District-based urban expansion monitoring using multitemporal satellite data: application in two mega cities.

Urban expansion is a process of urban development as a result of population growth. Urban sprawl, known as unplanned and unrestricted urban expansion, is among the most important topics in urban studies. In recent decades, many cities around the world in both developing and developed countries have experienced urban expansion. Istanbul and Sydney are two of those cities encountering the urban expansion. Thus, in this study, the spatial and temporal pattern of urban expansion of the most urbanized districts of Istanbul (Arnavutköy) and Sydney (Hills Shire) was analyzed using multi-temporal remote sensing data. Initially, the Landsat images were classified to evaluate the land use/land cover (LULC) changes. The change detection analysis revealed that urban area of Arnavutköy district has increased about 669% from 1997 to 2017 and urban area of Hills Shire Local Government Area (LGA) increased by 78% between 1996 and 2018. The relationship of land surface temperature (LST) and urban areas extracted by recoding the LULC maps was also evaluated in different buffer zones. The results showed that with the increase in urban area extent, the LST has also increased. Then, Shannon's entropy and spatial landscape metrics were used to analyze the district-based urban expansion. The results showed that both study areas expanded over the time but the main differences observed are that Arnavutköy has more fragmented and Hills Shire has a more compact urban growth process.

Urban change analysis and future growth of Istanbul.

This study is aimed at analyzing urban change within Istanbul and assessing the city's future growth potential using appropriate approach modeling for the year 2040. Urban growth is a major driving force of land-use change, and spatial and temporal components of urbanization can be identified through accurate spatial modeling. In this context, widely used urban modeling approaches, such as the Markov chain and logistic regression based on cellular automata (CA), were used to simulate urban growth within Istanbul. The distance from each pixel to the urban and road classes, elevation, and slope, together with municipality and land use maps (as an excluded layer), were identified as factors. Calibration data were obtained from remotely sensed data recorded in 1972, 1986, and 2013. Validation was performed by overlaying the simulated and actual 2013 urban maps, and a kappa index of agreement was derived. The results indicate that urban expansion will influence mainly forest areas during the time period of 2013-2040. The urban expansion was predicted as 429 and 327 km(2) with the Markov chain and logistic regression models, respectively.