Evaluating the Land Use/Land Cover Change with a Future Prediction Using Remote Sensing and GIS in the Elassona-Tsaritsani Basin of Thessaly (Central Greece)

Land use/land cover (LU/LC) change and forecast is a cutting-edge and important method of illustrating global changes. Over the last decades, it has been clear that the world is witnessing a demographic transformation, which has an indirect or direct influence on environmental and human health safety. In order to examine the LU//LC change and forecasting for the Elassona-Tsaritsani basin in Thessaly, Central Greece, which contains a significant groundwater reservoir that serves as the basis for all purposes, remote sensing (RS) and geographic information systems (GIS) methods are employed. To evaluate historical LU/LC trends as well as projecting future periods, specifically for 2025 and 2030 a sequence of satellite images were downloaded from the USGS Earth explorer from 1990 to 2020 with a five-year time step. Arc GIS software was used to perform LU/LC classification and area calculations while IDRISI Terrset was used for future prediction through cellular automata (CA)-Markov model. Over a period from 1990 to 2020 the results demonstrate significant changes for each LU/LC map. Urban area and irrigated land increased from 7.06 to 13.41% and 21.69 to 30.31%, respectively. Fallow land remained remarkably steady around 6% and non-irrigated land fell dramatically over the course of three decades, losing a total of 13.85%. The same LU/LC trend is dominant in the upcoming future with minor changes in all classes. However, the projection indicates a significant shift from 1990, as evidence by the irrigated land (which increased from 21.69 to 31%) and the urban area (which increased from 7.06 to 14.50%). The Elassona-Tsaritsani basin, which has undergone tremendous change in the past thirty years, particularly in the urban area and irrigated land, requires an urgent inquiry into the water quality due to anthropogenic effects. Decision-makers can view potential future environmental challenges thanks to the combination of RS and GIS.