Coastline extraction from repeat high resolution satellite imagery

Chang-lei, Dai; Howat, I. M.; Larour, Eric; Husby, Erik

doi:10.1016/j.rse.2019.04.010

Cited by 52 publications

(41 citation statements)

References 38 publications

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“…The availability of multispectral satellite images at very high resolution (VHR) allows, in fact, acquisition in a short time and simultaneously of long stretches of coast. The geometric accuracies of submetric to decimetric order are absolutely compatible with the specific application and the availability of different bands allows semi-automatic or automatic approaches [15][16][17] such as those proposed in this paper.…”

mentioning

confidence: 77%

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Alicandro

Baiocchi

Brigante

et al. 2019

JMSE

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Coastal erosion, which is naturally present in many areas of the world, can be significantly increased by factors such as the reduced transport of sediments as a result of hydraulic works carried out to minimize flooding. Erosion has a significant impact on both marine ecosystems and human activities; for this reason, several international projects have been developed to study monitoring techniques and propose operational methodologies. The increasing number of available high-resolution satellite platforms (i.e., Copernicus Sentinel) and algorithms to treat them allows the study of original approaches for the monitoring of the land in general and for the study of the coastline in particular. The present project aims to define a methodology for identifying the instantaneous shoreline, through images acquired from the WorldView 2 satellite, on eight spectral bands, with a geometric resolution of 0.5 m for the panchromatic image and 1.8 m for the multispectral one. A pixel-based classification methodology is used to identify the various types of land cover and to make combinations between the eight available bands. The experiments were carried out on a coastal area with contrasting morphologies. The eight bands in which the images are taken produce good results both in the classification process and in the combination of the bands, through the algorithms of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), spectral angle mapper (SAM), and matched filtering (MF), with regard to the identification of the various soil coverings and, in particular, the separation line between dry and wet sand. In addition, the real applicability of an algorithm that extracts bathymetry in shallow water using the "coastal blue" band was tested. These data refer to the instantaneous shoreline and could be corrected in the future with morphological and tidal data of the coastal areas under study. Author Contributions: Supervision, M.A., V.B., R.B. and F.R.; Methodology, M.A., V.B., R.B. and F.R.; Investigation, M.A., V.B., R.B. and F.R.; Writing-Review and editing, V.B., R.B. and F.R. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflict of interest.

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confidence: 77%

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Alicandro

Baiocchi

Brigante

et al. 2019

JMSE

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“…It also relies on spatial interpolation of the isobaths between successive waterlines, which can be problematic if the waterlines are sparse. Recently, alternative methods have been developed using a flood frequency map to estimate the coastal topography pixel-by-pixel (Dai et al, 2019;Tseng et al, 2017). In these approaches, reference water levels (for instance, minimum, average, and maximum) were assigned to reference flood frequencies (respectively 100%, 50%, and 0%).…”

Section: Topography Of Periodically Flooded Areasmentioning

confidence: 99%

Comment on essd-2021-32

Ligi¹

2021

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The characterization of estuarine hydrodynamics primarily depends on the knowledge of bathymetry and topography. Here we present the first comprehensive, high-resolution dataset of the topography and bathymetry of the Amazon River estuary, the world's largest estuary. Our product is based on an innovative approach combining space-borne remote sensing data, an extensive and processed river depth dataset, and auxiliary data. Our goal with this characterization is to promote the database usage in studies that require this information, such as hydrodynamic modeling or geomorphological assessments. Our twofold approach considered 500'000 sounding points digitized from 19 nautical charts for bathymetry estimation, in conjunction with a state-of-the-art topography dataset based on remote sensing, encompassing intertidal flats, riverbanks, and adjacent floodplains. Finally, our estimate can be accessed in a unified 30 m resolution regular grid referenced to EGM08, complemented both landward and seaward with land (MERIT DEM) and ocean (GEBCO2020) topography data. Extensive validation against independent and spatially-distributed data, from an airborne LIDAR survey, from ICESat-2 altimetric satellite data, and from various in situ surveys, shows a typical accuracy of 8.4 m (river bed) and 1.2 m (non-vegetated inter-tidal floodplains). The dataset is available at http://dx.doi.org/10.17632/3g6b5ynrdb.1 (Fassoni-Andrade et al., 2021). IntroductionThe Amazon River exports the largest discharge of freshwater (205'000 m 3 s -1 ; Callède et al., 2010) and the largest sedimentary supply (5-13 10 8 tons per year; Filizola et al., 2011) to the global ocean. However, there does not exist, up to now, any consistent, comprehensive, publicly available topographic dataset in the estuary that can support hydrodynamic, sedimentary, or ecological studies. The largest estuary in the world is home to energetic exchanges of momentum between the upstream river and the ocean, with a marked variability of the water level over a broad range of timescales, from the

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“…This data is available as atmospherically and terrain corrected 'analysis-ready' data processed to surface reflectance, allowing reliable spectra to be extracted with no additional processing or calibration required [44]. We focused on five commonly studied environments to explore the influence of contrasting spectral properties on waterline extraction performance: a) sandy beaches [1,6,28,33,34,38,42], b) artificial shorelines [32,45], c) rocky shorelines [10,29,45], d) wetland vegetation [46][47][48], and Remote Sens. 2019, 11, 2984 5 of 23 e) tidal mudflats [7,10,19,49].…”

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

“…These methods are inherently limited to the resolution of the satellite sensor, and are unable to resolve changes in waterline positions occurring at a scale of less than a whole pixel (e.g., 10 m for Sentinel-2 or 30 m for Landsat; [28]). Although high resolution satellite data from commercial providers such as Planet Labs and DigitalGlobe are increasingly available for these applications [29,30], these sources of data are typically prohibitively expensive to implement across regional to global extents, and lack either the temporal depth or systematic revisit frequency that are available for medium resolution satellite programs with coarser pixel resolutions (e.g., Landsat imagery available since 1972, [31]). Accordingly, there is a pressing need for the development of operational methods for extracting waterline information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management.…”

Section: Introductionmentioning

confidence: 99%

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

et al. 2019

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Accurately mapping the boundary between land and water (the 'waterline') is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu's method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50-3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

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Coastline extraction from repeat high resolution satellite imagery

Cited by 52 publications

References 38 publications

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Comment on essd-2021-32

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

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