Nowadays, coastal areas are exposed to multiple hazards of increasing severity, such as coastal floods, erosion, subsidence due to a combination of natural and anthropogenic factors, including climate change and urbanisation. In order to cope with these challenges, new remote sensing monitoring solutions are required that are based on knowledge extraction and state of the art machine learning solutions that provide insights into the related physical mechanisms and allow the creation of innovative Decision Support Tools for managing authorities. In this paper, a novel user-friendly monitoring system is presented, based on state-of-the-art remote sensing and machine learning approaches. It uses processes for collecting and analysing data from various heterogeneous sources (satellite, in-situ, and other auxiliary data) for monitoring land cover and land use changes, coastline changes soil erosion, land deformations, and sea/ground water level. A rule-based Decision Support System (DSS) will be developed to evaluate changes over time and create alerts when needed. Finally, a WebGIS interface allows end-users to access and visualize information from the system. Experimental results deriving from various datasets are provided to assess the performance of the proposed system, which is implemented within the EPIPELAGIC bilateral Greece-China project. The system is currently being installed in the Greek case study area, namely Thermaikos Gulf in Thessaloniki, Greece.

Today’s remote sensing data and technologies offer the capability to effectively monitor diverse and challenging environments around the world, such as coastal river and riparian zones. Coastal riparian zones and river deltas usually demonstrate extreme coastline changes in terms of the extent of water coverage of inland territories due to flood events, low and high tides, the climate, specific environmental characteristics, etc. In this paper, we exploit freely available multispectral time series data for previous decades, utilizing Landsat missions in order to develop an open-source-based image processing pipeline for the extraction of the actual yearly average coastline status of riparian river delta areas. The latter present significant temporal coastline changes between years, semesters, and months. Average mean maps are generated and then compared to several temporal levels in order to distinguish long-term significant changes and ecosystem threats. Additionally, a custom long short-term memory (LSTM) neural network is deployed to forecast the evolution of the coastline by exploiting the average value for each pixel across all available images as a training sample and producing a forecast output for the next period. The network achieves accuracy scores of 89.77% over 'non-water' depicting pixels and 84.26% over 'water' depicting pixels, for regions that present frequent changes between land and water coverage over time. The predicted map presents high statistical agreement with the respective average map generated in two different validation approaches, with kappa coefficients of 85.9% and 91.4%, respectively.