Coasts are continually changing and remote sensing from satellite has the potential to both map and monitor coastal change at multiple scales. This study aims to assess the application of shorelines extracted from Multi-Spectral Imagery (MSI) and Synthetic Aperture Radar (SAR) from publicly available satellite imagery to map and capture sub-annual to inter-annual shoreline variability. This is assessed at three macro-tidal study sites along the coastline of England, United Kingdom (UK): estuarine, soft cliff environment, and gravel pocket-beach. We have assessed the accuracy of MSI-derived lines against ground truth datum tideline data and found that the satellite derived lines have the tendency to be lower (seaward) on the Digital Elevation Model than the datum-tideline. We have also compared the metric of change derived from SAR lines differentiating between ascending and descending orbits. The spatial and temporal characteristics extracted from SAR lines via Principal Component Analysis suggested that beach rotation is captured within the SAR dataset for descending orbits but not for the ascending ones in our study area. The present study contributes to our understanding of a poorly known aspect of using coastlines derived from publicly available MSI and SAR satellite missions. It outlines a quantitative approach to assess their mapping accuracy with a new non-foreshore method. This allows the assessment of variability on the metrics of change using the Open Digital Shoreline Analysis System (ODSAS) method and to extract complex spatial and temporal information using Principal Component Analysis (PCA) that is transferable to coastline evolution assessments worldwide.

This research proposes the acquisition of a time series from optical satellites to observe changes in the Venice lagoon, an ecosystem which is very challenging to monitor by means of in situ survey activities, let alone using remote sensing techniques, given the presence of land and sandbars (vegetated intertidal areas). The work describes the specific validation process performed by ISPRA on the results obtained as applied on some target sites of the Venice Lagoon, both natural and partially artificial islands, using fully artificial islands as reference.

<p>Coastal areas are recognized as the most at risk due to climate change. They exhibit low-lying elevation, very high urban density and valuable economical assets. Sea level rise, storm events and coastal floods that are increasingly frequent and more powerful will increase damage to fragile coasts. Human activities (especially the reduction in natural defences), sediment balance and natural phenomenon are disrupted and increase the coast’s vulnerability.</p><p>As part of the GDA-DR initiative, we aim to produce some new indicators derived from EO to better understand the coastal system. As part of these new indicators, improved flooding maps are being developed using corrected elevation data and additional layers to better represent water behaviour in coastal cities. </p><ul><li>The Copernicus DEM GLO-30m is proving too crude to provide suitable flood modelling. It offers an accuracy of 4m, leading to an accuracy of earth features’ localization of less than 2.6 m. By using LIDAR measurements from the ISS-borne GEDI sensor and additional altimeter missions such as ATLAS on-board the ICESAT-2 satellite, we provide an improved and corrected DSM and DTM</li> <li>Additional layers produced for DEMs correction are used to improve floods modeling such as land cover maps to extract drag coefficients and 3D settlement layers to take into account channelling effects.</li> </ul><p>This new approach can provide improved flood maps to better support flood mitigation planning. We propose to present DSM and DTM, for Dili in Timor Leste, based on the most recent space-lidar data, GEDI (2019-2021) and of IceSAT-2/ATLAS (2019- on going) and hoe those new DEMs can impact flood risk assessment.</p>

Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.

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