Comparative Assessment of the Built-Up Area Expansion Based on Corine Land Cover and Landsat Datasets: A Case Study of a Post-Socialist City

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (13), p.2137
• Main Authors: Stoica, Ilinca-Valentina, Vîrghileanu, Marina, Zamfir, Daniela, Mihai, Bogdan-Andrei, Săvulescu, Ionuț
• Format: Article
• Language: English
• Subjects: datasets assessment; datasets limitations; multi-scale approach; post socialist dynamics; remote sensing; territorial planning
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12132137

Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets, respectively, Corine Land Cover and Landsat, to each other, as well as the ground truth, with the goal of identifying the most cost-effective and reliable tool. The analysis was based on the largest post-socialist city in the European Union, the capital of Romania, Bucharest, and its neighboring Ilfov County, from 1990 to 2018. This study generally represents a new approach to measuring the process of urban expansion, offering insights about the strengths and limitations of the two datasets through a multi-level territorial perspective. The results point out discrepancies between the datasets, both at the macro-scale level and at the administrative unit’s level. On the macro-scale level, despite the noticeable differences, the two datasets revealed the spatiotemporal magnitude of the expansion of the built-up area and can be a useful tool for supporting the decision-making process. On the smaller territorial scale, detailed comparative analyses through five case-studies were conducted, indicating that, if used alone, limitations on the information that can be derived from the datasets would lead to inaccuracies, thus significantly limiting their potential to be used in the development of enforceable regulation in urban planning.