Water resource management in tourism hotspot: A cost-effective, semi-automatic method for monitoring the diffusion of residential swimming pools - Giuseppe Cillis; Rosa Coluzzi; Luca Salvati; Andrea Colantoni; Bogdana Nosava; Vito Imbrenda; Maria Lanfredi (Ecological Informatics)
Reliable mapping of landscape transformations is getting essential to face (and operationally manage) the complexity of rapidly changing socio-environmental systems. Water-related services are particularly vulnerable to climate change and experience overexploitation in dry (or semi-arid) areas under increasing human pressure, as in tourism-specialized regions of the Mediterranean basin. Monitoring the spread of residential swimming over time and space is thus informative for a refined estimate of water consumption and for an indirect assessment of unsustainable settlement development as a result of urban sprawl. The present study illustrates a semi-automatic procedure mapping residential swimming pools diachronically, by interpreting freely available aerial photographs with GNU Image Manipulation Program (GIMP) platform. The procedure is intended for practical use in real-world operational contexts, where simple, transparent, and low-cost tools are often preferable to more sophisticated alternatives. In this study this procedure was applied to Mykonos Island (Greece), a tourism hotspot with dry landscapes dominated by second-home and tourism-driven residential sprawl. The approach reveals a 40 % increase in the number of swimming pools between 2010 and 2018, highlighting the trend growing pressure on freshwater resources and expanded land sealing. In spite of the intrinsic simplicity, the method achieved change detection accuracy (OA = 93.4 % for 2010 and 91.1 % for 2018) comparable to that reported in the literature for more sophisticated methodologies, such as those AI-based. Due to its low technical and financial requirements, this strategy can be replicated by local and regional authorities across the Mediteranean basin, particularly in data-scarce contexts.