**5. Ancillary data integration**

46 Landscape Planning

**Adana based** 

**city** 

**Object** 

**Adana city** 

Pixel based LDA classifier was failed in onion, sour orange, settlement, bulrush and sand dunes using March and April images. However, June and August images, distance from built up areas, distance from cost line were integrated in rule dependent object based classification and kappa coefficient was increased 28% in general. Sour orange, bulrush, sand dunes and settlement accuracy were raised impressively. One of the advantages in rule dependent classifiers was allowed to add new class during the classification. In this study, saline vegetation and natural grasslands were included to improve classification accuracy (table 12).

Wheat %100 %100 - Onion %48 %15 %33 Potato %84 %84 - Citrus %95 %23 %72 Water bodies %100 %100 - Fallow %96 %96 - Bareground %85 %89 %4 Bulrush %95 %34 %61 Forest %100 %88 %12 Sand dunes %100 %40 %60 Settlement %95 %30 %65 Natural grasslands %74 - - Saline vegetation %95 - - **Overall Kappa** %90 %62 %28 Table 12. Kappa accuracy of each LUC and difference between object and pixel based

**classification LDA Difference** 

Fig. 15. LDA pixel based and object based classification results of LSP

**LUC Object based** 

classifications

**LDA pixel b d**

Remotely sensed data may not be enough to map all LUC accurately alone. Ancillary data provide additional information on physical land dynamics, vegetation, climate, social geography and surface variability in LUC classification. When suitable ancillary dataset used, classification accuracy would be more accurate. In this chapter, only elevation (physical), texture (surface variability) and vegetation data (vegetation indices) were discussed in USP using DT and RT classifiers.
