Exploring long term Impervious Surface Areas (ISA) dynamics using Landsat imagery, Μachine Learning and GEE: The case of Attica, Greece

Accurate data on Impervious Surface Areas (ISA) are essential for various studies concerning urban environments, as the constant proliferation of these surfaces is a noticeable result of urbanization, especially in metropolitan cities. The present study proposes a methodology approach in performing a long-term mapping of ISA changes in Attica Prefecture, Greece, from 1984 to 2022, exploiting the Landsat archive and contemporary machine learning (ML) methods of geospatial data processing, namely Support Vector Machines (SVM) and Random Forests (RF). Using Google Earth Engine cloud platform, the SVM and RF classifiers are developed and implemented for four single dates (in years 1984, 1999, 2013 and 2022). Accuracy assessment of the classification maps was based on the computation of a series of statistical metrics based on the confusion matrix, ans the McNemar's chi-square test which was used to evaluate the statistical significance of the difference in the classification maps, derived from SVM and RF classifiers. Both SVM and RF provided very accurate results, with Overall Accuracy (OA) higher than 90% and kappa coefficient (Kappa) higher than 0.8 for all classification maps, with SVM performing better in 1984 and 2022 and RF outperforming SVM in 2013. In addition, the McNemar's test confirmed the statistical significance of the research findings reported herein. Change detection results, highlighted the wide sprawl of the urban fabric, especially in sub-urban areas, surrounding the metropolitan center of Athens. The employed methodology represents a significant advancement in the application of GEE, beyond their general use, by integrating cutting-edge ML techniques with available remote sensing data to create an automated analysis process. This innovative fusion not only enhances the precision and efficiency of ISA mapping but also establishes the basis for a pioneering standard in the field by harnessing the power of advanced technologies and accessible data sources.