A Novel GIS-Based Approach for Automated Detection of Nearshore Sandbar Morphological Characteristics in Optical Satellite Imagery

Abstract

Satellite remote sensing is a valuable tool for coastal management, enabling the possibility to repeatedly observe nearshore sandbars. However, a lack of methodological approaches for sandbar detection prevents the wider use of satellite data in sandbar studies. In this paper, a novel fully automated approach to extract nearshore sandbars in high–medium-resolution satellite imagery using a GIS-based algorithm is proposed. The method is composed of a multi-step workflow providing a wide range of data with morphological nearshore characteristics, which include nearshore local relief, extracted sandbars, their crests and shoreline. The proposed processing chain involves a combination of spectral indices, ISODATA unsupervised classification, multi-scale Relative Bathymetric Position Index (RBPI), criteria-based selection operations, spatial statistics and filtering. The algorithm has been tested with 145 dates of PlanetScope and RapidEye imagery using a case study of the complex multiple sandbar system on the Curonian Spit coast, Baltic Sea. The comparison of results against 4 years of in situ bathymetric surveys shows a strong agreement between measured and derived sandbar crest positions (R2 = 0.999 and 0.997) with an average RMSE of 5.8 and 7 m for PlanetScope and RapidEye sensors, respectively. The accuracy of the proposed approach implies its feasibility to study inter-annual and seasonal sandbar behaviour and short-term changes related to high-impact events. Algorithm-provided outputs enable the possibility to evaluate a range of sandbar characteristics such as distance from shoreline, length, width, count or shape at a relevant spatiotemporal scale. The design of the method determines its compatibility with most sandbar morphologies and suitability to other sandy nearshores. Tests of the described technique with Sentinel-2 MSI and Landsat-8 OLI data show that it can be applied to publicly available medium resolution satellite imagery of other sensors.

Analysis of Interannual and Seasonal Nearshore Bar Behaviour Observed from Decadal Optical Satellite Data in the Curonian Spit, Baltic Sea

“…The aim of this study is to assess the alongshore variability in interannual and seasonal bar cross-shore behaviour by adapting traditional analysis techniques to satellite-derived bar data using a case study of the multi-bar system, located along the Curonian Spit, south-eastern Baltic Sea. It is an extension of a previous study by Janušait ė et al [39] who suggested a methodology to automatically extract nearshore bars from satellite images. Another study by Janušait ė et al [2] proved that the medium-resolution satellite imagery used in this study is a powerful tool to study the phenomena of three-dimensional bar behaviour, particularly multi-bar switching, and its interplay with coastal evolution, whereas this study focuses on the analysis of two-dimensional bar behaviour.…”

“…We extracted bar data from satellite images following the approach proposed by Janušait ė et al [39]. It is a multi-step algorithm that consists of four stages used to detect various morphological features for bar analysis: extracting the shoreline, calculating the multi-scale Relative Bathymetric Position Index (RBPI) with nearshore morphological features, identifying the bar boundaries, and deriving the positions of the bar crests within defined boundaries.…”

“…It was determined that with this algorithm, bar crests were derived with an average root mean squared error (RMSE) of 5.8 and 7.0 m for PlanetScope and RapidEye sensors, accordingly. Between the inner and outer bars, the RMSE was between 3.8 m and 7.7 m [39]. To avoid the influence of possible errors on the results, we decided to exclude short-term changes in bar morphology from the analysis, and only seasonal and interannual bar behaviour were evaluated.…”

“…This study suggests a methodology to analyse cross-shore morphology and dynamics of bars solely based on optical satellite data as an alternative to resource-intensive bathymetric surveys or other state-of-the-art remote sensing techniques such as video monitoring. Previous studies have proven satellite-derived bar positions to be an accurate measure of bar morphology [38][39][40]42]. However, clouds, haze, and water turbidity, all of which limit water clarity, have a significant impact on the quality of satellite-derived data.…”

“…Only a very few of these studies focused on regions with alongshore lengths greater than tens [25,27] or hundreds of kilometres [17,35]. Recently, studies that used optical satellite imagery to detect nearshore bars emerged [38][39][40][41][42]. The methods proposed in these studies, when combined with Earth observation satellites, enable the possibility of studying bars over large spatial scales with a temporal frequency of days [43].…”