An Integrated Monitoring System for Coastal and Riparian Areas Based on Remote Sensing and Machine Learning

Abstract

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.

Comprehensive Analysis of the Use of Web-GIS for Natural Hazard Management: A Systematic Review

“…Integrating remote sensing with machine learning techniques has been employed to monitor natural hazards in coastal areas [53]. Platforms like the MOSAIC [54] utilise predictive models such as SCHISM and XBeach to interpret local coastal hazards effectively.…”

“…The ease of navigation and intuitive controls, as emphasised by studies [47,53,60,86,87], ensure that users can traverse through vast datasets and perform tasks with minimal cognitive load, including [53] where using a progressive web application for data visualisation and user engagement is beneficial. Advocating for accessibility ensures that users of varying technical proficiency can effectively engage with the system [71,[88][89][90].…”

“…GPS-based data collection strategies for locust tracking [84] emphasise efficient data synchronisation and spatial risk estimation for proactive hazard mitigation. Additionally, the integration of rule-based DSSs, real-time tide gauge measurements [53], and machine learning algorithms [42] for timely hazard detection and alert generation augments the effectiveness of early warning systems.…”

Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery

“…The ROI used for testing is the greater region of Thessaloniki, which includes Thermaikos Gulf and the Axios Delta National Park. The area was chosen as the Greek pilot site for the EPIPE-LAGIC project [43], as it is a coastal area that contains three river deltas and has different land uses as well as various economic activities, which in many cases have significant environmental impacts.…”

Land use change in coastal zones of China from 1985 to 2020

“…Noteworthy research dynamics in this realm have revolved around the application of remote sensing technology (Mouw et al, 2015;Adade et al, 2021;Wu et al, 2022), ecological restoration measures (Waltham et al, 2020;Li H. et al, 2022), and sustainable development paradigms (Ryabinin et al, 2019;Zou et al, 2019). The rapid evolution of remote sensing technology has endowed scientists with enhanced precision in land use data, facilitating dynamic monitoring of coastal land use changes and patterns (Abd El-sadek et al, 2022;Tzepkenlis et al, 2022;Ye et al, 2022;Liu et al, 2023). Concurrently, ecological restoration endeavors are geared towards transformative shifts in land use practices aimed at preserving coastal ecosystem integrity (Pena et al, 2021).…”