- Email: [email protected]
Analysis of landscape pattern: towards a ‘top down’ indicator for evaluation of landuse
Ecological Modelling 130 (2000) 87 – 94 www.elsevier.com/locate/ecolmodel
Analysis of landscape pattern: towards a ‘top down’ indicator for evaluation of landuse A. Bartel * Institute of Landscape Management and Landscape Architecture, Uni6ersity of Agricultural Science, Peter-Jordan Str. 82, A-1190 Vienna, Austria
Abstract Landscapes as highly complex systems are subject to many different assessment procedures despite the fact that their development is not really predictable. Every synthetic model of landscape functioning will probably fail in demonstrating the landscape behaviour due to the decreasing precision and relevance of its output with increasing complexity. Two approaches are presented here which use deductive methods to describe landscape behaviour as indicated by its spatial structure. The one is a correlative concept which is characterized by all the advantages and disadvantages of statistical regression methods. It is strongly dependent on data quality. In the second study an expert knowledge system is developed. Fuzzy set theory is applied to transfer rules of landscape ecological experience to image parameters derived from satellite data. Both approaches are compared regarding the problems faced in the work during implication. Theoretical restrictions and the applicability in landscape assessment is discussed. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Landscape structure; Landscape pattern; Structural indicators; Habitat suitability index; Deductive approach
1. Introduction The pattern of landuse is based on and influenced by a variety of factors and processes in different sectors: natural site conditions, cultivation traditions preferring regional techniques, social development, as well as economic forces and even religious rules are working intertwined in a complex system in the past and the present. They are all changing over time constrained by technical possibilities. The composition and configura* Tel.: +43-1-476547206; fax: +43-1-476547209. E-mail address: [email protected] (A. Bartel).
tion of landscapes is a highly integrative response to those (and even more) causes. The exact processes which control and produce this formation are not understood well enough so that a synthetical, inductive ‘bottom up’ approach to modelling development of landscape pattern is really promising. Nevertheless, looking at a landscape (or a picture of it) we are able to draw many sound conclusions on the underlying factors which are governing the development of the landscape and its pattern. Landscape ecologists intuitively feel (and there are scientific hints too) that there are correlations between pattern and process (Forman
0304-3800/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 8 0 0 ( 0 0 ) 0 0 2 1 4 - 3
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and Godron, 1986; Turner, 1989). For example we think that the more regularity and straight lines are present in a landscape (referred to as geometrization), the greater is the degree of human impact and thus the distance to natural conditions and sustainable flow rates. In this sense the spatial structure (shape, composition, configuration) provides traits of landscapes that can be used for their characterization The term structure refers to ‘‘the spatial distribution of energy, material, and species in relation to the sizes, shapes, numbers, kinds, and configurations of the ecosystems’’ (Forman and Godron, 1986). Therefore landscape structure can be seen as the spatial pattern (where), completed by the ‘what is where’ aspect. If we want to use pattern as an indicator for landscape functioning we have to develop methods for the quantification of structure or pattern (e.g. ‘regularity’); find out the relevant and important parameters of pattern which make a difference between landscapes (note the scale dependency!); find out about the indicated landscape processes behind the structural measurements; i.e. define indicator functions.
1.1. Understanding landscape pattern The cognitive process of understanding the structure of a landscape can be paralleled by the steps of the automatic analysis of landscape data (Fig. 1): the underlying basis is the existing pattern in the landscape whose characteristics are filtered by constraints of scale and resolution (in space and time) of the reception system. On the technical side this system is defined by physical sensors, the human version is called visual perception, including all its morphological and neurological characteristics. The next steps of processing, interpretation (1st level), and integration of additional (collateral) data are defined by algorithms and performed computational. Humans perform a cognitive act and rely heavily on experience and learned knowledge. The result can be a thematic interpretation of the picture of a landscape. A structural evaluation of landuse needs a further step, a 2nd level of interpretation: the descrip-
tion of the thematic pattern, if required combined with different thematic layers of the same region, leads to an understanding of the structure and facilitates the identification of states, changes, problems, and focus areas. The technical approach calculates metrics on the thematic maps and then needs an interpretation towards understanding. In short one tries to draw conclusions from a high grade interpretation back to its underlying processes without exactly knowing the functional relationships.
1.2. Describing pattern and structure With the landscape as study object remote sensing methods offer an ideal technique to collect data. The task is now to extract all the valuable information from this data and to reduce the ‘noise’. But what is the information asked for when dealing with landscape structure? In our natural language we can describe the pattern or structure of elements in terms which are not always straightforward, sometimes metaphorical or ambiguous. But trying to convert those concepts into an exact terminology which can be identified automatically in remotely sensed data, we end up in a dilemma: either we have measures but we do not know the (ecological) meaning, or we have an ecological interpretation, but we do not know how to measure and quantify automatically, e.g. the density of a network. The understanding that the spatial configuration of landscapes is central for their functioning and evaluation leads towards a practical need for structural indicators. Problems on the regional scale can only be addressed correctly if the assessment includes spatial aspects. Due to the limitations of a classical approach to those questions, we present and compare two concepts which use deductive methods for the evaluation of landuse structure.
2. Competing approaches Both examples presented here focus on cultural landscapes of middle Europe. The main data source is Landsat™, combined with collateral data if necessary due to the objectives. Monotem-
A. Bartel / Ecological Modelling 130 (2000) 87–94
poral datasets are used in both cases. The image processing and classification is performed with different more or less sophisticated methods (Schneider, 1993) yielding thematic maps together with a number of attributes. Those maps serve as the samples of landscape structure; they are analysed for a set of metrics
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which include the patch size, the patch shape, the elongation, the percent area covered by the patch, and the fractal dimension of the borderlines. For the different landuse classes the mean value of those metrics over a landscape is calculated. The herein very important presumption of the delineation of the landscape borders within the study
Fig. 1. The process of understanding landscape pattern. Different levels of perception and processing in the human world (right) compared to an automatically interpreted satellite image (left). Using pattern as indicator for function means to draw conclusions about deeply hidden complex processes (below) from the high level interpretation (top).
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Fig. 2. Examples of regression curves between probability of presence (incidence functions) of amphibian populations and four image parameters. Significance is \ 95% in all of them.
area is based on different approaches in both studies (see below). The calculation has been done partly using the public domain program fragstats (McGarigal and Marks, 1994), based on Arc/ Info7.1, partly through specially programmed software integrated with the image processing package (KBVision).
2.1. The correlati6e approach The study area of about 500 km2 is an intensively agricultural exploited landscape about 40 km north of Munich, Germany. The objective of this study is the generation of ‘habitat suitability indices’ for parameters derived directly from remotely sensed images. In the past a number of studies used a model of land cover derived from satellite data and applied a habitat model to predict species ranges. These habitat models have mostly been described in dimensions of the real landscape, e.g. in terms of types of vegetation, percent forest cover, distance to open water, sea level, and others. They have been tested and verified in the field, and the quality of potential range prediction depends substantially on the correctness and the thematic fitting of the classes separated during the image interpretation. But a thematic map derived from satellite data does not necessarily have to be a landuse map. Themes can also be the heterogeneity or variety of
grey values in a moving window, a texture parameter, a patch shape complexity measure like fractal dimension, and others. And they can represent much more structural information contained in the image, and though in the landscape, than landcover alone. The approach is to correlate these image parameters to the probability of the appearance of biological species and thus generate habitat suitability functions directly for image parameters. Different maps of metrics are calculated and displayed as raster maps. The reference data is the presence/absence of species in a point map. Then each image pixel is referred to as an independent observation and in a logistic regression the probability of the presence (incidence) of a species is plotted against the value of a metric. Regression curves with high statistical significance represent the indicator function (incidence function) of this metric. Despite the pixel based calculation of the incidence function, metrics partly include properties of their neighbourhood.
2.1.1. Examples of incidence functions Fig. 2 shows some examples which demonstrate the incidence of amphibians related to four different image parameters in the study area: The diversity of landuse classes within a moving circle window of 200 m radius (div200). The range of greyvalues in the first principal component of the imagery in a moving 5× 5cross neighbourhood (texture); like div200 a metric of diversity or roughness in the image. The fractal dimension as space filling property of borderlines in the landuse map (fract); describes the amount of borders in relation to areas. The normalized differential vegetation index (ndvi); in remote sensing well known as correlated to green biomass. For display fract and ndvi are multiplied by 100. Those regression curves can be seen as habitat suitability functions or incidence functions for structural dimensions of the landscape. All four examples have a small r 2 but, nevertheless, they
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are significant with greater than 95% confidence due to the high number of pixels. That shows, that even small differences in the metrics values represent a certain change in incidence. In this example different amphibian species have been put together in one map of the presence/absence of amphibians. The different habitat needs and dependencies on biotope structures should come out more distinctively when species or ecological guilds are analysed separately. Texture and ndvi have a closer relationship with incidence than div200 and fract. In texture there appears to be a threshold: higher incidence is correlated with ranges of greyvalues of more than 110 units. Most amphibian species are found near wet areas, which are rare but have a clearly different spectral profile than their neighbourhood. In div200, even if it has a similar thematic content like texture, this correlation is represented less obviously, because div200 is based on a landuse classification, followed by a median filtering. Thus small areas often are dissolved in their surrounding landuse classes and only big amphibian biotopes are represented in this metric. The negative correlation of ndvi with incidence is somewhat surprising. But it makes sense: amphibians often are found in more or less natural vegetation, they are usually not found in high productive fields. And those crops usually have a high ndvi, whereas in natural vegetation the higher amount of dead biomass lowers the ndvi. The use of fractal metrics to describe landscape complexity is a intensively discussed topic. The mathematical concept of fractals is not easily understood, and in landscape ecology it is often applied without the necessary background. Serious criticism on the use of fractal dimensions is pointed out, e.g. by Frohn (1998). In this example incidence regressed to fract performs a very low r 2. The values of fract are very similar in the whole study area; assuming one really measures ‘complexity’ with this metric, this piece of agricultural landscape seems to be fairly homogenous in its fractal properties.
2.2. The knowledge based approach The second approach discussed is used in a
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project which is aimed towards an evaluation of sustainability of landuse and focused on the structural properties of the landscape. In this paper the term ‘sustainability’ only refers to the ecological dimension of the originally 3-fold definition comprising ecological, economical, and social aspects. The targeted area is the whole of Austria. Due to the fact that Austria covers a range of very different landscape types, from high alpine areas, fluvial dominated valleys, down to the pannonic dry grasslands, the whole area has been divided into about 6000 landscapes which have been classified into 11 types of cultural landscapes (Wrbka et al., 1997). Those types correspond mainly to dominant landuse systems but also in structural characteristics as they appear in a satellite image. They have been visually delineated and classified from a satellite image zoomed to a scale of 1:200 000. The landscapes are the units of evaluation and the types serve as a stratification of data. Between the types there are essential differences in the system so that a common evaluation is not appropriate. A set of representative sample points has been chosen throughout the types by a special method of sampling design (Reiter et al., 1998). Those samples are studied intensively in their relations between structural features and functions related to sustainability of use (Wrbka et al., 1997). The expertise gained this way is used to validate and adjust the literature knowledge to the Austrian conditions in fine scale. The data for the evaluation covering the whole Austrian area stem from Landsat™. They are analysed in a segmentation and classification process which produces a landuse map of high spatial accuracy and 14 classes. The single polygons and the landscapes are attributed with metrics which describe the spatial structure of the landscapes. The step of scaling up the knowledge developed in fine scale samples to the coarser scale satellite measurements is done using a knowledge based fuzzy-set approach. Experts document their knowledge on the relationships between structure and eco-sustainability in a set of simple rules which operate on fuzzy sets of the landscape metrics. For each of the landscape types an
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adapted set of rules is developed. The fuzzy system can be seen on one hand as a formal documentation of indicator functions for sustainability of landuse, on the other hand it is a software tool which can digest a huge amount of data (Wrbka et al., 1998). To use such a fuzzy rule based system at this point has two major advantages: Discrete classes in other evaluation procedures may produce different output values out of very small input variances. A fuzzy system takes into account the inherent ‘uncertainty’ of borders and classes in landscapes. The complex concept of landuse eco-sustainability in coarse scale can be separated into a set of simple rules where each rule only describes one relationship. The integration is done by the fuzzy system, assumed that it is tuned well. The system of rules is then applied to the fuzzified structural attributes and produces an output value of ecological sustainability for each landscape. Only the landscapes of one type are treated with the same set of rules. For each type of landscapes a different set of rules has to be provided. Therefore, the result is a ranking of landscapes only within one type of cultural landscape. The area of Austria is covered by combining the maps of all the different types. The computer implementation of this evaluation system is realized with the MATLAB (v5.2) fuzzy logic toolbox (v 2.0) and its adaptation through macro scripts. The coupling of image processing /GIS and the fuzzy system uses ASCIIfiles for data transfer, organized through fields of landscape-ID and segment-ID.
3. Correlative versus knowledge based Both of this approaches have certain advantages and restrictions regarding their data, methods, and conceptual framework.
3.1. Definition of landscape borders Landscapes are the top spatial units of the assessment of spatial structure. In both approaches their delineation is an a priori informa-
tion which has great influence on the values of the most metrics. The correlative approach probably would prefer a landscape definition adapted to the reference data used. If a raster sampling design has been used in the mapping of species ranges, for the best spatial congruence the landscapes should be defined in the same grid. The knowledge based approach in contrast does not have a spatial sampling to rely on. It uses a landscape type classification instead which has been generated visually. As criteria structural properties as well as dominance of major landuses have been used. Due to this method the assessment units represent functional units and the evaluation rules can be developed on this background.
3.2. Measuring pattern The use of abstract metrics for the description of pattern has some critical implications: some metrics do not represent what they are supposed to! Theoretical preconditions for the use of structural indices are (Li, 1989, Borg (personal communication 1998)): invariance of direction invariance of scale invariance of size invariance of spatial resolution invariance of class resolution robustness unambiguity Because these presumptions are not really true in most cases, one has to be careful in using metrics. If possible the parameters used should have some relationship to the investigated topic to reduce the chance of artefacts. The transfer of models validated on one set of spatial data to another is only possible using invariant metrics, or after a proof of data consistency. Many of the widely used metrics are correlated to a high degree. In the development of a multi parameter model, therefore, a decorrelation has to be calculated. For example Riiters et al. (1995) identified in a multivariate analysis of pattern metrics of USA quadrangle landuse maps only six
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factors responsible for an 87% variation in the values of 26 widely used metrics.
3.3. The dimension of time In both of the examples presented the dimension of time has not yet been included in the studies. The correlative approach, therefore, models a status quo of species ranges and the development is not depicted. But the actual status does not have to be the potential or even optimum range; not all of the possible habitats might be really covered. Or, on the other hand, some of the still existing populations might be only the declining remainder of an actually dying population. This restriction can be overcome in the knowledge based approach. If data are available rules can be developed which integrate the temporal aspects. But that implies a good knowledge about processes and their principles in space and time.
3.4. Conceptual obstructions The advantage of the correlative approach is that it can detect dimensions of — in this case — habitat descriptions which have not been seen so far. Especially it is possible to find out the role of aspects of neighbourhood and distance for the distribution of species. But this focus on spatial structure is a disadvantage too. Of course spatial properties are not the only relevant dimensions of a habitat description. The availability of food or certain requisites for breeding, competition or predation phenomena, and many other factors with a substantial influence on the distribution, are not represented in an analysis of the spatial structure of images. Even new remote sensing sensors with high resolution can not give information on those factors. The use of fuzzy sets in the knowledge based approach allows us to use knowledge and experience even if precise quantification is not available. But a rule based system has to be parameterized carefully and, therefore, it needs a training dataset to test the plausibility of its output. It can only give a formal reproduction of experts knowledge, but it can not give new insights into a modelled reality. Nevertheless, if it is tuned well once, such a system
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is able to represent the complex knowledge of experts and can be applied automatically. Thus it can be used to evaluate scenarios of possible developments.
4. Discussion Landscape pattern respective structure is a complex product of many underlying processes. And in return structure defines a spatial framework for process manifestation and puts certain constraints on them. Landscape pattern and landscape processes have a mutual impact. For understanding and evaluating a landscape we, too, have to think in both directions. To leave the spatial structure out of sight means to reject the existence of lateral relations between landscape compartments. Modern landscape ecology is still trying to understand the interrelationships of different landscape elements. One of the main conclusions is that landscape evolution can not be predicted. The principle of incompatibility of precision and complexity (Zadeh, 1973) says that ‘‘as the complexity of a system increases, the ability to make precise and yet significant statements about its behaviour diminishes…’’. Already a minor change in surrounding conditions (climatic, economic, social,…) may facilitate a development in a very different direction. Catastrophic events and pure chance are basic factors in the evolution of landscapes. In this sense a landscape matches the criteria for a complex system and a model of it has necessarily to deal with uncertainty. In terms of systems theory emergent properties are substantial characteristics of landscapes. Emergent properties can not be derived only by combining the properties of holons below. Thus an inductive ‘bottom up’ modelling approach will fail in depicting the landscape system as a holon at a high level. The deductive view focuses intentionally on those emergent properties.
5. Conclusions Landscapes are systems of high complexity and therefore an analytic modelling approach is not
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very promising. Nevertheless the importance of landscape structure urges the trial of deductive modelling approaches in assessment studies on the landscape level. Even without an exact cause – effect explanation it is worth detecting relationships and to apply even only qualitative experience in the assessment and evaluation of landscapes. Metrics on landscape structure can be used as indicators for landscape functioning under certain restrictions: Models based on landscape metrics are only valid in a regionspecific context. The dependence on input data quality restricts the use of those models to a similar data quality like the validation data. The landscape border problem must be taken into account. Correlations between metrics can produce weightings not intended. Landscape structure is a highly integrative outcome of the complex processes acting in landscapes. Seen as ‘frozen processes’ and with appropriate restrictions in mind, pattern and structure provide valuable information for different landscape assessment tasks.
Acknowledgements These studies have kindly been funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF) in the framework of the Research Network ‘Forschungsverbund Agraro¨kosysteme Mu¨nchen’ and by the Austrian Federal Minister of Science and Transport (BMWV) in the research program ‘Cultural Landscapes’. Many colleges have done a substan-
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tial amount of work on both of these projects and have discussed my ideas. To all of them I owe a warm thank you.
References Forman, R.T.T., Godron, M., 1986. Landscape Ecology. Wiley, New York. Frohn, R.C., 1998. Remote Sensing for Landscape Ecology. CRC Press, Boca Raton, FL. Li, Z.C., 1989. Computer Transformation of Digital Images and Patterns. World Scientific, Singapore. McGarigal, K., Marks, B., 1994. A spatial pattern analysis program for quantifying landscape structure, Ver. 2.0. Oregon State University of Corvallis, OR, ftp: ftp.fsl.orst.edu. Reiter, K., Gottfried, M., Pauli, H., Grabherr, G., 1998. Application of satellite images for designing sampling strategies in terrestrial ecology. Proc. Int. Scantram Meeting, Arktikum, Rovaniemi. Riiters, K.H., O’Neill, R.V., Hunsaker, C.T., et al., 1995. A factor analysis of landscape pattern and structure metrics. Landsc. Ecol. 10 (1), 23 – 39. Schneider, W., 1993. Landuse mapping with subpixel accuracy from Landsat™ image data. In: Remote Sensing and Global Environmental Change. Proc. 25th ERIM Symposium, Vol. II, pp. 155 – 161. Turner, M.G., 1989. Landscape ecology: the effect of pattern on process. Annu. Rev. Ecol. Syst. 20, 171 – 197. Wrbka, T., Szerencsits, E., Reiter, K., 1997. Classification of Austrian cultural landscapes for nature conservation and sustainable development. Proc. II Int. Conf. on Culture and Environment. UNESCO-Chair of Ecological Awareness, Banska Stiavnica, SLK, 1997. Wrbka, T., Reiter, K., Szerencsits, E., Beissmann, H., Maudl, P., Bartel, A., Schneider, W., Sappan, F., 1999. Landscape structure derived from satellite images as indicator for sustainable landuse. In: Nieuwenhuis, G.J.A., Vaughan, R.A., Molenaar, M. (Eds.), Operational Remote Sensing for Sustainable Development. A. Balkema, Rotterdam/ Brookfield, p. 119. Zadeh, L.A., 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Manag. Cybern. 1, 28 – 44.
Analysis of landscape pattern: towards a ‘top down’ indicator for evaluation of landuse A. Bartel * Institute of Landscape Management and Landscape Architecture, Uni6ersity of Agricultural Science, Peter-Jordan Str. 82, A-1190 Vienna, Austria
Abstract Landscapes as highly complex systems are subject to many different assessment procedures despite the fact that their development is not really predictable. Every synthetic model of landscape functioning will probably fail in demonstrating the landscape behaviour due to the decreasing precision and relevance of its output with increasing complexity. Two approaches are presented here which use deductive methods to describe landscape behaviour as indicated by its spatial structure. The one is a correlative concept which is characterized by all the advantages and disadvantages of statistical regression methods. It is strongly dependent on data quality. In the second study an expert knowledge system is developed. Fuzzy set theory is applied to transfer rules of landscape ecological experience to image parameters derived from satellite data. Both approaches are compared regarding the problems faced in the work during implication. Theoretical restrictions and the applicability in landscape assessment is discussed. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Landscape structure; Landscape pattern; Structural indicators; Habitat suitability index; Deductive approach
1. Introduction The pattern of landuse is based on and influenced by a variety of factors and processes in different sectors: natural site conditions, cultivation traditions preferring regional techniques, social development, as well as economic forces and even religious rules are working intertwined in a complex system in the past and the present. They are all changing over time constrained by technical possibilities. The composition and configura* Tel.: +43-1-476547206; fax: +43-1-476547209. E-mail address: [email protected] (A. Bartel).
tion of landscapes is a highly integrative response to those (and even more) causes. The exact processes which control and produce this formation are not understood well enough so that a synthetical, inductive ‘bottom up’ approach to modelling development of landscape pattern is really promising. Nevertheless, looking at a landscape (or a picture of it) we are able to draw many sound conclusions on the underlying factors which are governing the development of the landscape and its pattern. Landscape ecologists intuitively feel (and there are scientific hints too) that there are correlations between pattern and process (Forman
0304-3800/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 8 0 0 ( 0 0 ) 0 0 2 1 4 - 3
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and Godron, 1986; Turner, 1989). For example we think that the more regularity and straight lines are present in a landscape (referred to as geometrization), the greater is the degree of human impact and thus the distance to natural conditions and sustainable flow rates. In this sense the spatial structure (shape, composition, configuration) provides traits of landscapes that can be used for their characterization The term structure refers to ‘‘the spatial distribution of energy, material, and species in relation to the sizes, shapes, numbers, kinds, and configurations of the ecosystems’’ (Forman and Godron, 1986). Therefore landscape structure can be seen as the spatial pattern (where), completed by the ‘what is where’ aspect. If we want to use pattern as an indicator for landscape functioning we have to develop methods for the quantification of structure or pattern (e.g. ‘regularity’); find out the relevant and important parameters of pattern which make a difference between landscapes (note the scale dependency!); find out about the indicated landscape processes behind the structural measurements; i.e. define indicator functions.
1.1. Understanding landscape pattern The cognitive process of understanding the structure of a landscape can be paralleled by the steps of the automatic analysis of landscape data (Fig. 1): the underlying basis is the existing pattern in the landscape whose characteristics are filtered by constraints of scale and resolution (in space and time) of the reception system. On the technical side this system is defined by physical sensors, the human version is called visual perception, including all its morphological and neurological characteristics. The next steps of processing, interpretation (1st level), and integration of additional (collateral) data are defined by algorithms and performed computational. Humans perform a cognitive act and rely heavily on experience and learned knowledge. The result can be a thematic interpretation of the picture of a landscape. A structural evaluation of landuse needs a further step, a 2nd level of interpretation: the descrip-
tion of the thematic pattern, if required combined with different thematic layers of the same region, leads to an understanding of the structure and facilitates the identification of states, changes, problems, and focus areas. The technical approach calculates metrics on the thematic maps and then needs an interpretation towards understanding. In short one tries to draw conclusions from a high grade interpretation back to its underlying processes without exactly knowing the functional relationships.
1.2. Describing pattern and structure With the landscape as study object remote sensing methods offer an ideal technique to collect data. The task is now to extract all the valuable information from this data and to reduce the ‘noise’. But what is the information asked for when dealing with landscape structure? In our natural language we can describe the pattern or structure of elements in terms which are not always straightforward, sometimes metaphorical or ambiguous. But trying to convert those concepts into an exact terminology which can be identified automatically in remotely sensed data, we end up in a dilemma: either we have measures but we do not know the (ecological) meaning, or we have an ecological interpretation, but we do not know how to measure and quantify automatically, e.g. the density of a network. The understanding that the spatial configuration of landscapes is central for their functioning and evaluation leads towards a practical need for structural indicators. Problems on the regional scale can only be addressed correctly if the assessment includes spatial aspects. Due to the limitations of a classical approach to those questions, we present and compare two concepts which use deductive methods for the evaluation of landuse structure.
2. Competing approaches Both examples presented here focus on cultural landscapes of middle Europe. The main data source is Landsat™, combined with collateral data if necessary due to the objectives. Monotem-
A. Bartel / Ecological Modelling 130 (2000) 87–94
poral datasets are used in both cases. The image processing and classification is performed with different more or less sophisticated methods (Schneider, 1993) yielding thematic maps together with a number of attributes. Those maps serve as the samples of landscape structure; they are analysed for a set of metrics
89
which include the patch size, the patch shape, the elongation, the percent area covered by the patch, and the fractal dimension of the borderlines. For the different landuse classes the mean value of those metrics over a landscape is calculated. The herein very important presumption of the delineation of the landscape borders within the study
Fig. 1. The process of understanding landscape pattern. Different levels of perception and processing in the human world (right) compared to an automatically interpreted satellite image (left). Using pattern as indicator for function means to draw conclusions about deeply hidden complex processes (below) from the high level interpretation (top).
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Fig. 2. Examples of regression curves between probability of presence (incidence functions) of amphibian populations and four image parameters. Significance is \ 95% in all of them.
area is based on different approaches in both studies (see below). The calculation has been done partly using the public domain program fragstats (McGarigal and Marks, 1994), based on Arc/ Info7.1, partly through specially programmed software integrated with the image processing package (KBVision).
2.1. The correlati6e approach The study area of about 500 km2 is an intensively agricultural exploited landscape about 40 km north of Munich, Germany. The objective of this study is the generation of ‘habitat suitability indices’ for parameters derived directly from remotely sensed images. In the past a number of studies used a model of land cover derived from satellite data and applied a habitat model to predict species ranges. These habitat models have mostly been described in dimensions of the real landscape, e.g. in terms of types of vegetation, percent forest cover, distance to open water, sea level, and others. They have been tested and verified in the field, and the quality of potential range prediction depends substantially on the correctness and the thematic fitting of the classes separated during the image interpretation. But a thematic map derived from satellite data does not necessarily have to be a landuse map. Themes can also be the heterogeneity or variety of
grey values in a moving window, a texture parameter, a patch shape complexity measure like fractal dimension, and others. And they can represent much more structural information contained in the image, and though in the landscape, than landcover alone. The approach is to correlate these image parameters to the probability of the appearance of biological species and thus generate habitat suitability functions directly for image parameters. Different maps of metrics are calculated and displayed as raster maps. The reference data is the presence/absence of species in a point map. Then each image pixel is referred to as an independent observation and in a logistic regression the probability of the presence (incidence) of a species is plotted against the value of a metric. Regression curves with high statistical significance represent the indicator function (incidence function) of this metric. Despite the pixel based calculation of the incidence function, metrics partly include properties of their neighbourhood.
2.1.1. Examples of incidence functions Fig. 2 shows some examples which demonstrate the incidence of amphibians related to four different image parameters in the study area: The diversity of landuse classes within a moving circle window of 200 m radius (div200). The range of greyvalues in the first principal component of the imagery in a moving 5× 5cross neighbourhood (texture); like div200 a metric of diversity or roughness in the image. The fractal dimension as space filling property of borderlines in the landuse map (fract); describes the amount of borders in relation to areas. The normalized differential vegetation index (ndvi); in remote sensing well known as correlated to green biomass. For display fract and ndvi are multiplied by 100. Those regression curves can be seen as habitat suitability functions or incidence functions for structural dimensions of the landscape. All four examples have a small r 2 but, nevertheless, they
A. Bartel / Ecological Modelling 130 (2000) 87–94
are significant with greater than 95% confidence due to the high number of pixels. That shows, that even small differences in the metrics values represent a certain change in incidence. In this example different amphibian species have been put together in one map of the presence/absence of amphibians. The different habitat needs and dependencies on biotope structures should come out more distinctively when species or ecological guilds are analysed separately. Texture and ndvi have a closer relationship with incidence than div200 and fract. In texture there appears to be a threshold: higher incidence is correlated with ranges of greyvalues of more than 110 units. Most amphibian species are found near wet areas, which are rare but have a clearly different spectral profile than their neighbourhood. In div200, even if it has a similar thematic content like texture, this correlation is represented less obviously, because div200 is based on a landuse classification, followed by a median filtering. Thus small areas often are dissolved in their surrounding landuse classes and only big amphibian biotopes are represented in this metric. The negative correlation of ndvi with incidence is somewhat surprising. But it makes sense: amphibians often are found in more or less natural vegetation, they are usually not found in high productive fields. And those crops usually have a high ndvi, whereas in natural vegetation the higher amount of dead biomass lowers the ndvi. The use of fractal metrics to describe landscape complexity is a intensively discussed topic. The mathematical concept of fractals is not easily understood, and in landscape ecology it is often applied without the necessary background. Serious criticism on the use of fractal dimensions is pointed out, e.g. by Frohn (1998). In this example incidence regressed to fract performs a very low r 2. The values of fract are very similar in the whole study area; assuming one really measures ‘complexity’ with this metric, this piece of agricultural landscape seems to be fairly homogenous in its fractal properties.
2.2. The knowledge based approach The second approach discussed is used in a
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project which is aimed towards an evaluation of sustainability of landuse and focused on the structural properties of the landscape. In this paper the term ‘sustainability’ only refers to the ecological dimension of the originally 3-fold definition comprising ecological, economical, and social aspects. The targeted area is the whole of Austria. Due to the fact that Austria covers a range of very different landscape types, from high alpine areas, fluvial dominated valleys, down to the pannonic dry grasslands, the whole area has been divided into about 6000 landscapes which have been classified into 11 types of cultural landscapes (Wrbka et al., 1997). Those types correspond mainly to dominant landuse systems but also in structural characteristics as they appear in a satellite image. They have been visually delineated and classified from a satellite image zoomed to a scale of 1:200 000. The landscapes are the units of evaluation and the types serve as a stratification of data. Between the types there are essential differences in the system so that a common evaluation is not appropriate. A set of representative sample points has been chosen throughout the types by a special method of sampling design (Reiter et al., 1998). Those samples are studied intensively in their relations between structural features and functions related to sustainability of use (Wrbka et al., 1997). The expertise gained this way is used to validate and adjust the literature knowledge to the Austrian conditions in fine scale. The data for the evaluation covering the whole Austrian area stem from Landsat™. They are analysed in a segmentation and classification process which produces a landuse map of high spatial accuracy and 14 classes. The single polygons and the landscapes are attributed with metrics which describe the spatial structure of the landscapes. The step of scaling up the knowledge developed in fine scale samples to the coarser scale satellite measurements is done using a knowledge based fuzzy-set approach. Experts document their knowledge on the relationships between structure and eco-sustainability in a set of simple rules which operate on fuzzy sets of the landscape metrics. For each of the landscape types an
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adapted set of rules is developed. The fuzzy system can be seen on one hand as a formal documentation of indicator functions for sustainability of landuse, on the other hand it is a software tool which can digest a huge amount of data (Wrbka et al., 1998). To use such a fuzzy rule based system at this point has two major advantages: Discrete classes in other evaluation procedures may produce different output values out of very small input variances. A fuzzy system takes into account the inherent ‘uncertainty’ of borders and classes in landscapes. The complex concept of landuse eco-sustainability in coarse scale can be separated into a set of simple rules where each rule only describes one relationship. The integration is done by the fuzzy system, assumed that it is tuned well. The system of rules is then applied to the fuzzified structural attributes and produces an output value of ecological sustainability for each landscape. Only the landscapes of one type are treated with the same set of rules. For each type of landscapes a different set of rules has to be provided. Therefore, the result is a ranking of landscapes only within one type of cultural landscape. The area of Austria is covered by combining the maps of all the different types. The computer implementation of this evaluation system is realized with the MATLAB (v5.2) fuzzy logic toolbox (v 2.0) and its adaptation through macro scripts. The coupling of image processing /GIS and the fuzzy system uses ASCIIfiles for data transfer, organized through fields of landscape-ID and segment-ID.
3. Correlative versus knowledge based Both of this approaches have certain advantages and restrictions regarding their data, methods, and conceptual framework.
3.1. Definition of landscape borders Landscapes are the top spatial units of the assessment of spatial structure. In both approaches their delineation is an a priori informa-
tion which has great influence on the values of the most metrics. The correlative approach probably would prefer a landscape definition adapted to the reference data used. If a raster sampling design has been used in the mapping of species ranges, for the best spatial congruence the landscapes should be defined in the same grid. The knowledge based approach in contrast does not have a spatial sampling to rely on. It uses a landscape type classification instead which has been generated visually. As criteria structural properties as well as dominance of major landuses have been used. Due to this method the assessment units represent functional units and the evaluation rules can be developed on this background.
3.2. Measuring pattern The use of abstract metrics for the description of pattern has some critical implications: some metrics do not represent what they are supposed to! Theoretical preconditions for the use of structural indices are (Li, 1989, Borg (personal communication 1998)): invariance of direction invariance of scale invariance of size invariance of spatial resolution invariance of class resolution robustness unambiguity Because these presumptions are not really true in most cases, one has to be careful in using metrics. If possible the parameters used should have some relationship to the investigated topic to reduce the chance of artefacts. The transfer of models validated on one set of spatial data to another is only possible using invariant metrics, or after a proof of data consistency. Many of the widely used metrics are correlated to a high degree. In the development of a multi parameter model, therefore, a decorrelation has to be calculated. For example Riiters et al. (1995) identified in a multivariate analysis of pattern metrics of USA quadrangle landuse maps only six
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factors responsible for an 87% variation in the values of 26 widely used metrics.
3.3. The dimension of time In both of the examples presented the dimension of time has not yet been included in the studies. The correlative approach, therefore, models a status quo of species ranges and the development is not depicted. But the actual status does not have to be the potential or even optimum range; not all of the possible habitats might be really covered. Or, on the other hand, some of the still existing populations might be only the declining remainder of an actually dying population. This restriction can be overcome in the knowledge based approach. If data are available rules can be developed which integrate the temporal aspects. But that implies a good knowledge about processes and their principles in space and time.
3.4. Conceptual obstructions The advantage of the correlative approach is that it can detect dimensions of — in this case — habitat descriptions which have not been seen so far. Especially it is possible to find out the role of aspects of neighbourhood and distance for the distribution of species. But this focus on spatial structure is a disadvantage too. Of course spatial properties are not the only relevant dimensions of a habitat description. The availability of food or certain requisites for breeding, competition or predation phenomena, and many other factors with a substantial influence on the distribution, are not represented in an analysis of the spatial structure of images. Even new remote sensing sensors with high resolution can not give information on those factors. The use of fuzzy sets in the knowledge based approach allows us to use knowledge and experience even if precise quantification is not available. But a rule based system has to be parameterized carefully and, therefore, it needs a training dataset to test the plausibility of its output. It can only give a formal reproduction of experts knowledge, but it can not give new insights into a modelled reality. Nevertheless, if it is tuned well once, such a system
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is able to represent the complex knowledge of experts and can be applied automatically. Thus it can be used to evaluate scenarios of possible developments.
4. Discussion Landscape pattern respective structure is a complex product of many underlying processes. And in return structure defines a spatial framework for process manifestation and puts certain constraints on them. Landscape pattern and landscape processes have a mutual impact. For understanding and evaluating a landscape we, too, have to think in both directions. To leave the spatial structure out of sight means to reject the existence of lateral relations between landscape compartments. Modern landscape ecology is still trying to understand the interrelationships of different landscape elements. One of the main conclusions is that landscape evolution can not be predicted. The principle of incompatibility of precision and complexity (Zadeh, 1973) says that ‘‘as the complexity of a system increases, the ability to make precise and yet significant statements about its behaviour diminishes…’’. Already a minor change in surrounding conditions (climatic, economic, social,…) may facilitate a development in a very different direction. Catastrophic events and pure chance are basic factors in the evolution of landscapes. In this sense a landscape matches the criteria for a complex system and a model of it has necessarily to deal with uncertainty. In terms of systems theory emergent properties are substantial characteristics of landscapes. Emergent properties can not be derived only by combining the properties of holons below. Thus an inductive ‘bottom up’ modelling approach will fail in depicting the landscape system as a holon at a high level. The deductive view focuses intentionally on those emergent properties.
5. Conclusions Landscapes are systems of high complexity and therefore an analytic modelling approach is not
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very promising. Nevertheless the importance of landscape structure urges the trial of deductive modelling approaches in assessment studies on the landscape level. Even without an exact cause – effect explanation it is worth detecting relationships and to apply even only qualitative experience in the assessment and evaluation of landscapes. Metrics on landscape structure can be used as indicators for landscape functioning under certain restrictions: Models based on landscape metrics are only valid in a regionspecific context. The dependence on input data quality restricts the use of those models to a similar data quality like the validation data. The landscape border problem must be taken into account. Correlations between metrics can produce weightings not intended. Landscape structure is a highly integrative outcome of the complex processes acting in landscapes. Seen as ‘frozen processes’ and with appropriate restrictions in mind, pattern and structure provide valuable information for different landscape assessment tasks.
Acknowledgements These studies have kindly been funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF) in the framework of the Research Network ‘Forschungsverbund Agraro¨kosysteme Mu¨nchen’ and by the Austrian Federal Minister of Science and Transport (BMWV) in the research program ‘Cultural Landscapes’. Many colleges have done a substan-
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tial amount of work on both of these projects and have discussed my ideas. To all of them I owe a warm thank you.
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