What Can Remote Sensing Provide for Biodiversity Assessment?

BioAssess a project example

Barbara Koch1 & Eva Ivits2

1,2Dept. Remote Sensing and Landscape Information Systems University of Freiburg, 79085 Freiburg, Tennenbacherstr. 4, Germany

Phone: ++49 761 203 3694 Fax: ++49 761 203 3701 e-mail: barbara.koch@felis.uni-freiburg.de

e-mail: jochen.krueger@felis.uni-freiburg.de

Presented at ForestSAT Symposium Heriot Watt University, Edinburgh,

August 5th-9th of August 2002

ABSTRACT

The article starts with a definition of biodiversity. An exemplary illustration of the present discussion about the term biodiversity is highlighted. Ecodiversity is understood as a sum of criteria for bio- and geodiversity. Possible cirteria are discussed assessment of ecodiversity are discussed. According to the definition of the Convention on Biodiversity ecodiversity is the term which covers the diversity of species and the diversity of landscapes. Only if the diversity of landscapes is integrated then the requested holistic approach of diversity is possible. A discussion of the selection of landscape features and their extraction from remote sensing data for biodiversity assessment is started. It shows the problem of scale dependency and highlights different approaches to select the appropriate scale for landscape feature assessment. Based on the project BioAssess an example of a pragmatic way to extract objects from a very high resolution panchromatic image and the Landsat ETM image is demonstrated. For this approach improved filter techniques and a segmentation based method is presented. The results of an object classification after segmentation are discussed as basis for diversity indices. Future research gaps and the final objectives of the BioAssess project are presented.

Keywords and phrases: biodiversity monitoring, remote sensing, filter techniques segmentation based classification, e-cognition, scale space theory

1.0 INTRODUCTION

The assessment and monitoring of biodiversity is a topic of increasing interest throughout the world, as loss in biodiversity has been identified as a major global environmental problem. The EU and all its Member States are parties to the Convention on Biological Diversity adopted at the Rio de Janeiro ‘Earth Summit’ in June 1992.

Since then a need to monitor changes in biodiversity has been especially recognised and is explicitly included in, amongst other places, the Convention on Biological Diversity (CBD) as well as the EU Biodiversity Strategy. The CBD requires development of indicators to monitor the status and trends of biological diversity. However, a monitoring programme to assess changes in all components of biodiversity is clearly impossible because of the high number of species present in any place and the number of experts that would be involved. Thus scientists are increasingly expressing the need for indicators of biodiversity and other methods of ‘rapid biodiversity

assessment to assess the overall biodiversity (OLIVER AND BEATTIE, 1993). An ideal indicator will accurately reflect changes in most components of biodiversity. There is an urgent need for indicators that reflect those components of biodiversity that are particularly threatened or valued. An ideal indicator of biodiversity should also be one that provides an early warning of changes in biodiversity, particularly in relation to possible threats to biodiversity. Some scientists have suggested that single groups of plants and animals may serve as indicators for overall biodiversity. However surveys (PRENDERGAST ET AL., 1993) show highly variable responses of different groups to a disturbance. Therefore, reliance on a single indicator would give a poor measure of overall biodiversity. To overcome this problem, authors have proposed ‘predictor sets’ of taxa (STORK 1995).

All discussion on biodiversity monitoring is still mainly linked to species diversity. Nevertheless biodiversity is much more than species diversity and from a landscape ecology point of view landscape structures itself are a component of biodiversity (JEDICKE 2001). This is also supported by the Convention on Biological Diversity which explicitly includes the diversity of ecosystems. According to JEDICKE (2001) the assessment of ecodiversity (landscape diversity) would be a more adequate approach to describe the biological diversity because ecodiverstiy comprises biodiversity and geodiversity. Many investigations proofed that geodiversity and landcover or landuse pattern have a close link to species diversity and abundance (JEDICKE 2001). In addition NAVEH et al (1998) believes that only through ecodiversity (landscape diversity) the close interrelation between biological diversity, ecological heterogenity and cultural diversity can be expressed. Especially the inclusion of human influence on biodiversity is an advantage of assessing ecodiversity because it is doubtless that long time interactions between man and nature influenced biological diversity decisively. LESER & NAGEL (1998) highlight that diversity of landscapes reflect effects of interactions between abiotic and biotic systems on one hand and between human communities and techniques on the other hand. This ecosystem oriented approach also might become a key to open the species focused discussion towards an integrated strategy of (bio)diversity assessment and monitoring in order to serve the requested holistic nature protection approach including abiotic resource as well as process protection.

2.0 QUANTIFICATION OF ECODIVERSITY

If ecodiversity is an appropriate approach to assess biological diversity as requested by the Convention on Biological Diversity then of course it has to be asked how to measure ecodiversity? Ecosystems are complex systems which are organized hierarchically (ALLAN &STARR 1982 in HAY ET AL 2001). The hierarchy theory predicts that complex ecological systems are composed of interrelated subsystems until the lowest level is reached (HAY ET AL 2001.), nevertheless each scale reflects different ecological objects or different modulations of the same object and in each scale level the same object might have a different ecological functionality. As described in INNES & KOCH (1998) this necessitates an assessment of biodiversity or ecodiversity at different scales as well as the possiblity to cross these scales. In reference to JEDICKE (2001) 5 levels of scale are suggested for ecodiversity monitoring the geosperical level which is on a global scale the region level like bioregions landscape level habitat level and micro habitat level JEDICKE (2001) indicates that a comparison of ecodiversity only within the same scale level is allowable. Even so it is accepted that ecosystems have to be measured in different scale levels the answer what is the appropriate scale level can only be provided if it is clear what informations are needed to assess the overall biological diversity. Due to the complexity of ecological systems the definition what information is needed and how to measure them is an unsolved problem in eco-science. Nevertheless it is clear that measurements have to be reduced to a set of feasible indicators. The integration levels for the overall biodiversity indicators are in general divided into type diversity (species), spatial diversity, diversity in time and diversity in functions. Until today species diversity is considered to provide the basic information for assessment and evaluation of biodiversity, even so it is only one part of biodiversity. Independent from the fact that the selection of indicator species is still under discussion, the assessment of species is problematic. This is due to the variability over time, the time intensive observations and the sampling character of data collecting. On the other hand some investigations show that there is a significant correlation between faunistic species and structural diversity of vegetation (Fig. 1).

Fig. 1: Structural diversity of the forest stands and species diversity of avifauna (Jedicke 2001)

If species diversity and species abundance is related to the spatial structure of vegetation complexes then the assessment of vegetation complexes and their spatial distribution seems to be a useful indicator for the assessment of biodiversity. Nevertheless it was highlighted that species indicator or the assessment of vegetation complexes is not enough to describe ecodiversity. The structure of habitats, land cover types and land use forms is basic information to describe ecodiversity. In order to develop a feasible method to assess the criteria of ecodiversity including vegetation complexes a set of methods is needed which allows the exploitation of data on landscape level. Remote sensing is one data type which can substantially provide information on landscape level therefore for ecodiversity assessment. Remote sensing data can provide horizontal, vertical, multispectral and multitemporal information on different scales. Thus remote sensing has the potential to provide a range of different data for ecodiversity studies and its use should be enforced.

Discussing the benefits of remote sensing as a tool for ecodiversity assessment this article will not focus on the different quality of data types because this was extensively discussed in the article INNES & KOCH (1998), but rather highlight the question how remote sensing data can be exploited for ecodiversity assessment. The exploitation of remote sensing data and the linked questions will be discussed on the basis of a research project called BioAssess which has its focus on the assessment and monitoring of biodiversity based on different indicator species integrating remote sensing data.

3.0 BIOASSESS – BIODIVERSITY MONITORING BASED ON LINKED INFORMATION FROM REMOTE SENSING DATA AND SPECIES INDICATORS

In the frame of a European project Biodiversity Assessment Tools (BioAssess) under the Global Change, Climate and Biodiversity Key Action of the Energy Environment and Sustainable Development Programme, a method for biodiversity monitoring will be developed for the fast assessment of the ecosystem diversity status on a European level. Test sites in different biogeographical regions have been selected (Finland, Ireland, UK, Hungary, Switzerland, France, Spain and Portugal) (Fig. 2). Each European test site consists of six spatially related test areas called landuse units (LUU) representing a gradient of different land cover diversity and anthropogenic influence.

Fig. 2:Overview of BioAssess partner and partner countries

The landuse units (LUU) in all European partner countries have each a size of 1x1 km and cover the same gradient from relatively natural forests to intensively managed agricultural areas (Tab. 1).

LSU Criteria (% cover) 1 Old-growth forest Old-growth forest >50%

Other forests-woodland-shrubland >10% Other land-uses?

2 Managed forest Managed forest >50% Other forests-woodland-shrubland >10% Other land-uses?

3 Mixed-use dominated by forest or woodland

Forest-woodland-shrubland >50% Grassland >10% Crops >10%

4 Mixed-use not dominated by a single land-use

Forest-woodland-shrubland >25% Grassland >25% Crops >25%

5 Mixed-use dominated by pasture Grassland >50% Crops >10% Forest-woodland-shrubland >10%

6 Mixed-use dominated by arable crops

Crops >50% Grassland >10% Forest-woodland-shrubland >10%

Tab. 1: Description of the six landuse units (LUU)

In all LUU biologists sample groups of plants and animals (birds, butterflies, soil macrofauna, collembola, carabids, plants, lichens) as indicator species for biodiversity . Parallel remote sensing images have been acquired covering all selected areas. Within remote sensing a methodology for the assessment of the landscapes and landscape structures is developed as well as diversity indices are calculated and tested in respect to their qualification for ecodiversity assessment. Finally the indices based on the indicator species and the indices derived from remote sensing will be correlated to bridge the missing linkage between remote sensing and ground based methods. This will be the first time that biodiversity indicators from ground based methods and remote sensing will be compared on a European approach for a variety of indicator species.

3.1. Selection of Scale

As mentioned before ecodiversity monitoring is scale dependent. The selection of the aformentioned indicator species for biodiversity monitoring requests an assessment in different scale levels. In order to integrate the different scale levels one value is needed for the respective reference units (LUU). In order to come up with one value for each LUU a sampling grid was defined with 16 samples in each LUU unit for the assessment of the plant and animal species (Fig. 3). Based on the data take at the 16 sampling points for each LUU species biodiversity indicators will be calculated.

Fig. 3: One landuse unit (LUU) with the gird of 16 sampling plots.

To find the appropriate remote sensing scale to link the terrestrial data with the remote sensing data it is necessary to know which landscape features are relevant for the selected indicator species respectively which landscape features are in general relevant for overall biodiversity studies. Taking into account the discussion above one approach might be the mapping of habitats (JEDICKE 2001). In BioAssess the habitat structures of the indicator species are well defined by the specialists but taking into account the selected species it becomes clear that remote sensing is not the right tool to describe directly the habitat quality for some of the selected species. So what can remote sensing provide? Remote sensing can provide information on land cover types, landscape diversity and landscape structures and their changes over time. The assessment of land cover pattern and structure is linked with ecosystem functions and habitat quality. As TISCHENDORF (1995) indicates on species and populations level the information on land cover and landscape structure is most relevant to have an approach to the steering biodiversity parameters like habitat quality, isolation, fragmentation and connectivity. On the level of society the land cover pattern and landscape structures give information on the intensity of interaction by mankind. Even so it is clear that information on landscape level is closely linked to ecodiversity which is related to biodiversity the question how detailed land cover features have to be assessed is still open. There are no binding lists or recommendations but has to be defined in each case separately. Only if a set of indicator species is defined as a standard set for overall biodiversity monitoring and the interaction of landscape features with the indicator species are investigated then the appropriate answer might be possible.

Ideally a method for scale definition would relate on the image inherent objects structures without any a priori knowledge. One method to find the optimal scale for landscape feature is the scale-space theory approach. Based on the mathematical description by LINDEBERG (1993 & 1999) HAY ET AL. (2001) is presenting the theory, method and utility to explore scale dependent complex landscapes based on the scale-space theory. Scale-space theory is an uncommitted framework for early visual operations that has been developed by the computer vision community to automatically analyse the scale of an object and where to search for it. In order to use the method high resolution data are requested for the start. Based on this the scale is iterated from fine to coarse scale by application of the Gaussian filter. All images produced by the Gaussian filter from one data set are put into a layer stack (Fig. 4). The second component of the multiscale analysis is then the feature detection. Four techniques can be applied for feature detection the edge, ridge, corner or blob detection. HAY ET AL. (2001) describes the blob detection as a method for detection and linking of dominant image objects through scale. Blobs are features which can be 3-D separated in space (x,y) and in grey level intensity (z) over different scales. Complex computer operations are needed to finally derive the blob features through scales but at the end it will indicate the areas on the image where dominate object structures are inherent in the image (Fig. 5 a,b,c). Even so this seems to be a promising approach for feature extraction through space the request of high resolution data, the intense computing operations with an enormous amount of data and as a result the pure object structure information, still not indicating the nature of the object, are weak points of the method which restrict its practicability for today.

Fig 4: Layer stack after use of Gaussian Filter (BLASCHKE & HAY 2001)

Fig. 5a: Hyper blob stack Fig. 5b:Idealized hyper blobs Fig. 5c: Ranked blobs overlaid with the image

(BLASCHKE &HAY 2001)

Until today for practical solutions first the landscape features, which should be extracted, are defined, like forest, forest types, hedges, meadows a.s.o. and second the data set which is assumed to provide this information is selected. This approach was also used for the BioAssess project. Based on the habitat requirements from the indicator species a list of related landscape features has been worked out. This list was then adjusted to the CORINE land cover classes to be compatible with the European land use classification system. In a next step the image data sets have been selected. This is the approach most biodiversity projects integrating remote sensing data use for today. Nevertheless this is still a ideal situation because in most cases data selection is restricted by data availability and costs. Therefore the selection of remote sensing data sets is in most cases a compromise between feasibility in respect of costs and size of landscape features which are considered important. Nevertheless for the practical approach the most critical point is the existing gap of knowledge how the different landscape features are related to the diversity of indicator species.

For the investigations in the BioAssess project there are two different approaches an pragmatic one which will use Landsat ETM and IRS 1C pan data for all test sites and a research oriented one with image data of different scales from very high resolution to Landsat ETM resolution in a selected super test site in Switzerland. For the latter the interdependence between different remote sensing scales, remote sensing derived diversity indices and biodiversity indices from indicator species will be tested.

3.2. Optimisation of image layers for information extraction

Before images can be used several steps of preprocessing and processing have to be provided in order to extract the wanted information. The steps for preprocessing are well known and will not be discussed here. Having in

mind that the BioAssess project is looking for a standardised method to apply it with small adaptations to different data sets over test sites in different bioregions the method has to be transparent and flexible. Only with a standardised data set remote sensing based biodiversity monitoring will be possible. In respect to a stable biodiversity monitoring for Landsat ETM and IRS 1C-pan data a segmentation based approach was tested. In addition the object based assessment gives better reference to terrestrial mapping or photo interpretation, the usual form information is provided for diversity indices calculation. As in many cases the data scale of Landsat ETM and IRS 1C pan seem not really appropriate for diversity monitoring in a 1x1 km land use unit but due to financial restrictions the affordable given data had to be used. In order to extract the most possible inherent information from the two provided data sets two forms of processing are needed first the correction of image distortions, as inherent in IRS images, and second the establishment of optimal information layers for segmentation and classification.

For the IRS 1C pan restoration several filters have been tested to eliminate the stripes in the image. The modified sigma filter provided best results. The sigma filter is widely used in radar remote sensing to eliminate noise in SAR images by averaging pixels within two sigma standard deviation from the value of the central pixel.

To achieve in a next step an optimisation of information extraction from the two existing data sets it is best to have the information of both data sets in one that means to combine the rather high spatial resolution with rather high spectral resolution. Data fusion techniques are appropriate to integrate two data sets. There are different fusion methods available. POHL (1996) gives an extended overview on different procedures. Most of the fusion algorithms lead to alterations of the multispectral grey values by the panchromatic data set. For classification it is most desirable to keep the original values of the multispectral data set. Therefore the adaptive image fusion filter (AIF) (STEINNOCHER 1999) was introduced for image fusion. The AIF uses the above-mentioned modified sigma filter for the fusion of the panchromatic (IRS) and the multispectral (Landsat) images. In this method, a local moving window averages those pixels in the higher resolution panchromatic image, which are located within two-sigma distance from the central pixel. Edges are conserved because they do not belong to the two sigma distance. The detected edges of the panchromatic image form the objects. These selected objects are then filled with the values of the averaged multispectral band values. An important advantage of the AIF method is, that no spectral information is transferred from the panchromatic image into the multispectral values (STEINNOCHER, 1999) thus the original radiometry is kept. The use of such a filter sharpens the edges of objects inherent in the high resolution image, while the multispectral grey values within each single object are smoothed (Fig 6) This approach provides the extraction of landscape features within high resolution images without a priori knowledge, but the output is always restricted to the input scale level. For the object identification the pure multispectral values can be used. The sharpening of edges is an important pre-condition for a clear segmentation of objects with decreased distortion of pixel based edge effects.

Fig. 6:AIF filtered image (left) and orignal image (right) (FRITZ1999)

The major limitation of AIF is the loss of texture information within the objects, which hinders the segmentation of fine details. The local variation of grey values in the higher resolution image unfortunately cannot be reconstructed without distorting the spectral characteristics of the multispectral band. Therefore, an additional processing step was needed to re-import the structural information from the IRS data into the AIF filtered fused image. FRITZ (1999) reported good results with the intensity, hue, and saturation (IHS) transformation of the AIF and the high-resolution image. When compared to other methods, the “blockiness” from the Landsat 30m resolution pixels completely disappeared from the AIF-IHS fused results. This is very important for the segmentation of objects, which form the basis for landscape indices calculations. During the IHS transformation, the intensity channel has to be replaced with the panchromatic, high-resolution band, from the IRS band in the respective case (Fig. 7). The AIF-IHS transformation is also especially helpful for the visualisation of the data often the basis for land cover interpretation maps.

Figure 7. AIF-sigma-IHS transform (IVITS &KOCH 2002)

3.3. Segmentation of landscape features

Only recently the segmentation of objects became a practicable tool for image classification. The advantage for segmentation based approaches are manifold. One major advantage is the extraction of homogenous objects which corresponds to our perception of environment. This is also true for biodiversity studies on landscape level which is based on the delineation of basic units like habitats or ecotops. For biodiversity studies landscapes are divided into geometrical units with sharp borderlines. Even so it is well known that sharp borderlines and homogenous units seldom reflect reality it seems to be the only feasible way to come up with a practical approach for landscape diversity characterisation. The problem using segmentation based classifications is the definition of thresholds which influences strongly the delineation of objects. The set of thresholds is based on empirical approaches and requests a thorough a priori knowledge on the kind of units which are of interest. There are different segmentation algorithms available nevertheless all of them request the interaction with the elaborator. Of course this is true for pixelbased classification algorithms too, even for unsupervised classifications a tuning of the statistics is possible. The problem with interactions by the elaborator is the problem of standardisation. But having in mind that this influence is inherent to most processes, like terrestrial mapping, sampling of indicator species (location of samples, sampling time, arrangement of sampling a.s.o.) then the factor of individual influence of the elaborator to some extent has to be accepted.

The e-cognition software used for segmentation in the BioAssess project is based on a region growing algorithm. To reduce the influence of the elaborator as much as possible the scale factor which is the definition of a threshold was selected in a way that even fine greyvalue differences were taken into consideration, leading to small but high number of objects. Iteratively this process was refined checking if the segments follow visible border lines and changes in the multispectral dimension. The segmentation was applied to the AIF-sigma-IHS images. Based on these fused images the segments provided good visual appearance, the structural information contained in the IRS image is almost perfectly kept, and even the grey-level differences within objects are carried through. Accordingly, the segmentation resulted in a very good delineation of object edges and multispectral inner structures. In the AIF-sigma-IHS image, based on sigma-filtered IRS, even the stripes in the IRS 1C pan image were reduced to such an extent that the shape of the segmented objects was not influenced anymore (Fig. 8). With other methods, like Principal Component Analysis, Brovey-, and IHS-fusions the blockiness from Landsat ETM data could not be avoided in the fused images which delivered bad segments.

Fig. 8: Segmentation results based on different image processing levels (IVITS & KOCH 2002)

3.4. Classification of landscape features

For classification the definition of classes is most important. The definition of classes often depends on the expectation of the user, what information they finally need. The expected classes should drive the scale and data type used to extract the information, nevertheless often restrictions due to availability of data drive the selection of data type. Even so it is not possible to get out more from a data set then is in a data set there are still differences possible in extraction of information. For the BioAssess project as for most projects in biodiversity studies the classification system is user driven. Features on landscape level were defined which are assumed to have a relation to the diversity of the indicator species. In BioAssess specialists for the species indicators defined relevant landscape features. These have been revised according to their feasibility to assess them with the given data set and have then been adjusted to the CORINE classification system. A hierarchical segmentation based classification scheme was used, classifying the segments in two levels. On the first level coarse classes have been selected which most possibly can be directly transferred to all test sites in the eight countries (project level). The classes are: urban, rock, bare-soil, gravel, asphalt, shadow, forest, and other vegetation areas. On the second level (country level), forest and other vegetation areas were further classified to follow country specific characteristics. Based on the classification mask of the first level, forest areas were further divided into deciduous, evergreen, mixed, open forest classes and small forest habitats. Other vegetation classes were grouped into grassland and agricultural areas (Fig. 9). Segments smaller then 1ha and not surrounded by other forest objects were classified in the category small forest habitats.

Fig. 9: Classification hierarchy and input image for classification (IVITS &KOCH 2002) The object based classification was carried out with the eCognition software. In BioAssess the nearest neighbour classifiers (NN) have been used. On a higher hierarchy level, classes were separated in a Standard Nearest

Neighbour (Std. NN) feature space and on the lower level the NN feature space was adjusted to the classification of vegetated areas. Different image layers were produced as optimal input for the classification.

Optimisation task is often driven by the low input information and high quality output problem. Biodiversity studies often require the assessment of many small habitats from remote sensing images, in order to correlate the information with the field data. For this purpose not only the multispectral but also the structural information (texture) from the higher resolution panchromatic band might be useful for the classification. The most often used method to measure texture is the so-called grey level co-occurrence-matrix (GLCM) (MUSIC & GROVER, 1990, HARALICK ET AL., 1973). Therefore GLCM texture images were also included into the layer stack for the classification.

4.0 RESULTS AND CONCLUSIONS

The accuracy assessment in BioAssess proved that the segmentation based classification (Fractal Net Evolution Approach FNEA) commercially introduced by BAATZ & SCHÄPE (1999) provides a good basis for further calculation of diversity indices. The object-oriented half-automatic classification proved to be very suitable for different landscape types like in Hungary and Switzerland. Accuracy was in both countries over 95%. On this scale the smallest forest object correctly recognized was 0.022ha (225m2). In order to get from rather coarse data like it was provided for the BioAssess project relative fine structural information a fusion of high resolution panchromatic images with images of high spectral resolution is needed. In addition for the classification all information has to be used inherent in the images the structural information from the high resolution panchromatic image in form of texture image layers as well as the multispectral information. For the multispectral information as input for the classification the AIF filtered images and their derivatives (NDVI a.s.o) seem to be more appropriate for object classification because the filtered image follow the segments delineated from the panchromatic high resolution image. With the help of the texture and slope images, non-vegetated features were correctly identified. Homogeneity was very useful when separating homogeneous from heterogeneous non-vegetated areas like asphalt, bare soil, and gravel surfaces from urban areas. Between homogeneous non-vegetated surfaces, the Standard Deviation and the Dissimilarity measures delivered good results.

The identification of structural information by creating classified objects serves as important basis for landscape ecological analysis. The FNEA and its fuzzy logic method proved to be very useful when a flexible but stable classification system is needed to serve different landscapes which is a pre-condition for a biodiversity monitoring system. Nevertheless the objects once defined are homogeneous. This might be a loss of information for diversity. Therefore a combination of object based classification with pixel based classification might improve the basis for diversity studies. In Fig. 10 the pixel based classification shows slightly different structures than the object based classification. One approach might be to have a object based classification and within the objects, at least when a minimum object size is given a pixel based classification. This might be useful to refine the results. As a consequence diversity indices can be calculated on the basis of the objects to give information on the landscape unit level and second within objects on pixel basis to give further information on the diversity character of the object.

Fig. 10: Object based classification and pixel based classification of the same landscape unit

5.0 FUTURE RESEARCH

The use of remote sensing data for ecodiversity studies respectively biodiversity studies is just at the beginning. Until today most biodiversity studies are concentrated on species indicator assessment studies which restricts the studies to terrestrial sample studies. The discussion in the past indicate that the landscape diversity is one important indicator for biodiversity monitoring taking into account that diversity of species is nested in diversity of landscapes. Terrestrial sampling cannot provide the holistic information on a landscape level. This points towards the need to integrate remote sensing methods in biodiversity studies. Nevertheless the methods to use remote sensing data in a way to serve the information needed for biodiversity studies asks for more investigations in order to fill the gaps of knowledge in respect of scale dependent feature extraction, automatisation of feature extraction and on the interrelation between indicator species diversity and diversity of landscape features. In addition a number of well known landscape diversity indices has to be tested to provide information on the influence of scale and data processing on them. BioAssess will answer some questions especially the stability of indices over scale will be tested based on a stack of data sets with different resolution. Also the interdependence of the diversity of species and landscape features will be a major objective of investigation.

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