landscape-ecological mapping of the netherlands

Landscape Ecology vol. 5 no. 3 pp 145-162 (1991) SPB Academic Publishing bv, The Hague

Landscape-ecological mapping of the Netherlands

Kees J. Canters I, Cees P. den Herder 2, Aart A. de Veer 3, Paul W.M. Veelenturf 2. and Rein W. de Waal 3 1Centre for Environmental Studies, Leiden University, P.O. Box 9518, NL-2300 RA Leiden, 2National Physical Planning Agency, P.O. Box 90618, NL-2509 LP The Hague, 3 Winand Staring Centre for Integrated Land, Soil and Water Research, P.O. Box 125, NL-6700 AC Wageningen

Keywords: geographical database, GIS, grid map, landscape ecology, land classification, nature conservation, susceptibility, significance, vulnerability

A b s t r a c t

The Landscape-ecological Mapping of the Netherlands project (LMN project) started in 1983 with the aim of establishing a landscape-ecological database for use in developing and evaluating national land-use plans. The project, working with grid cells of 1 km 2, has four working objectives: a) development of mapping potential for basic landscape-ecological data, b) assessment of susceptibility to interventions, c) evaluation of significance for nature conservation and d) production of vulnerability maps, as a combination of susceptibility and significance. In addition to information on soil, groundwater, ecotopes, flora and fauna, the database also incorporates information on physiographical features and entire landscapes. The resulting database is a geographic information system (GIS). This article describes the second phase of the project (1985-1989), covering the 'Randstad' area, and focusses on the methods and the applications potential of the database.

I n t r o d u c t i o n

In the past few decades a variety of environmental inventories has been carried out in the Netherlands, the majority of which has now been completed. The country measures around 40,000 km 2 from which ca. 60% is agricultural land; the remaining parts consist of a.o. waters, urban areas, infrastructure, forests and nature reserves. Figures 1 and 2 give an impression o f the complexity of the landscape in the Netherlands.

Most of the inventories currently in progress con- cern flora and vegetation. In the early 1970s already, an attempt was made to compose an overall map of the ecological status of the Netherlands based on available information. However, the resultant National Environmental Mapping (Kalk-

hoven et al. 1976) met with substantial criticism, as was the case with other, similar projects. The major shortcomings of the maps developed during this period were: - lack of adequate detail; - insufficient insight into current status of flora

and fauna; - no information on topological (= vertical) and

chorological (= horizontal, spatial) relationships;

- no distinction between susceptibility to interventions and significance for nature conservation;

- no indication of vulnerability, combining susceptibility and significance.

In an endeavour to resolve these problems, in 1984 the National Physical Planning Agency initiated the

* P r e s e n t address." Province of Limburg, P.O. Box 5700, NL-6202 MA Maastricht.

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Fig. 1. Part-dug peat landscape with meadows in centre of study area: "The Green Heart of Holland" (Photo: KLM-Aerocarto b.v.: 18260).

Landscape-ecological Mapping of the Netherlands project (LMN project; in Dutch: 'Landschapseco- logische Kartering van Nederland'). These were not the sole aims of the project, however. There was also a need for a flexible database system with easy access and permitting rapid data retrieval, while also allowing for ready updating of data. In addition, there was a growing need for landscape-ecological maps covering the whole of the Netherlands. Moreover, it was foreseen that the quantity of data yielded by environmental inventories would only increase in the years ahead, without there being adequate capacity for using the data at a national level. To this end it was necessary to investigate the possibilities to combine heterogeneous data, both abiotic and biotic, at a single spatial scale. Together, these considerations formed the incen-

tives for a more coordinated approach made by the National Physical Planning Agency towards the processing and utilization of floristic and faunistic inventory data (see also: Beenhakker et al. 1989).

This article describes some of the more general aspects of the LMN project, focussing on the methods developed and the potentials of the database. For more detailed information the reader is referred to Veelenturf et al. (1985, 1987) and De Veer et al. (1986, 1988).

Aims o f the Landscape-ecological Mapping o f the Netherlands

In light of the above considerations, the objective of the LMN project was formulated as follows: to create an automated landscape-ecological database

147

Fig. 2. Ice-pushed ridge with woodland (front) and arable land at clays at the background (Photo: KLM-Aerocarto b.v.: 16059).

for national land-use planning. To reach this aim,

the following four working objectives were defined for the project: a. to create a database for preparing basic maps, in-

cluding description of the incorporated data; b. to develop simple conversion procedures based

on intervention-effect relationships to produce susceptibility maps;

c. to develop conversion procedures based on ex- plicit and practical evaluation criteria to produce significance maps;

d. to combine susceptibility assessment and significance evaluation to produce vulnerability maps.

Figure 3 shows how these working objectives are related. It can be seen that a well-functioning system requires both information on intervention-

its side (middle) in eastern part of 'Randstad' area; grassland on fluvial

effect relations and criteria with which to evaluate

significance. A maj or advantage of this approach is that basic data (objective a) are kept separate from interpreted data (other objectives). The same distinction holds for susceptibility assessment and significance evaluation (objectives b and c).

For objective c (= significance evaluation), an important precondition was that not only pattern criteria such as rarity, diversity and authenticity should be taken into account, but also process criteria such as naturalness, degree of disturbance of cycles and regulation mechanisms and connectivity between ecosystems (cf. Canters and Udo de Haes 1986). Outside the Netherlands, too, in landscape ecology there is a growing appreciation of the significance of relationships between landscapes or landscape elements (e.g. Forman and Godron 1986;

148

', interventions

I assessment ]

objetive b

susceptibility maps I

inventory of biotic and abiotic

components and landscapes

objective a

basic data

maps

r c r i ter ia 3

I eva,uati~ [

vulnerability

maps

objective c

l significance maps

S objective d

Fig. 3. The four working objectives of the LMN project and their interrelationship.

Schreiber 1988). It can be stated that taking into account the processes within and between landscape elements, viz. exchanges of energy, matter (dead or living) and information, is a prerequisite to a true

landscape-ecological approach. With respect to the actual and potential occur-

rence of species, the chosen procedure utilizes current, real-world data, i .e. species actually observed in a given area. By linking up various data blocks, however, the system also permits the potential occurrence of plant and animal species to be determined and used. In such cases an assessment is made as to whether a given grid cell satisfies the habitat requirements of the species concerned. This might be an extra, new working objective (e) for the future, i.e. the production of suitability maps.

Basic considerat ions

In addition to the concrete working objectives, a

number of more general aims can be distinguished.

For operationalizing a database of the given scope, a number of requirements must be satisfied. The

database ought to: - be user-friendly; - fit the kind of questions posed in everyday prac-

tical work, yielding maps of a scale appropriate for national physical planning;

- p r o d u c e outcomes which are verifiable and modifiable, i.e. the procedures should be easy to follow and utilization of alternative criteria and weighing factors ought to yield comparable results;

- permit both updating and extension to include

different and/or more recent data.

E s t a b l i s h i n g a l a n d s c a p e - e c o l o g i c a l d a t a b a s e o f t h e

N e t h e r l a n d s

The LMN project, which was commissioned by the National Physical Planning Agency of the Nether- lands (RPD), is being carried out by two institutes. The Centre for Environmental Studies, Leiden, is responsible for the biotic side of the project, with the Winand Staring Centre, Wageningen, handling the abiotic side. In addition, the latter institute is responsible for development of the LMN data model.

Project programming

The LMN project is programmed in three phases. In the first phase (1984-1985), a method was developed for operationalizing the basic ecological data; the method was then tested in a sample area measuring 320 km 2 in the province of Utrecht (cf. Vee- lenturf 1987). During the second phase (1986- 1989) the method was further elaborated and maps were produced of an area measuring approx. 4,000

km 2 in the west of the Netherlands ( 'Randstad' area).

The third phase, started in 1988, is less concerned with methodological aspects, but aims at a nationwide database with 1 km 2 as grid cell (see below). When necessary, there is scope for adapting the developed methods and programs. The third phase of the project is anticipated to take at least five years.

Source data

The basic data stored in the database have been taken largely from material (either published or un- published) collected by outside agencies. In some cases, these data have been incorporated directly into the database. In most cases, however, the data have first been processed to some extent to match the scale of working. The following source material has been used:

149

- data on soil and geomorphology, mainly from the Winand Staring Centre;

- data on soil and surface water, based on litera- ture and cartographic materials from the Minis-

try of Transport & Public Works; - d a t a on vegetation and flora, provided by

regional (i.e. provincial) authorities; - data on fauna, collected under joint private/

government schemes and usually involving volunteer surveys of animals.

A large proportion of the faunistic data are under the administration of the Biogeographic Informa- tion Centre (BIC) of the Ministry of Agriculture, Nature Conservancy & Fisheries. In the case of avi- faunal data, administration is by the Central Sta- tistical Office (CBS), Ministry of Economic Af- fairs. These data sets differ considerably in design and detail, resulting from differences in principles and objectives, and a variety of technical and finan- cial constraints. There is consequently considerable variation in such matters as: up-to-dateness, ac- curacy and (spatial) completeness. In some cases, the differences are bound up with the character of

the ecosystem component. Soil type, for example, can be determined fairly accurately; moreover, it changes only very slowly. This is in marked contrast

to faunistic data. In other cases, differences can be traced to the different efforts of inventory. The result is a database in which a wide variety of information is integrated with the aim of bringing together heterogeneous data at a single spatial scale.

Scale and mapping units

In the LMN project grid cells rather than polygons have been taken as the mapping unit. The main con-

siderations underlying this choice were (see e.g. Patti et al. 1970):

- problems in interpreting maps having different - and therefore overlapping - polygons for (sub-)components can be avoided;

- in analyzing data, statements are always made at the grid-cell level, thus avoiding pseudo-accura- cy, viz. with respect to polygons representing much smaller areas;

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SOCIETAL ACTIVITIES SECTORS (examples)

MINING, QUARRYING ~ [ clay or sand digging

[ ~ . _ [ housing estate HOUSING construction

TRAFFIC, TRANSPORT ~ motorway construction

[ WATER MANAGEMENT ] > [ river regulation

[ industrial site INDUSTRY ~ construction

~ energy generation, ENERGY PRODUCTION transport

[ WASTE TREATMENT I ~_ [ water-treatment plant construction reallotment of farming land

E AGRICULTURE intensification of

farming operations

FORESTRY ~ commercial afforestation

[ RECREATION ] :~ [ outdoor recreation

I DEFENCE >- [ use of training area

[ NATURE CONSERVATION ] > [ reserve management

INTERVENTIONS affecting the whole:

] ~ paving,eXcavati~ ]}

land development ]

disturbance of relationships] (e.g. by watertable lowering)

~ affecting components:

changes in Cgrouod I water levels

acidification

eutrophication, �9 impovedshment

intoxication

~[ grazing, mowing, felling, planting, "set-aside"

~{ trampling

disturbance by noise, trapping, killing

EFFECTS ON:

ECOSYSTEM

Fig. 4. The chain of cause and effect: activities in different societal sectors result in interventions, who are simple to describe and limited in number; interventions lead in turn to effects on the ecosystem components or on the overall ecosystem.

- many of the available data are already stored in grid cells, permitting relatively easy transforma- tion to the LMN database.

As mentioned above, the objective of the LMN project is to render landscape-ecological data accessible for national physical planning activities. This means that the scale of the maps must be compati- ble with that employed in physical planning: max. 1 : 2 5 0 , 0 0 0 (Veelenturf 1987). At this scale, the basic mapping unit of polygon maps is 4*4 mm (1 km 2 in the field) (cf. Klijn 1988). A choice for a grid cell of 1 km 2 does not necessarily imply the neglect of within-cell variation. For certain components the relative areas occupied by the two largest (for soil and freatic groundwater) or different (for example landuse) types within the grid cell are stored in the data base. For faunistic elements this is impossible because of the mobility of animals.

The choice of target maps for the LMN project was based on an analysis of their relevance for physical planning purposes (cf. Fig. 4, illustrating the relationships between interventions and effects within and on an ecosystem). Table 1 lists the maps produced in the second phase of the project.

C o n t e n t s o f the d a t a b a s e

If a large database is to be used efficiently, data must be stored in a format accessible to a powerful database managing system. For the LMN project, ORACLE, a powerful system designed for managing large databases, is run on a VAX 11/750 computer.

Table 1. List of maps made in Phase II of the LMN project.

Component Objec- Topic tive

ABIOT1CA a largest soil unit a second largest soil unit a largest geomorphological unit a groundwater relationships b susceptibility of soil to

(ground)water lowering b susceptibility of geomorphology

to levelling c diversity of soil and groundwater

classes c major physiographical features c undisturbed nature of surface-

water relationships d vulnerability of soil and ground-

water classes

IPI-ECOTOPES dominant land use: woodland vegetation structure linear nutrient-poor ecotopes planar nutrient-poor ecotopes planar wet ecotopes linear woodland ecotopes planar woodland ecotopes susceptibility of planar ecotopes to eutrophication susceptibility of planar ecotopes to watertable lowering rarity of linear ecotopes rarity of planar ecotopes replaceability of planar ecotopes vulnerability of planar ecotopes to eutrophication

FLORA no. of plant species of nutrient- poor environment no. of plant species of wet terrestrial environment no. of seepage-indicating plant species susceptibility of flora to (ground)water lowering susceptibility of flora to eutrophication

FAUNA no. of meadow bird species no. of bird species of marshes and reedbeds no. of wintering geese no. of wintering widgeons no. of species of wintering birds of prey

Table 1. Cont.

Component Objec- Topic tive

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LANDSCAPES a d

no. of species of mammals no. of species of amphibians no. of species of butterflies occurrence of badger occurrence of hedgehog occurrence of otter occurrence of weasel number of species of common mammals structure of community susceptibility of meadow birds to intensification susceptibility of breeding birds to disturbance susceptibility of mammals to fragmentation susceptibility of amphibians/ reptiles to watertable lowering endangered status of breeding birds significance for waterfowl rarity of mammals endangered status of amphibians vulnerable breeding birds

landscapes vulnerability of landscapes to groundwater extraction

The L M N data model

The L M N data model comprises the following ele-

ments (files):

- A B I O T I C A : abiotic characteristics

- I P I - E C O T O P E S : land-use and ecotope char-

acteristics

- F A U N A

- F L O R A

- L A N D S C A P E S

: f a u n i s t i c c h a r a c t e r i s t i c s

: f l o r i s t i c - e c o l o g i c a l c h a r a c -

t e r i s t i c s

: l a n d s c a p e u n i t s

T h e f i le A B I O T I C A c o m p r i s e s al l p e d o l o g i c a l , geo-

m o r p h o l o g i c a l a n d h y d r o l o g i c a l d a t a , as wel l as

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Table 2. Fields in ABIOTICA file of LMN database, showing length (max. no. of positions) and type of data (I = integer, R = real).

Field name Description Length Type

CEL coordinates of grid cell 6 I

SLGC1 largest soil type/groundwater class combination (= s.g. combination) 5 R

area of largest s.g. combination 3 I second largest s.g. combination 5 R

area of second largest s.g. combination 3 I largest groundwater class 1 I

area of largest groundwater class 3 I second largest groundwater class 1 I area of second largest groundwater class 3 I

anthropogenic disturbance of soil 1 I

number of s.g. combinations 2 I

largest geomorphological unit 3 I

area of geomorphological unit 2 I number of geomorphological units 2 I

source of geomorphological information 1 I groundwater relationships (non-freatic) 4 I

surface-water relationships 2 I

largest physiographical feature 2 I

area of physiographical features 2 I genesis of largest physiographical feature 2 I

SLGC1-AREA SLGC2

SLGC2-AREA GC1

GC1-AREA GC2 GC2-AREA

DISTSL

SLGC-DIVER

GEOM

GEOM-AREA GEO-DIVER

GEOSCE RELGRWA

RELSURWA

PHYFEAT

PHYFEAT-AREA GEAGEN

data on significant physiographical features I (see Table 2). Information on soil and freatic groundwater has been derived from the "Soilmap of the Netherlands" (1:50.000) (Anonymous). The detailed information was first clustered into five main groups, based on geogenesis: peat soils, marine clay soils, fluvial clay soils, dune sandy soils and Pleisto- cene sandy soils. These five categories were then subdivided on the basis of the following differentiating characteristics: pedogenesis, calcium con- tent, topsoil type, nutrient availability, texture and horizon type. Only the two soil types occurring most extensively in each grid cell have been stored in the LMN database.

The file IPI-ECOTOPES comprises all land-use and vegetation data (see Table 3). IPI's, "inter- provincial inventory units", are used for inventory purpose by the provinces. They are based upon

1These physiographical features (see for examples: Fig. 7b) reflects the fact that inventory procedures do not always enable a strict distinction to be made between objectives a ( - basic data) and c (= assessment of significance).

uniformity of land use. Examples are: a woodland or a complex of grasslands. IPI's typically have an area of several hectares. A grid cell contains on an average ca. 10 IPI's. Within these IPI's, "ecotopes" are distinguished. Runhaar et al. (1987) de- fine an ecotope as "a spatial unit homogeneous with respect to vegetation structure, succession stage and abiotic factors governing plant growth". Identification of these ecotopes and assessment of their relative extent within IPI's are carried out using ecological (plant-species) groups (of. Stevers et al. 1987), elaborated further in the component FLORA. Species presence is based on the checklists used in the field by provincial authorities; when making these inventories only urban areas were left o u t .

For the LMN project, information on ecotope presence yields useful data on such aspects as nutrient availabitity, moisture regime, acidity and vegetation structure. An IPI consists mostly on 2 - 3 ecotopes; this number is only occassionally higher. The smallest resolution of IPI's and ecotopes is 0.1 ha or 0.1 km.

153

Table 3. Fields in IPI-ECOTOPES file of LMN database, showing length (max. no. of positions) and type of data (I = integer, A = alphanumeric).


CEL coordinates of grid cell 6 I PROVINCE province 2 A IPITYPE land-use type 3 I INV-NMB code of floristic inventory unit 3 I INV-YEAR year of inventory 2 I IPITYPE-P area of land-use type (planar) 3 I IPITYPE-L length of land-use type (linear) 2 I ECOCOMNR combination of ecotope type 1 I ECOCOMN-P area of combined ecotope types (planar) 3 I ECOCOMN-L length of combined ecotope types (linear) 2 I ECOTYP1 largest single ecotope type 3 I ECOTYP1-P area of largest ecotope type (planar) 3 I ECOTYP1-L length of largest ecotope type (linear) 3 I

ECOTYP4 fourth (and last) .. . etc. 3 I

Table 4 lists the characteristics stored in the files FLORA (= species groups), FAUNA and LAND-

SCAPES. With the exception of LANDSCAPES

(see above) these components contain either origi- nal data or generalized data, i.e. data that have un-

dergone little or no manipulation. The file FLORA

contains data on the number of species observed in each ecological group. These ecological groups are

defined in terms of abiotic and biotic characteris-

tics, in such a way that the assignment of plant species to ecological groups can be verified by means of direct measurement of the (a)biotic differentiating characteristics, viz. vegetation structure, salini-

ty, substratum, moisture regime, nutrient availability, acidity and ecosystem dynamics (sand drift,

trampling etc.) (Runhaar et al. 1986). Each differentiating characteristic has been subdivided into several classes, which can be combined to de-

fine different types of habitat, the "eco topes" mentioned earlier.

Individual plant species are not yet introduced in the database for several reasons: these data i) can not be obtained for the whole country, ii) are for

the time being too much detailed for the LMN project, iii) ask a lot of megabytes and are difficult to handle. Nevertheless, it is not ruled out that in the future the presence of individual plant species in the grid cells is introduced into the LMN database. In this way it will be possible to make new or some

different groupings of plant species for certain future applications. Informat ion about the presence

of individual plant species might also be interesting

for significance evaluation, specially because re-

cently the first Red Data List of Dutch plants has been published (Weeda et al. 1990).

Selection, conversion and presentation o f data

Appropriate selection and processing of data f rom

the LMN database is used to assess susceptibility, or to evaluate significance and vulnerability. This is

achieved with the aid of a user-friendly interface

(UFI), incorporating an interactive application processor (IAP) for retrieval, updating and correc- tion of basic data. Following selection, straightfor-

ward (automatic) conversion procedures are em-

ployed to convert data for use in the various types

of maps. Presentation of results on a grid-cell map is han-

dled by the program MAP (Tomlin 1980). For the

basic data, especially, the maps prepared by MAP

give a highly simplified picture, however, because only a limited number of (monotone) hatchings can be employed. With MAP, therefore, no more than 12 legend elements can be distinguished without compromising legibility. In Phase III of the LMN project this problem will be overcome by linking

154

Table 4. Fields in FLORA, FAUNA and LANDSCAPES files of LMN database, showing length (max. no. of positions) and type of data (I = integer, A = alphanumeric, C = alphanumeric or integer).


FLORA

CEL coordinates of grid cell 6 I TESPS-A1 number of terrestrial species in group 1 3 I

TESPS-A72 number of terrestrial species in group 72 3 I AQSPS-A1 number of aquatic species in group 1 3 I

AQSPS-A10 number of aquatic species in group 10 3 I SPSPS-A seepage-indicating species 3 I F A U N A

CEL coordinates of grid cell 6 I GROUP faunal group 11 A SPECIES species 2 I INV-YEAR year of inventory 6 I INDV-NMB number of individuals 4 I DIVERSE miscellaneous remarks 2 C L A N D S C A P E S

CEL coordinates of grid cell 6 I LANDSCAPE type of landscape 3 I

ARC-INFO with the ORACLE system used until now. ARC-INFO has greater graphics potential (incl. also a full-colour printer), although it is less powerful in the database sense than ORACLE. By combining the two, however, the features of both systems can be put to their best advantage.

Landscape types

Besides data at the component level, the database also comprises integrated data in the form of landscape type. A landscape typology has been developed on the basis of geohydrological relationships, geomorphology and vegetation structure (see Fig. 5). These landscapes types encompass integrated information to maps providing information on individual ecosystem components. Landscapes are described in terms of (cf. Canters and Udo de Haes 1986): - spatial structure (including presence of gra-

dients, vegetation structure) - abiotic processes (e.g. sedimentation, erosion) - s t ruc ture of foodweb (producers, predators,

etc.) - abiotic and biotic spatial or chorological rela-

tionships (e.g. surface water or birds).

For (sub-)landscapes (see the examples in Figs. 1 and 2) a susceptibility or vulnerability index can be established, based on the effects of interventions on the characteristics of the landscape or ecosystems. As an example of this kind of application, Fig. 6 shows the vulnerability of landscapes to groundwater lowering from extraction. For this purpose the following ecosystem characteristics were used: spatial structure, abiotic processes, and chorological relations. The vulnerability was based on middling the scores per characteristic and (sub-)landscape in combination with the susceptibility of these characteristics for groundwater withdrawal.

S o m e r e s u l t s f r o m P h a s e II ( ' R a n d s t a d ' a r e a , t h e

w e s t e r n N e t h e r l a n d s )

Some of the results will now be presented, i.e. one of the applications: the routing options proposed for the Rapid (TGV) rail link in the west of the Netherlands. Previously the more general maps obtained so far in relation to the four working objectives will be mentioned (cf. Table 1).

~ . - - groundwater relations

~ - - ~ - - geomorphology

,: vegetat ion structure

Fig. 5. Differentiating criteria for the landscape typology in Phase II of the LMN project.

The maps

In Phase II of the LMN project, a large number of maps were produced for the various different working objectives (a = 30; b = 10; c = 10 and d = 4).

As illustrated in Fig. 3, maps providing information on a given component can be considered in relationship to one another. The first step consists on selection and presentation of the basic data (objective a). These data are then processed to yield the susceptibility to a given intervention (objective b) and the conservation significance (objective c). Fi- nally, combination of susceptibility and significance results in the vulnerability map (objective d). Maps can also be prepared at an integrated level, e.g. landscape maps.

For the 'Randstad' area of Holland, three such series of maps have been produced, showing (cf. Veelenturf et al., in press): 1. the vulnerability of soil and groundwater to

lowering the watertable, with respect to diversity

155

of soil types and groundwater classes 2. the vulnerability of vegetation to agricultural eu-

trophication, with respect to replaceability 3. the vulnerability of avifauna to intensification of

agriculture, with respect to endangered species.

An application: the Rapid (TGV) rail link in the west of the Netherlands

One concrete application of the LMN database and

the developed methodology is a pilot study on the environmental impact of construction and operation of the Rapid (TGV) rail link between Rotter- dam and Amsterdam, in the west of the Nether- lands. The results of Phase II have been used for a general assessment of the routing options proposed for the TGV project in 1987 (see Fig. 7). Fig. 7a shows the three options. Using the material available in the database, assessments could be made for routes A, B and C, all of which traverse the 'Rand- stad' conurbation. Construction of this rail link in- volves raising a sand bank about 7 metres high and 50 metres wide.

Landscape-ecological effects result from erection

of the sand bank and the presence and operation of the railway. The accompanying interventions are habitat destruction and fragmentation, lowering the watertable, and more intensive agricultural use (= intensification). As the precise routing of the three options is not yet known, an assessment was

made of potential effects in a 2-kilometre zone on either side of the track. These zones are also shown in Fig. 7a. Table 5 lists the maps that might be used for assessing the impact of the various different routes.

For purposes of illustration, four maps have been selected here (Figs. 7b-7e) , one for each of the identified interventions. Using these maps, a count was made of the number of en-route grid cells scoring high or very high on significance or susceptibility; the results are shown in Table 6. As can be seen, route B is the least and route C the most at- tractive of the available options.

It should be noted, and stressed, that an assessment of alternative rail routed encompasses more than only the mentioned considerations. The draft

156

VULNERABIL ITY OF LANDSCAPES TO G R O U N D W A T E R LOWERING

LEGEND

• ve~ high nigh

[ ] moderale [ ] low

[ ] no applicable inlofmation

not in LMN database

SOURCE 0ATA=L~N ~*T~ASE

=q[] []

Fig. 6. Vulnerability of landscapes to groundwater lowering; each grid cell measures 1"1 km.

Table 5. LMN maps available for assessing the impact of alternative Rapid rail routes

Intervention Available significance map or susceptibility map

habitat destruction: (= significance map)

watertable lowering:

fragmentation: intensification:

- physiographical features (Fig. 7b) - diversity of soil/groundwater dynamics (Fig. 7c) - endangered breeding birds - replaceability of ecotopes - susceptibility of soil/groundwater dynamics - susceptibility of ecotopes - susceptibility of mammals (Fig. 7d) - susceptibility of meadow birds (Fig. 7e)

env i ronmenta l impact s ta tement on the Rapid rail

l ink also covers such aspects as t ransect ion of ma jo r

landscape elements, t ransect ion of areas that have

been assigned a certain policy status (e.g. nature

reserves, sanctuaries) , size of popu la t ion exposed to

noise nuisance, costs (e.g. route length, n u m b e r of

viaducts and tunnels) and so on.

In weighing up all these aspects, env i ronmenta l

considerat ions other t han those relat ing to land-

scape ecology may lead to a choice for a different

157

use in the future in plan preparing and plan evaluation.

Phase Ill: project completion

THE]

4

Fig. 7a. Alternative routes (A, B and C) for Rapid rail link in 'Randstad' area.

option from the one assessed as being most favour- able on the basis of the LMN data. In this context it is also important to consider the possibilities for mitigating measures. Such measures can lessen the impact in terms of landscape ecology (e.g. construction of railway (sections) on concrete pillars reduces fragmentation), as well as in terms of environmental hygiene (e.g. construction of noise barriers).

Discussion and conclusions

The results obtained up to now will be discussed in the light of the current phase of the LMN project: phase III in which the entire Netherlands are mapped. Emphasis is given on the completion and advantages of the database, and the possibilities for

Phase II ( 'Randstad' area) has yielded an opera- tional database of ecological data suitable for national physical planning purposes. The chosen data model enables maps to be prepared quickly and in a flexible manner. The system design is such that additional data can be added and existing data cor- rected and updated. Using the basic database, there is also potential for other applications. Use of the database with the combined ARC-ORACLE program, package enables a variety of geographical analyses to be performed on the basic data.

A valuable feature of the LMN database is the potential for correlative studies. In a number of cases, for example, the faunistic data were found to

be too generalized or incomplete. For this reason, the faunistic maps have as yet only limited usage potential. In addition, there is still inadequate un- derstanding of the susceptibility of faunal elements to various interventions. In the current phase of the

project, we are endeavouring to downscale the faunistic data currently available for 5*5 km cells to data for 1" 1 km cells. Our approach is to combine knowledge of ecology and regional occurrence of species with data available in the LMN database, viz. by translating the habitat requirements into habitat characteristics, e.g. vegetation structure, whose availability in a given grid cell can be established with the aid of the database.

During the third phase a nationwide database is to be built up, incorporating the components described above. The data model has undergone several modifications, and now offers greater oper- ational flexibility. As necessary, the model can be more readily adapted to new advances in under-

standing. In 1988 the database was extended to include new data relating to soil and groundwater classes and interrelationships, as well as flora and vegetation data for the western provinces of the Netherlands. In 1989 the components soil, geomorphology, surface-water dynamics, flora and vegetation have been added for several other provinces

158

genese occupied aeolic marine fluvial organo- >1 feature surface genous with different in grid cell genese

1-10ha [ ] [ ] [ ] E~ -

10-50 ha [ ] ~ [ ] [ ] [ ]

50- 100 ha [ ] [ ] [ ] [ ] [ ]

�89

C

W/~WA~

[]

Fig. 7b. Presence of physiographical features in investigated and potentially affected zones 2 km wide on either side of alternative Rapid rail routes; empty grid cells are not mapped (i.e., urban area or outside the study area) or contain non of the mapped features (for points of reference, see Fig. 7a).

Table 6. Number of cells within 2 km zone either side of Rapid rail routes, A, B and C with high or very high susceptibility, as function of intervention and selected component .

Route A B C

Intervention Susceptible No. of cells with component high or very high assessed susceptibility

destruction physiogr, features 13 30 0 watertable lowering soil 35 59 32 intensification meadow birds 141 149 95 fragmentation mammals 50 69 62

159

I I very hig l~ high [ ] modera [ ] low or

absent

==-

-_'@_

L -f

L - - ~

b I

/:Tg. 7c. Susceptibility of soil and groundwater to watertabie lowering in investigated zones on either side of alternative Rapid rail routes; empty grid celis are not mapped (i.e., urban area) or lay outside the study area (for points of reference, see Fig. 7a).

and input of faunistic data has been started. In the second phase of the project the landscape types were stored as separate data in the database. In Phase III this will be approached differently, with the landscapes being derived directly from the individual components stored in the database.

In summary, the LMN project can be character- ized as having the following advantages:

- the database contains information on the occurrence of a wide variety of biotic and abiotic components, representing (in the light of current

availability) a reasonably complete set of landscape-ecological data;

- for each component, the database contains just that information which can be responsibly used at the chosen scale and also effectively employed in national physical planning activities;

- by making use of the provincial vegetation inventories and the major national faunistic inventories, the database contains up-to-date information on the occurrence of flora and fauna;

- the database has powerful capabilities for deriv-

160

�9 very [ ] high f~ mode [ ] low o [ ] no

obser

Fig. 7d. Susceptibility of mammals to fragmentation of habitats in investigated zones on either side of alternative Rapid rail routes; empty grid cells are not mapped (i.e., urban area) or lay outside the study area (for points of reference, see Fig. 7a).

ing the potential occurrence of given faunal and

floral elements (so-called suitability maps, an

extra working objective); - the methods developed permit strict distinction

among susceptibility, significance and vulnera-

bility; - the data model is designed such that modifica-

tions, additions and/or extensions can be fairly easily implemented.

The LMN database is intended for use in the field

of national physical planning and policy execution.

Besides the initiating agency, other agencies con-

cerned with problems of planning at a national or supraregional scale will obviously also be interested

in the LMN system and the data it comprises.

Because the LMN project is being implemented by the government, with reports on the research ef-

forts being published, the LMN data will be more or less public. For various reasons, this public ac- cessibility may discourage holders of source data from participating in the project. At the moment

161

�9 ver! higt

[ ] mo( [ ] low

abs

Fig. 7e. Susceptibility of meadow birds to intensification in investigated zones on either side of alternative Rapid rail routes; empty grid cells are not mapped (i.e., urban area) or lay outside the study area (for points of reference, see Fig. 7a).

we are seeking an appropr ia te mode for "open ing

u p " the system, meanwhile guaranteeing that the rights o f data suppliers will be respected.

F u t u r e o u t l o o k

After complet ion o f Phase I I I , the database and the developed me thodo logy will be available for opera-

t ional use. The person-power invested in the total project per year including overhead etc. (during a

ten years period: 1984-94) , is now be estimated on

one senior scientist and two junior scientists with both an assistent.

At the end of the project the total database will

measure ca. 100 megabytes consisting o f some 4 - 5 million records. In principle, it will then also be feasible to extend the system to encompass infor-

mat ion on, e.g. lower plants, aquatic fauna or but-

terflies as well as physico-chemical parameters. To

ensure op t imum use, it will remain essential to sup- plement the database at regular intervals (e.g. by

162

filling in blank cells) and to update the data. However, such activities may be regarded as part and parcel of the usual management, maintenance, use and ongoing development.

Besides the capabilities of the LMN system as a geographic information system per se, interlinkage with other databases currently used or under development at the National Physical Planning Agen- cy offers interesting prospects. So far, the LMN data for the 'Randstad' area have been employed to evaluate a number of plans for improving connectivity (= ecological infrastructure) for various faunal groups (Harms et al. 1988). One of the elements of this project entails regenerating alluvial woodland on the floodplains of the main river system of the central part of the Netherlands (de Bruin

et al. 1987). In addition, the database constitutes a powerful tool for a variety of analyses of scientific nature (e.g. studies relating to correlation, impacts

and distribution).

Acknowledgments

We thank Frans Klijn who gave valuable comments to a draft version of the manuscript, and Kees

Groen for critically reading the manuscript. Han Runhaar did very valuable work in preparing the database, especially concerning the information about ecotopes. Jan Pieter Smolders and Wim Baas did most of the work for filling-up the database, to whom we are very obliged. We also are obliged to Joke van der Peet, who did a lot of typing work, to

Yke van Randen for making the maps, to Martin Brittijn and H. Heijn for making the figures, and to Peter Twisk who made the illustrations in Fig. 7.

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