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eCognition User Meeting, October 2002, München

Landscape connectivity studies on segmentation based classification and manual interpretation of remote sensing data

Eva Ivits1, Barbara Koch2

1,2 Department of Remote Sensing, Freiburg University, Germany; email: 1Eva.ivits@felis.uni-freiburg.de, 2 barbara.koch@felis.uni-freiburg.de

Thomas Blaschke

ZGIS, University of Salzburg, Austria; email: thomas.blaschke@uni-tuebingen.de

Lars Waser WSL, Birmensdorf, Switzerland; email: lars.waser@wsl.ch

Abstract Analysing landscape structure and landscape pattern through indices is relatively widespread in landscape ecology and landscape planning. The lack of comparability of the results between different case studies and across spatial resolutions limits the potential usefulness of landscape metrics, in a context where multi-scale GIS and high resolution remotely sensed data are becoming increasingly available. In this paper, an object-based methodology to analyse and quantify connectivity at a landscape level as a general measure is described. Connectivity is to some degree species-dependent. What is regarded as ‘connected’ for one species can be unreachable for another species. In landscape planning and for many conservation efforts at a landscape level, more general measures of connectivity of patches are needed. Recently some progress has been made in the foundation of fragmentation indices, but connectivity measures are still immature. We analyse categorical data derived from fused Landsat-ETM imagery and aerial photography using multiscale image segmentation techniques in eCognition© software. In an object-based semantic network which links several segmentation levels, rules are formulated to exploit neighbourhood/distance relationships of the resulting patches. The work is done in the context of the European Union funded project BioAssess (EVK2-CT1999-00041). 1 Introduction Quantification of landscape patterns like fragmentation and connectivity through spatial indices is currently becoming a common practice in landscape ecology and related disciplines. These indices capture and summarise some of the spatial characteristics that have been found to be relevant for different ecological or physical processes. In particular, landscape fragmentation indices derived from remotely sensed data are being increasingly used for landscape condition assessment and land cover change characterization (Gulinck et al. 1993, Frohn et al. 1996, Chuvieco 1999, Griffiths et al. 2000). Satellite images are used as the primary source of spatial information because they provide the digital mosaic of landcovers required for the computation of these indices (Chuvieco 1999). At the same time, the development of remote sensing and geographical information systems has made available a wide variety of spatial data. It is now possible to handle, compare and integrate landscape data corresponding to different scales.

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In this context, there have been different studies devoted to the effect of spatial resolution on landscape indices (Turner et al. 1989, Benson and MacKenzie 1995, Frohn 1998, Blaschke and Petch 1999). These previous researches have described the effect of spatial resolution and have provided relevant insights in the understanding of their behaviour. However, it is not yet fully understood how fragmentation and connectivity indices in particular are affected by spatial resolution. It is generally assumed that it is not possible to compare the values of these indices when they are measured on landscape data with different spatial resolutions. More research is necessary to address this topic, since the lack of comparability across scales seriously limits the potential usefulness of this kind of quantitative analysis of landscape patterns. Information extraction from remote sensing data is a key issue in environmental monitoring and many landscape ecological applications (Innes and Koch 1998). The usefulness of the resulting landscape objects is commonly measured by their respective area information based on the amount of classified pixels. Therefore, in most cases, the exploitation of land-use/land cover data is restricted to land-use statistics. This information is indispensable but not sufficient to capture landscape properties. In addition to the share of individual land-use/land cover types their spatial distribution and arrangement need to be considered (Wiens 1997). Classification of remote sensing data is a major source of error in projecting patterns of biodiversity. Therefore, accuracy assessment of objects has to be expanded beyond statistical measures. For instance, rather then an accuracy measure of 86% of a class "grassland" the aim is to know how well small patches are identified, how well they reflect the environment, what their respective mean patch size is, or what compactness ratio they exhibit. Spatially explicit consideration is a key issue in operalization of the patch-matrix concept in landscape ecology (Farina 1998, Gustafson 1998). It considers a landscape as a geographic area composed of patches, which in the real world are such entities as forest types, farmland, residential areas, lakes etc. This patchiness exists at all scales of perception. In fact, what we define as a patch is dependent on our scale of interest and measurement system. In remote sensing papers, attention is mostly put on the influence of the sensor and its spatial resolution on the resulting land use/land cover classes. We know much less about how well we can reproduce the characteristics of the spatial configuration (e.g. shape or adjacency of different patch types). The configuration and its perception, respectively, again depend on the scale of consideration. By exploiting the multi-scale image segmentation approach in eCognition© (see section 2) we aim to address several scales simultaneously within one image. Jelinski and Wu (1996) concluded from a thorough literature review that there was no suitable encompassing theory for indicating how sensitive results are to the scale of the analysis and to variations in the way in which data are represented. As Gardener (1998) states, the identification of appropriate scales for analysis and prediction is an interesting and challenging problem. The problem of scale definition is not subject of this study but when defining a methodology for measuring connectivity and putting multiscale object-based analysis in practice landscape patterns investigations are performed at several scales within one image. In this study, we compare a segmentation-based classification approach with manual interpretation of two remote sensing data sets, namely a fused Landsat ETM/IRS image (5m resolution) and CIR aerial photographs (60cm resolution). The test area (LUU=Land Use Unit) is 1 by 1 km, being the working unit in the EU-funded research project, BioAssess. From the variety of landscape metrics describing patch properties few indices were chosen which are relevant to landscape fragmentation and connectivity. Fragmentation and

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connectivity of landscape elements is one of the key issues in BioAssess that focuses on developing biodiversity assessment tools at a European level. In this project, methods are needed that allow rapid, standardized, and comparable assessment of the recent state of biodiversity in several countries at the same time and measures of landscape structure are just one part of a larger whole. We carried out several comparisons based on the segmented patches to test which indices describe the landscape best. This will be evaluated in follow up papers. Here rather the potential of the proposed methodology as well as some difficulties arising from the methodological gap of object-based and visual image classification and postclassification procedures are elucidated. 2 Landscape metrics and landscape connectivity indices Several studies have shown (but mostly assumed) that landscape features such as connectivity or fragmentation, or simply meausres like patch extent might have an influence on species diversity. Connectivity is a complex concept and somehow linked with fragmentation, although it is not just the opposite of the latter. Different aspects related to fragmentation can analogously be evaluated and emphasized by different indices which is analogously true for aspects related to connectivity. Thus, a set of pattern metrics is usually employed to characterise these landscape metrics. Landscape metrics can be categorized into (i) patch area metrics which measure the number and size of patches, (ii) edge and shape metrics which quantify the occurrence of ecotones, (iii) diversity metrics and (iv) landscape configuration metrics. Some of these indices (mean patch size, largest patch index, edge density) are commonly used in landscape pattern analysis. Patch cohesion (Schumaker 1996), effective mesh size and landscape division (Jaeger 2000) have been more recently introduced, and present relevant properties or potential improvements over existing indices that may make them widespread in a near future. Other connectivity and fragmentation metrics different from those considered here are also available; however, many of them are combinations or variations of the previous ones, being highly correlated with them. For this study several landscape indices were calculated from both visual interpretation and segmentation based classification of the same areas with the software FRAGSTATS 3.1. Only those indices were included in the calculation that did not require a scale dependent weight since different species percept their environment on very different scales. Therefore, when describing the landscape it is no possible to define a unique value for those indices. The only exceptions were the proximity and the connectance index because of their importance in connectivity studies; the searching radius for proximity was considered as 500m (at 1km test area) and the connectance in 25m (pixel size = 5m). At later phase of the project these indices and the rest not considered here will be recalculated with species specific distances (Ivits and Koch, 2002). 3 Image Segmentation Although image segmentation is not new (see Blaschke and Strobl 2001) it has gained much attraction recently. Especially within the last two years many new segmentation algorithms as well as applications were developed, but not all of them lead to qualitatively convincing results while being robust and operational. One reason is that the segmentation of an image into a given number of regions is a problem with a huge number of possible solutions. The high degrees of freedom must be reduced to a few which are satisfying the given

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requirements. A new algorithm, called “fractal net evolution approach” (Baatz and Schäpe 2000) is becoming popular in image processing of remotely sensed data. Hierarchic object-based image analysis (Figure 1.) is advantageous to visual interpretation of landscape objects when aiming to describe patch properties for landscape ecological studies. Firstly, the different segmentation levels allow for the perception of landscape objects at different scales even when the same sensor type is used. Secondly, the eCognition© classification procedure (Baatz and Schäpe 2000) enables the user to utilize objects at different levels within different user specific contexts, e.g. landuse units (extensive and intensive grassland) on a coarse scale, habitat types (deciduous forest and wetland) at a meso scale, or landscape features (small forest habitats, roads, artificial surfaces) on a detailed scale. Thirdly, small habitat patches (stepping stones biotopes) that have importance in connectivity studies are mostly left out from visual interpretation while hierarchical image segmentation enables a standardised assessment of their presence, distribution, and connectivity within and between the respective landscape elements (or corresponding image super objects, respectively).

Fig. 1: Object-based image analysis of a fused Landsat-IRS satellite image for landscape ecological studies

The conceptual entities and practical working units of landscape ecology are patches where it is assumed that the internal heterogeneity relatively little and the gradient of a phenomenon under consideration to its surroundings is relatively steep. These patches serve as basis when calculating indices describing fragmentation, connectivity or anthropogenic influence. That requires image processing techniques to produce homogeneous objects and well defined object edges (Ivits & Koch, 2002). Segmentation groups pixels into homogeneous entities where the varying reflectance values are smoothed out. Homogeneous landcape objects reduce the “salt and pepper effect” of pixel-based methods that influences classification results. Single incorrectly classified pixels have negative effect on landscape ecological studies as well. Considering connectivity, the salt and pepper effect underlying a ‘pixel-based’ landscape analysis might lead to missleading results (Figure 2.).

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4 Requirements of the pan-European project BioAssess for connectivity

measures For a European-wide application standardisation and repeatability of the methodology is a key issue. In principle, the hierarchic structure and the shape/area information of objects in

eCognition© result in transferability and increases the comparability of classifications of different landscapes and countries. It can be assumed that certain landcover classes in different bioregions show some similarities: a forest is a collection of trees and grassland is a landscape feature with smooth texture. That makes the classification scheme of general classes on a higher hierarchy level transferable between countries. Classification on a lower hierarchy level on the other hand enables country specific assessment (Figure 3), e.g. the differentaiation of deciduous, coniferous and mixed stands. The flexible classification strategy in eCognition©, i.e. inactivation of classes that are not to be involved in the

classification process, further facilitates the transferability of the classification scheme. Hedges or linear group of trees have spatial extents that can only be represented as lines in visual interpretation. This, in some cases might exclude these objects from further landscape ecological analysis where indices can only be calculated from polygon features. Segmentation, on the other hand, is able to build polygons few pixels of a size the extent of which keeps more stable across different images. Figure 4. shows a fused Landsat ETM/IRS

Fig. 3: hierarchic classification for pan-European usage

?Figure 2.: Connectivity of forest elements from pixel-based (left) and object-based (right) classification

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satellite image with 5m ground resolution (A) and two classification results(A1, A2). One is produced using eCognition© (A1) and the other with visual interpretation (A2). A1 displays objects like groups of trees or small forest stands (red circle) fuzzy and well defined. These objects cannot be captured by a human eye (A2) in a way that could be standardised among several interpreters. Typically, landscape objects will either be interpreted in a strongly varying size among interpreters or might not even be captured. In case of aerial photos (B, B2) this problem is even more serious. Automatic methods (A1, B1), on the other hand, operate on a more sensitive level where little differences between values not visible to the human eye make the decision criteria.

To assure standardisation of visual methods across several European countries, small landscape patches under a certain limit in size cannot be included in the interpretation. Therefore, connectivity of landscapes calculated from segmentation and visual interpretation will differ in its pattern considerably. Figure 5 shows two diagrams displaying connectivity of six landscapes of the same size (LUU1-LUU6). The left diagramm shows the Connectance Index calculated from segmentation and right the index from visuel interpretation. In several cases a landscape might even be reported unconnected after visual interpretation since inside the defined distance no patch of the same type was found. On the other hand, segmentation is able to capture small landscape features e.g. “stepping stone habitats” that reports the landscape more connected as with visual interpretation.

Fig.4. Comparison of segemntation and visual interpretatin on landscape features; A1= segmentation, A2 visualinterpretation of a fused Landsat-IRS image; B1 = segmentation, B2 = visual interpretation of an

orthophoto

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Fig. 5: Connectivity of six landscapes (luu1-luu6) after segmentation (left) and visual interpretation (right) of

fused Landsat and IRS images A European wide usage requires a fast and effective methodology to process and evaluate remote sensing data. In high resolution aerial photos (here 60cm) or satellite images (e.g. Quickbird with 70 cm) the variance between the information content of single pixels increases

to an extent where pixel-based classification methods reach their limits. Thus, what may be considered as a relatively homogeneous deciduous forest on Landsat data, will be a complicated mosaic of tree elements, bare soil, small forest roads etc. on higher resolution images. This is a challenge even for object-based methods but through the use of hiererchycal methods, sub-objects and super objects assure new ways to deal with this type of complexity. When a small segmentation

scale is used (Figure 6., left), tiny landscape objects are segmented very well. However, the small segmentation scale still leads to a high number of segments and does not make classification of the objects fast and effective. A user might even end up selecting as many training areas that would correspond to visual interpretation – which is not an automatic way of image processing. Although working with a larger scale parameter reduces variance to an extent that makes classification much easier (Figure 6., right), object edges, in many cases, will not be segmented correctly. Hierarchical segmentation methods might solve this problem effectively, although their operability still need to be developed. 4 Methodological Problems The first analysis step following various data preparation tasks (data import, georeferencing, image fusion etc., see Ivits and Koch, 2002) was an image enhancement operation which turned out to be necessary for the given data sets. Regarding the orthophoto, we changed the input image in various ways using a-priori knowledge of the study area. Thus, we were forcing the segmentation algorithm “to do what we want it to do”. We used several image

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Figure 6.: Segmentation of an orthophoto on two different scales

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processing algorithm. Edge sharpen filter were used to enhance object edges with window sizes from 3 till 19 pixels (Figure 7.). The local region filter was used to smooth out variance between neighbouring pixels in an iterative way where the output of the first iteration was the input for the second iteration. The nine edge enhancement filters and the iterative local filter then were segmented with the same scale parameters to test which delivers the best result. Together with the segmentation of the original aerial photo, 11 results needed to be tested for how well they represent the real landscape objects in the image. Since the reality is based on what can be seen, manual delineation of objects on the aerial photo is a good basis to varify the segmentation.

Fig. 7: Segmentation and visual interpretation of an orthophoto; arrows show how a segmented object changes according to the segmentation input (object-primitives were merged into one obejct)

Verification of complete segmented images would involve several thousand objects, which is not operational at the moment. Therefore, a selection of objects was included in the assessment. This includes the risk that selected objects indicate very good segmentation, while none-selected image objects much worse results. Additionally, whether objects under consideration can be considered the same or similar has to be defined. Four basic properties of the objects are suggested here to solve the problem:

• their area • their shape • their direction • and their geometric position.

Only for combinations of the suggested parameters, each of them fulfilling a given threshold, we can consider the respective objects as being similar or “the same”. Object similarity is also

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a common question in change detection applications where the area of e.g. forest habitats are compared across different years. When we leave the pure statistical view (84% class 1 at time 0, 82% at time -1), we are entering a surprisingly unexplored field of object identification. Potentially, in eCognition© several object features (included those mentioned above) can be calculated and exported to a GIS for further analysis. We compared the segmented objects in the above described data sets using descriptive statistics, i.e. the mean, standard deviation and variance of the object parameters. When we assume that the delineation of objects based on visual interpretation describe the reality best, a series of segmentations can be assessed based on how close they come statistically to it. 5 Discussion There is indeed a growing demand of quantitative knowledge about connectivity at a landscape level. The primary question to be addressed more thoroughly is whether it is possible to compare connectivity indices measured on landscape data with different spatial resolutions. The first attempt to address this problem might be to analyse different patch definitions within images through multiscale segmentation. Another part of the comparability problem consists in scaling-up (aggregating) categorical landscape patterns, so that indices at coarser resolutions can be estimated by computing them on resultant aggregated data. This aggregation is usually accomplished through majority rules, but these do not fully replicate the way remote sensors actually acquire radiation from the objects on the ground. The signal that remote sensors assign to a given pixel includes besides the area corresponding on the ground to that pixel, also objects located in neighbouring pixels. Additionally, there is a need for an operalisational way of object comparison also to be used in change detection studies. In addition to methodological problems of object definition and scale aspects, the ecological interpretation of landscape metrics is a major challenge. Many researchers investigate the survival of species and populations as a function of landscape structure. Questions of the habitat loss and fragmentation, isolation and corridors, require autecological knowledge and are often species-dependent. Landscape parameters have been shown to contribute to the explanation of species presence and abundance, namely for mammals, birds, amphibians and reptiles (Mazerolle and Villard, 1999). In further steps of the BioAssess project, connectivity and other landscape indices will be tested for their relevance in the description of landscapes and landscape ecological processes. These results and other remote sensing derived indicators will then be correlated with species diversity data in the framework of the BioAssess project. References 1. Baatz, M. and Schäpe, A. 2000. Multiresolution Segmentation – an optimization approach

for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (eds.): Angewandte Geographische Informationsverarbeitung XII, Wichmann-Verlag, Heidelberg, 12-23.

2. Benson, B.J. and MacKenzie, M.D. 1995. Effects of sensor spatial resolution on landscape structure parameters. Landscape Ecology 10: 13-120.

3. Blaschke, T. and Petch, J. 1999. Landscape structure and scale: comparative studies on some landscape indices in Germany and the UK. In: Maudsley, M. and Marshall, J. (eds.), Heterogeneity in landscape ecology: pattern and scale. IALE UK, Bristol, 75-84.

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4. Blaschke, T. and Strobl, J. 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS – Zeitschrift für Geoinformationssysteme vol. 14(6): 12-17.

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6. Farina, A. 1998. Principles and methods in landscape ecology. Chapman & Hall, London. 7. Frohn, R. 1998. Remote sensing for landscape ecology: new metric indicators for

monitoring, modeling and assessment of ecosystems. CRC-Lewis Publishers. Boca Raton, USA.

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9. Griffiths, G.H., Lee, J. and Eversham, B.C. 2000. Landscape pattern and species richness; regional scale analysis from remote sensing. International Journal of Remote Sensing 21: 2685-2704.

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