Landscape structure and scale: comparative studies on some
landscape indices in Germany and the UK
T. Blaschke and J. Petch
Manchester Metropolitan University
Department of Environmental & Geographical Sciences
Chester Street, Manchester M1 5GD, UK
e-mail: [email protected]; [email protected]
Abstract
Management of all types of landscape from urban to natural needs ways of
measuring and modelling structure. Systematic measurements relevant to the effects
of measuring and modelling landscape structure are presented. It is investigated how
context and scale dependent the output of landscape metrics are. Specific
communities and species have specific scales and dimensions which need to be
considered. Recent studies in landscape ecology highlight the behaviour of some
indices, but are mainly employing different resolutions based on computer -generated
artificial landscapes. In this paper, real management situations from the UK and
Germany are used to systematically investigate some landscape metrics and their
scale and resolution dependency and specifally to study if it is really possible to
compare “the structure” of different landscapes.
Scaling
“Processes in nature don't have a scale in and of themselves” (Wiens, 1995).
Landscapes are dynamic systems that occur in spatio -temporal dimensions. As we attempt to
interpret the landscape issues of scale are inevitably important. The scale of a landscape,
however, is not a property of the landscape alone but something generated by sampling the
landscape (Allen and Hoekstra 1991). How we view the heterogeneity of a landscape is different
from how an organism utilises mosaic of patches, corridors and matrices. Recognising also that
the dynamics of a landscape is the flux of energy, materials, nutrients, and species among the
components, we can appreciate that processes at various scales create the mosaic of a landscape
(Urban et al., 1987). Forman and Godron (1986) suggest a lower limit for landscapes at a “few
kilometres in diameter,” although they recognise that most of the principles of landscape ecology
apply to ecological mosaics at any scale. This definition corresponds to our human perception of
the environment. Indeed, a high percentage of studies are undertaken at a certain scale, the
”landscape scale” (Lavers and Haines-Young, 1993). McGarigal and Marks (1994) used an
organism-centred perspective of a landscape and advise users of their Fragstats program to take
into account that each organism scales the environment differently. From an organism-centred
perspective, a landscape could range in absolute scale from an area smaller than a single forest
stand (for example, an individual log) to an entire ecoregion. Empirical studies have shown that if
this organism -centred definition of a landscape is accepted, then a logical consequence of this is a
mandate to manage wildlife habitats across a range of spatial scales. Each scale, whether stand or
watershed, or some other scale, will likely be important for a subset of species, and each species
will likely respond at more than one scale (Wiens and Milne, 1989, Kotliar and Wiens, 1990).
The scaling of environmental processes is to take information at one scale and use it to derive
processes at another scale. For many applications relating to global environmental change,
information on plant and vegetation processes is scaled up to larger spatial scales and longer
temporal scales. Information on weather, historical or predicted, is scaled down to match, as far is
possible, the spatial and temporal scales of the processes at the scale of interest. For these models
to have a flexible, predictive value, they must contain explicit descriptions of the particular
processes on which the perturbations are known to act, or known to be best observed.
Landscape metrics and scale
Landscape metrics are used to quantify and describe landscape pattern. Efforts to develop
methods to quantify these spatial patterns began in the 1980s (Krummel et al., 1987, O’Neill et
al., 1988) and have accelerated so that there are now literally hundreds of quantitative measures
of landscape pattern (Gustafson, 1998). Recent studies have examined how data-, scale- and
resolution-dependent many of the now widely used landscape indices are.
Scale of landscapes can be defined in terms of grain and extent. The grain of the landscape is
measured by the average diameter or area of all patches present (Forman and Godron, 1986). It
has been clear for a long time to those describing spatial patterns that the nature of a pattern (e.g.
the dispersion of individuals) changes with changes in the scale of analysis. What is a boundary
or a patch or a corridor at one scale may disappear or become a different structure at another
scale (Gosz, 1991). Several studies have demonstrated clearly that patterns in community
organisation or landscape complexity (e.g. Krummel et al., 1987) differ as the scale of analysis is
changed. Exactly how patterns or processes change with changes in scale, however, is still
unresolved. If patterns and processes change continuously with scale, then results at one scale can
possibly be extrapolated to other scales, once the scaling function is known. On the other hand, if
ecological phenomena change discontinuously with scale, then extrapolation is only possible
within scale domains in which certain pattern-process relationships hold (Wiens, 1989).
Translating among scales then becomes a major problem (Turner et al.; 1989. King, 1991).
But what makes upscaling a challenge is non-linearity in relations between processes and
variables (Wiens 1995). It is this heterogeneity in properties that determines the rate of processes.
At different scales, heterogeneity of structure and processes may be randomly distributed or
organised into patches with distinct properties that distinguish one patch from another. So scale
becomes a crucial aspect of heterogeneity. One recent research objective in landscape ecology,
therefore, is to develop metrics that behave predictively with changes in spatial resolution while
being most sensitive to various predictable gradients of change. As Frohn (1998) points out
however, if landscape metrics are unpredictable with changes in spatial resolution, then changes
in metric values may be a function of spatial resolution in addition to changes of landscape
pattern. This makes their values unpredictable and their meaning unclear. Scaling issues,
however, are not confined to landscape studies; they affect all ecological investigations (Wiens,
1989). Perhaps because it is usually practised at a broader scale of resolution than the local
habitats historically studied by ecologists, landscape ecology has become closely associated with
ecological scaling (Urban et al., 1987, Turner et al., 1989; Wiens, 1992; Allen et al., 1993).
Indeed, the problem of interrelating processes occurring at different scales may be “the central
problem of theoretical biology” (Wiens, 1995).
Case studies and research objectives
The need to measure landscape structure is based on an understanding of the properties and
behaviour of certain phenomena being dependent on the spatial arrangement of themselves and of
the elements of their environment. These ideas have been expressed in the patch-matrix concept
and many of the most frequently used indices attempt to measure landscape structure on the
assumption of a patch-matrix model. Since measures of landscape composition are essentially
calculating diversity, many of the indices are derived from indices of species diversity used in
community ecology. Since many of the common indices are extensively described and defined
by McGarigal and Marks (1994), Haines-Young and Chopping (1996) and in other publications,
they will not be described here. Instead, we will investigate the behaviour of different indices
across different spatial and thematic (class) resolutions and also the meaning of the discrete
selection of the study area (“study area bias”). Because many landscape indices are highly
correlated we concentrate here on 4 indices measuring basic aspects of landscape structure and
which are not directly correlated. Underlying research questions of these comparative studies are:
•
•
•
•
How sensitive is the metric to a change in spatial and thematic resolution of data?
Does the discrete boundary of a single study area significantly affect the metric? In other
words: How much would a minor shift of the study area affect the results for the landscape?
How do some commonly used measures behave across different landscapes?
Is it generally possible to compare “the structure” of different landscapes using different
datasets?
Two common issues considered for all methods for four different study areas in Germany and the
UK (Figure 1) were: the method of mapping physical phenomena, and the metrics used to
establish the properties of the mapped phenomena. Mapping methods are based on different
approaches to define landscape units. Aerial photo interpretation and the derivation of categorical
maps is used in all 4 studies, while additional classification of satellite data is used in study areas
1, 3 and 4. These two methods result in two different types of data, vector and raster. For
comparison, the vector data are rasterised and the influence of the spatial resolution is
investigated. Land cover and vegetation data were all exported to ASCII raster files for
subsequent analysis using FRAGSTATS software (McGarigal and Marks, 1994). For a given
landscape mosaic, FRAGSTATS computes a range of statistics at the patch, class and landscape
levels, providing various indices which describe the spatial composition and diversity of a given
landscape.
Study area 1 is the alluvial floodplain of the river Salzach from north of the city of Salzburg
(Austria) to north of Burghausen, where the Salzach flows into the Inn. It is situated mostly in the
south-easternmost part of Germany, covers about 5700 ha and has a core area of natural and nearnatural riparian forests of about 2000 ha. For this area, very detailed vegetation and landcover
maps exist (Tabble 1) (1:5.000 core area, 1:10.000, 1:25.000 and Landsat TM-derived data with
30 m resolution for the whole area) and structural diversity was investigated comprehensively
(Blaschke, 1997). Study area 2 is a part of the German province of Lower -Saxony south of the
Harz mountains (“Harzvorland”) and incorporates a moderately hilly rural landscape dominated
by forest and agriculture. Here, two different categorical maps exist, both derived from 1:10.000
aerial photos. The study areas 3 and 4 are located in Northern England, in the southern Greater
Manchester/northern Cheshire area (3) and the Pennines (4). Cloud-free Landsat TM images were
used, six for the Pennines (three from May 1985, three from May 1992) which would allow
assessment of land cover change, and four for the Greater Manchester area (May 1988, June
1997, two each). For all satellite imagery supervised classification was adopted and the resulting
landcover maps are investigated. For both UK study areas, digital orthophotography was used
(pixel resolution 0.25m and an aggregated 1m version). In addition, SPOT data have been
purchased for the area and are currently under investigation.
A number of landscape configuration metrics can be calculated for individual patches and for the
whole landscape, depending on the emphasis sought. For example, fractal dimension is a measure
of shape complexity that can be computed for each patch and then averaged for the landscape, or
it can be computed from the landscape as a whole (by using the box-count method) (McGarigal
and Marks, 1994). Similarly, core area can be computed for each patch and then represented as
mean patch core area for the landscape, or it can be computed simply as total core area in the
landscape. Obviously, one form can be derived from the other if the number of patches is known,
and so they are largely redundant. In this comparative study only 4 relatively independent
measures are analysed: Shannon-Wiener Index (SHI) as a commonly used diversity index, Mean
Patch Size (MPS), Mean Shape Index (MSI) describing the size and shape of the patches at a
landscape level and Total Core Area Index (TCAI) indicating the percentage of core area given a
specified edge distance. Fractal dimension at the landscape level and contagion as a measure for
landscape composition based on adjacency likelihood’s have been investigated for both German
study areas earlier but did not lead to ecologically meaningful results.
Table 1: Overview data, scale and resolutions
1 Salzach
2 Lower -Saxony
3 Great. Manchester
4 Pennines
Finest
scale
1:5.000
1:10.000
1:5.000
1:5.000
Broadest
scale
Landsat TM
1:10.000
Landsat TM
Landsat TM
finest
pixel size
5m
10m
1m
1m
Coarsest
pixel size
30m
30m
30m
30m
Categorical classes
6, 8, 12, 21, 22, 23, 40
7, 16, 23
9, 16, 18
12, 14
4
3
2
1
Fig. 1: Overview study areas
Preliminary Investigations
Spatial resolution
Systematic studies with raster data at various resolutions show clearly that rasterisation of vector
data does not automatically lead to a significant change in the value of a metric. Below a critical
resolution (threshold) no significant change in the results for most landscape indices was
observed. Here, this “critical scale” was detected empirically. This has not been considered
feasible for most studies, so more advanced techniques have been developed recently (Keitt et al.
1997). In this study, the empirically detected scale could be correlated to the smallest mapping
units. Table 2 exhibits the effects of different raster resolution (cell size) on four of the indices
calculated with FRAGSTATS. For the 1:10.000 mapped data of study area 2, a threshold can be
observed around a 20 m resolution. Rasterisation of the vector data with cell sizes between 5 and
20 do not cause significantly different results, while 25m cells do. This empirical approach still
needs some sophistication.
Table 2: Effects of rasterisation on 4 widely used landscape indices: SHI: Shannon-Wiener Index,
MPS: Mean Patch Size, MSI: Mean Shape Index, TCAI: Total Core Area Index (at the same edge
buffer distance of 20 m).
Index
SHI Area 1
SHI Area 2
SHI Area 3
SHI Area 4
MPS Area 1
MPS Area 2
MPS Area 3
MPS Area 4
MSI Area 1
MSI Area 2
MSI Area 3
MSI Area 4
TCAI Area 1
TCAI Area 2
TCAI Area 3
TCAI Area 4
5m
1.34
1.76
10 m
1.34
1.78
15 m
1.33
1.77
4.61
5.69
3.43
5.68
2.59
5.78
2.10
1.69
1.90
1.65
1.57
1.61
68.8
72.4
69.0
72.5
75.3
73.1
20 m
1.33
1.76
1.90
1.48
2.77
6.04
1.65
1.91
1.46
1.53
1.60
1.61
69.4
73.2
33.2
33.4
25 m
1.31
1.76
2.87
5.76
1.39
1.49
64.5
61.2
30 m
1.32
1.80
1.91
1.48
3.43
4.58
0.74
0.96
1.38
1.45
1.26
1.33
59.7
55.30
24.3
26.1
The insensitivity of the Shannon-Wiener and Simpson indices is demonstrated further by data on
their values for classified satellite images with 7 and 21 classes in which pixels have been
aggregated to an extreme degree (Table 3). This shows clearly the complete insensitivity of the
indices to spatial resolution. The values are determined solely by the count of pixels in each class
and not by any aspect of their spatial arrangement.
Table 3: Demonstrating the independence of Shannon-Wiener (SDI) and Simpson’s diversity
index (SIDI) against spatial resolution. Both are absolutely insensitive against spatial aggregation
even when the resolution gets so coarse that smaller patches are lost.
Resolution (m)
5x5
10x10
20x20
40x40
50x50
62,5x62,5
100x100
125x125
200x200
(7 classes)
SDI
1.16
1.16
1.16
1.16
1.15
1.15
1.15
1.16
1.16
SIDI
0.64
0.64
0.64
0.64
0.64
0.64
0.64
0.64
0.65
(21classes)
SDI
SIDI
1.78
0.79
1.78
0.79
1.78
0.79
1.78
0.79
1.77
0.79
1.78
0.79
1.78
0.79
1.79
0.79
1.77
0.79
Thematic resolution
The number of classes in which we normally divide a landscape has enormous consequences for
most landscape indices describing landscape composition. Only for some simple categorisation of
a landscape (such as binary systems of forest and non-forest, water and land, urban and nonurban) are the number of classes more or less given. For most other objectives the thematic
resolution plays a crucial role. It could be shown that not only diversity indices such as Shannon-
Wiener are heavily dependent on class numbers but some other frequently used indices. Dividing
a landscape into 8, 10 or 12 classes using the same data source gave completely different figures.
Even using the same class scheme, various analysts will get different results using different
training sets or classification algorithms (these investigations are ongoing).
Fig. 2: Various resolutions (cell size) for the same input data, shown here 10 m (left), 30 m
(middle) and 50 m resolution (right). Stepwise aggregation of pixels did not result in significant
loss of information below a critical threshold at which a number of small patches are lost.
Study area bias – Stability of indices
For study area 1 and 2 (3 and 4 are under investigation) experiments with shifting study area
boundaries have been performed. The study areas were reduced by 10% in all 4 directions and the
metrics recalculated and compared with shifting the boundaries by 5 % of its north-south or eastwest extent in one direction at each time. For the 4 indices the variation was significant only for
the Mean Patch Size (MPS). Although in every case the absolute number of patches was large
(between 742 and 842 in area 1 and between 1046 and 1217 in area 2), the variation in patch size
is surprisingly high with an average of 8.6% (area 1) and 11.4% (area 2) of the original value.
The greatest difference between the average patch sizes of all shifting windows and a single
window was 33%. Therefore it is very questionable what the MPS index means and if it can be
used to compare different landscapes. The three other indices do not differ significantly with
deviations of 4.3% and 2.8% for MSI, 3.4% and 1.1% for TCAI and 1.9% and 2.2% for SDI.
Satellite imagery specifics
Filter algorithms employed on satellite imagery-derived data have significant effects on
landscape metrics. In most recent studies cleaning routines were applied before the landscape
metrics were applied. Although these are necessary in most cases to eliminate the “salt and
pepper effect”, the consequences are enormous (Table 4).
Table 4: Consequences of filtering algorithms on landscape indices for a classified Landsat TM
scene for the Greater Manchester area.
SDI
MPS
MSI
TCAI
Original
classification
1.89
0.74
1.26
23.5
3*3 majority
filter
1.84
1.96
1.24
49.3
5*5 majority
filter
1.69
2.59
1.16
63.3
Embedding the results into landscape ecological theory
As one of the consequences of these preliminary results it seems that it is very risky to compare
absolute figures of landscape metrics between different study sites – which is of course a core
objective of landscape ecology! Only under very clear restrictions can landscape metrics of
different areas be compared. The results are corroborated by some recent studies showing the
importance of class levels on some indices (Riitters et al., 1995; Frohn, 1998). Strong
dependency on the “thematic resolution” is shown, not only for diversity indices such as
Shannon-Wiener, which have been adopted from “a-spatial” applications, indicate but for spatial
indices as well. Furthermore, there is a need to investigate more thoroughly whether or not
satellite imagery-derived data on a pixel by pixel basis are useful for landscape metrics
applications. Typically classified images, in contrast to vector maps of landscape, show complex
elements of structure, for which simple metrics of shape or diversity seem not to apply (Figure 3).
One assumption behind the “patch-matrix paradigm” (Forman and Godron, 1986; Kotliar and
Wiens, 1990; Wiens 1989; 1995) is the idea that a patch is a relatively discrete and internally
homogeneous entity. While of course homogeneous entities rarely exist in a landscape, discrete
patches in a map commonly exhibit spatial gradients of phenomenon under investigation that can
be established either in the field or on an orthophoto, for instance. Yet, despite such limitations,
for some areas of research, patch theory has been extensively developed and is used widely in
landscape studies today.
Fig. 3: Mapped patches in vector (left) and raster format (middle) vs. Satellite imagery-derived
classification (right). While the rasterisation of vector data does not automatically lead to a loss
of information (compare fig. 2) it also does not necessarily result in significantly altered areas
and shape indices.
One specific concern from this study is that diversity indices such as Shannon-Weaner and
Simpson diversity index may not have any spatial meaning at all! Although Shannon-Weaner is
often used to describe the diversity of a landscape, it is concluded from tables 3 and 4, that both
Shannon-Weaner and Simpson Diversity Index are absolutely insensitive against spatial
resolution. It is also insensitive against the pattern of some patches. That means, there is no
difference at spatial configurations as long as the number of different patches is the same.
Landscape as an individual entity
There are also specific findings for some of the study areas. The number of study areas is not
large enough to statistically compare variation in between but one point should be highlighted:
For the best investigated area (1), the riparian landscape of the Salzach river there is the highest
level of variance in all indices (except Shannon-Weaner) when changing resolution and when
shifting the study area systematically. Although this has to be proofed with other data sets, it
coincides with the idea behind, that a riparian landscape is made up of a number of ecosystem
types and exhibits a highly complex conglomerate of floodplain forest, levees, abandoned
channels and relicts of a system of ridges and swales of a formerly meandering river bed. The
study area consists of a relatively narrow and long-stretched river corridor. Study area 2 is
dominated by large forests and various agricultural areas. At a level of consideration, where it is
not distinguished between forest types, these large areas are regarded as huge homogeneous
patches and consequently dominate most landscape metrics measured. The upland areas of the
Northern Pennines (study area 4) are totally different but are exhibited to a similar semantic
problem: If they are regarded as one coarsely defined class “natural and semi-natural upland
areas” large patches result and most landscape indices describe them as being relatively
homogeneous. A finer classification which distinguishes 8 classes for semi-natural and natural
upland areas (Cheesman, 1998) from Landsat T M-imagery delivers a very patchy combination of
“upland bog”, “bracken”, “bare soil and rock exposures”, “Molinia moorland” etc.. The only
statistically proved finding from landscape metrics analysis using this data is, that the patches are
relatively small (in relation to the spatial resolution of 30x30 m) and irregularly in shape.
These considerations together with the experiences from other studies (Blaschke, in press) lead to
the conclusion, that if we are dealing with landscape metrics and comparin g descriptive indices
across landscapes, we have to have measures for the homogeneity of patches, or the “within patch
diversity” (Blaschke 1995). But very few studies have been undertaken which explicitly model
homogeneity /heterogeneity or class uncertainty, but a promising approach has been described by
Aspinall and Pearson (1995).
Implications for ecological applications and the research agenda
These preliminary results from different countries across Europe emphasise the critical
dependencies of some landscape heterogeneity indices on specific data properties. These
dependencies are circumvented by studies that concentrate on binary pictures of a phenomenon
under investigation, e.g. forest/non-forest, suitable habitat/non-suitable habitat or that are
performed with artificial computer-generated data. The problems with the “real world” are much
more complex however. For real world data this study shows that;
•
•
•
•
•
Many indices are heavily dependent on data, scale and resolution. Using different data for the
same landscape, totally different figures for landscape structure and spatial heterogeneity
indices may result.
“Thematic” resolution (number of classes) is for some indices far more consequential than
spatial resolution, as long as spatial resolution is not reduced so significantly that whole
patches are lost.
Many factors make studies individual so that they cannot be repeated nor can comparative or
multi-temporal studies be conducted
The selection of boundaries for a study area influences the result (“study area bias”). Thus, a
value for the “stability” or “variability” of an index is needed which reflects the non-linearity
of the bias.
This leads to the conclusion that it is extremely difficult to compare “the structure” of
different landscapes. Any results have to be considered in the context of data properties, preprocessing and the nature of the metrics being applied.
In many environmental disciplines, interest in analysing spatial pattern has grown. Aided by more
powerful spatial statistics, some of this growth has led to increased sophistication in the
description of spatial patterns, in exploring animal patterns (Maurer, 1994) and in examining
landscape patterns as functions of various processes in the landscape (Farina, 1998). Most of
these developments are driven and enabled by the data processing power of tools such as GIS and
remote sensing and by increasing computational power. These tools all enhance our ability to test
spatially explicit theory and modellers have developed simulation models that incorporate spatial
variation by allowing individual cells in a spatial grid to undergo dynamics that are spatially
linked in various ways. Interest in spatial problems reflects also an increasing focus on spatial
aspects of ecological theory. Spatial theory, especially metapopulation theory (Wiens, 1997), is
behind an increasing number of applications in ecology, e.g. the elaboration and extension of
patch-based population models.
The questions that remain about the growth in our understanding of spatial ecological phenomena
are about the power of our spatial ecological theories and about the validity of our metrics.
Leaving aside the former issue, though recognising that developments in both are inextricably
linked, it is clear that the field of landscape metrics is still immature and there are many areas of
uncertainty. The results described in this paper reflect problems with landscape indices and
landscape metrics investigated in recent studies (Hargis et al., 1998; Frohn, 1998; Gustafson,
1998). Nevertheless, a rapidly increasing number of applied studies by landscape planners,
planning authorities or conservation bodies show dangerous or misleading use of landscape
metrics. Especially in the German speaking countries young researchers recently favour
landscape metrics in combination with GIS and remote sensing without due regard to theoretical
issue of using metrics (Blaschke, in press). It is believed that, potentially, landscape metrics can
help scientists and planners in specific issues within a landscape and in understanding how their
values are altered within landscapes. At present however, there seem to be no landscape metrics
that quantifies spatial arrangement of a landscape in a way that one can compare different
landscapes. Not all landscape metrics are really spatially explicit. The Shannon-Wiener Index is
very insensitive to changes in spatial resolution and spatial composition. But also other measures,
such as mean patch size and patch density, at either the patch or landscape level do not depend
explicitly on the spatial character of the patches or their relative location. One obvious conclusion
is, that the absolute value of an index has little interpretative value: instead it should be used as a
comparative value. But even this conservative approach is regarded as suspect by the authors of
this paper after preliminary studies.
The implications for the research agenda in landscape ecology are clear. There is a acute need
and a likely continuing need for substantial research on;
• critical evaluation of established methods of measuring landscape structure.
• comparative analysis of set of empirically established and theoretically based measures of
structure in relation to scale, data product and phenomenon under study
• comparative analysis of measures of structure based on different theoretical constructs.
• development of truly spatial measures of structure
• development of measures of complex elements of landscape structure
• establishing the suitability of measures in relation to specific questions, to specific
phenomena and to specific empirical data sets.
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