SPATIOTEMPORAL LAND USE CHANGE ANALYSIS AND FUTURE URBAN GROWTH SIMULATION USING REMOTE SENSING: A CASE STUDY OF ANTALYA
- 1Akdeniz University, Faculty of Science, Department of Space Sciences and Technologies, 07058, Antalya, Turkey
- 2Akdeniz University, Faculty of Architecture, Department of Urban and Regional Planning, 07058, Antalya, Turkey
Keywords: LULC Change, Urban Expansion, Urban Growth Simulation, Cellular Automata, Artificial Neural Network method
Abstract. The objectives of this study are: to create land-use maps by 5-year interval from 1995 to 2015, to analyse the land use change and urban development, and to estimate future land-use pattern and urban growth for the years: 2030, 2045 and 2060. Antalya, which is the 5th biggest city of Turkey, was selected as study area. In this study, there are basically three stages: (i) preprocessing and preparing additional bands, (ii) spatiotemporal land use detection using image classification and (iii) land use simulation using urban growth models. Firstly, atmospheric correction was applied to the Landsat 5 TM and Landsat 8 OLI images and land-cover indices, ASTER Global Digital Elevation Model (GDEM), and Nighttime data were prepared to use them as additional bands during the classification process. Secondly, Landsat images were classified using Random Forest (RF) machine-learning algorithm. Thirdly, urban simulations were performed for the years 2005, 2010, and 2015 and land-use pattern and urban growth was estimated for the years 2030, 2045 and 2060. The RF classification accuracies range from 84.44% to 92.82%. The urban areas increased from 49.56 km2 to 96.25 km2 from 1995 to 2015. The simulation accuracies were computed above 80%. According to the 2030, 2045 and 2060 simulation results, the urban areas were computed as 133.61 km2, 148.27 km2 and 156.85 km2, respectively. As a result, it was seen that the urban area of Antalya has almost doubled between the years 1995–2015 and the urban expansion is expected to continue increasing up to 1960.