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.

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.

Accurate and consistent mapping of the boundary between land and water (the ‘waterline’) is critical for tracking coastal change and coastal management. Earth Observation satellite remote sensing provides a unique cost-effective alternative to traditional methods. Waterlines from satellites are often derived by methods based on spectral indices that lead to the separation between land and water. The validation strategy for these products requires a complex approach from accuracy assessment (quantifying error) to verification of its suitability for monitoring applications. Traditionally the accuracy of EO products is reduced and simplified to the resolution of the sensor or satellite that collects the data. However, environmental variables (sea conditions, weather, vegetation, anthropic) that may have a direct effect on the sensor and on the coastline that we are trying to monitor are not taken into consideration. Segments of Sentinel-2-derived waterlines were selected in North Bull Island for further analysis in the creation of a new benchmark dataset for understanding the waterline models of eastern Ireland. In our novel approach, we propose that horizontal accuracy assessment is performed by using the mean absolute distance between the GNSS reference line and the Sentinel-2-derived waterline. The vertical accuracy assessment was then calculated by the difference between the attributed waterline height compared with the mean GNSS elevation at the intersection points. Results were then compared with Dublin Port tide gauge height record. The development of reference validation models can allow more efficient application of satellite data for monitoring, and understanding how environmental variables affect each case study.

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.

This work proposes the use of automatic co-registered satellite images to obtain large, high frequency and highly accurate shorelines time series. High resolution images are used to co-register Landsat and Sentinel-2 images. 90% of the co-registered images presented vertical and horizontal shift lower than 3 m. Satellite derived shorelines presented errors lower than mission’s precision. A discussion is presented on the applicability of those shorelines through an application to Tordera Delta (Spain).