger EU contribution to averting global biodiversity loss

Methodology

Methodology for indicator calculation

MAES ecosystem classes correspond to the following CORINE sub-classes:

(1)   Urban: 1.1.1., 1.1.2., 1.2.1., 1.2.2., 1.2.3., 1.2.4., 1.3.1., 1.3.2., 1.3.3., 1.4.1., 1.4.2.,

(2)   Cropland: 2.1.1., 2.1.2., 2.1.3., 2.2.1., 2.2.2., 2.2.3., 2.4.1., 2.4.3., 2.4.4.,

(3)   Grassland: 2.3.1., 3.2.1.,

(4)   Woodland and forest: 3.1.1., 3.1.2., 3.1.3., 3.2.4.,

(5)   Heathland and shrub: 3.2.2., 3.2.3.,

(6)   Sparsely vegetated land: 3.3.1., 3.3.2., 3.3.3., 3.3.4., 3.3.5.,

(7)   Inland wetlands: 4.1.1., 4.1.2.,

(8)   Rivers and lakes: 5.1.1., 5.1.2.,

(9)   Marine inlets and transitional waters: 4.2.1., 4.2.2., 4.2.3., 5.2.1., 5.2.2.,

(10)  Marine: 5.2.3.

The marine ecosystem is currently represented only by the MAES Marine class equivalent to CORINE Land cover class 5.2.3. 'Sea and ocean' that represents the zone seaward of the lowest tide limit and extends to the limit of 12 nautical miles out to sea. It does not include coastal lagoons and estuaries.

Methodology for gap filling

No gap filling was used in this indicator.

Methodology references

• Mapping and Assessment of Ecosystems and their Services. An analytical framework for ecosystem assessments under action 5 of the EU biodiversity strategy to 2020. Maes J, Teller A, |Erhard M, Liquete C, Braat L, Berry P, Egoh B, Puydarrieux P, Fiorina C, Santos F, Paracchini ML, Keune H, Wittmer H, Hauck J, Fiala I, Verburg PH, Conde S, Schagner JP, San Miguel J, Estreguil C, Ostermann O, Barredo JI, Pereira HM, Stott A, Laporte V, Meiner A, Olah B, Royo Gelabert E, Spyropoulou R, Petersen JE, Maguire C, Zal N, Achilleos E, Rubin A, Ledoux L, Brown C, Raes C, Jacobs S, Vandewalle M, Connor D, Bidoglio G (2013). Publications Office of the European Union, Luxembourg.

Data specifications

EEA data references

• Corine Land Cover 2012 raster data provided by European Commission
• Corine Land Cover 2006 - 2012 changes provided by European Commission
• Corine Land Cover 2006 raster data provided by European Commission

Data sources in latest figures

Uncertainties

Methodology uncertainty

Datasets have been processed according to the land accounting methodology. Both for facilitating computation and visualising spatial change, land accounts are processed using a grid of 1x1 km. Each cell contains the exact CLC values but spatial aggregations are made of entire grid-cells, which may lead to some very limited marginal uncertainty for the border of a given national or regional land unit.

Differences in CLC change mapping technology (1990-2000 and 2000-2006):
the
In CLC1990-2000 changes were mapped by countries usually by intersecting CLC1990 and CLC2000 stock layers. The results were not always cleaned and non-changed parts might have remained in the CLC 1990-2000 changes dataset. On the other hand, isolated changes below 25 ha could not be mapped by this technology. In CLC 2000-2006 and CLC 2006-2012, changes were mapped directly. This way all changes exceeding 5 ha were mapped and non-changed areas were better excluded from CLC-Changes.

Data sets uncertainty

Geographical and time coverage at European level: 28 EU Member States are covered by all CLC 2000, 2006 and 2012 results. Land cover changes in Liechtenstein remained below the detection level of Corine Land Cover change methodology. In all EEA countries, the number of years between two CLCs is 6 years.

Representativeness of data on national level: At the national level, time differences between regions may happen in most countries and these are documented in the CLC metadata.

Rationale uncertainty

MAIN DISADVANTAGES OF THE INDICATOR

The use of remote sensing data implies that some degree of detail is lost. The Corine land cover data set is based on a minimal unit of 25 hectares and this implies that smaller areas of certain habitat types and linear features may not be adequately detected. Other data sets (e.g. statistical offices reporting for forests, cropland, grassland area) cannot be combined in this indicator calculation because the different definitions used as well as the different frequencies in updating will produce incomparable trends.