Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator

Almonacid-Caballer, Jaime; Sánchez-García, Elena; Pardo-Pascual, Josep E.; Balaguer-Beser, Ángel; Palomar-Vázquez, Jesús

doi:10.1016/j.margeo.2015.12.015

Cited by 115 publications

(74 citation statements)

References 13 publications

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“…This lack of frequency may cause problems when measuring natural beaches, as the shoreline position may be so affected by the sea level that its position loses geomorphological meaning. However, compiling tens of instantaneous shorelines during a specific time period may be useful for estimating mean shoreline positions and trends during the medium and long term [22].…”

Section: Introductionmentioning

confidence: 99%

“…A final evaluation relative to the seawalls shows that the standard deviations were [42] As mentioned earlier, the main goal of this type of work is the study of beach dynamics. The three-step workflow described (sub-pixel extraction, LUFT georeferencing, and PRC) has already been used in several tasks: storm impact on beach studies [49]; coastal evolution studies [50]; or annual mean shoreline extraction [22]. These studies characterize beach trends on a specific micro-tidal (up to 20 cm) area.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

et al. 2018

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This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

et al. 2018

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“…For each pixel, the output is composed of the median value of all the Landsat images within each year at that location, which can effectively remove the noise caused by outliers and poorly removed edges of clouds [25]. For estuarine islands mapping, such median-composing method can minimize the short-term coastal changes associated with sea level variabilities, wave run-up, sedimentary seasonal variations, and coastal storms [18]. Note that the pixels in the Landsat 7 SLC-off gaps were not included in the generation of the annual Landsat images owing to their non-value characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, Landsat imagery with a 30-m spatial and 16-d temporal resolution was selected as the main data source for tracking the temporal change of estuarine islands [18]. Unlike previous studies [19][20][21][22], this study aims to obtain an annual map of the estuarine islands and track their temporal changes at an annual scale rather than an interval of several years, which can provide more temporally detailed information on the estuarine islands' variations.…”

Section: Introductionmentioning

confidence: 99%

Continuously Tracking the Annual Changes of the Hengsha and Changxing Islands at the Yangtze River Estuary from 1987 to 2016 Using Landsat Imagery

Jia

Ding

et al. 2018

Water

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Abstract:The evolution of estuarine islands is potentially controlled by sediment discharge, tidal currents, sea level rise, and intensive human activities. An understanding of the spatial and temporal changes of estuarine islands is needed for environmental change monitoring and assessment in estuarine and coastal areas. Such information can also help us better understand how estuarine islands respond to sea level rise in the context of global warming. The temporal changes of two estuarine islands in Shanghai near the Yangtze River Estuary were obtained using Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper) images from 1987 to 2016 on an annual scale. First, a composite image was generated by using the multi-temporal Landsat images for each year. Then, a modified normalized difference water index (MNDWI) was applied to the annual estuarine island maps using a threshold segmentation method. Finally, we obtained the temporal changes of the estuarine islands in Shanghai during the period 1987-2016. The results suggest that (1) Landsat TM/ETM+ images can be used for estuarine island mapping and change detection; (2) the two estuarine islands have expanded significantly during the past three decades; (3) human activities are the main driving factor that caused the expansion of the estuarine islands; and (4) the sea level can also partly explain the change in the estuarine islands. This study demonstrates that Landsat data are useful for determining the annual variations in the land area of two estuarine islands in Shanghai during the past 30 years. In the future, other factors and their contributions to estuarine island changes should be further investigated.

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“…Pardo-Pascual et al [23] proposed an automatic method to extract shorelines from Landsat TM and ETM+ with subpixel precision, where the RMSE obtained in shoreline location ranges from 4.69 to 5.47 m. As the nature beaches are always changing over time, Almonacid-Caballer et al [24] applied the annual mean shoreline position extracted from Landsat images to largely decline the short-term variability of shoreline, and the extracted shorelines were biased from the seaward by around 4 to 5 m. Foody et al [25] evaluated a soft classification method in mapping coastline from a degraded simulated Landsat imagery, and the result presented coastline obtained at subpixel scale and the RMSE was 2.25 m. While, these methods are always complex and difficult to be achieved in real application. In the processing, only spectral information of Landsat imagery was utilized and the improvement of accuracy is limited.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

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Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator

Cited by 115 publications

References 13 publications

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

Continuously Tracking the Annual Changes of the Hengsha and Changxing Islands at the Yangtze River Estuary from 1987 to 2016 Using Landsat Imagery

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

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