INTEGRATION OF GIS AND OBJECT-BASED IMAGE ANALYSIS TO MODEL AND VISUALIZE LANDSCAPES

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1 INTEGRATION OF GIS AND OBJECT-BASED IMAGE ANALYSIS TO MODEL AND VISUALIZE LANDSCAPES Thomas Blaschke*, Lucian Dragut** * Department of Geography and Geoinformatics, University of Salzburg, Austria, thomas.blaschke@sbg.ac.at ** Faculty of Geography, Babeş-Bolyai University of Cluj-Napoca, Romania, lucian@geografie.ubbcluj.ro Keywords GIS, object-based image analysis, DEM analysis, image segmentation ABSTRACT Although a number of digital terrain analysis techniques were developed since the early 90ies terrain information is not utilized enough in landscape research even if topography is a key variable in a wide range of environmental processes. One explanation of this situation is the two-dimensionality of the main paradigm in landscape ecology, namely the patch-corridor-matrix model. Calculations of landscape metrics based on such definitions of landscape elements can lead to biased or misleading results if the topographic situation plays an important ecological role. Landscape spatial indices (e. g. mean patch size, fragmentation, diversity etc.) are strongly influenced by landforms which act as natural breaks in landscape patterns (steep slopes), or conversely, as spatial connectors (valleys). To account for these relationships, an image segmentation-based methodology of landform classification is proposed with the objective to integrate it into the definition of landscape units. 1. INTRODUCTION Landscape ecology and landscape metrics Landscape composition and configuration are important factors influencing ecosystem function and habitat quality (Turner, 1989). Landscape pattern analysis has received increasing attention in landscape research (e.g. Hessburg et al., 1999). Much theoretical research in landscape ecology has focussed on quantifying various aspects of pattern and understanding the effect of disturbance processes, both natural and humaninduced, on the vegetation mosaic (Forman, 1995; Wiens 1995). The term 'mosaic' implies discreetness of elements, the existence of clear boundaries between neighbouring patches. 'Landscapes', of course, may also be composed of elements that grade gradually into one another or in which the boundaries are blurred (Müller, 1998). If we are to make progress in developing landscape-level theory, however, it seems best to begin with the multi-patch, mosaic extension of patch-matrix approaches rather than to attempt to deal with the full range of possible landscape expressions (Wiens, 1995). To study mosaics, then, we must be able to define and map boundaries. These ideas led to the foundation of the patch-matrix paradigm in landscape ecology (Forman and Godron 1986; Forman, 1995). If we understand how ecological patterns are affected by the configuration of landscape mosaics, we may be able to develop a mechanistic foundation for landscape ecology (Wiens et al., 1993; Wiens, 1995). A basic part of landscape analysis is characterizing the elements (i.e., patches) within a landscape. Patches are often defined by visually obvious but arbitrary criteria (e.g., woodlots versus all other cover types), and this is often sufficient. Numerous metrics are used to evaluate structure; too many to cover in detail in this paper. Landscape pattern measurements, or metrics, can be classified into three categories: patch, class, and landscape (McGarigal and Marks 1995; Gustafson, 1998). Patch metrics describe the attributes of individual patches of vegetation. The size, shape, edge, or nearest-neighbour relations of individual patches are measured. Class metrics describe those same patch attributes as the mean, minimum, maximum, or variance for a class of mapped landscape attributes (e.g., latesuccessional forest). Landscape pattern metrics describe these and other attributes for all landscape classes combined without distinction between different classes. For example, mean patch size might be measured for all patches in a landscape, instead of for just one vegetation type (class). Descriptions of landscape metrics as well as software packages that calculate these metrics can be found in McGarigal and Marks (1995), Baker and Cai (1992), Mladenoff et al. (1993), or Mladenoff and de Zonia (2002). Figure 1. Patch delineation as a prerequisite for landscape metrics calculation. The two version illustrate that there is no single correct solution rather then an appropriate or inappropriate segmentation of reality in relation to objectives and data scales. The importance of the extent of patches of a particular vegetative or habitat type is intuitive. Assuming a direct correlation between species and habitat type, it follows that the amount of habitat directly affects species abundance. In absence of the required habitat type for a species, the landscape can't support that species. The more habitat present, the greater the potential capacity of a landscape to support a species. The threshold size for species or ecosystem functions is totally dependent on the species or process of interest. A metric used to characterize the extent of patches is simply # of patches.

2 Patch size determines the amount of resources available for species as well as the buffering capacity of a patch. Resource availability decreases with decreasing patch size. The susceptibility to environmental catastrophes as well as random events of extinction increases with decreasing patch size. Acreage of patches by type is typically the metric used to quantify patch size. Confounding the effects of patch size is the shape of a patch. The significance of patch shape is related to the amount of patch edge. Patches can be of similar area but have varying amounts of edge depending on shape. e.g., Circular patches have a minimal perimeter/area ratio whereas a highly irregular polygonal patch will have a high perimeter/area ratio. In a landscape context, with increasing edge there is a greater amount of surface area in contact with adjacent patches. Gustafson s (1998) review suggests that new indices will continue to be derived or modified, and so ecologists will be faced with an increasingly longer list of descriptive metrics. This proliferation encourages a shotgun approach to landscape characterization. Which metrics to use should reflect some explicit hypothesis about the patterns of interests and the mechanisms underlying that pattern. Fig. 1 illustrates how difficult it is to delineate reality into meaningful pieces. Consequently, it is even more difficult to choose the proper metrics which make sense for a particular application. Critique of the patch-matrix model We start this paper from the assumption that it is crucial to explore, analyse and model the effects of spatial context on ecological phenomena. But before we are ready to calculate landscape metrics and indices based on spatial entities, we must add topographic aspects into the two-dimensionality of the patch-matrix concepts. Only recently, Dorner et al. (2002) discuss a set of methods for landscape pattern analysis that address the relationships between vegetation pattern and topography. While some studies have generally addressed topographic influence by treating topographic characteristics as separate variables, these authors focus primarily on integration of topography into descriptors of landscape pattern. They demonstrate how measures of landscape pattern can be corrected for the underlying topography, and develop a set of indices that measure pattern with respect to topographic gradients. As Dorner et al. state, the theoretical framework of landscape ecology to date does not provide a well-developed methodology for analysing pattern and dynamics in landscapes with strong topography, or, more generally speaking, landscapes with a strong underlying physiographic structure. New methods are required to address research questions arising from the interplay between the physical terrain mosaic and ecosystem dynamics. It is obvious that e.g. vegetation pattern at two different times in a mountainous valley exhibit the spatio-temporal dynamics of pattern in landscapes. The pattern can be substantially influenced by the topographic relief. Topography shapes pattern indirectly through its influence on disturbance regimes and potential successional pathways, and directly, by creating permanent natural breaks in vegetation pattern (Swanson et al. 1998; Turner 1989). Terrain analysis, or geomorphometry, the science of describing and measuring various aspects of topography is well developed (see, e.g., Florinsky, 1998 or Dorner et al., 2002, for an overview). Digital datasets describing topography at various scales exist for many geographic areas, and software for manipulating topographic information has become widely available, both in form of standalone programs and as an integral part of many GIS packages (Pike 2000). A basic concept in landscape ecology is heterogeneity or the differences and diversity within a landscape. A natural forest landscape, for example, normally includes a variety of species of trees, shrubs, herbs, animals, and microorganisms, as well as a diversity of ecological stand types, varying according to moisture, slope, elevation, aspect, soil, and so forth. The same is true for alpine meadows or for mire systems: what appears to be relatively homogeneous according to the mapping conventions can be exposed to very different topographic situations and stand conditions. We assume that the central premise of landscape ecological research is that the structure of mosaics, not just patches, affects ecological patterns and processes. Patch-matrix theory will continue to be useful for addressing problems in which the island analogy is appropriate. In many situations, however, patch models may be inadequate to capture the complexity of spatial interactions. We should recognize that, even though nature usually exists as heterogeneous mosaics, not all problems in ecology require a spatially explicit analysis or solution. Models that assume (within-patch) spatial homogeneity may be applicable in some situations, topography-based models in others. These landscape ecological principles are combined with object-based image processing (Blaschke and Strobl, 2001) and we demonstrate for a highly complex mountain landscape the methodology of landscape unit definition based on a DEM. 2. METHODOLOGY Figure 2. Resulting patch-based landscape structural map without taking into account the Third dimension of relief. The research workflow integrates digital terrain analysis and image analysis performed upon DTMs and its derivatives utilizing an object-oriented software approach. While the latter is becoming more widespread in image processing of satellite data it is hardly applied for non-spectral data sets yet (Blaschke and Strobl, 2003). The digital terrain data were processed in a stand-alone GIS software called DiGeM on the basis of a 3x3 sub matrix analysis. Besides elevation, the following geomorphological characteristics were taken into account for morphometric description: profile curvature, plan curvature, slope gradient, and slope aspect. The latter was not used in the segmentation process but in the following classification step. The resulting data sets with the exception of the aspect layer

3 were segmented into relatively homogenous objects, using ecognition 3.0 software. This way, the independent terrain derivatives were transformed into coherent objects in respect with integrated geomorphic properties. In the empirical work, objects are classified into landform elements following the ideas of Dikau (1989) and using modern image segmentation techniques for the delineation and fuzzy rules for the classification. An important initial mechanistic step is the segmentation into relative homogeneous landscape units. Dorner et al. (2002) discussed approaches for incorporating topography into landscape analysis and divided them into three categories: 1) adjustment of area and distance calculations to avoid systematic biases in landscape statistics; 2) design of indices that capture characteristics of vegetation pattern in relation to topography; and 3) use of statistical models to describe broad-scale relationships between topographic characteristics and vegetation pattern. Figure 3. Schematic representation of patch delineation based on terrain homogeneity and relative abrupt changes between the patches. Our approach shall be open to all of these strategies. We aim for the delineation of relatively homogenous topographic areas. The resulting patches can further be analysed in any GIS environment in combination with vegetation or land use data layers through overlay techniques or through statistical analysis. First, we perform image segmentation techniques on the DEM and the derivates (slope and curvature). Secondly, we developed an automated methodology to classify landforms based on the objects from the segmentation process. We developed a nine class system building on the work of Dikau (1989). He distinguished nine classes based on combinations of convex, straight, and concave profiles. We believe that in fact only five combinations are important, the other four being mixtures between main classes. Mixed elements can be attached to different main classes, depending on their curvature values, both in plan and in profile. The fuzzy logic enables such a flexible classification. For instance, elements with straight profile, but convex plan are classified as side slope if the convexity value is close to zero, or as nose slopes when this value is too high. Landforms with concave profile and convex curvatures or conversely are more complex, allowing four different assignments in accordance with specific value combinations. Besides the five classes as used by Dikau (1989), another four categories, irrespective of curvature values, were established. Their definition is built on the dominance criterion and on the slope gradient one. The dominance criterion is an alternative to the use of absolute altitudinal values in classification. Obviously, forms situated at higher altitudes are dominant in respect with forms characterized by lower values of elevation. Thus, a mathematical expression of these relationships replaces very well the absolute values of elevation, which are too specific for a certain dataset, especially when extracting landforms situated at extremities (peaks and toeslopes). This criterion separates therefore between dominant forms (peaks), and dominated ones (toeslopes). Slope gradients less than 20 are expressions of flat areas, while higher than 450, of steep slopes. The nine classes established as described above have been embedded into a class hierarchy and grouped in similar object classes. The landform classification has been hierarchically built on three levels. At the highest level uplands, midlands and lowlands were set up using as criterion the relative altitude. We have chosen to deal with relative altitudes as one of the research aims was to develop a classification system applicable for different datasets. In this way the classification system become irrespective of specific datasets, all altitude values being stretched between 0 and 255. Thus, the membership function was set up in a very simple and flexible manner. These classification rules are also inherited by the children classes at lower levels. The data obtained from the classification are directly integrated in a GIS software. Landform types were visually analyzed, by draping them over DTMs of study areas. As Blaschke (2002) pointed out, the results of DTMs processing are difficult to be verified quantitatively because of the lack of ground truth data for geomorphologic features beyond altitude (pp. 23), so that geovisualisation is a first solution for evaluating the accuracy of landform elements. Visual analysis was used for similar purposes in other studies (Dikau et al., 1995). Obviously, the geomorphic categories resulted from the classification agree with the topographic surface, describing very well the geomorphology of study areas. A problematic subject in digital terrain modelling is the ground truthing. How well do the objects fit to real landforms? As the multi-level object-based image analysis methodology used mimics some ways of human perception, the visualization seems to be an adequate method to assess the classification results. Moreover, this evaluation method is the only one possible if no ground truth data for geomorphologic features beyond altitude exist. In this paper, we first applied visualization techniques to set up the scale parameter (e.g. the degree of the maximum heterogeneity per object) in the segmentation process. The segmentation outputs were draped over the DTMs of the study areas and their fittings with the surface topography, expressed both in a two-and threedimensional perspective, were visually analyzed. The same procedure has been applied to evaluate the classification results (e.g. landform categories). We demonstrate in detail for two geomorphologically different study areas in Romania and Germany that the fuzzy rules used for the classification of the landscape characters provide a high flexibility and transferability between DTMs and landscapes. The rules which are stored in a database can readily be queried and modified when a new understanding of the mapped scene is introduced. We finally discuss that the this methodology allows for better detection and mapping of complex topographic land-use units and, by incorporating patch generation rules, for improved landscape monitoring and scenario modelling.

4 3. RESULTS AND DISCUSSION The methodology discussed herein is relatively scaleindependent but it does not aim for microrelief forms rather then for landscape level geomorphological entities. It is in principle possible to up- or downscale the results but what makes upscaling a challenge is non-linearity between processes and variables. Is this heterogeneity in properties that determinate the rate of processes. We introduced a methodology that is relatively transferable to determine geomorphologic units which can then be combined with land use/ land cover information and auxiliary information. This way, the resulting spatial entities consist of distinct properties that distinguish one patch from another. This is one attempt to tackle the problem that scale is a crucial aspect of heterogeneity. Changes in scale are frequently associated with new or emergent properties. Upscaling can hide features present at a lower level. That is, an increase in the extent can lead to averaging of variation and the increasing of the grain, can transform a variable into a constant. As grain refers to the coarseness in texture or granularity of spatial elements composing an area (Forman, 1995), it determines, by consequence, the fineness of the distinctions that can be made in an observation set. In current research, we are investigating to which extent our methodology supports the calculation of landscape metrics and makes it less data (scale)-dependent. The mechanical nature of measuring pattern has lead to development and use of numerous metrics to characterize landscape structure. Relating landscape metrics to processes, however, is a much needed area of research. Associations between pattern and processes exist, but tend to be very species or taxa specific. We propose to be more general in nature. Making land-use decisions by assigning characteristic processes to patterns without geomorphologic information in addition to a thorough understanding of ecosystem process-pattern relationships may have certain negative consequences. The type of metric(s) used in evaluating a landscape must be a function of the objective(s). Measuring just one characteristic of a landscape in isolation is often not appropriate (e.g., the amount of edge of a specific patch type may provide little understanding of potential edge-related problems; the quantity and type of patches sharing a common edge may be of greater value). Also, use of just one metric doesn't tell the whole story. Several measures will likely be more appropriate to identify differences between somewhat similar yet distinct patterns. Towards more realistic patch definition The "patch" in landscape ecology is a unit of one ecosystem type within the landscape. As with the definition of landscape, the size of a "patch" varies with the perspective of the viewer. Larger animals require larger home ranges, so that a patch which is sufficiently large for a mouse is very small for a cougar. Ecologically functional landscapes must contain patches of habitat which are large enough to support all of the animal species which depend on the landscape. Our methodology supports this endeavour in one respect: it produces geomorphological units which are relatively stable across scales and relatively independent to a shift in data resolution. The results of visual assessments were very positive. The border lines follow ridges and edges where visible and are only in relatively few instances not explainable (see Fig. 4). Although a comprehensive statistical assessment and overlays with manual geomorphological interpretations based on stereo images is not undertaken yet, we conclude that the resulting patches are very realistic. In subsequent applications they might serve as containers for assemblages of species if their respective requirements can be defined in absolute spatial terms. Figure 4. Resulting landscape units draped on a DEM and a black and white orthophoto (from Blaschke, 2002). Demands for specific landscape visualisation techniques arise. We briefly refer to a recent paper by Blaschke and Tiede (2003). That paper examined how remote sensing and GIS data can be processed in an integrated RS/GIS object-based methodology in order to characterize landscapes, and to test how the results derived from this data source can be combined with visualization techniques. Two strategies were highlighted: block representations of objects based on sub-objects and virtual trees created on corresponding patches and their respective DEM information. In both cases, all steps are performed within the GIS software world (ArcGIS plus extensions and scripts). According to the nomenclature in Burnett and Blaschke (in press) the authors refer to whole forest patches in the case study area as the focal (level 0) patches. Single bushes, islands of clear-cut, meadows and other homogeneous sub-areas comprise the 1 level. This can be said to be a mechanistic level because they do not correspond to our focal scale and level of detail but help us to analyse and express quantitatively the complexity of the focal patches. The level +1 is the landscape, consisting of a mosaic of forest patches and open land such as meadows. Semantic rules are formulated in a commercial software environment (ecognition). Height information is encountered by integrating external forest data at for sub-stand units but not for individual trees. Typical heights range from 8 to 15 m, although some trees look higher due to relief variations. Individual trees in figure 4 are between 10 and 15 meter in height whereby different species at chosen. The two different ways of integration und visualisations are highlighted in figure 5. If the resulting landscape units can be proven to be ecologically meaningful they can serve as basic spatial units for further modelling including the modelling of flows of energy and material. This way we support the transferability of results and the comparability between studies. Many landscape metrics applications are purely exploratory, aimed at discovering and summarizing pattern in the study area but with very limited possibilities to compare different data sets. Spatial modelling of ecological change provides an opportunity to evaluate dynamics of landscapes. Modelling offers a formal opportunity to synthesis existing information, to formulate and test hypotheses, and to make futuristic predictions of system

5 states. Testing of model results, however, is difficult owing to the temporal extent of predictions. Spatial modelling does provide us with an ability to view potential results given our current understanding and aids us in making better, or at least, more informed decisions. Blaschke, T., A multiscalar GIS/image processing approach for landscape monitoring of mountainous areas. In: Bottarin, R. and Trappeiner, U. (eds.): Interdisciplinary Mountain Research, Blackwell Science, pp Blaschke, T. and Strobl, J., What s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS Zeitschrift für Geoinformationssysteme, 6/2001, pp Blaschke, T. and Strobl, J., Defining landscape units through integrated morphometric characteristics. In: Buhmann, E., Ervin, S. (eds.): Landscape Modelling: Digital Techniques for Landscape Architecture, Wichmann-Verlag, Heidelberg, pp Blaschke, T. and Tiede, D Bridging GIS-based landscape analysis/modelling and 3D-simulation. Is this already 4D? In: Schrenk, M. (ed.): CORP 2003 Geo Multimedia 03, Vienna, pp Burnett, C. and Blaschke, T., A multi-scale segmentation / object relationship modelling methodology for landscape analysis. Ecological Modelling (forthcoming). Dikau, R., The application of a digital relief model to landform analysis in geomorphology. In: Three dimensional applications in Geographical Information Systems (ed. by J. Raper, Taylor & Francis, London. pp Dikau, R, Brabb, E. E., Mark, R. K., Pike, R., Morphometric landform analysis of New Mexico. Z. Geomorph, Suppl. Bd. 101, pp Dorner, B., Lertzman, K., Fall, J., Landscape pattern in topographically complex landscapes: issues and techniques for analysis. Landscape Ecology 17. pp Florinsky I.V., Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in Physical Geography 22. pp Figure 5. Two visualisation approaches for object-based classifications (from Blaschke and Tiede, 2003): object-based entity modelling and geometric modelling which builds on 3- D geometric representations of individual landscape features. Our approach to developing landscape-level research goes beyond particular aspects of landscape structure - corridors, boundaries and the like. In an attempt to develop a methodology, we have simply coalesced DEM analysis and landscape ecology methods. The next step is to develop theory inductively, by gathering enough empirical information about mosaic effects to permit us to generate testable propositions. This paper provides a perspective for further research based on what we know and what we need to know to take this step. References Baker, W. L. and Cai, Y., The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology 7, pp Forman, R., Landscape Mosaics. the ecology of landscapes and regions. Cambridge University Press, Cambridge. Forman, R. and Godron, M Landscape ecology. John Wiley & Sons, New York. Gustafson, E., Quantifiying landscape spatial pattern: What is the State of the Art? Ecosystems 1, pp Hessburg P.F., Smith B.G. and Salter R., Detecting change in forest spatial patterns from reference conditions. Ecological Applications 9. pp McGarigal, K. and Marks, B Fragstats: spatial pattern analysis program for quantifying landscape structure. US Forest Service General Techn. Report PNW-GTR-351. Corvallis. Mladenoff, D.; White, M.; Pastor, J.; Crow, T Comparing spatial pattern in unaltered old forest and disturbed forest landscapes. Ecological Applications. 3. pp Mladenoff, D. and dezonia, B APACK 2.22 Analysis

6 Software. User s guide. Müller, F., Gradients in ecological systems. Ecological Modelling 108. pp Pike R.J., Geomorphometry diversity in quantitative surface analysis. Progress in Physical Geography 24. pp Scheiner, S. M., Measuring pattern diversity. Ecology. 73. pp Swanson F.J., Kratz T.K., Caine N., Woodmansee R.G., Landform effects on ecosystem patterns and processes. Bio- Science 38. pp Turner, M., Landscape ecology: the effect of pattern on process. Annual Review of Ecology and Systematics. 20. pp Turner, M., Spatial and temporal analysis of landscape pattern. Landscape Ecology. 4. pp Wiens, J., Landscape mosaics and ecological theory. In: Mosaic Landscapes and Ecological Processes. Ed. by L. Hansson, L. Fahrig, G. Merriam, Chapman & Hall, London, pp