Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale

Bishop‐Taylor, Robbi; Sagar, Stephen M.; Lymburner, Leo; Beaman, Robin J.

doi:10.1016/j.ecss.2019.03.006

Cited by 78 publications

(43 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various applications of this method have been attempted, including the monitoring of coastline changes [10], estimation of sediment transport [11], and data assimilation in a coastal morphodynamic model [12]. At larger scale, Bishop-Taylor et al developed an intertidal digital elevation model (DEM) for Australia coast at 25 m resolution using a relative intertidal extent model developed from 30 years of Landsat archive and global tidal modeling [13,14].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

et al. 2019

View full text Add to dashboard Cite

The intertidal zones are well recognized for their dynamic nature and role in near-shore hydrodynamics. The intertidal topography is poorly mapped worldwide due to the high cost of associated field campaigns. Here we present a combination of remote-sensing and hydrodynamic modeling to overcome the lack of in situ measurements. We derive a digital elevation model (DEM) by linking the corresponding water level to a sample of shorelines at various stages of the tide. Our shoreline detection method is fully automatic and capable of processing high-resolution imagery from state-of-the-art satellite missions, e.g., Sentinel-2. We demonstrate the use of a tidal model to infer the corresponding water level in each shoreline pixel at the sampled timestamp. As a test case, this methodology is applied to the vast coastal region of the Bengal delta and an intertidal DEM at 10 m resolution covering an area of 1134 km 2 is developed from Sentinel-2 imagery. We assessed the quality of the DEM with two independent in situ datasets and conclude that the accuracy of our DEM amounts to about 1.5 m, which is commensurate with the typical error bar of the validation datasets. This DEM can be useful for high-resolution hydrodynamic and wave modeling of the near-shore area. Additionally, being automatic and numerically effective, our methodology is compliant with near-real-time monitoring constraints.

show abstract

Section: Introductionmentioning

confidence: 99%

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

et al. 2019

View full text Add to dashboard Cite

show abstract

“…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%

“…Initially, we computed a series of common water extraction indices. The normalised difference water index (NDWI, [50]) is one of the most commonly used remote sensing water indices, having been applied to facilitate waterline delineation in a wide range of papers across inland [23,24,26] and coastal environments [7,10,25,51]. This index ranges from -1.0 (land) to 1.0 (water), and uses the ratio of visible green and near-infrared (NIR) reflectance to separate water pixels from land based on water's high reflectance of visible green light and low reflectance of NIR, and the high reflectance of NIR by dry soil and terrestrial vegetation:…”

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

“…This threshold scenario was intended to compare the best-possible waterline extraction performance in an application where the ideal water index threshold was known. As knowing the ideal threshold ahead of an analysis is often not possible, the 'zero' water index threshold scenario used a constant zero threshold to extract waterlines, a procedure which is commonly used in operational waterline extraction applications conducted across large spatial extents [7,10]. Finally, procedures for automatically deriving threshold values based on the histogram distribution of water index values have been increasingly used to provide improved waterline extraction performance in complex heterogeneous environments where a single threshold (i.e., 0) would produce poor results [6,39,42].…”

Section: Waterline Extractionmentioning

confidence: 99%

“…By organising freely available medium resolution satellite imagery such as Landsat or Sentinel-2 into spectrally and geometrically calibrated stacks of analysis-ready data, these platforms support the development of new automated and cost effective workflows for consistent, rapid and repeatable operational monitoring of environmental change across time and space [4]. Data cube approaches leveraging multi-decadal remote sensing time series have so far been used successfully in a wide range of coastal and inland water applications, including monitoring 33 years of coastal change across global sandy beach shorelines [6], mapping the topography and extent through time of threatened intertidal ecosystems [7][8][9][10], and tracking changes in the distributions of inland waterbodies at both continental and global scale [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds

et al. 2022

View full text Add to dashboard Cite

For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallowwater Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow-water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel subsites in the Florida Bay (application site) under a different time period (application period). We used foraging little blue herons (Egretta caerulea) as the ecological unit with which to constrain the model's "water depth window," that is, range of water depths to estimate shallow-water availability.At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability, which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by subsite and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow water has implications for monitoring habitat conditions for tidally influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.

show abstract

Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale

Cited by 78 publications

References 78 publications

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

High-Resolution Intertidal Topography from Sentinel-2 Multi-Spectral Imagery: Synergy between Remote Sensing and Numerical Modeling

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

Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds

Contact Info

Product

Resources

About