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

Abstract

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).

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

“…Therefore, the shoreline extracted is discontinuous with segments approximately 600 m in length and 500 m gaps [47]. The assessments in this paper have been made using the Landsat SWIR1 infrared band-corresponding to the L7 (band 5) and L8 (band 6)-because this approach led to the most accurate and robust sub-pixel shorelines in previous works [31,33]. The different spectral response of water and land in the infrared bands is the basic principle underlying the shoreline search.…”

“…Therefore, the final solution for each fixed Y-value is calculated as the average of all the approximate sub-pixel shoreline points obtained through the different initial pixels. Other works such as [33], calculate this average by weighing each solution point according to its distance from the initial pixel (which consequently reduces the RMSE). However, the current paper does not deal with this question as it is focused on assessing exclusively the inner technical core of the sub-pixel methodology (step 2(c) exposed in Section 1).…”

“…Pardo-Pascual et al [33]-coming from the methodology of [27]-uses a fifth-degree polynomial expression computed using the LSM and a stencil formed with an upsampled 7 × 7 fixed and Figure 7. Sub-pixel shoreline obtained using various methodologies (described next in Table 2) on the 8 October 2016 at El Saler beach and compared with the true shoreline measured by GPS.…”

“…Obtaining accurate shorelines is crucial in the use of the Landsat series for mapping the past with a coastal evolution indicator [31,32]. While the initial implementation of that methodology [27,29], was evaluated on some rigid seawalls, in [33] the same methodology was assessed on natural beaches, since the goal is having a tool to obtain satellite-derived shorelines (SDS) for subsequent geomorphological and management works. Along the publications in which this workflow has been used, the implementation of the workflow has changed from Matlab and IDL to Python as presented in [34].…”

Three Decades of Coastal Changes in Sindh, Pakistan (1989–2018): A Geospatial Assessment

“…Toure et al [37] reviewed various approaches used for coastline delineation from remote sensing imagery using various image processing methods. These methods to determine the coastal changes include pixel frequency count, image histogram thresholding [38], edge filter algorithm and tasseled cap transformation [18], and image classification through iterative self-organizing data analysis (ISODATA) [39]. We adopted an automatic coastline delineation method that consists of the following steps [40]: (a) band selection based on spectral profile curve of the images; (b) selection of the optimal index for coastline delineation; and (c) coastline delineation based on histogram threshold.…”