Abstract
This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic resolution aimed at these specific categories, according to their density and continuity. The classification was implemented using the Random Forests (RF) machine learning algorithm and the presented methodological framework involved a high degree of automation. The results revealed that the majority of the expansion of the built-up areas took place at the expense of agricultural land. Moreover, mapping and quantifying the LUC changes revealed three uneven phases of development, which reflect the socioeconomic circumstances of each period. The discontinuous low-density urban fabric started to increase rapidly around 2003, reaching 7% (from 2.5% in 1991), and this trend continued, reaching 12% in 2016. The continuous as well as the discontinuous dense urban fabric, almost doubled throughout the study period. Agricultural areas were dramatically reduced to almost half of what they were in 1991, while forests, scrubs, and other natural areas remained relatively stable, decreasing only by 3% in 25 years.
Highlights
Research efforts related to land use/cover (LUC) change have intensified since the mid-1970s, with the realization that processes taking place on the Earth’s surface directly and indirectly affect the climate and the environment [1]
Adopting Earth Observation (EO) techniques and relying on satellite data involves facing a trade-off between spatial scale and cost: very high resolution (VHR) imagery are expensive (e.g., Worldview, IKONOS), which acts as an obstacle to carrying out large-scale and multi-temporal approaches
This paper presented a methodological framework for the accurate detection of LUC changes that occurred in the Attica region of Greece over a 25-year period
Summary
Research efforts related to land use/cover (LUC) change have intensified since the mid-1970s, with the realization that processes taking place on the Earth’s surface directly and indirectly affect the climate and the environment [1]. Earth Observation (EO) technologies, along with Geographic Information Systems (GIS), can be combined to successfully provide spatially consistent and detailed LUC information, a prerequisite for monitoring the Earth’s surface effectively [7,8] To this end, the recent increase in the available EO data [9] can facilitate the growing demand for multi-spectral and multi-temporal information over a wide range of scales and data formats (e.g., [10,11,12]). The same applies to research efforts focusing on LUC transformations in forested areas and/or cropland To avoid these limitations, studies that aim to assess LUC changes occurring in a specific area and period of time cannot often rely on existing ‘hard’ classified datasets, such as the CORINE [25] or the GLOBELAND 30 [26]. It is fully transferable to other regions, contains a high degree of automation, and can act as a baseline for the continuous monitoring of LUC using medium-scale EO data
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