One Ecosystem :
Research Article
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Corresponding author: Karsten Grunewald (k.grunewald@ioer.de)
Academic editor: Carl Obst
Received: 30 Jan 2020 | Accepted: 30 Apr 2020 | Published: 04 May 2020
© 2020 Karsten Grunewald, Burkhard Schweppe-Kraft, Ralf-Uwe Syrbe, Sophie Meier, Tobias Krüger, Martin Schorcht, Ulrich Walz
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Grunewald K, Schweppe-Kraft B, Syrbe R-U, Meier S, Krüger T, Schorcht M, Walz U (2020) Hierarchical classification system of Germany’s ecosystems as basis for an ecosystem accounting – methods and first results. One Ecosystem 5: e50648. https://doi.org/10.3897/oneeco.5.e50648
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Information on changes in the area of different ecosystems is needed in order to establish an accounting system for ecosystem conditions and services. Currently, there are no comprehensive field mappings for the German federal states that obey a uniform mapping system. To create a nationwide “ecosystem accounting”, it is necessary to develop a uniform system of ecosystem classifications that can consistently deal with diverse nationwide data sources on the extent and condition of ecosystems, some of which use their own forms of classification. Against this background, we present a concrete proposal on how to combine and blend GIS land-use and ecosystem data that is compatible with EU-wide approaches with other regularly collected data sources, for example, from sample-based surveys, so as to generate a complete, updatable picture of the state of Germany’s ecosystems. The area shares of ecosystem types (ETs) can be shown in maps. Allocation tables with different classes or levels (layers) enable an ecosystem extent accounting, which are used to help draw up balances (area balance, status balance, service balance) and can be further detailed, depending on the task at hand. First results and trends of areal changes of main and sub-ecosystem types in Germany, based on the proposed classification system, are presented and discussed. However, the brevity of the considered timeframe (the three periods 2012-2015-2018) does not yet allow us to pinpoint trends or migratory movements, as these may be masked by methodological changes in the classification of land use and land cover. Nonetheless, the presented system for accounting changes in ecosystem areas should be continued and developed in the future in order to create a useful tool for biodiversity monitoring in Germany.
accounting, area changes, biodiversity, classification, CORINE land cover, ecosystem service, federal level, habitat type, monitoring
In 2011, the member states of the EU made a commitment to map and assess the state of ecosystems and their services and to integrate the results into European and national reporting systems by 2020 (
The MAES framework provides the following modules for the assessment of ecosystems and their services (
In accordance with the requirements of the EU Biodiversity Strategy 2020, a system of national initial assessment and evaluation of ES for Germany was first developed and coordinated. The assessment of ecosystem condition comprised mainly pressures, the natural condition of ecosystems and the state of biological diversity. Ecosystem services were measured and evaluated primarily based on land use, official data for agricultural and timber production, water use and population data (
In addition to the EU Biodiversity Strategy, other international conventions and institutions also address the link between the state of ecosystems, biodiversity and ES, such as:
The consideration of ES (and the use of appropriate terminology) is also embedded in other EU policies, such as the Green Infrastructure Strategy, the Forestry Strategy, the Regulation on Invasive Alien Species, the Marine Framework Directive and the Common Agricultural Policy (CAP)/Rural Development (
In order to create an exhaustive accounting of ecosystem conditions and services, we require a shared informational basis of changes in the spatial extent of diverse ecosystems (i.e. ecosystem extent account). The ecosystems must be classified in such a way as to meet the requirements of the subsequent accounting of their condition and services, which can vary greatly depending on regional and local ecological, economic and social conditions. The aim is to demonstrate the importance of ecosystems for society and to record their changes as a basis for political action. As in other statistically-orientated work (and in contrast to the field of planning), this is done here in the field of SEEA without the development of direct proposals for action.
Currently, Germany’s federal states do not follow a uniform mapping system to provide an exhaustive recording of ecosystems. To establish nationwide “ecosystem accounting”, it is therefore essential to develop a common structure of ecosystem classifications, with the help of which diverse national datasets on the extent, condition and services of ecosystems (some of which use their own forms of classification) can be consistently integrated to create a standard ecosystem accounting system. With this aim in mind, we present here a concrete proposal on how uniform and spatially accurate national datasets of GIS data on land uses and ecosystems, which are compatible with EU-wide approaches, can be integrated with other regularly updated data sources, including sample-based surveys, so as to generate as complete and up-to-date picture of Germany’s ecosystems as possible, that can be filled with the available data on condition and linked with data and required models on ecosystem services.
The challenge to be overcome is to find the degree of thematic detail, especially whether and which functional characteristics should be included in order to support the further accounting steps, e.g. analysis of spatial migration between ecosystems, integrating relevant condition parameters and applying very different models and valuation approaches to assess ecosystem services, including the contribution of the various ecosystems to biodiversity protection on the national scale.
It is important to note at this point that the ecosystem extent account, presented here, was developed in a research project that aimed to develop pilot accounts for three ecosystem services. Criteria for the selection were: a broad spectrum of services, a good data basis and the development of internationally innovative methods, based on German experience. The decision was in favour of the contribution of ecosystems to agricultural production, ecosystem services of urban green spaces and the role of different ecosystem types for conserving biological diversity in Germany.
It is also worth noting that the condition account, which is normally regarded as a necessary link between extent and service account, was skipped against the following background. In Germany (East and West), in the scientific discussion in landscape planning and physical geography in the 1970s, the position gained acceptance that there is neither a single ecosystem classification nor a single set of condition parameters which is suitable for all land use decisions and can therefore guide land use planning in general. Instead, the concept of "Partial Ecosystem Potentials" was developed, where for each type of use, such as agriculture and forestry, groundwater recharge and extraction, recreation or nature conservation, different types of quality and condition parameters are used (
In the following, we first discuss the basis for recording data on ecosystems at EU and national level (Sections 2 and 3). Based on this, we present a concrete proposal for processing high-resolution data to classify ecosystems in Germany that is compatible with EU-wide approaches. With the ecosystem contribution to conserve biodiversity as an example, we show how this system can be complemented with detailed special information to assess certain services.
In Section 4, we present first results and trends of areal changes of main and sub-ecosystem types in Germany, based on the proposed classification system. Other area monitoring systems, in particular the area statistics of the Federal Statistical Office (German: Statistisches Bundesamt or simply Destatis) and the Monitor of Settlement and Open Space Development (IOER Monitor), will also be included to map the ecosystem extent and changes to this over the last years and decades. The paper closes with some conclusions and comments on existing shortcomings, data restrictions and challenges for the future. The use of ETs to assess the condition and services of ecosystems is only briefly mentioned here, because this can vary greatly from case to case and is not the focus of the paper.
Since the middle of the 20th century, ‘ecosystem’ has been a guiding scientific term for the pragmatic observation of ecological units (
Ecosystem research is a conceptual approach particularly identified by natural scientists, since it encompasses the creation of analytical models to examine the structure and dynamics of spatial regions (
Ecosystems must be understood holistically. This means that their emergent system properties (interactions between their components) behave in a way that cannot be explained by simply analysing each individual component. Hence, we must attempt a holistic understanding, also in terms of our indicators and mappings. Furthermore, landscapes and ecosystems can be viewed, on the one hand, as the result of influential factors of the natural environment or, on the other hand, as a construct of human perception, i.e. as artificial units, whereby the criteria for the selection of the ecosystem components and their delimitation are determined by the particular interests of the observer (landscape as perceived by people). The Biodiversity Convention (
For land cover mapping at the pan-European level, there exist established data sources such as Corine Land Cover (CLC), Land Use and Land Cover Survey (LUCAS) and the Farm Structure Survey (FSS). New high‑resolution Copernicus land‑monitoring products can increase the precision and relevance of these data (
While land cover describes the physical material on the surface of the earth and its characteristics (e.g. grass, asphalt, trees bare ground, water etc.), land use is, by contrast, a description of how people use the land. Examples for land use are urban and agricultural land use, but also institutional land, sports grounds, residential land etc. (
When classifying ecosystems at national level for ecosystem accounting, EU member states are recommended to use the pan-European CORINE Land Cover (CLC) data on land use (
The general MAES typology of ecosystems (
The European Environmental Agency provides a dataset aimed at contributing to a better biological characterisation of terrestrial and marine ecosystems across Europe (EEA-39). It represents probabilities of the presence of EUNIS (European Nature Information System) habitats in terrestrial, freshwater and marine ecosystems. The work supports the Mapping and Assessment of Ecosystems and their Services (MAES), namely Action 5 in Target 2 of the EU Biodiversity Strategy to 2020, established to achieve the Aichi targets of the Convention on Biological Diversity (CBD). Input information and methods are documented in a technical paper (
With regard to the individual approaches of various EU countries in gathering the needed data, we can expect a differentiated picture. This will be outlined below by means of a few examples.
Estonia established a very short project of 18 months, creating a constraint in terms of time and workload (
In Slovakia, a generalised nationwide map of ecosystems has been created using agricultural, forestry and environmental data (
The work in Poland was divided into the following stages (
The resolution of the database corresponds to scale 1:100,000. Minimal mapping units are:
In the Czech Republic, a more detailed map of the ecosystems, the so-called Consolidated Layer of Ecosystems of the Czech Republic (CLES), was developed in cooperation with the Czech Nature Conservation Agency. CLES presents a detailed map of the extent of natural, as well as artificial ecosystems in the national territory. It was developed by means of detailed habitat mapping in the Czech Republic, as well as by exploiting other data sources on agricultural land, urban areas and water bodies. The advantage of CLES is the detailed resolution of ecosystems down to the local level. However, in its current form, CLES cannot be used to track changes in the value of ecosystem services (
The Italian ecosystem types were identified and mapped by integrating the land cover database with additional spatial datasets that focus on biophysical features of the environment, such as bioclimate and vegetation (
The mapping of ecosystems in Bulgaria was based on CLC data and on MAES typology. Drawing on the CORINE dataset, nine main ecosystem types (according to MAES typology) can be delineated in Bulgaria. The scale of the output product was fixed at 1:100,000, giving a spatial precision of the CLC database of 100 m (N).
The following criteria were determined in developing the typology (classification) of ecosystems:
The European CLC classification system was used to define those ecosystem classes that, on the basis of the Digital Land Cover Model for Germany (Digitales Landbedeckungsmodell für Deutschland or LBM-DE), can be delimited with sufficient clarity. Of the 44 existing land cover classes for Europe, identified in the CLC nomenclature, 37 are relevant for Germany (
Proposal for a Systematic Approach to Ecosystem Types (ETs) in Germany (CLC – Corine Land Cover).
Main-ET |
Sub-ET |
CLC code |
CLC class name |
1 Semi-natural open areas |
11 Natural grassland and heathland |
321 |
Natural grassland |
322 |
Moors and heathland |
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12 Wetlands |
411 |
Inland marshes |
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412 |
Peatbogs |
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421 |
Coastal salt marshes |
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13 Open spaces with no or little vegetation |
331 |
Beaches, dunes and sand plains |
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332 |
Bare rock |
||
333 |
Sparsely vegetated areas |
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334 |
Burnt areas |
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335 |
Glaciers and perpetual snow |
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2 Forest and grove areas |
21 Forest |
311 |
Broad-leaved forest |
312 |
Coniferous forest |
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313 |
Mixed forest |
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22 Grove |
324 |
Transitional woodland/shrub |
|
3 Agricultural land |
31 Arable land |
211 |
Non-irrigated arable land |
221 |
Vineyards |
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222 |
Fruit tree and berry plantations |
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32 Grassland |
231 |
Pasture, meadows and other permanent grasslands under agricultural use |
|
33 Heterogeneous agricultural area |
242 |
Complex cultivation patterns |
|
243 |
Land principally occupied by agriculture, with significant areas of natural vegetation |
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4 Water |
41 Streams |
511 |
Watercourses |
42 Inland water bodies |
512 |
Water bodies |
|
43 Marine waters |
521 |
Coastal lagoons |
|
522 |
Estuaries |
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523 |
Sea and ocean |
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423 |
Intertidal flats |
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5 Settlement and artificial modified areas |
51 Buildings and transportation area |
111 |
Continuous urban fabric |
112 |
Discontinuous urban fabric |
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121 |
Industrial and commercial units |
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122 |
Road and rail networks and associated land |
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123 |
Port areas |
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124 |
Airports |
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133 |
Construction sites |
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52 Mining and dump sites |
131 |
Mineral extraction sites |
|
132 |
Dump sites |
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53 Urban vegetated areas |
141 |
Green urban area |
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142 |
Sport and leisure facilities |
Fig.
The proposed classificatory system has been aligned with the EUNIS classification (
The aggregation of types required by the ET classification system takes account not only of the characteristics of land use and land cover, but also the rules of aggregation and the reliability of the data sources. The rules of aggregation state that small elements must be assigned to that ET of which they can be considered as spatial components (e.g. pastoral forests, hedges or bushes to the open land they are located in, springs to wetlands etc.). In order to keep the number of ETs manageable and to be able to work efficiently with the catalogue, land types that very rarely occur in Germany, which can change with great rapidity or are often recorded incorrectly by remote-sensing methods, were assigned to classes from which they sometimes differ to a large degree. This applies, for example, to glaciers (extremely rare), burnt areas (highly dynamic) or certain natural grassland types (rare and difficult to detect). For the sake of reliability, CLC classes that can hardly be distinguished from one another by analysing the base data, are grouped into relatively undifferentiated ETs, for example, coniferous, mixed and deciduous forests, as well as wooded dunes, are assigned to the class forest or wetlands, which also encompasses lowland moors (undetectable from space). Semantic ambiguity – such as the historically or technically controversial distinction between natural, fortified, straightened, diverted or canalised rivers and their (sometimes more natural but nevertheless artificially created) interconnected millraces and “real” canals – was also avoided in view of the small total extent of running waters. Ultimately, the relatively broad typology was chosen to avoid any unnecessary imprecision through the introduction of uncertainties inherent in finer classifications.
The specification of EU-wide approaches and data can be realised for Germany by means of the “Digital Basic Landscape Model” (Digitales Basis-Landschaftsmodell or Basis-DLM), derived from the “Official Topographic-Cartographic Information System” (ATKIS) and the “Digital Land Cover Model for Germany” (LBM-DE) (
Since 2009, the LBM-DE has been used to derive the CLC dataset for Germany (
Alongside the derivation of CLC, the LBM-DE is favoured as a basis for ET mapping due to the fact that data are regularly updated by the BKG, namely every three years. The LBM-DE records land cover throughout Germany and (in part) marine areas semi-automatically by analysing satellite images (RapidEye), basic DLM and auxiliary data (topographic maps, orthophotos) (
As the reporting in LBM-DE is more finely differentiated than the original CLC system, mixed types such as 242 and 243 (complex parcel structure, as well as agricultural areas with a significant proportion of natural biotopes) do not appear in the LBM mapping. To retain consistency with CLC, some biotope types, for which separate classes might otherwise have been created, had to be assigned to the existing CLC classes. The most prominent example of these are the various small structures and biotopes found in the agricultural landscape (see note in Table B in the supplementary material 2). It is essential that no information is going to be lost when making assignments to superordinate types, but that the ultimate value of each superordinate type is entirely determined by its subtypes.
Since the LBM classification is based, amongst other things, on remote sensing data, there is some uncertainty in the assigned ETs. In order to minimise errors in distinguishing arable land from grassland, satellite data of different vegetation periods over the timeframe of one year were used to generate the LBM-DE (
Linear infrastructure and small structural elements (
The applied widths of spatial elements and the assignment to the CLC classes are shown in Table
The ecologically valuable small structures and infrastructure from the German official topographic-cartographic information system (ATKIS) added to the digital land cover model of Germany (LBM-DE), their width and CLC-classes.
Small Structures and infrastructures from ATKIS |
Type of geodata in ATKIS |
Generated widths for linetype geodata |
CLC-class |
Railway traffic (railway stations, stopping points and operational aboveground railway tracks) |
lines/ areas |
single / double track |
122 |
standard gauge |
7.5 / 10.5 m |
||
tramway |
3 / 6 m |
||
(Historical) narrow-gauge train |
2 / 4 m |
||
Road traffic (inclusively hard shoulders and adjacent bicycle tracks and sidewalks; in built-up areas, sidewalks were considered by 2.5 m) |
lines |
according to number of traffic lanes |
122 |
motor-way |
5.5 – 17.5 m |
||
miscellaneous roads |
4.5 – 12.5 m |
||
sealed roads (forestal and agricultural purposes) |
4.5 – 16.5 m |
||
Air traffic (footprints, runways) |
lines/ areas |
5 - 90 m |
124 |
Lanes and paths |
lines |
122 |
|
unsealed roads (forestal and agricultural purposes) |
4.5 – 16.5 m |
||
tracks for bicyling, walking and horse riding, fixed rope routes, steep tracks, footpaths, pedestrian bridges etc. (mostly purpose-built, paved and unpaved) |
3 – 25.5 m |
||
Running waters (perennial and temporäry surface waters) |
511 |
||
width > 12 m |
areas |
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width < 12 m |
lines |
1.5 – 9 m |
|
Rocks |
332 |
||
spires, boulders |
lines |
6 m |
|
Vegetation |
lines |
322 |
|
tree rows, conifers tree rows, conifers and broadleafs tree rows, broadleafs |
6 m |
||
hedges |
6 m |
The cartographic representation of ETs is realised at federal level by means of a 1 km2 grid, whereby the principle of dominant value is applied. Special evaluations, for example on the hemeroby/natural condition of a landscape section are also often calculated in a 1 km² grid. Here the original vector geometries of the LBM-DE are used so as not to distort the original extent of the areas and to ensure the greatest possible accuracy. This is also the case when calculating ratios or changes for individual administrative levels, such as federal states or for the whole German territory.
When determining areal ratios based on the total national territory, it is essential to determine the reference area correctly. Therefore, the entire terrestrial area inclusive inland surface waters of the Federal Republic of Germany (approx. 35,767,570 ha, based on municipal geometries) should be considered to ensure comparability with other statistics. From the point of view of the Ecosystem Extent Account, however, marine and other areas should also be included, as these contain a range of significant biotopes that must be reflected in the analysis. The total area actually represented and investigated therefore currently covers 106.6% of the terrestrial area.
The administrative geometry VG25 of the ATKIS-Basis-DLM includes administrative units that can be simply assigned to the national terrestrial area (municipality geometries) and other areas (marine areas, Lake Constance, German-Luxembourg border area) (
Biodiversity is an essential criterion for the condition of ecosystems (
Therefore, the accounting framework should be structured in a way that all regularly collected data, relevant for the assessment of biodiversity, can be evaluated within the accounting framework in a consistent way. Such data sources are:
In the near future, it may also be possible to integrate data from the planned sample-based ecosystem-monitoring.
It is obvious that the above-mentioned sample-based data sources cannot deliver spatially explicit data. They can, however, be used to determine average values for the composition of the DLM-classes on the national level (i.e. what is the share of the different FFH-forest ecosystems in the overall forest coverage of Germany). Furthermore, they provide relevant information for biodiversity conservation on the condition of the DLM-classes as a whole (i.e. ecological status of water bodies) or on parts of them (i.e. conservation status of FFH-habitats).
As a basis for the quantitative physical assessment of the contribution of ecosystems to biodiversity, so-called "biotope points" are used. Biotope points consider (average) features of ecosystems like natural condition, age, occurrence of endangered species or degree of the endangerment of the ecosystem itself. They are widely used in Germany to determine the no-net loss under the nature conservation law in cases where impacts on biological diversity have to be compensated for by the upgrading or development of new habitats. Biotope points can thus be considered as physical exchange values for the function of ecosystems to conserve biodiversity.
We used the current federal list of biotope points (
At present, there is no database on the coverage or spatial distribution of these 500 biotopes in Germany. One prerequisite to integrate this list into the accounting system is, therefore, a reference system to assign the categories of the list to the ecosystem classification developed here for accounting purposes. Similar reference systems were developed between the biotope list and the categories used in the above-mentioned surveys and between the survey categories and the CLC-classes used for accounting.
The hierarchical overview of the EUNIS habitat types (
Even for the sample data of the Federal Forest Inventory, ways could be found to interlink between biotope points, inventory data and CLC. If the composition of the forests changes, for example, with regard the age of the tree stocks or the proportion of native tree species, the assignment of the frequencies of combinations of characteristics from the Federal Forest Inventory to biotopes present on the biotope value list enables us to estimate the change in the average biotope value of the forests. Corresponding statements can be made for grassland, for example, if the proportion of HNV grassland changes.
As the biotope values are not understood in a deterministic way, but define an average value as well as an upper and lower value depending on the characteristics, additional information on the condition of ecosystems, habitat types etc. can be used to specify in greater detail their value and the value of aggregates. Information, for example, from the reporting of the Habitats Directive or the WFD on the conservation status or ecological status, is therefore used to determine the respective assigned biotope value in more detail via additions and deductions in clearly defined calculation steps.
The result of the classification and evaluation system, presented here, are quantitative values for the contribution of the various ecosystems classified by CLC to the conservation of biological diversity. As the values are partly based on random samples, they do not (strictly speaking) apply explicitly to each individual ecosystem, but only to the respective aggregate at the German level. Aggregated values can be calculated not only for the CLC types, but also, for example, for HNV farmland or individual classes of FFH habitats. Although such a system may appear complex and error-prone at first glance, it is precisely the complexity and the resulting need for coherent processing of many different types of information that should make it relatively robust against the incorrect attribution of individual values. In this way, the approach appears suitable for national reporting within the framework of environmental accounting.
Since the above-mentioned information on particular biodiversity relevant issues (FFH-monitoring etc.) is regularly gathered at very different intervals, it is necessary to conduct interpolations and trend updates if results of the ecosystem accounting are to be available continuously at intervals of one or several years.
One shortcoming is the still relatively low level of information on the composition and status of less valuable ecosystems, meaning that the assignment of values in this area is relatively imprecise. However, this weakness should be removed by the implementation of the planned ecosystem monitoring (
The evaluation and presentation of the main and sub-ecosystem types (ETs) in the 1 km² grids according to the dominance principle gives an idea of the distribution of dominant ETs throughout Germany (Fig.
For the visual representation of the distribution of ETs at federal level, it makes sense to use grid cells of 1 km x 1 km. At smaller pixel sizes, an excessive number of differently categorised raster cells directly adjacent to one another can prevent the viewer from recognising large contiguous areas. Under the principle of dominant value, the raster cells of 1 km x 1 km are categorised according to the CORINE-Land-Cover (CLC) class with the largest proportionate area within the cell. This is not necessarily the largest undivided area occurring within the 1 km2 cell. In this way, areas, such as meadows and pastures (CLC 231, sub-ET 32) in the Bergisches Land and in the foothills of the Alps, which are present in large numbers in the LBM-DE (Digital Land Cover Model for Germany), but which in each case often have only a relatively small individual size, can nonetheless determine the categorisation of this sub-ET, since the proportion of such areas is high overall. Similarly, high ratios of grassland for the above-mentioned regions are also found in the Thünen Atlas (
The visual representation by the 1 x 1 km2 raster grid only displays the rough spatial distribution of ET, but it did not serve as a calculation basis for the ET areas. The concrete areal ratios were derived from the vector-based spatial elements of the LBM-DE along with the additional small-scale and infrastructure elements from the ATKIS-Basis-DLM (Digital Basic Landscape Model from the Official Topographic-Cartographic Information System) (Suppl. material
The introduction of linear infrastructures and small structures (Table
When considering the areal ratios for the year 2018, it becomes clear that the ratios of the main ETs correspond relatively well with known figures from Destatis land use statistics (
In addition to the reference year 2018, the CLC classes for the years 2015 and 2012 were calculated using the indicated data (LBM-DE/ATKIS) and aggregated to sub-ET or main-ET. However, the relative brevity of the considered timeframe (three time periods) does not allow us to reliably determine any trends or shiftings, as these may be masked by methodological changes in the classification of land use and land cover in the LBM-DE (especially between the reference years 2012 and 2015) (
Main ecosystem types were assessed uniformly at federal level. In cases where it is appropriate and the data situation permits, the spatial framework was extended to the district level. The map representations were limited to the illustrations necessary for understanding.
Agroecosystems occupy about half of the territory of Germany. Most of this land is used for arable farming, followed by grassland (about two thirds to one third). Approximately 2,300 m² of agricultural land is available per inhabitant, of which 1,500 m² is arable land. The degree of self-sufficiency in Germany is over 100% for many agricultural products (cereals, potatoes, meat), while for fruit and vegetables, it is well below 50% (
Official statistics show an ongoing reduction in the extent of land used by Germany’s farmers. Whereas in 1990 about 18 million ha were still utilised for agricultural purposes, by 2018 the figure had fallen to only about 16.65 million ha, a decline of approx. 7% (
Evaluations, based on the IOER Monitor, also show a clear trend in decreased ratios of agricultural land to total national territory strongly in the period 1995 to 2018. At district level, such ratios fell by up to 16% (Fig.
With regard to grassland, the IOER Monitor data does not reveal any uniform trend across the country. Some regions, especially along Germany’s north-western coast or in Bavaria in the country’s south-east, experienced strong decreases. However, we also find sporadic regional increases in grassland ratios, especially in some regions of Bavaria, Baden-Württemberg and the Saarland (Fig.
Making up approx. 30% of the national territory, forests and woodlands constitute the second largest form of land use in Germany. Large forest areas are mainly found in the low mountain ranges and on less-favoured soils in the north-east.
In the period 2002-2012, a small increase of 0.4% (50,000 ha) was detected in the extent of land used for forestry. This is the finding of the Federal Forest Inventory 2012 (
The main-ET forest and grove areas calculated from the LBM-DE data show a slight downwards trend from 2012 to 2018 (-0.5%, Suppl. material
Without human influence, Germany would be predominantly covered by deciduous forest. After various phases of deforestation and special forms of agricultural use that severely decimated the tree population, targeted reforestation began towards the middle of the 19th century, mainly with conifers. The tree species composition is one of the criteria that can be used to classify the condition of forests. Other criteria include the stratification of the forest, the age of the trees or the proportion of old and dead wood.
Today, coniferous forest – as defined by CLC – is the predominant type of woodland (54% of all woodlands in 2015), followed by deciduous (31%) and mixed forests (13%) (Fig.
Extent and change of the area of different forest types in Germany (source BMEL 2014).
Area in ha |
Change in % |
Percentage of total forest area |
|||
---|---|---|---|---|---|
2002 |
2012 |
2002 |
2012 |
||
Deciduous forest |
2,264,453 |
2,380,235 |
5.11 |
21.04 |
21.94 |
Deciduous forest mixed with conifers |
1,884,042 |
2,158,835 |
14.59 |
17.50 |
19.90 |
subtotal |
4,148,494 |
4,539,070 |
9.41 |
38.54 |
41.85 |
Coniferous forest |
3,324,268 |
2,961,466 |
-10.91 |
30.88 |
27.30 |
Coniferous forest mixed with deciduous trees |
3,173,922 |
3,296,067 |
3.85 |
29.49 |
30.39 |
subtotal |
6,498,190 |
6,257,533 |
-3.70 |
60.37 |
57.69 |
Equal proportion of deciduous and coniferous trees |
117,495 |
49,837 |
-57.58 |
1.09 |
0.46 |
Total |
10,764,179 |
10,846,440 |
0.76 |
100 |
100 |
Around 13-14% of Germany’s landmass was mapped as main-ET “settlement and artificially modified areas” (Suppl. material
Calculations in the LBM-DE showed an increase of 339,374 ha in the main-ET “settlement and artificially modified areas” from 2012 to 2018. This can be attributed to strong growth in the “buildings and transportation areas” (383,700 ha), while in the same period, “urban vegetated areas” decreased by 47,640 ha (Suppl. material
Fig.
For the new settlement and transport areas added in the period 2013-2018, we can determine the different various ratios of the specific forms of pre- and post-land use. To simplify the model, the previous types of use (or origin) of settlement and transport areas (Fig.
Limiting the growth of settlement and transport areas continues to be an important goal of the National Sustainability Strategy (
According to official statistics, Germany’s total surface water is about 8,500 km² or 2.3% of the national territory. The maps of the ecosystem types (Figs. 1 and 2) also encompass the lakes of the Federal Republic, the German part of Lake Constance and the German Exclusive Economic Zone (EEZ) in the North Sea and Baltic Sea. Together, these bodies of water cover about 3 million ha, with only marginal changes between 2012, 2015 and 2018 (Suppl. material
At the regional level, however, we can detect some transformations in surface waters. For example, Table
Growth in surface waters in the period 2008-2018 due to the flooding of opencast mines in eastern-German lignite-mining districts (“Central German mining area”, “Lusatian mining area”).
Ligniteregion |
Reporting unit |
admin. area [km2] |
water area [% 2018] |
water area [km2 2018] |
water area [% 2008] |
water area [km2 2008] |
difference (% of adm. area) |
difference absolute [ha] |
water surface increase [%] |
Central German |
Leipzig (Rural district), Saxony |
1651.3 |
4.0 |
65.9 |
2.9 |
47.9 |
1.1 |
1801.4 |
37.6 |
Burgenlandkreis, Saxony-Anhalt |
1419.9 |
0.8 |
11.5 |
0.7 |
9.9 |
0.1 |
156.0 |
15.7 |
|
Leipzig (city), Saxony |
297.8 |
3.0 |
9.1 |
2.7 |
8.0 |
0.3 |
105.9 |
13.2 |
|
Wittenberg, Saxony-Anhalt |
1942.8 |
2.1 |
41.4 |
2.0 |
38.9 |
0.1 |
254.3 |
6.5 |
|
Lusatian |
Oberspreewald-Lausitz, Brandenburg |
1223.0 |
6.0 |
73.5 |
4.8 |
58.7 |
1.2 |
1479.6 |
25.2 |
Bautzen, Saxony |
2395.6 |
4.9 |
118.3 |
4.3 |
103.0 |
0.6 |
1529.0 |
14.8 |
|
Cottbus, Brandenburg |
165.6 |
1.5 |
2.5 |
1.4 |
2.3 |
0.1 |
18.1 |
7.8 |
|
Spree-Neiße, Brandenburg |
1657.0 |
2.4 |
39.1 |
2.3 |
38.1 |
0.1 |
99.0 |
2.6 |
|
Görlitz, Saxony |
2111.1 |
3.3 |
70.6 |
3.3 |
69.7 |
0.0 |
93.3 |
1.3 |
|
Germany |
357,680 |
2.0 |
6985.3 |
1.9 |
6795.92 |
0.1 |
18938.0 |
2.8 |
This sub-chapter gives an overview of the remaining terrestrial ecosystems, largely located outside urban areas. The main ET “semi-natural open areas” covers only 1.8% of Germany’s land mass. It is divided into the three subtypes, namely “grassland and heathland”, “wetlands” and “open spaces with no or little vegetation”, which in 2018 encompassed 418,536 ha, 180,033 ha and 46,976 ha, respectively (Suppl. material
Although highly heterogeneous, these ecosystems have one or more of the following shared characteristics:
So far, no clear trend can be identified from the LBM data for the main-ET “semi-natural open area” (Suppl. material
The areal ratios of certain ecosystem types do not yet tell us anything about the composition and structure of larger spatial components, such as administrative districts or larger grid cells (e.g. 10 x 10 km2). Yet, it is precisely such aspects of the spatial arrangement and composition of the individual elements of land use or ecosystem types that strongly influence the condition of ecosystems, as these influence the inherent functions and processes of each ecosystem (see Sect. 5).
The concept of hemeroby analyses current forms of land use with regard to human impact. For this, the distance between the current vegetation and a constructed final state of self-regulated vegetation in the complete absence of human intervention (so called potential natural vegetation (PNV)) is measured. Hence, the hemeroby is an inverse measure of the closeness to nature (
For this purpose, individual objects of the land cover model for Germany (LBM-DE) and the ATKIS-Basis-DLM were each assigned one of the seven hemeroby levels ranging from natural (ahemerobic) to artificial (metahemerobic) (
Our analyses show that the main trends of land cover change observed in the EU (
Only from the LBM-DE (Suppl. material
A clear delineating of ecosystem types in a reliable manner is important for many current and emerging issues regarding ecosystem assessments and accounting, which ultimately helps to support decision-making. A number of good proposals have been made internationally in this context (Sect. 2), but it is still a dynamic field (
As discussed and confirmed in the SEEA revision process (
However, it can be argued tha,t on the one hand, it remains uncertain if the results would have high accuracy, due to different data being collected at very different scales. Often, even they are not available at all or only in very coarse scales for the whole of Germany. On the other hand, all these resulting small homogenous patches would be aggregated again – but in what way – to make any meaningful statements about them with regard to their extent?
To conduct a nationwide analysis of ETs, in our opinion, it is more plausible to reduce the complexity and use ecosystem classifications, like that of the CLC ecosystem types. In general, land cover, in principle, quite accurately reflects the relevant characteristics of the ecosystems (e.g. forests on steep slopes, peat bogs in wet habitats, arable land on fertile soils etc.). If we need to refine the analysis in order to determine ecosystem conditions or services, however, it is reasonable to add additional information: has the land at the urban fringe converted to buildings resulted in higher natural soil productivity than average cropland? Is the water level of a mire ten centimetres higher or lower, which is decisive for carbon oxidation and greenhouse gas emissions? Is the cropland located on steep slopes so that soil erosion should be reduced by additional hedges or conversion to grassland?
If ecosystem services and ecosystem conditions are in question, additional information is obviously required. However, unlike in the ecosystem extent account, this information can then be used in a very targeted manner, depending on the respective land use, the status parameter under investigation and the ecosystem service that is assessed.
We have shown that the application to the German context, with practical realities considered, has transpired into emerging results. Although we start from the CLC classes, we use the German LBM-DE model, including some more characteristics than just land cover. Additionally, in the geodata system developed here, linear ATKIS elements (e.g. roads, watercourses or even small structures, such as rows of trees and hedges) are converted into polygons by means of buffering and integrating into the polygonal mapped ET and CLC classes. Care has been taken to ensure that no overlapping occurs, thus avoiding potential cases of double recording, thereby confounding the validity of result accuracy (Sect. 3.2).
However, a balance must be achieved between results that are sufficiently ecologically meaningful rather than being simply pragmatic and on which ET classification level this is relevant. We therefore believe that our proposed national system be processed in four levels (Main-ET, Sub-ET, CLC-classes, further subdivision into habitats to integrate biodiversity relevant information, Sect. 3). This would offer a flexible, but also simultaneously best appropriate, approach in this respect.
First evaluations have been realised and discussed (Sect. 4). In addition to the reference year 2018, the CLC classes for the years 2015 and 2012 were also calculated on the basis of the available data (LBM-DE/ATKIS), aggregated to Sub-ET and Main-ET, respectively, giving some results that allow for tentative interpretations. However, the brevity of the timeframe (three reference years) does not yet permit a reliable identification of trends or shiftings. Nevertheless, the results confirm the feasibility of conducting a national spatial monitoring of Germany’s ecosystems (Ecosystem Extent Account) in the future and of identifying possible changes on the basis of LBM-DE/ATKIS data.
The CLC classes defined in this way, when combined with other available information, are already sufficient for the evaluation of many ES (e.g. recreation, erosion control) and their conditions (e.g. hemeroby/natural condition,) . For example, in the case of natural soil fertility for the CLC class “arable land”, there is a combined analysis possible by overlapping the ETs with a number of other nationally-available datasets (including soil water balance, soil type, climate, slope inclination), which together enable an assessment of natural soil fertility, according to the Müncheberger Soil Quality Rating (SQR) (
The system of ET/CLC classes (Table
To provide a spatial structuring of ecosystems, ‘condition indicators of landscape ecosystems’ can be used to measure the ratio of structural elements in the open landscape. This includes, for example, the IOER Monitor indicator “woodland-dominated ecotone density”, which reports on the density of linear elements, such as hedges, tree rows, woody plants and forest margins. It is precisely such elements that are of great ecological importance, as they represent transitional areas between ecosystems and are home to special communities of species that are thus particularly significant for the provision of ecosystem services.
Conversely, main roads or railway lines often have negative effects on the condition of ecosystems, as they act as barriers for wildlife and humans, thus impeding or even completely preventing key ecosystem functions. This aspect of the condition of ecosystems can be measured, for example, using indicators on landscape fragmentation as a whole or specifically forest fragmentation (see indicators on landscape fragmentation in the IOER Monitor: https://monitor.ioer.de).
Besides the classification system for ecosystem types (ETs), the table of the area of various ETs (Suppl. material
Furthermore, we propose that the presented system to record the areal change in ecosystems should be developed into an integral component of biodiversity monitoring in Germany (
For the long-term observation of ETs, a consistent and stable data-gathering methodology for the production of the main German data base, the LBM-DE, should be implemented to help realise a representative system of ecosystem monitoring. Only in this case, area changes of land use/land cover types can be mapped in a reliable manner. The results so far, which have been fully calculated for the German state area (terrestrial, inland surface waters, marine) on the basis of available data for the years 2012, 2015 and 2018, are still relatively uncertain with regard to trend developments or shifts, as these may be masked by methodological changes in the classification of land use and land cover (especially between the time periods 2012 and 2015) (B). There are also challenges in the consistency of the results (How can we deal with significant deviations from existing accounts, for example, forest and agriculture?).
Further challenges to be overcome are related to the degree of thematic detail that can be entailed, especially considering whether, which and with what kind of detail, functional characteristics of ecosystems should be included in order to support the realisation of the respective objectives at the national level (e.g. biodiversity protection, identification of services, prioritisation of damaged ecosystems to be restored, analysis of changes in the extent of specific ecosystems in Germany).
The contribution is part of the pilot project "Integration of ecosystems and ecosystem services in environmental-economic accounting", funded by the BfN with the support of the BMU (FKZ 351780060B). We would like to thank Mr. D. Hendersen for the language check of the text.
No conflicts of interest.
Suppl. material 1 contains a table with the proposal of a classification system for ecosystem types (ETs) in Germany, assignment to the European ecosystem types according to EUNIS and to the CLC types of the database LBM-DE.
Suppl. material 2 contains a table with supplementation of ecosystem types (ETs) by more differentiated spatially and non-spatially explicit data (system of assignment of biotope and habitat types relevant for nature conservation to ETs.
Suppl. Material 3 contains a table with the area and share of main ecosystem types and sub ecosystem types (ETs, see Table 1) in the German land cover model (LBM-DE) for the time periods 2012, 2015 and 2018. Linear elements such as small scale structures and infrastructures from the topographic-cartographic Information system (ATKIS) were added to the land cover model.
The table contains a detailed matrix of pre- and post-use of settlement and transportation areas in Germany from 2013 to 2018.