**6.2. Fuzzy standardization and factor weighting**

**6. Results**

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**6.1. Urban change detection by RS**

In this study, urban change in time has the key role for standardization and weighting of the factors. Therefore, RS science is into play in this point to create LUC layouts and define urban

Landsat imageries taken on August 2002 and 2015 were classified using object-based classifi‐ cation approach. A post-classification change detection technique was applied and atmos‐ pheric or radiometric correction is not necessary in this technique. Also, Landsat imageries have already been corrected geometrically. Segmentation parameters were modified after a small experimental application. Seven LUCs were classified, except Van lake area: agriculture,

August 2002 and 2015 overall *κ* classification coefficiencies were obtained as 0.89 and 0.92, respectively. The urban built-up area was 2066 ha in 2002 and increased to 7694 ha in 2015. Then, 3841 ha agriculture, 1265 ha grassland, 328 ha bareground, 89 ha woodland, and 18 ha bulrush areas were transformed to the urban. Particularly, after the 2011 earthquakes, urban growth system was changed and new residential areas were established on the far regions

change. Then, a GIS-based MCE process is applied according to the changed areas.

grassland, bareground, settlement, bulrush, woodland, and inland water.

from the city center. This was affected urban built-up area change fast (**Figure 5**).

**Figure 5.** LUC of Van central city area (2015–2002) and changes in urban built-up areas.

Factors were reclassified based on natural breaks in ideal intervals. LUC and LUA data sets were already categorical. However, other inputs were continuous data format. Elevation data were reclassified into eight categories with 100 m intervals. Slope was divided into five categories with 15° intervals as very low (flat), low, medium, high, and very high. Hillshade was reclassified into five categories based on sun effect as very high, high, medium, low, and very low. Distance from roads and distance from urban built-up areas were classified as 10 intervals for each 1000 m. Urban area change was recorded as 5628 ha and all changed areas were separated according to urban change areal diversity to each category for each factor (**Table 2**).


\*(1) Refers lowest value range of the factor.

\*\*(1) Refers the highest value of the factor.

\*\*\*(1) Agriculture, (2) grassland, (3) bareground, (4) woodland, and (5) bulrush.

**Table 2.** Areal diversity of urban change areas to each category inside the factors.

All factors must be evaluated alone based on the suitability degree for fuzzy standardization. In this frame, following fuzzy standardization functions were applied to the factors and all inputs were standardized between 0 and 1 (**Table 3**; **Figure 6**).



**Table 3.** Fuzzy standardization rules of the factor for urban built-up suitability.

**Figure 6.** Standardized factors by fuzzy approach for urban sprawl suitability.

Weighting the factors was performed according to the SD of urban change in each factor. Areal diversity of urban change was already obtained for each category of factors. SDs were used to see the heterogeneity of urban change diversity in the factors. The SD values and weights are shown in **Table 4**.


**Table 4.** Weights of factors.

**Categories Technique Function Explanation**

**Table 3.** Fuzzy standardization rules of the factor for urban built-up suitability.

**Figure 6.** Standardized factors by fuzzy approach for urban sprawl suitability.

Almost linear If an area far from the road network, suitability of urban

Almost linear If an area far from the road network, suitability of urban

sprawl is decreased

sprawl is decreased

LUA Optimal values User defined Suitability degree of each category was defined based on

urban growth distribution from past to current LUC

Monotonically decreasing

Monotonically decreasing

Distance from

218 Sustainable Urbanization

Distance from built-up areas

road

As a result of the weights, slope and hillshade were the most significant factors for urban sprawl. The lowest effect was recorded in LUA because the urbanization aspects from past to current were not the care of LUA for urban sprawl. However, these results showed that urban sprawl in time was not sustainable if this situation continued, because city growth on the agricultural areas that are limited in the region and there is no too much option for income, except agriculture and tourism in the region.

#### **6.3. Scenario applications**

Scenarios are described in Section 5.3.2. In the application stage, there are several approaches to apply a scenario. Some of these approaches were applied modifying the weights of each factor for each scenario. However, in this study, all factor weights have been already obtained based on urban change, and physical variables, such as hillshade, elevation, slope, and distance factors, are not changed according to the our scenarios. Therefore, the weights of these data were used the same in all scenarios. However, LUC and LUA suitability can be changed according to the economic, ecological, and sustainable scenarios.

In the economic scenario, the fuzzy standardization of the LUC and LUA was defined without any limitation. For example, agricultural areas were transformed to urban areas in 13 years dominantly. In the economic scenario, this situation was continued; in the ecological scenario, some of the agricultural areas were protected, which were located in the first, second, and third zones of the LUA. Also, in the ecological scenario, wetland, coastal line, and nearby and natural grassland usage was limited for urban sprawl.

In the sustainable scenario, only the first zone of the LUA was protected because, if first three zones were protected, there would not be enough urban sprawl area in the future and this situation would not be sustainable.
