Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

Abstract

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

Spatial and seasonal variation in macrozoobenthic density, biomass and community composition in a major tropical intertidal area, the Bijagós Archipelago, West-Africa

“…450 km 2 of intertidal flats [ 48 , 49 ]. Its topographical characteristics promotes large tidal amplitudes reaching up to 4.5 m in spring tides [ 50 ]. The climate is tropical and bi-seasonal, with a wet season from May to October, and a dry season from November to April.…”

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

“…The images from different dates of the Sentinel-2 time series may present discrepancies in their reflectance values because of different acquiring conditions among the days (see [69]). Likewise, the reflectance values of the two adjacent Sentinel-2 scenes that encompass the entire area of interest were not directly comparable, mainly because of the inherent differences in the reflection of light captured by the twin satellites, despite the fact that the acquisition times were virtually the same (Figure S1).…”

“…We followed an established method, relying on a Sentinel-2 time series to identify and select the intertidal area to be classified [69,71,72]. We calculated the normalized difference water index (NDWI; [73]) for each image to enhance the contrast between the water and land pixels (NDWI is obtained from the green (Band 3) and the near-infrared (Band 8) bands).…”

“…The concept behind this method is that permanent water bodies and land masses present relatively stable conditions (and show NDWI values with low temporal variance), whereas intertidal areas will have a higher variability in the NDWI values across the tidal time series of images as they are subject to cyclic inundation periods. A water and a land mask were then derived to be used in the subsequent steps of the methods, using the value of 0.2 as the variance threshold to identify intertidal areas (Figure S2; see [69,72]) for methodological details).…”

“…For each date (n = 29), two adjacent scenes were downloaded (T28PCT and T28PDT), totalling 58 images. Tidal heights (water height at the exact time of image acquisition) for each image were calculated following the methods described in [69]. Table S2: Performance statistics and respective equations.…”