Quantifying Coastal Shoreline Erosion Due to Climatic Extremes Using Remote-Sensed Estimates from Sentinel-2A Data

Ruheili, Amna M. Al; Boluwade, Alaba

doi:10.1007/s40710-021-00522-2

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…Sentinel-2 satellite images are frequently employed for the purpose of coastline extraction, owing to their high spatial resolution and multispectral capabilities. In [24], shoreline changes in the Al Batinah region of Oman and the impact of Cyclone Kyarr are analyzed using Sentinel-2 images and the Digital Shoreline Analysis System (DSAS). In [25], the effectiveness of MODIS, Landsat 8, and Sentinel-2 in measuring regional shoreline changes is compared.…”

Section: Introductionmentioning

confidence: 99%

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Andria

Scarpetta

Spadavecchia

et al. 2023

Sensors

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Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation.

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Section: Introductionmentioning

confidence: 99%

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Andria

Scarpetta

Spadavecchia

et al. 2023

Sensors

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“…The technological advances of the past decades, such as the development of AI algorithms for satellite EO data, the deployment of high-end computing architectures, effective data-handling procedures (Munawar et al 2022), as well as the introduction of additional non-satellite approaches such as UAV monitoring and lidar captures (Rovithis et al 2017), have further expanded the potential use cases to coastline environments. The continuous progress made in new processing techniques and technological approaches alongside the availability of more open-source solutions is allowing the parallel monitoring of several case studies or even climate phenomena all round the world (Donchyts et al 2016;Ruheili et al 2021). Supervised and unsupervised classifications (Taveneau et al 2021), automatic change detection techniques, and machine and deep learning (DL) applications using sophisticated artificial neural network (Makantasis et al 2018) architectures, among other methods, are constantly being improved using more sophisticated mathematical models or fusion approaches (Zhu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification and analysis of long-term changes in river deltas and riparian zones using time-series multispectral data

Kontopoulos

Kitsiou

Chlorokostas

et al. 2022

Euro-Mediterr J Environ Integr

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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.

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Remote Sensing Techniques for Investigating Natural Resources Deterioration: Application on Agricultural Degradation in Sultanate Oman

Al-Mulla,

Ali,

Alalawi

et al. 2024

Earth and Environmental Sciences Library

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Quantifying Coastal Shoreline Erosion Due to Climatic Extremes Using Remote-Sensed Estimates from Sentinel-2A Data

Cited by 13 publications

References 41 publications

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Identification and analysis of long-term changes in river deltas and riparian zones using time-series multispectral data

Remote Sensing Techniques for Investigating Natural Resources Deterioration: Application on Agricultural Degradation in Sultanate Oman

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