**6. Conclusions**

48 Landscape Planning

moment provided larger accuracy and the most appropriate window size was 7X7 and 9X9. Multiscale texture measures should be incorporated with original spectral wavebands to improve classification accuracy (Shaban and Dikshit 2001, Podest and Saatchi 2002, Butusov 2003). Recently, the geostatistic-based texture measures were found to provide better classification accuracy than using the GLCM-based textures (Berberoglu et al. 2000). For a specific study, it is often difficult to identify a suitable texture because texture varies with the characteristics of the landscape under investigation and the image data used. Identification of suitable textures involves determination of texture measure, image band, the size of moving window, and other parameters (Chen et al. 2004). The difficulty in identifying suitable textures and the computation cost for calculating textures limit the extensive use of textures in image classification, especially in a large area (Lu and Weng

To test the texture data on classification accuracy, five different GLCM was derived such as, variance, contrast, dissimilarity, homogeneity, entropy. These measurements incorporated with Landsat spectral wavebands in Eastern Mediterranean region. Overall accuracy was unchanged, however accuracies of settlement and agricultural land classes were increased 4- 5%. However, accuracy of bareground and sand dunes decreased using DT classifier.

Vegetation metrics are another ancillary data for more accurate LUC mapping. Deriving the metrics is dependent on the spectral resolution of an optical image. A vegetation index derived from combination of image wavebands. The most used vegetation indices are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), green vegetation index (GVI) and perpendicular vegetation index (PVI) at the literature. Vegetation indices indicate health condition (NDVI) and water content (NDWI) of the vegetation canopy. There are many textbooks and papers about calculation of vegetation indices. These indices provide extra information for LUC

For instead, NDVI calculated using red and near infrared band (NIR) combination as shown

NDVI data was included to Landsat TM data to show the effect of a vegetation index on LUC mapping. Overall accuracy was unchanged significantly, but sand dunes, baregrounds,

Besides, there are some indices specifically designed for sensors. For example; Envisat MERIS data has own chlorophyll index called MERIS terrestrial chlorophyll index (MTCI). Additionally, vegetation metrics such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI) and fraction of green vegetation covering a unit area of horizontal soil (fCover) can be obtained using specific equations from MERIS data. Berberoglu et al. (2009) used this vegetation metrics to improve RT soft classification accuracy using MERIS data. When only MERIS wavebands used to determine the tree cover

NIR – RED / NIR + RED (5)

2007).

**5.3 Vegetation indices** 

classification to discriminate subtle classes.

in following equation (Rouse et al 1974);

deciduous classes were classified more accurately.

This chapter has demonstrated various issues in LUC classification including, ability of optical remotely sensed data, different classifiers, training data size and ancillary data in the example of Eastern Mediterranean region. Parametric, non-parametric hard and soft LUC mapping techniques in local scale were assessed. Main findings of this chapter are:

Selection of a classification scheme and the optical data are vital for a reliable result in LUC mapping. Remotely sensed data must be defined according to the mapping scale and study purpose. LUC classification scheme and level should be defined based on optical data ability such as spatial and spectral resolution. Image pre-processing such as, geometric registration, atmospheric correction, geometric correction and radiometric calibration are essential parts in change detection studies.

Training data size, quality and mapping details are also important to select suitable classifier for LUC mapping. MLC, LDA, and DT techniques are useful for hard classification outputs. On the other hand, to derive a continuous map like cover percentage of each LUC or probability of each LUC needs soft classifiers such as RT and LMM. Training data size and quality affect the classification accuracy and classifier selection. Although model based classifiers has potential when strong training data set was used. In this case, data dependent classifiers can be chosen for better accurate LUC map. Linear techniques are suitable if mixture degree is small in a pixel. LMM is ideal if there are enough training data and pure pixel for each LUC. However, if training pixel size and pure pixels are weak, non-linear techniques like RT or ANN are suggested.

Hard classifiers were performed inaccurately with coarse spatial resolution images (e.g. MODIS, MERIS, NOAA, SPOTveg) because of mixed pixel problem. Fuzzy classifiers are reduced this problem and provided better accuracy than hard classification. Hard pixel based mapping techniques were successful using medium spatial resolution data (e.g. Landsat TM/ETM, Aster and Alos AVNIR) in regional and local scale, however, for the specific purposes like detailed crop pattern mapping or urban pattern mapping, object based classification approach was recommended for more reliable LUC mapping. Object based classification is appropriate when using very high spatial resolution data (e.g. rapid eye, Ikonos, Aerial photos, Geoeye). In segmentation stage of object based classification, pixels were merged to create each segment or object according to spectral, structural and textural similarities. This method is tolerated the pixel misclassification if there is a pixel noise in an area (Figure 15).

In this chapter ancillary data integration were also discussed using several data from satellite remote sensing sensors. Three types of ancillary data were integrated to the DT hard classifier. DEM resulted the largest improvement in overall classification accuracy among others. Surface texture and vegetation indices were improved the accuracy of specific land cover types. When all data used together, overall classification accuracy were reduced. Additionally, more ancillary data is not important to enhance classification accuracy. Success of the ancillary data varies based on classification target, study area characteristics and remotely sensed data.

Land Use/Cover Classification Techniques

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**1. Introduction** 

**3** 

Matthias Pietsch

*Germany* 

**GIS in Landscape Planning** 

Landscape planning supports sustainable development by creating planning prerequisites that will enable future generations to live in an ecological intact environment (Bfn, 2002). It breeds to a full-coverage strategy with the aim of maintaining landscape and nature as well as facilitating municipal and industrial development (von Haaren, 2004; BfN 2002). Contrary to the design approach (McHarg, 1969) it has been developed to an institutionalized planning system based on analytical processes (Schwarz- v. Raumer & Stokman, 2011). Objectives will be derived from scientifically based analysis and normative democratically legitimized goals (Riedel & Lange, 2001; Jessel & Tobias, 2002; von Haaren, 2004 a.o.).

Existing Geographic Information Systems (GIS) offer the needed capabilities concerning the whole planning cycle (Harms et al., 1993; Blaschke, 1997; von Haaren, 2004; Pietsch & Buhmann, 1999; Lang & Blaschke, 2007; a.o.). Data capturing for inventory purpose, scientific-based analysis, defining objectives, scenarios and alternative futures and planning measures can be carried out by using GIS (Schwarz-v. Raumer, 2011; Ervin, 2010; Steinitz, 2010; Flaxman, 2010). For the implementation and sometimes necessary updates environmental information systems can be developed for specific purpose (Zölitz-Möller, 1999; Lang & Blaschke, 2007). Nowadays required models (e.g. process, evaluation, decision) can be defined and interchanged for different scopes (Schaller & Mattos, 2010). The technical evolution of hard- and software enable planners and designers to improve participation processes and decision-making using visualization and WebGIS-technologies (Warren-Kretzschmar & Tiedtke, 2005; Paar, 2006; Lange, 1994; Wissen, 2009; Bishop et al., 2010; Buhmann & Pietsch, 2008a and b; Pietsch & Spitzer, 2011; Richter, 2009; Lipp, 2007). Transforming the existing planning process to a process-oriented one with new ways of interaction technical enhancements are necessary as well as a new planning and design style (Ervin, 2011). Therefore teaching methods must be changed to a more process- and workflow oriented thinking (Steinitz, 2010; Ervin, 2011) using the advantages of the different software tools like GIS, CAD, visualization and Building Information Models (BIM) (Flaxman, 2010). GeoDesign as a "new" term had been discussed the last years. While some planners contribute that they are doing this for years (Schwarz-v. Raumer & Stokman, 2011) requirements had been defined for a more collaborative and process-oriented planning

(Francica, 2012; Ervin, 2011; Steinitz; 2010; Flaxman, 2010; Dangermond, 2009, 2010).

In this paper the different terms will be explained and the special German definition of landscape planning will be described. Based on this definition the use of GIS in the different

*Anhalt University of Applied Science* 

