Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices

Kelly, Joshua T.; Gontz, Allen

doi:10.1016/j.jag.2017.10.007

Cited by 55 publications

(41 citation statements)

References 49 publications

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“…The method described in this work start from a rough shoreline at the pixel level for each of the Landsat scenes that defines the set of initial pixels where the analysis starts. Note that this initial line can be obtained in various ways such as the thresholds implemented in [20,[49][50][51]. Any other accessible line can also be used (such as the shoreline provided by the Instituto Hidrográfico de la Marina for the Spanish territory) but usually these are biased in a magnitude about one Landsat pixel (25-35 m).…”

Section: New Sub-pixel Methodological Solutionmentioning

confidence: 99%

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

et al. 2019

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This paper presents a new methodological process for detecting the instantaneous land-water border at sub-pixel level from mid-resolution satellite images (30 m/pixel) that are freely available worldwide. The new method is based on using an iterative procedure to compute Laplacian roots of a polynomial surface that represents the radiometric response of a set of pixels. The method uses a first approximation of the shoreline at pixel level (initial pixels) and selects a set of neighbouring pixels to be part of the analysis window. This adaptive window collects those stencils in which the maximum radiometric variations are found by using the information given by divided differences. Therefore, the land-water surface is computed by a piecewise interpolating polynomial that models the strong radiometric changes between both interfaces. The assessment is tested on two coastal areas to analyse how their inherent differences may affect the method. A total of 17 Landsat 7 and 8 images (L7 and L8) were used to extract the shorelines and compare them against other highly accurate lines that act as references. Accurate quantitative coastal data from the satellite images is obtained with a mean horizontal error of 4.38 ± 5.66 m and 1.79 ± 2.78 m, respectively, for L7 and L8. Prior methodologies to reach the sub-pixel shoreline are analysed and the results verify the solvency of the one proposed.

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Section: New Sub-pixel Methodological Solutionmentioning

confidence: 99%

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

et al. 2019

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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%

“…Recently, a modification of the NDWI which substitutes short-wave infrared (SWIR) in place of NIR has seen increasing popularity for waterline extraction [22]. This modified normalised difference water index (MNDWI) has been suggested as a more accurate alternative to NDWI, particularly in environments affected by white water from surf or high levels of turbidity [1,33]. However, this index can produce poor results in intertidal environments where wet substrate remaining after high tide is often mapped as open water [19,20].…”

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

“…The 'optimal' water index threshold scenarios allowed us to assess the theoretical limits of sub-pixel waterline extraction performance under ideal conditions where the value of the best threshold value was known. However, threshold selection remains one of the key limitations of satellite-derived waterline extraction, particularly where reference data is not available to tailor the threshold to contrasting heterogeneous coastal or inland water environments [1,63]. This is frequently the case for operation analyses aimed at modelling waterlines across large spatial extents (e.g., at regional, continental or global scale).…”

Section: Effect Of Water Index Thresholdmentioning

confidence: 99%

“…Accurately mapping the boundary between land and water (the 'waterline') is critical for tracking coastal change and managing water resources in an era characterised by the increasing environmental impacts of development and anthropogenic climate change. By providing repeated observations of dynamic coastal zones and inland waters through time, satellite remote sensing provides a powerful and cost-effective alternative to traditional land-based surveys or waterline extraction methods based on sporadically acquired aerial data [1]. Historically, the use of satellite data for operational environmental monitoring has been limited by data access restrictions, inconsistent data structures and formats, a requirement for time and resource-intensive pre-processing before analysis could be conducted, and rapidly increasing data volumes which made desktop-based analyses of large extents or long time-series impractical [2].…”

Section: Introductionmentioning

confidence: 99%

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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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Marine mucilage mapping with explained deep learning model using water-related spectral indices: a case study of Dardanelles Strait, Turkey

Yilmaz,

Tonbul,

Kavzoglu

2023

Stoch Environ Res Risk Assess

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Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices

Cited by 55 publications

References 49 publications

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

A New Adaptive Image Interpolation Method to Define the Shoreline at Sub-Pixel Level

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

Marine mucilage mapping with explained deep learning model using water-related spectral indices: a case study of Dardanelles Strait, Turkey

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