Molnar et al.vp - PTE Természettudományi Kar

Folia Geobotanica 42: 225–247, 2007
A GRID-BASED, SATELLITE-IMAGE SUPPORTED,
MULTI-ATTRIBUTED VEGETATION MAPPING METHOD
(MÉTA)
Zsolt Molnár1), Sándor Bartha1), Tibor Seregélyes2), Eszter Illyés1), Zoltán Botta-Dukát1),
Gábor Tímár3), Ferenc Horváth1), András Révész1), András Kun1), János Bölöni1),
Marianna Biró4), László Bodonczi5), Áron Deák József6), Péter Fogarasi7), András
Horváth1), István Isépy8), László Karas9), Ferenc Kecskés10), Csaba Molnár11), Adrienne
Ortmann-né Ajkai12) & Szilvia Rév13)
1) Institute of Ecology and Botany of the Hungarian Academy of Sciences, H-2163 Vácrátót, Hungary; e-mail
molnar@botanika.hu (Molnár), sanyi@botanika.hu (Bartha), illyese@freemail.hu (Illyés), bdz@botanika.hu
(Botta-Dukát), jboloni@botanika.hu (Bölöni), kun@botanika.hu (Kun), ahorvath@botanika.hu (Horváth A.),
horvfe@botanika.hu (Horváth F.), revesz@botanika.hu (Révész); 2) Tünde köz 5, H-2481 Velence, Hungary;
e-mail calypso@mail.datanet.hu; 3) State Forest Service, H-2600 Vác, Hungary; e-mail timar.gabor@aesz.hu;
4) Alkotmány u. 2-4., H-2163 Vácrátót, Hungary; e-mail mariann@botanika.hu; 5) Alszer 28/a, H-9941
Õriszentpéter, Hungary; e-mail bodonczi@axelero.hu; 6) University of Szeged, Department of Climatology and
Landscape Ecology, H-6722 Szeged, Hungary; e-mail aron@geo.u-szeged.hu; 7) Fazekas u. 64/a, H-2890 Tata,
Hungary; e-mail kickingbird@freemail.hu; 8) Eötvös Loránd University, Botanical Gardens, H-1083 Budapest,
Hungary; e-mail isepy@yahoo.com; 9) Team-Work Consulting and Training, H-2000 Szentendre, Hungary;
e-mail laszlo.karas@axelero.hu; 10) Budai Secondary School Táncsics Mihály, English Bilingual
Grammarschool, H-1126 Budapest, Hungary; e-mail kecskes@tmgbp.hu; 11) István u. 52., H-3036
Gyöngyöstarján, Hungary; e-mail minuartia@freemail.hu; 12) Báránytetõ u. 2., H-7635 Pécs, Hungary;
e-mail: aadrienn@dravanet.hu; 13) E-misszió Environmental Association, H-4400 Nyíregyháza, Hungary;
e-mail revszilvi@e-misszio.hu
Abstract: In this paper we present the main characteristics of a new, grid-based, landscape-ecology-oriented,
satellite-image supported, field vegetation mapping method, called MÉTA (MÉTA stands for Magyarországi
Élõhelyek Térképi Adatbázisa: GIS Database of the Hungarian Habitats). The goals of the MÉTA method based
vegetation mapping program (MÉTA mapping) include the following: (1) to map the actual (semi-)natural
vegetation of Hungary; (2) to evaluate Hungarian (semi-)natural vegetation heritage for conservation purposes;
(3) to evaluate the present state of Hungarian landscapes from a vegetation point of view; (4) to collect
vegetation and landscape ecological data for the prognosis of future changes of vegetation and the landscape.
Spatial resolution, mapped attributes and mapping methods were developed to meet these goals.
The MÉTA method uses a hexagon grid with cells of 35 hectares. In the hexagons, habitat types are listed,
then the area, naturalness-based habitat quality, spatial pattern in the hexagon, effect of the neighbourhood,
connectedness, and threats are recorded for each habitat type. Other attributes are recorded in the hexagons:
potential natural vegetation, area occupied by invasive plant species, area of old fields, land use of grasslands,
and landscape health status (naturalness and regeneration potential of the landscape in general). One hundred
hexagons form a quadrat – mainly for practical, organizational reasons, but also for collecting certain vegetation
data at this spatial scale. For standardization of mapping, three different pre-printed data sheets and two different
kinds of guides have been composed (Mapping Guide and Habitat Guide) and field trainings were organized. For
standardization of estimation of naturalness-based habitat quality and regeneration potential field examples were
prepared for each habitat type and each category of these attributes.
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Keywords: GIS database, Habitat quality, Landscape ecology, Large area survey, Nature conservation,
Regeneration potential, Standardization
Nomenclature: SIMON (2000)
INTRODUCTION
For conservation of our natural heritage and for developing sustainable landscape
management strategies for Europe, it is essential to know what kind of interventions must be,
should be or should not be done and in which particular areas (Habitats Directive,
92/43/ECC). Partly for these reasons the phytosociology-based knowledge of actual
vegetation has re-gained its importance in many countries of Europe.
Responses of vegetation science to the increasing landscape and vegetation knowledge are
manifested in the recent phytosociological monographs and swiftly developing databases
with detailed documentation of European vegetation and habitat types (MUCINA et al. 1993,
RODWELL 1991–2000, SCHAMINÉE et al. 1995–1999, FREMSTAD 1997, CHYTRÝ et al. 2001,
RODWELL et al. 2002, STANOVÁ & VALACHOVIÈ 2002), the potential natural vegetation
maps of countries (e.g. ZÓLYOMI 1989, NEUHÄUSLOVÁ & MORAVEC 1997), regions
(NIKLFELD 1973) and of the whole of Europe (BOHN & NEUHÄUSL 2000–2003), the
ecological map of Europe (OZENDA & BOREL 2000) and vegetation mappings covering larger
areas (e.g. BARR et al. 1993, RUZ¡IÈKOVÁ et al. 1996, LOIDI 1999, MÄGI & LUTSAR 2001,
KALIGARIÆ et al. 2003, NOWOTNY 2003, GUTH & KUÈERA 2005).
To satisfy at least part of the demands for actual landscape knowledge, the European Union
has established the CORINE Land Cover mapping project (CLC). This is the one and only
large area actual landscape mapping project for all of Europe (ANONYMOUS 1995, BÜTTNER
et al. 1995, 2000, 2002). CLC developed a new, efficient and standardized methodology to
accurately map the landscape of large areas (based on the interpretation of satellite images).
Although land-cover maps can be considered as a certain kind of detailed actual vegetation
map, they have serious deficiencies from the vegetation and landscape ecologists’ point of
view: too many (semi-)natural habitat types are aggregated in one land-cover category, so
they are not really suitable for the analysis of (semi-)natural areas (CUDLÍN et al. 2005).
Land-cover maps also do not provide information for example on naturalness and diversity of
vegetation patches. These features can only be observed in the field (MILLINGTON &
ALEXANDER 2000) and with the proper background in vegetation science (FULLER et al.
1998, WYATT 2000). Though satellite images provide ever greater help for field botanists,
and methodological developments have “promising results” (KERAMITSOGLOU et al. 2005,
FANELLI et al. 2005), even the newest images (IKONOS, Quickbird) with the newest
technologies have serious limitations in mapping habitat quality and diversity (see the very
limited results of BOCK et al. 2005 and KERAMITSOGLOU et al. 2005).
Field mapping of actual vegetation has old traditions in Europe because a vegetation map is
one of the easiest ways of communicating knowledge on vegetation (KÜCHLER 1967, FEKETE
1998). Nowadays, however, the traditional methodology has to face new challenges: larger
areas, increased number of desired attributes (not only vegetation type, but also quality,
representativity or fragmentation), and more frequent repetition (monitoring). For scientific
and applied purposes actual vegetation data are needed for ever larger areas in ever higher
MÉTA vegetation mapping
227
spatial and thematic resolution, and in multi-attributed, GIS databases. Fortunately, the
development of technology (remote sensing, digital data processing, advanced sampling
methods) equips us to elaborate and utilize new, more efficient and more complex, more
standardized vegetation mapping methods (KÜCHLER & ZONNEVELD 1988, MILLINGTON &
ALEXANDER 2000). At the same time, however, very few publications deal with the problems
of the methodological improvement and the necessary standardization (CHERRILL &
MCCLEAN 1995, 1999, MILLINGTON & ALEXANDER 2000). However, there are some case
studies on the repeatability of local field vegetation mapping (JANSSEN 2004, SANDERS et al.
2004) and on the use of satellite images for habitat mapping (BOCK et al. 2005, FANELLI et al.
2005, KERAMITSOGLOU et al. 2005). Two field-survey based fine-scale large area vegetation
mappings exist in Europe, both motivated by the European Natura 2000 program: in Spain
(RIVAS-MARTÍNEZ 1994, LOIDI 1999), and in the Czech Republic (GUTH & KUÈERA 2005).
Neither of them discusses the methodological problems of their methods.
In this paper we present (1) the development of a new vegetation mapping method; (2) the
attributes to be mapped; (3) the rules of field mapping and (4) the process of standardization.
Because the method was developed for large area field mappings, and was used in the
so-called MÉTA mapping project (BARTHA et al. 2002), we also present those aspects of
the MÉTA project that we think are relevant for future application and further development
of the MÉTA method.
DEVELOPMENT OF THE MÉTA METHOD
Having reviewed the recent and near-future scientific and practical needs for any kind of
Hungarian vegetation data, we realized that a country-level vegetation mapping project has to
reach the following goals: (1) mapping in detail the actual (semi-)natural vegetation of
Hungary (93 000 km2); (2) evaluating our (semi-)natural vegetation heritage for conservation
purposes; (3) evaluating the present state of our landscapes from a vegetation point of view;
(4) collecting vegetation and landscape ecological data for the prognosis of future vegetation
and landscape changes. The MÉTA method was developed between 2001 and 2003. We had
two different sources of experience:
(1) the skills gained by the long tradition of phytosociological vegetation mapping (e.g.
KÜCHLER 1967, KÜCHLER & ZONNEVELD 1988, DIERSCHKE 1994, FALIÑSKI 1994a,b,
GRECO et al. 1994, WATERTON 1997; in Hungary: SOÓ & ZÓLYOMI 1951, ZÓLYOMI et al.
1954, JAKUCS 1965, FEKETE 1980, 1998, BAGI 1991, 1997), and
(2) the experiences of non-traditional vegetation mappings and vegetation database
constructions, such as:
- CORINE Land Cover and Biotope mapping (BÜTTNER et al. 1995, 2000, 2002,
KOVÁCS-LÁNG et al. 1997),
- British Countryside Survey (ANONYMOUS 1990, BARR et al. 1993, CHERRILL &
MCCLEAN 1995, 1999, SMART et al. 2003),
- Hungarian Biodiversity Monitoring System (KOVÁCS-LÁNG et al. 2000, MOLNÁR et al.
1998, KUN & MOLNÁR 1999),
- Preparation for Implementing the Habitats Directive (92/43/ECC) in Hungary
(HORVÁTH et al. 2000, MOLNÁR et al. 2001a,b),
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Fig. 1. 2834 quadrats covering the whole area of Hungary. There are approximately 100 hexagons in each
quadrat (267 813 hexagons in the country). The habitat types and their areal proportion in the hexagon are
shown. The radius of the circle corresponds to the total area of (semi-)natural vegetation in the hexagon, the
proportions of the different habitat types are shown as well.
- woodland-type mapping (DANSZKY & ROTT 1964)
- conservation-oriented vegetation mappings (e.g. SEREGÉLYES & CSOMÓS 1995, BIRÓ et
al. 2006a,b).
The method was developed in an iterative process based on a series of workshops, field
tests (in 14 landscapes) and 9 trainings in 34 different landscapes with the contribution of 160
botanists.
Constraints
As usual in large-area biological surveys, the most important constraint to overcome
during the development of the MÉTA method were time and money (BURBIDGE 1991). The
actual vegetation map of Hungary had to be prepared “as soon as possible”. The need for this
map arose as early as in the 1950s, but the mapping could not start because of the lack of
capacity (SOÓ & ZÓLYOMI 1951, FEKETE 1998), because the estimated time of work required
to accomplish this map on the scale of 1:10 000 is about 100 000 field days.
Prior to the MÉTA mapping, a questionnaire survey was established, which revealed that
the total capacity of ca. 300 Hungarian botanists is ca. 10 000 person-days of field work
annually, from which ca. 2000 person-days can be spent on this vegetation mapping (taking
the load, means of subsistence, field experience and the distribution of the experts’ age into
consideration – see MORAVEC 1995). If we have 2.5 growing seasons to accomplish the
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MÉTA vegetation mapping
Table 1. Applicability of different Hungarian external data sources for vegetation mapping. +++ – very well
applicable, ++ – well applicable, + – poorly applicable, - – gives no suitable information.
Habitat type
Military
map
Euhydrophyte and non-woody fen and mire vegetation
Phragmites/Typha/Schoenoplectus/Carex beds in marshes
Alkali vegetation
Molinia meadows, mesotrophic marsh and hay meadows
Annual salt pioneer swards, open sand and rock grasslands
Steppe grasslands
Swamp and mire woodlands
Riverine willow-poplar woodlands
Closed oak and beech woodlands
Acidophilous woodlands
Quercus pubescens scrub woodlands
Forest-steppe oak woodlands
Ravine, slope and rock woodlands
Acid coniferous woodlands
+
+
+
++
++
+
++
++
+
Aerial
photo
+
++
+/++
+
+++
+++
+
++
++
+
+++
++
+
+++
Satellite Forestry Soil/geol.
image database
maps
+
++
+++
+
++
+
+
++
+++
++
+
+++
+
++
+++
+++
+
++
+++
++
+
+++
++
++
++
++
++
+++
+
+++
+
project, then each mapper should map ca. 2000 ha per day (15% of that is semi-natural
vegetation, 85% arable land, settlements etc.). Mapping of ca. 2000 ha a day on average can be
achieved only if we put ca. eight times less effort on mapping the man-made habitats than on
the mapping of natural parts of the landscape. It is similar to the approach of GUTH & KUÈERA
(2005), in which the so-called contextual mapping served to complete detailed habitat
mapping in areas where no occurrence of natural or semi-natural habitats was assumed.
Our previous experiences in the Hungarian vegetation monitoring program
(KOVÁCS-LÁNG et al. 2000) and the Natura 2000 program (HORVÁTH et al. 2000, MOLNÁR
et al. 2001a,b) made us aware of the considerable, yet often hidden, heterogeneity of the
botanists’ perception. This was the second biggest contraint after time limitation. Since the
MÉTA mapping was to be accomplished by the joint work of ca. 200 botanists,
methodological guides for the standardization were prepared and field trainings were planned
from the very beginning.
External data sources
Increasing the efficiency of the mapping (which mostly means the efficiency of the survey
route) was a fundamental requirement. After reviewing the possible sources of background
data (Table 1), field mapping was supported by the following external sources: (1) topographic
maps (scale 1 : 25 000, 1985–1990 and partially 1983–1992), (2) false-coloured, enhanced but
not processed SPOT4 satellite image-maps from 1998–1999 for orientation, planning of
mapping route and interpretation (pixel size: 10 ´ 10 m), with the superimposed hexagonal
grid, (3) some other additional GIS layers (e.g. roads, brooks, contour lines, borders of
settlements); (4) data of the National Forestry Database of Hungary (list of tree species and
their abundance per forestry unit). GPS-devices and aerial photos were not used due to their
high price, while soil and geological maps at a suitable scale were inaccessible.
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Zs. Molnár et al.
Testing the trade-off between spatial resolution and thematic richness
We decided to use a grid for the MÉTA mapping to overcome the problems of patch
delineation. A hexagonal grid was chosen from the different possible shapes (e.g. circle,
triangle, quadrat, hexagon), because: (1) it covers the surface without overlappings and gaps;
(2) is most similar to the circular panorama that is observed in the field; and (3) is more easily
handled in landscape ecological analysis, because a hexagon has six neighbouring cells, all
the same distance from the centre of the hexagon, while e.g. a square-shaped cell has four
closer neighbours and four distant ones (WHITE et al. 1992, SCHMIDTLEIN 2000, DE CLERCQ
& DE WULF 2004). One hundred hexagons form a quadrat – mainly for practical,
organizational reasons, but also for collecting certain vegetation data at this spatial scale.
The first testing used 25 ha hexagons and the attributes of the Hungarian Natura 2000
program (the list of vegetation types, the area covered by each type in the hexagon,
naturalness and the chance of local survival of vegetation patches; HORVÁTH et al. 2000,
MOLNÁR et al. 2001a,b). The selection of hexagon size was based on expert judgement, but
was successfully tested in Natura 2000 habitat data collection (HORVÁTH et al. 2000). The
first MÉTA testing (ILLYÉS, unpubl.) provided the following basic results:
(1) in one day of field work, 1000–3000 ha could be mapped with these hexagons by
combining walking together with the use of a car;
(2) vegetation in 80–95% of the study area could be mapped, i.e., 5–20% of the hexagons
are left out intentionally or unintentionally from the field observations, e.g. some of those
lying in an isolated area, those that are hard to reach (fence, military area), or certain inside
parts of vast homogeneous patches (e.g. reed beds, homogeneous pastures), or solitary
hexagons missed by chance, but these missing data could be interpreted quite effectively
based on the satellite image and/or the previous knowledge of the particular landscape;
(3) important characteristics of the landscape are not documented by the Natura 2000
attributes at all (e.g. area of invasive species and old fields, connectedness, regeneration
potential);
(4) though the documentation of the vegetation attributes on the data sheets takes much
time (ca. 30% of the daily work), even more time is needed for the exploration of the area; so
recording more vegetation attributes would not lengthen the work excessively.
Based on the results of the testing, we included new vegetation attributes (see below) and
enlarged the size of the hexagons to 35 ha, thus decreasing the time required for exploring and
documenting vegetation of a given area.
DESCRIPTION OF THE MÉTA METHOD
Data collection
Mapping route
To standardize mapping and to increase mapping efficiency, the mappers had to follow
these rules:
(1) there are “compulsory” and “non-compulsory” hexagons, the former containing more
than 25% of (semi-)natural vegetation (in most cases satellite images and maps help decide
whether a hexagon is compulsory or not);
MÉTA vegetation mapping
231
(2) during the field mapping each “compulsory” hexagon has to be crossed and its most
common (dominant) habitat type recorded, as well as those types covering at least 25% of the
hexagon; moreover, the vegetation patches found “on the way” should also be recorded (note
that this rule led to the omission of many small fragments);
(3) in fragmented landscapes (dominated by arable fields, settlements and plantations) all
grassland and woodland patches of at least 12 ha that are expected to be more or less natural,
should be recorded;
(4) vegetation data of “non-compulsory” hexagons should be documented, if these
hexagons are crossed by the mapping route or the data can be derived from the satellite image;
(5) at least 80% of the compulsory hexagons and the same proportion of the vegetation data
recorded in the compulsory hexagons has to be mapped; ca. 20% of the data can be interpreted
based on satellite image and local field experience;
(6) if the mappers have earlier local vegetation data (not older than 10 years), these can also
be used.
Documentation
Three different pre-printed data sheets have to be filled in during the field mapping: the
data sheet of compulsory hexagons, the data sheet of non-compulsory hexagons and the data
sheet of the quadrat. In the case of non-compulsory hexagons only the area of old fields and
invasive species, and the potential vegetation have to be recorded. Data are recorded as codes,
thus minimizing the amount of text within the database.
About 20% of our data is extrapolated and/or interpreted (based on the satellite images).
Extrapolation means that a homogeneous vegetation patch mapped in a hexagon continues in
the neighbouring hexagons. Interpretation means that the colour and pattern of the patch on
the satellite image is similar to that of another one already mapped. In the database it is
documented whether a particular data derives from recent field mapping, former vegetation
data or interpretation/extrapolation.
Mapped attributes
Habitat types
In a wider sense there are three different approaches used for mapping landscapes and
documenting the actual vegetation in Hungary:
(1) The phytosociological plant associations (SOÓ & ZÓLYOMI 1951, SOÓ 1964–1980,
BORHIDI et al. 1999) proved to be suitable for local mapping of areas with (semi-)natural
vegetation, on a fine (1:5 000 – 1:10 000) scale, though degraded patches or those of
a transitionary state are usually hard to classify (VIRÁGH & FEKETE 1984, BAGI 1991, 1997,
SEREGÉLYES & CSOMÓS 1995). The number of categories is ca. 400, many of them weakly
defined, with frequent but undocumented overlaps and gaps.
(2) For a general mapping of the whole landscape (scale 1:50 000 – 1:100 000) the
CORINE Land Cover classification was developed (BÜTTNER et al. 1995, 2000, 2002). The
new, 1:50 000 scale version of this classification uses 78 well-defined categories, but only
22 of them are related to (semi-)natural vegetation. These categories are similar to the
categories of topographic maps.
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Table 2. Summary of data collected by the MÉTA method.
For each habitat type
For the landscape in the hexagon
Hexagon level
Quadrat level
area, naturalness-based habitat
quality, pattern, neighbourhood,
connectedness, threats
invasive species,
connectedness,
regeneration potential
potential natural vegetation, area of
invasive species and old fields, land-use
type, landscape health status
-
(3) For an intermediate spatial and thematic resolution, a new habitat classification system
was developed in Hungary in 1996, called Á-NÉR (the Hungarian abbreviation stands for
General National Habitat Classification System; FEKETE et al. 1997). This system has 112
habitat types, all with detailed and standardized descriptions.
For the purposes of the MÉTA the Á-NÉR system seemed to be the most adequate:
(1) relatively simple; (2) with a limited number of categories; (3) well-tested in many different
landscapes (ca. 200 vegetation maps); (4) vicariant geographic variants are omitted;
(5) knowledge of all the plant species is not as vital as for the phytosociological system. For
the MÉTA method the Á-NÉR system was partly extended and thoroughly revised (BÖLÖNI
et al. 2003).
We record these Á-NÉR habitat types as lists for each hexagon. Stands larger than the
determined minimal size (differing between the types, but usually some 10–100 m2) are to be
recorded. Mapping includes all habitat types that belong to our natural vegetation heritage.
We document the patches of the same habitat type in the hexagon as a compiled record.
Patches of transitional state should be classified as two (or more) separate habitats. According
to our experiences, the mean number of habitat types in a hexagon is 1.5–2 in the whole
country, and 4–6 (not more than 8–12) in hexagons where the proportion of natural vegetation
is high.
Area and spatial pattern
Without a thorough exploration of the hexagon we can only roughly estimate the area of the
habitat types. Therefore the areal cover of each recorded habitat type has to be given as
a proportion of the hexagon using the categories < 1, 1, 10, 50, 100%. Satellite images help
make the estimation. Though the exact pattern of the vegetation in a hexagon is not mapped,
spatial pattern of each type should be documented as follows: it forms only 1–2, 3 or several
distinct patches, or it has a diffuse spatial pattern in the hexagon.
Naturalness-based habitat quality
To prepare the natural vegetation heritage inventory for Hungary, we had to evaluate the
habitat quality of each vegetation type in the hexagon. Our evaluation was standardized,
though scientifically not fully sound (see Discussion). The following system of
naturalness-based habitat evaluation was used, which has proven to be efficient during the
15 years of its application: (1) totally degraded state; (2) heavily degraded state;
(3) moderately degraded state; (4) semi-natural state; (5) natural state (for definitions see:
NÉMETH & SEREGÉLYES 1989). This system is more-or-less in accordance with the
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conventions used in some other European countries (DIERSCHKE 1984, BASTIAN 1996,
RUZ¡IÈKOVÁ et al. 1996, GRABHERR et al. 1998, MÄGI & LUTSAR 2001, BARTHA 2003a).
Naturalness-based habitat quality was recorded separately for each habitat type. The
naturalness-based habitat quality of different patches of the same habitat type in the same
hexagon had to be merged into one (or two) value(s). If for up to 10% of the stands a higher
category was relevant, it had to be indicated (e.g. coding 5r4 means: 4 dominates, but with less
than 10% of the total area of a habitat type in category 5 is also present). That 10–25%
of a particular patch belongs to a lower category of naturalness is not documented. Selection
of the proper category of naturalness-based habitat quality is supported by a large set of
examples in the Habitat Guide (see below).
Threats
In many cases, human disturbances threaten the survival of the remnant vegetation patches.
From 22 threat types (listed below) the most characteristic ones had to be selected that
actually threaten the survival and maintenance of the habitat type in the hexagon in the next
10–15 years. The strength of the threats is not recorded. The threatening factors (listed on the
data sheet) are as follows: improper water management, improper pasturing or mowing,
drainage, encroachment of shrubs and trees, burning, afforestation with improper species,
woodland patches managed homogeneously, improper selection of trees for timber
extraction, logging trees at low age, inappropriate plantation, keeping high densities of game,
colonization by invasive plant species, tillage, building and construction, gardening, mining,
establishment of a pond, trampling, pollution, rubbish, commercial collection of plants.
Neighbourhood
Prediction of future changes of vegetation patches can be supported by the evaluation of
the direct effect of neighbourhood (< 200 m) on the mapped stands. This evaluation means
deciding whether the neighbouring patches will aid or hinder the survival of the particular
patch in the next few (10–15) years. The categories are: (1) definitely positive (sustaining
neighbourhood), (2) slightly positive, (3) indifferent, (4) slightly negative, (5) definitely
negative (destructive neighbourhood). The neighbourhood is negative, e.g., if there is an
intensively used arable field (chemicals, infiltration of fertilizer), expanding settlement, or
spreading populations of invasive species surrounding the patch. Also an alder woodland
around a patch of Molinia meadow may accelerate the colonization of trees thus causing the
disappearance of the meadow. Neighbourhood is positive, if it serves as a source of species
(a forest rich in species around a well-established bush vegetation), provides proper
micro-climate (e.g. woodland around a bog), buffers against degrading factors (forest strip
between a grassland and an arable field). Selection of the proper category is supported by a set
of examples in the Mapping Guide (see below).
Connectedness
In the field, we document connectedness at two spatial scales: several hundred meters
(hexagon), and several kilometers (quadrat). Connectedness at coarser scales can be derived
from the database. By connectedness we mean the potential of dispersal of the species of one
vegetation stand from the surrounding areas. We look for the presence and quantity of the
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species of the particular habitat type in the surroundings within the distance of several
hundreds of metres. We record whether the patches are (1) isolated (typical species of the
habitat are not present in the surroundings), (2) connected (species are abundant) or (3) the
connectedness is intermediate.
Connectedness is also documented at the quadrat level. We examine the area between the
stands of the particular habitat type for two different aspects: whether the species pool of the
habitat type exists there and whether there is any possibility for dispersal through this area
(motorways or tree lines hindering dispersal). Categories indicate that stands are properly
connected, moderately connected or isolated. For example, in marshes it is evaluated whether
the marsh patches are connected by brooks or channels, or in steppes whether there are steppe
species in the forests, mesic grasslands or hedges between the steppe patches. Selection of the
proper category is supported by a set of examples in the Mapping Guide (see below).
Regeneration potential
To gather information on which landscape patches have the possibility to survive for a long
time or to regenerate after degradation (BARTHA 2003b), regeneration potential should be
documented. We estimate it on the level of quadrats for each habitat type, assessing three
different aspects: (1) potential for regeneration of the existing stands in the case of mean
degradation; (2) potential for regeneration in the place of a neighbouring habitat type (e.g.
woodland encroaching on an abandoned pasture, meadow developing in the place of a drying
marsh); (3) potential for regeneration on a nearby abandoned arable field. Categories of
regeneration potential are the following: good, mean, poor, or it has no place to regenerate
(BÖLÖNI et al. 2003). A large set of examples in the Habitat Guide (see below) also support
selection of the proper category of regeneration potential.
Potential natural vegetation
Mapping of potential natural vegetation (TÜXEN 1956, KOWARIK 1987, HÄRDTLE 1995,
CHYTRÝ 1998) does not have a long tradition in Hungary (but see PÓCS et al. 1958, SZMORAD
1997). From the many different approaches to potential vegetation, we use the following
definition in the MÉTA project: potential natural vegetation is defined as the most complex
(climax-like) vegetation that would develop in a site “in no time”, so ignoring the constraints
of succession, if we exclude the present and future direct influence of humankind, but taking
the history of previous changes of site conditions into consideration (KOWARIK 1987,
HÄRDTLE 1995). The potential climax woodland vegetation, not the “potential replacement
vegetation” (CHYTRÝ 1998), should be documented except in naturally non-wooded areas. If
the mapper is unsure whether the grassland could be potentially afforested, then he/she has to
choose some kind of forest as potential vegetation. We are aware that this is quite an arbitrary
decision, thus detailed quality assurance is planned before publishing the potential vegetation
map of the country. The vegetation categories of Á-NÉR are applied for the documentation.
Selection of potential vegetation type must be based on the investigation of the macro-, mesoand microclimate, bedrock, soil type, hydrology and present and past land use. Detailed
guidelines for each vegetation category are given in the Mapping Guide. Potential vegetation
is recorded in each hexagon, giving not more than five habitat types, listed in the order of their
dominance.
MÉTA vegetation mapping
235
Invasive alien plant species
We have stressed the importance of the invasive species among the threats, because in the
coming decades they might cause the most serious damage to the natural and semi-natural
vegetation in Hungary (MACK et al. 2000, TÖRÖK et al. 2003). The most important invasive
plant species in Hungary (TÖRÖK et al. 2003, and references therein) include Acer negundo,
Ailanthus altissima, Amorpha fruticosa, Asclepias syriaca, Aster spp., Celtis occidentalis,
Echinocystis lobata, Elaeagnus angustifolia, Fraxinus pennsylvanica, Phytolacca spp.,
Prunus serotina, Reynoutria spp., Solidago spp., Robinia pseudoacacia, Vitis vinifera and
V. rupestris.
We estimate the total area occupied by invasive species in each hexagon, based on the
categories < 1, 1, 20, 100%. On the quadrat level we record the existence and the potential
negative effect of the 15 most important invasive plant species for each habitat type
separately.
Old fields
About 10% of the territory of Hungary is covered by old fields, but no data are available
about their exact distribution, actual vegetation, regeneration potential etc. In each hexagon
the total area of old fields abandoned after World War II and at least two years ago, is recorded
with the categories < 1, 1, 20, 100%. Military maps drawn 15–20 years ago are especially
useful for recognizing old fields that can also be discerned in the field because of their typical
vegetation pattern and species composition.
Land use
As a result of decreasing numbers of domestic animals, the use of grasslands is declining all
over the country, which leads to successional changes (BÖLÖNI et al. 2003). There are no
exact data on the process, because on the country scale, statistics are given only for the gross
number of animals per settlement or per farmer, while there is no information on the way of
keeping (e.g. stalling or grazing) or feeding (e.g. hay or other forage).
Therefore we also document pasturing or mowing of the grasslands at the hexagon level:
mowing, pasturing with cattle, sheep, or with other domestic animals (e.g. goats, geese,
horses). More than one category can be marked if appropriate. Data on pasturing or grazing
are obtained by observation: either the site has just been pastured or mowed at the time of the
mapping or the mapper makes a decision based on the presence of excrements (pasturing) or
the lack of dry stalks from the previous year (mowing).
If evidence of threats, invasive species, old fields and land use is lacking in the field, the
code “not observed” should be marked. Thus we can avoid the misinterpretation due to mere
lack of data.
Landscape health status
This is a synthetic attribute that describes the (semi-)natural vegetation in its landscape
context. It is based on the actual naturalness and regeneration potential of the whole
landscape. The hexagons are classified into one of the following categories: (1) region with
high value of naturalness and with little or no potential for regeneration (“relict” landscapes);
(2) region with high value of naturalness that has the potential to regenerate in case of future
236
Zs. Molnár et al.
Table 3. Preliminary evaluation of the accuracy of identification of habitat types, based on the expert judgement after
quality check of 1500 quadrats (53%).
High
High-medium
Medium
Poor
Nymphaea, Nuphar, Utricularia,
Stratiotes euhydrophyte habitats
Aquatic communities
of fens
Trapa, Lemna, Salvinia
and Ceratophyllum
communities
Phragmites beds of fens
Artemisia salt steppes
Eu- and mesotrophic
reed and Typha beds
Helophyte beds
Lowland rich fens
Tussock sedge communities
Non-tussock beds of
large sedges
Salt marshes
River bank vegetation
Transition mires and raised bogs
Alkali mud habitats
Rich fens on highlands
Lowland steppe thickets
Salt meadows
Closed rocky grasslands
Molinia meadows
Open sand steppes
Slope steppes on stony
grounds
Mesotrophic floodplain
meadows
Open rocky grasslands
Closed steppes on loess,
clay and tufa
Tall-herb communities
Lowland alder woodlands
Oak-hornbeam
Arrhenatherum hay
woodlands on highlands
meadows
Lowland hornbeam woodlands
Closed thermophilous
oak woodlands
Beech woodlands
Rock woodland habitats
Forest-steppe meadows
and other semi-dry
grasslands
Closed sand steppes
White oak scrub woodlands
Willow mire woodlands
Sand steppe oak woodlands
Riverine woodland habitats
Poplar-juniper steppe woodlands
Acidophilous woodland habitats
Thicket vegetation of rocks
Turkey oak woodlands
Acidophilous coniferous woodlands
degradation; (3) region with mean value of naturalness, but with the potential of regeneration;
(4) region with mean value of naturalness, lacking the potential of regeneration; (5) degraded,
depreciated region (see also BASTIAN 1996). Selection of the proper category of landscape
health status is supported by a large set of examples in the Mapping Guide.
STANDARDIZATION IN THE MÉTA METHOD
Methodical Guides and field trainings
Two different kinds of guides have been compiled: Mapping Guide (MOLNÁR 2003) and
Habitat Guide (BÖLÖNI et al. 2003).
The Mapping Guide accurately defines the terms, provides detailed descriptions of the
terms often used inconsistently in Hungary, gives pieces of advice for the planning of the
mapping route, determines the rules of the data sheet completion, and lists the most common
mapping mistakes. Each attribute has an entry constructed as follows: (1) why do we collect
this attribute?; (2) definition of the attribute; (3) detection of the attribute in the field;
(4) categories of the attribute; (5) abbreviations used on the data sheet.
MÉTA vegetation mapping
237
The Habitat Guide includes the modified version of the Á-NÉR habitat classification
system with descriptions of the habitat types with the following structure (1500–2000 words
for each type): definition, site conditions, characteristic species, vegetation context, subtypes
(with short descriptions), types not belonging here (the correct category is given), the pattern
on the satellite image characteristic for the certain type, characterization of naturalness-based
habitat quality and regeneration potential with examples. The chapters of “types not
belonging here” and the examples of naturalness-based habitat quality and regeneration
potential were prepared to help standardization.
To decrease differences in perception, compulsory field trainings were organized.
Conceptual ground for the field trainings was the idea of experience learning: mapping in
shuffled pairs, followed by discussions, in four to six different landscapes for three days.
Standardization of habitat identification
The basic tool for this was the preparation of the Habitat Guide written by many botanists
and reviewed by all mappers. Field trainings were used to motivate mappers to use the Habitat
Guide in every case when they are uncertain in the identification.
Habitat types of rather extreme site conditions, very species-poor sites and habitats very
rich in characteristic species proved to be the least problematic ones, as well as some zonal
habitat types. Most of the habitat types comprising different transitional states have lower
accuracy; these will be analyzed better after aggregating with similar/related habitat types
(Table 3).
Standardization of naturalness-based habitat quality and regeneration potential
After the preparation of the general definitions for the categories of naturalness-based
habitat quality (based on NÉMETH & SEREGÉLYES 1989), we compiled field examples for
each habitat type, for each category of naturalness-based habitat quality and regeneration
potential. The overall number of examples given were 522 for naturalness-based habitat
quality and 678 for regeneration potential.
Advantages of this approach are as follows: (1) habitat specific differences (species
richness, structural and dynamic features) can be handled more easily; (2) the mapper can
compare the actually mapped vegetation patch to certain habitat-specific examples instead of
abstract general criteria; so (3) examples (instead of definitions) are easier to use and more
reliable. Disadvantages of this approach include the following: (1) much harder work to
prepare; and (2) more specialists are needed than for writing five abstract definitions.
Comparing the experiences of the Natura 2000 mapping project (only categories were used
and definitions for the categories were given; MOLNÁR et al. 2001b) and the MÉTA mapping,
degree of standardization improved considerably, but further efforts are still needed.
When preparing the descriptions for the examples of naturalness-based habitat quality, the
following criteria were taken into account: species composition (e.g. number and abundance
of characteristic species, dominant species, weeds and disturbance tolerant species),
structural characteristics (e.g. patchiness, horizontal structure), origin and age (e.g. primary or
secondary vegetation), site conditions (e.g. water-supply, erosion), landscape context (e.g.
species richness) and land use (e.g. overgrazing by game, burning). When elaborating the
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Zs. Molnár et al.
examples of regeneration potential, the following attributes were considered: the conditions
of the stand (e.g. species richness, regeneration ability of the species populations, competitive
ability of disturbance tolerant ones), site conditions (e.g. water supply, soil-nutrient balance),
conditions of the landscape (e.g. propagule sources, mobility of species related to their
distance to the propagule source), type of land use (e.g. forest management, pasture).
The standardization was successful, but not perfect (see Table 4 for examples of
naturalness-based habitat quality). The proportion of the values given by the mappers and
considered as unacceptable errors, decreased from 23.7% to 0% (values in bold), while the
proportion of identical values increased from 21.1% to 63.4%.
The standardization of naturalness-based habitat quality and the regeneration potential is
more difficult than the standardization of habitat identification, because the habitat type first
has to be identified, and then the habitat quality and the regeneration potential can be chosen
from the examples. It is, however, easier to do, because the examples of similar habitats are
also similar, thus in case of incorrect identification of the habitat type the naturalness-based
habitat quality and the regeneration potential can be still correct.
Selection of mappers
In the announcement for the participation in the mapping it was stated that at least few
years of field experience is needed. Nearly all Hungarian botanists indicated their interest,
though there were many withdrawals during the standardization process (because of the lack
of capacity, knowledge or true interest). The selection was not based on any exam, but on
voluntary withdrawal in parallel with the standardization process.
Before the field trainings, all mappers had to read the Mapping Guide (54 pages) and at
least the descriptions of those habitat types from the Habitat Guide that occur in the landscape
they were going to map (ca. 50–100 pages). Ten habitat types had to be reviewed thoroughly
and the reviews had to be sent to the editors of the Habitat Guide. Later we examined the
accuracy of the content of each mapper’s documentation of her/his first quadrat, and corrected
and sent back the mapper-specific mistakes and misunderstandings. We compiled the
questions and deficiencies in the lists and asked the mapper for corrections. We estimated that
by this time about 30–35% of the potential future mappers withdrew from the mapping.
The selection proved to be successful; 85% of the finished quadrats are acceptable,
approximately 10% needed supplementary mapping (for too few attributes or too few
hexagons), and only 2% needed to be re-mapped.
DISCUSSION
Many practical decisions made during the development of the methodology were mostly
determined by the available resources. Such decisions included the following: selection of
either patch or grid map, spatial resolution, size of the hexagon, type of data sources, number
of mapped attributes, and recording basic or synthetic attributes. Supported by the outcomes
of similar projects, our own previous experiences and the literature, we thoroughly discussed
all of these issues before the final decisions. Here we would like to present some thoughts
from this discussion.
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MÉTA vegetation mapping
Table 4. The values of naturalness-based habitat quality given by the pairs of mappers on the first and on the last
(usually the 7th) site of the field trainings. Mapper pairs were shuffled 5–6 times during the three days. Each
number indicates the values given by one pair (for coding see section: Naturalness-based habitat quality). The
proportions of identical, acceptable and non-acceptable values given for naturalness-based habitat quality are
significantly different on the first site and on the last site of the field trainings (chi-square = 19.44, P = 0.00006).
Values given by the pairs of participants
of the mapping course
Standard value according
to the Habitat Guide
First site of the field training
4r2, 4r3, 3r2, 4, 2
5-4, 5, 5, 3, 5r4
4, 5-3, 4r3, 4, 3, 3
4, 3, 4, 2, 3r2, 4
4r3, 4r3, 4r3, 4r3, 3-2
5, 5r4, 5r4, 3r2, 4, 4
5, 5r3, 4r3, 5, 3
3r2
5r4
4
3r2
4r3
4r3
4-3
Last site of the field training
3, 4, 3, 4, 4, 4, 4
4, 4-3, 4, 4r3, 3, 4r3, 4
5r4, 4, 4, 4, 4, 4, 4
3, 3, 4, 3, 3
5, 5, 5-4, 5r4
4, 4, 5r4, 5r4, 4
4, 4, 4, 4, 4, 3
4
4r3
4
3
5r4
4
4
Scaling and resolution
Principally any scale of mapping, any kind of resolution can be “good”. The important
thing is that the aim of the mapping, the mapped attributes and the scale/resolution must
match. Generally, a map with finer resolution can be used better and for more things, and it is
possible to make a coarser map from the finer, while the opposite way is limited. Though there
are relevant scales in each particular type of landscape, in an area of the size of Hungary these
relevant scales change between different landscapes (e.g. fine-scale alkali steppe mosaic vs.
coarse-scale mosaic of zonal woodland communities). Any scale chosen for vegetation
mapping of a country is therefore arbitrary. We have chosen the finest possible scale
according to our capacity. We have also taken into consideration that CLC has a fine
polygon-pattern and the CLC mapping will be repeated regularly, so MÉTA was designed to
“complement it” with actual detailed vegetation data.
Traditional vegetation mapping methods document the landscape in patch maps, which is
a product of a “thorough” exploration of the area (KÜCHLER & ZONNEVELD 1988).
Delineation of patches is time-consuming, and it needs much attention, hence the parallel
mapping of attributes with different patch patterns is rather difficult, and therefore rarely done
(KÜCHLER & ZONNEVELD 1988). To meet our four goals, we had to map many different
attributes of the vegetation and landscape at the same time, therefore we were forced to find a
compromise between spatial resolution and thematic richness. If there is a given amount of
resource for the mapping of an area, the patch map and the grid map will mainly differ from
each other in the emphasis on the spatial or thematic resolution. We chose the grid map,
because the high thematic resolution was more important for our purposes.
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Zs. Molnár et al.
Advantages of grid mapping include the following: (1) the parallel mapping of many
different attributes is easier (BAGI 1991, 1997, DIERSCHKE 1991); (2) areas with vegetation
transitions and with differently scaled mosaics are easier to document (e.g. grasslands with
encroachment of bushes). Disadvantages include the following: (1) the patterns finer than the
grid are blurred, while (2) the patterns much coarser than the grid are documented redundantly
cell by cell; (3) it is hard to visualize many different attributes at the same time.
Combination of data sources
Aerial photographs and satellite images can highly improve the spatial resolution and data
reliability of vegetation maps (KÜCHLER & ZONNEVELD 1988, MILLINGTON & ALEXANDER
2000). If (semi-)natural regions are dominantly field surveyed, while fragmented regions
partly interpreted with remotely sensed material (as is the case in the MÉTA method), it is
a good example of the combined data source utilization proposed by MILLINGTON &
ALEXANDER (2000). Actually, the MÉTA method uses two main data sources: (1) the actual
landscape and (2) the satellite image.
It is important to emphasize that in the MÉTA method we combine two approaches:
(1) in-door vegetation mapping by satellite image interpretation (e.g. CORINE Land Cover
mapping; BÜTTNER et al. 2002) (although we did it out-door as well!); (2) field mapping
aided by remotely sensed material (KÜCHLER & ZONNEVELD 1988). We agree with
KÜCHLER & ZONNEVELD (1988) and others (e.g., FALIÑSKI 1994a, MILLINGTON &
ALEXANDER 2000, STEVENS et al. 2004) that the combination and local calibration of these
background data sources allows for the preparation of more accurate maps containing more
information. According to our experiences, the accuracy of extrapolations and “out-door”
satellite image interpretations is satisfactory particularly at this spatial scale, if it is based on
good knowledge of the landscape.
Basic vs. synthetic data
Both basic (inductive) and synthetic (deductive) data have advantages and disadvantages:
collection of synthetic data takes less time, so the spatial resolution can be much higher
(calculated for the same time spent in the field), but the possibilities of subsequent correction
and re-interpretation are limited. Recording basic data demands much more effort, but they
can be analyzed in a more flexible way, including various finer analyses (SANDERS et al.
2004). Basic data are scientifically more sound and easier to repeat (monitoring). Nature
conservation, however, often needs a large quantity of spatially detailed data that cannot be
produced reasonably based on basic data collection (e.g. site evaluation).
Large differences in the possibilities of standardization in these two data types exist. These
types differ so much that CHERRILL & MCCLEAN (1999) consider the collection of basic data
the most efficient (or even the one and only) way of full standardization, because this way the
mapper working in the field is excluded from the final decisions on the mapping process.
Basic data are worthy of being collected if the mapped object can be classified objectively
and its quantity or pattern can be given exactly (e.g. ten individuals of Acer tataricum).
However, collecting synthetic data is worthy if many field data and/or many different
attributes are needed for the categorization (e.g. naturalness-based habitat quality,
MÉTA vegetation mapping
241
regeneration potential). If the habitat identification is made in the traditional way, based on
species composition, the data collected in the field can be considered as basic data in
a well-documented, unambiguous case. However, in a hardly identifiable, transitional or
unique situation the collected data can be considered as synthetic. Vegetation maps are based
on synthetic data, unless there are phytosociological relevés taken in the patches with
adequate regularity, and the categorization of the patches is created a posteriori, after the
analysis of the relevés (CHERRILL & MCCLEAN 1999). The data are synthetic if we document
historical events or predicted future changes without having documented the vegetation
dynamics with basic data.
In the MÉTA method we made a compromise between resolution, type (basic or synthetic)
and number of attributes and scientific soundness. We decided to use synthetic data for
attributes such as habitat type, naturalness-based habitat quality, landscape health status and
potential vegetation, and basic data for the attributes such as area of habitat types, area of
invasive plant species, area of old fields and threats (Table 5).
Scientific vs. practical approach
An important task of vegetation science is to make botanical evaluations and formulate
suggestions responding to the challenges of the society. Quite often science is not prepared
enough to fulfill the demands of the society, e.g. there are no tested adequate methods or
capacity. In these cases compromises are needed. In our days the evaluation of habitats, their
naturalness and prognosing their possible future is one of the most important demands of
nature conservation.
Because we do not have scientifically sound, objective methods for measuring “habitat
quality” (or conservation value), the comparison of the present and an ideal or abstract state is
usually used to estimate “habitat quality” for use in nature conservation (for the first time:
BERNÁTSKY 1905, later e.g. JALAS 1955, ELLENBERG 1963, SUKOPP 1969, DIERSCHKE
1984, ANDERSON 1991, 1992, GÖTMARK 1992, BASTIAN 1996, GRABHERR et al. 1998,
MÄGI & LUTSAR 2001, BARTHA et al. 2003, BARTHA 2003a, GUTH & KUÈERA 2005). In
these cases we decide according to our – consciously subjective and anthropocentric –
nature-conservation-based evaluation, what is considered to be in a better or worse natural
state: richness in “preferred” species (e.g. habitat specialists), state of the structural elements
(e.g. canopy layers) and of the natural processes (litter accumulation, small-scale successional
patchiness). If more positive values can be related to a certain patch and fewer negative ones,
we consider it to be of “higher quality”. While defining the “good” state, sometimes the
spontaneous vegetation dynamics are emphasized more (e.g. in pioneer habitats), sometimes
the high species diversity (e.g. in some meadow communities) or the structural diversity (e.g.
woodlands) are. Therefore the habitat-quality categories were defined for each vegetation
type separately in the MÉTA mapping.
The practice-oriented attributes of MÉTA will be used for evaluations and strategic
planning. In the determination of preciousness or nature conservation priority of an area
beyond the habitat type, the size of the site, naturalness, surrounding landscape and zoological
and aesthetic aspects should be taken into consideration. Therefore collective analysis of
several different attributes will nearly always be needed. Although any kind of evaluation
Habitat type
Area
Naturalness-based habitat quality
Pattern
Neighbourhood
Connectedness (hexagon scale)
Threats
Potential natural vegetation
Landscape health status
Invasion (%, hexagon scale)
Old fields
Land use
Invasion (effect, quadrat scale)
Connectedness (quadrat scale)
Regeneration potential of existing stands
Regeneration potential on neighbouring areas
Regeneration potential on old fields
B<SY
B
SY
B
B<SY
B<SY
B<SY
B<SY
B<<SY
B
B
B
B<SY
B<<SY
B<<SY
B<<SY
B<<SY
+++
+++
++
+
++
++
++
++
+
++
+++
++
++
+
++
+
+
S>P
S»P
S<<P
S»P
S<<P
S<P
S<<P
S»P
S<<P
S»P
S»P
S<<P
S»P
S<P
S<P
S<<P
S<<P
Basic/
Possibilities
Scientific/
synthetic
of
practical
data
standardization
+++
+++
++
+++
+
+
++
++
+
+++
+++
+++
++
+
+
+
+
Scientific
soundness
+++
+++
++
+
++
+
-
1st goal:
atlas,
vegetation
geography
+++
+++
+++
+
+++
++
+++
++
+++
++
++
+++
++
-
2nd goal:
evaluation
of
vegetation
for
conservation
+++
+++
+++
+
+++
+++
+++
++
++
+++
+++
+
+++
+++
++
++
++
3rd goal:
evaluation
of
landscape
+++
+++
+++
++
+++
+++
+++
++
+++
+++
+++
++
+++
+++
+++
+++
+++
4th goal:
prognosis
of
vegetation
and
landscape
changes
Table 5. Different characteristics and planned applications of the MÉTA attributes (B – basic; SY – synthetic; S – scientific; P – practical; <, <<, », >>,
>– comparative suitability for these purposes; +++ – high; ++ – medium; + – low; - – none).
242
Zs. Molnár et al.
MÉTA vegetation mapping
243
involves weighting different attributes, the results will never be objective. Consequently, for
nature conservation purposes different evaluations with different weighting systems can be or
even should be made.
There are some attributes (e.g. connectedness and neighbourhood), which can be used for
basic studies in landscape ecology and for designing ecological networks as well. The
country-scale distribution of different habitat types is suitable for basic studies in geobotany
and for replenishing the Natura 2000 databases. Based on the data on landscape health status,
regions can be selected where the nature protection should have priority even on the
agricultural areas (zones with different levels of protection). During any analysis scientific
soundness and standardization should always be taken into consideration. We indicate the
possible future applications of the attributes in Table 5.
FUTURE PROSPECTS
At the beginning of year 2006, 91% of the area of Hungary was already surveyed.
Preparation of the MÉTA maps has already started (see www.novenyzetiterkep.hu/META/en).
After the detailed quality assurance, the first version of “The Atlas of Hungarian Habitat
types” is going to be published. It will serve as a basis for phytogeography (e.g. the
relationships between flora and vegetation, and climatic, geological, hydrological and soil
factors) and landscape ecology (e.g. the study of fuzzy classification of landscape types or
landscape gradients). We are also going to publish the considerably extended version of the
Habitat Guide. We have the 1st opportunity to prepare the detailed map of naturalness-based
habitat quality of the country, to evaluate the threat of alien plant invasion, to delineate the
potential ecological networks or the sites of Important Plant Areas (IPA). We can also enrich
the database of Natura 2000, support the strategic regional planning procedures, and
participate in the preparation of training packages for primary, secondary or higher education.
Acknowledgements: Many persons have supported the elaboration of the method and the writing of the article
by providing advice, opinions, questions, own data and the aid in finding local manuscripts and publications: all
MÉTA participants, Gábor Fekete, Milan Chytrý, Karel Prach, Rense Haveman, Miklós Kertész, István Bagi,
John Rodwell, Jiøí Guth, Helge Bruelheide, Gabriella Magyar, Dobromil Galvánek, Eli Fremstad, Andrew C.
Millington, Petr Petøík, Branka Trèak, Ferenc Csillag†, and three anonymous reviewers. The English translation
is by Zsolt Erõs-Honti, and was improved by Mark Searle.
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Received 28 November 2005, revision received 7 August 2006, accepted 10 August 2006
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