Determination of Urban Areas Using Google Earth Engine and Spectral Indices; Esenyurt Case Study

Abstract

Identifying impervious surfaces for monitoring urban expansion is important for the sustainable management of land resources and the protection of the environment. Remote sensing provides an important data source for urban land use/land cover mapping, and these data can be analyzed with various techniques for different purposes. If the aim is to extract information easily and rapidly, using spectral indices is the most appropriate solution, and there are many indices created for this purpose. The study carried out on the Google Earth Engine (GEE) platform, Esenyurt, the most populous district of Istanbul, was investigated using Sentinel 2 MSI image, with eight urban spectral indices and three vegetation indices. In addition, classification was made, and the results were evaluated. As a result of the urban index applications, it has been seen that the roofs are more or less mixed with the bare soil areas, and Normalized Difference Tillage Index (NDTI)gives the best results. Accuracy assessment is performed for index results and classification using the same points, and due to the urban area density in the application area, it is determined as 0.95% and 0.95% for NDTI and Normalized Difference Vegetation Index (NDVI), and 97% for classification, respectively. In GEE, a high (-0.79) negative correlation is observed between May mean values and 2007-2022 population data when the NDVI time series was applied to the entire area within the district borders using Landsat 5 and Landsat 8 images between 1990-2022. The rapidly increasing population in the district leads to rapid urbanization, and green areas are disappearing at the same rate.

Utilization of Landsat Imagery and Google Earth Engine to Analyze the Urban Growth Dynamics in Thuan An City, Binh Duong Province

“…[2] . The utilization of Google Earth Engine (GEE) has seen a growing trend in environmental and urban studies, primarily because of its cloud-based geospatial processing capabilities and the availability of a vast collection of geospatial datasets such as Landsat, MODIS, Sentinel, and various free satellite data [7][8][9][10][11]. Therefore, this study was conducted to monitor the urban development process and evaluate the changes during the period from 2012 to 2022 in Thuan An city.…”

Google Earth Engine Based Spatio-Temporal Changes of Bafa Lake from 1984 to 2022

“…al., 2021;Khanal et. al., 2019;Kaya and Dervisoglu, 2023), climate change and air pollution (Ghasempour et. al., 2021;Bahsi et.…”