**3.2.1 Multi-criteria evaluation (MCE)**

Analyzing landscape functions (e.g. soil erosion) different information (e.g. land use, gradient, rainfall) must be taken in consideration. Using scientific-based methods the potential, risk or existing conflicts can be calculated. Depending on the selected methodology and the available information / datasets multi-criteria evaluation (MCE) is a very powerful tool. Therefore a reduction of complex environmental factors into a cohesive spatial concept is necessary. Overlay-functions (raster or vector-based) in combination with evaluation or impact models can be used to calculate e.g. suitable areas for farming or settlement or to perform impact analyses (see Fig. 6.)

Fig. 6. Overlay (example)

#### **3.2.2 GIS-based habitat models**

In conservation biology and conservation planning there is a great diversity of GIS-based species distribution, habitat or population models (Blaschke, 1997; Blaschke, 2003; Taeger, 2010; Guisan & Zimmermann, 2000; Gontier, 2007; Gontier et al., 2010; Pietsch et al., 2007; Amler et al., 1999). Habitat suitability models based on empirical data versus models based on expert knowledge can be distinguished. On the other hand the level of detail (e.g. individuals, populations, species occurrences or species communities) is another way to describe the different models (Gontier et al. 2010).

There has been a lot of discussion about the possibilities to implement habitat suitability analysis (HSI) in environmental and landscape planning (Kleyer et al., 1999/2000; Schröder, 2000; Blaschke, 1999 and 2003; Rudner et al., 2003). They are established in environmentaland bio-science but because of the data requirements and the time- and cost-consuming modeling used only in a few planning examples (Jooß, 2003 and 2005; Pietsch et al., 2007; Gontier, 2007; Rudner et al., 2004; Schröder, 2000).

That means that different models and methods are needed to integrate science in the planning process (Blaschke, 1997; Lang & Blaschke, 2007; Schwarz-v. Raumer & Stokman,

Analyzing landscape functions (e.g. soil erosion) different information (e.g. land use, gradient, rainfall) must be taken in consideration. Using scientific-based methods the potential, risk or existing conflicts can be calculated. Depending on the selected methodology and the available information / datasets multi-criteria evaluation (MCE) is a very powerful tool. Therefore a reduction of complex environmental factors into a cohesive spatial concept is necessary. Overlay-functions (raster or vector-based) in combination with evaluation or impact models can be used to calculate e.g. suitable areas for farming or

In conservation biology and conservation planning there is a great diversity of GIS-based species distribution, habitat or population models (Blaschke, 1997; Blaschke, 2003; Taeger, 2010; Guisan & Zimmermann, 2000; Gontier, 2007; Gontier et al., 2010; Pietsch et al., 2007; Amler et al., 1999). Habitat suitability models based on empirical data versus models based on expert knowledge can be distinguished. On the other hand the level of detail (e.g. individuals, populations, species occurrences or species communities) is another way to

There has been a lot of discussion about the possibilities to implement habitat suitability analysis (HSI) in environmental and landscape planning (Kleyer et al., 1999/2000; Schröder, 2000; Blaschke, 1999 and 2003; Rudner et al., 2003). They are established in environmentaland bio-science but because of the data requirements and the time- and cost-consuming modeling used only in a few planning examples (Jooß, 2003 and 2005; Pietsch et al., 2007;

2011). Some examples will be given in the following chapters.

settlement or to perform impact analyses (see Fig. 6.)

**3.2.1 Multi-criteria evaluation (MCE)** 

Fig. 6. Overlay (example)

**3.2.2 GIS-based habitat models** 

describe the different models (Gontier et al. 2010).

Gontier, 2007; Rudner et al., 2004; Schröder, 2000).

GIS-based models based on expert knowledge normally use presence datasets of specific species. Using the knowledge about the habitat preferences it's possible to analyze the suitability. Actual land use maps or other thematic information about habitats and specific structures or qualities (e.g. hydrological situation, soils, water quality) are used to evaluate the actual situation (Blaschke, 1997; Jooß, 2003; Taeger 2010). In contrast to ecological models using statistical methods models based on expert knowledge have a great potential to be used in landscape planning (see table 1). They are not as precise as ecological models but easier to interprete and applicable in larger areas.


Table 1. Types of habitat models (adapted from Jooß, 2003)

They can be used for several species using existing species information. Based on the prediction model future conditions and different scenarios can be simulated to evaluate the impact of land use changes in the planning process (Gontier et al., 2010; Taeger, 2010; Pietsch et al., 2007; Blaschke, 1997). The visualization of future habitat suitability is possible. GIS offers the capability to create models (Fig. 7) based on existing datasets (species, land use, previous impact, structures, qualities) to analyze the actual and future suitability.

It's possible to create evaluation models, to create scenarios to improve the situation for one specific or several species, to develop measures to reduce negative impacts or create new habitats (Hunger, 2002; Hennig & Bögel, 2004) and they are useful to evaluate the negative impact of a plan, project or program in the context of an SEA or EIA (Gontier, 2007; Pietsch et al, 2007; Lang & Blaschke, 2007; Blaschke, 1999). In combination with connectivity analysis (see chapter 3.2.3) physical and functional links in ecological networks can be examined.

GIS in Landscape Planning 67

In graph-theory a graph is represented by nodes (e.g. habitats) and links (dispersal). A link connects node 1 and node 2 (see Fig. 9) (Tittmann, 2003; Urban & Keitt, 2001; Saura & Pascual-Hortal, 2007; Wolfrum, 2006). If the distance between two nodes is longer than the specific dispersal rate the link is missing, if the distance is in the dispersal range there is an

Fig. 9. Scheme of nodes and landscape graph representing habitats and connections (Pietsch

The graph-theory models can be distinguished in binary and probability models (Pascual-Hortal & Saura, 2006; Saura & Pascual –Hortal, 2007; Bunn et al., 2000; Urban & Keitt, 2001). Using binary models it's only possible to analyze if there is a link or not, while using probability models it's possible to analyze the existing situation (if there are links or not) and to evaluate each specific patch (habitat) (see Fig. 10) (Bunn et al., 2000; Urban & Keitt, 2001; Zetterberg et al., 2010). The distance between the nodes can be represented as edge-to-edge interpatch distance, as Euclidian distance or as least-cost path (Tischendorf & Fahrig, 2000; Ray et al., 2002; Adriaensen et al., 2003; Nikolakaki, 2004; Theobald, 2006, Zetterberg et al., 2010).

Fig. 10. Evaluation of specific habitats of Zootoca vivipara (example) (the bigger the more

valuable) (left); patches and connectivity zones (right)

existing link (Pietsch & Krämer, 2009; Zetterberg et al., 2010).

& Krämer, 2009)

Fig. 7. Example for a habitat suitable model (Schmidt, 2007)
