Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Pucino, Nicolas; Kennedy, David M.; Young, Mary; Ierodiaconou, Daniel

doi:10.1016/j.rse.2022.113293

Cited by 19 publications

(7 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, no corrections were made to the shorelines obtained through the developed methodology. However, intending to increase the number of case studies and test dates, the availability of this kind of information can be of fundamental importance, as attested by several authors (Zollini et al, 2023) (Pucino et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Angelini,

Angelats,

Luzi

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This work aims to compare and assess the performance of certain methodologies for shoreline mapping based on the use of medium (10 m) and high resolution (3 m) multi-spectral imagery, provided by Sentinel-2 (S2) and PlanetScope (PS), respectively. Being Sentinel-2 part of the Copernicus missions, its data are freely available. PS imagery are also freely available for scientific research, upon approval by the European Space Agency of a related project proposal. Several spectral indices, including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Index (WI), were used for shoreline detection. In particular, two unsupervised classification techniques, the Gaussian Mixture Model (GMM) and K-means clustering were deployed as shoreline extraction methods. The outcomes of such approaches were validated using reference shorelines derived from aerial orthomosaics, generated from images acquired as close as possible to the satellite imagery dates, and the ”baseline and transect” approach for accuracy verification. Three tide-less Mediterranean beaches were used as study cases for comparison: the beach between Castelldefels and Gava in Spain, Feniglia and Marina di Grosseto in Italy. The results demonstrated sub-pixel accuracy in shoreline extraction, with Mean Absolute Distances ranging from 2 m to 5 m for S2 data and 1.5 m to 2 m for PS data. These findings highlight the potential of freely available satellite data for semi-automatic shoreline detection. Results obtained by using the combination of different indices and methodologies show that the best option may change depending on the considered context, hence future investigations should be dedicated to the development of a procedure for automatically determining the context-based (close to) optimal index-classifier combination.

show abstract

Section: Discussionmentioning

confidence: 99%

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Angelini,

Angelats,

Luzi

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Not only will it provide a testbench for new features accessible to all developers, but it will also enable researchers to have a standard set of metrics used for reporting the accuracy of SDS time-series to the coastal community and its end users (e.g., coastal scientists, managers, and engineers). For instance, there have been many new developments in this space only in the last couple of years, including the use of increasingly high-resolution satellite imagery (e.g., 3 m/pixel PlanetScope imagery 52 ), the development of automated co-registration 65 algorithms, and the use of deep learning to automatically detect the shoreline position 66,67 . In this context of rapid development and innovation, this benchmarking framework will help test how these new developments are improving the accuracy, precision, and reliability of satellite-derived shorelines.…”

Section: Discussionmentioning

confidence: 99%

Benchmarking satellite-derived shoreline mapping algorithms

Vos,

Splinter,

Palomar-Vázquez

et al. 2023

Commun Earth Environ

View full text Add to dashboard Cite

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.

show abstract

“…The eligible papers that mapped and/or monitored DSM. DSM (1) Avulsion sites TanDEM-X (12 m) No -[340] -DSM (1) Coastal aquaculture ponds -Yes [212] -[210] DSM (1) Coastal forested wetland UAV No -[341] -DSM (1) Coastline change -Yes [150,269,342,343] [ [344][345][346] [347] DSM (1) Coastline change UAV No [348] -DSM (1) Coastal [350] DSM (1) Coastal vulnerability assessment -Yes [116] -[166,351] DSM (1) Coastal vulnerability assessment ASTER Yes --[102] DSM (1) Coastal vulnerability assessment Pleiades (0.5-2 m) No --[352] DSM (1) Coastal vulnerability assessment SRTM DEM, USGS (30 m) Yes -[275] -DSM (1) Coastal vulnerability assessment VENµS No --[274] DSM (1) Coastal wetland classification Airborne lidar No -[353] -DSM (1) Flood extent -Yes --[109] DSM (1) Flood extent Pleiades stereo image (0.5-2 m) No [62] [354] -DSM Flood risk -Yes --[60] DSM (1) Habitat -Yes --[355] DSM (1) Habitat Airborne lidar No -[277] -DSM (1) Habitat NASADEM Yes [215] --DSM (1) Habitat Sentinel-1 (~10 m) No [165] --DSM (1) Habitat UAV No - [277,[356][357][358] [279,359,360] DSM (1) Habitat UAV-LiDAR No - [142,361] DSM (1) Intertidal polychaete reefs DJI Phantom 4 Multispectral UAV No - [8...…”

Section: Parameter Phenomena Remote Data or Dataset Available Product...mentioning

confidence: 99%

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

View full text Add to dashboard Cite

Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

show abstract

Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Cited by 19 publications

References 61 publications

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Shoreline Extraction Methods from Sentinel-2 and PlanetScope Images

Benchmarking satellite-derived shoreline mapping algorithms

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Contact Info

Product

Resources

About