Change Detection of Urban Areas in Ankara through Google Earth Engine

Abstract

Rapid urbanization with inadequate planning can have negative impact on the cities with growing population. Maintaining sustainable urban development and planning becomes one of the major necessity for the government and policy makers. Thus, being able to monitor the changes and the enlargement of the cities provides a valuable information for those decision-making bodies. This study investigates the possibilities of identifying the changed areas with Google Earth Engine (GEE) providing a fast and easy-to-use platform with its geospatial analysis tools for applications such as change detection. In this study, C-band Synthetic Aperture Radar (SAR) images of Sentinel-1 and Multispectral Instrument (MSI) images of Sentinel-2 were utilized to identify the changed areas, such as new built-ups or soil excavation, of 21 neighborhood units of capital city of Turkey through GEE platform. The image subtraction of MSI images were integrated with image subtraction of SAR images of 2015 and 2017. The image indices such as Difference Built-up Index (NDBI), Bare Soil Index (BSI) and Soil-adjusted Vegetation Index (SAVI) were also utilized. A binary supervised classification was performed by using Random Forest classifier. Finally, a post-processing with morphological operators was conducted to reduce the effects of pixel-based classification and to achieve higher test accuracy. With the post-processing, 91% overall test accuracy and kappa value of 0.82 were achieved. The study reveals an average of 5.9% change of the total area in those selected 21 neighborhood units of Ankara and Erler neighborhood unit is the most altered with 14.5% of its total area.