Marianthi Stefouli

Climate change could exacerbate floods on agricultural plains by increasing the frequency of extreme and adverse meteorological events. Flood extent maps could be a valuable source of information for agricultural land decision makers, risk management and emergency planning. We propose a method that combines various types of data and processing techniques in order to achieve accurate flood extent maps. The application aims to find the percentage of agricultural land that is covered by the floods through an automatic map estimation methodology based on the freely available Sentinel-2 (S2) satellite images and machine learning techniques.

The amount of water flowing into the seas and oceans has slowly but steadily increased in recent years, signifying a possible speeding up of the water cycle due to climate change. The detection of the water outflows and the exploitation of them are very important to the rational water management. Remote sensing techniques has been proven a valuable tool for the detection and identification of submarine groundwater discharge-SGD (Submarine Groundwater Discharge). In this study the island of Cephalonia in Ionian sea, Greece is considered as a case study. A 6 Km sea buffer zone around the island is studied using Landsat images. Submarine groundwater discharge has been detected through remote sensing methods and the results have been combined with the tectonism and hydrogeological conditions of the island. A quantitative analysis of temperature in the buffer zone was carried out. The sea thermal anomalies in the same zone were also investigated.