Department of Urban and Regional Planning, Rivers State University, Port Harcourt, Nigeria
Changes in the shoreline position are a result of climate change-induced sea level rise and morphological changes caused by coastal processes. The delineation of shoreline positions relies on robust techniques and data sources, with remote sensing being particularly advantageous due to its cost-effectiveness and technological advancements. The study focuses on the eastern Niger Delta region of Nigeria, utilising mid-resolution multispectral datasets from Landsat-8 OLI, Sentinel-2 MSI, and PlanetScope to compare shoreline positions derived from different water indices (NDVI and NDWI) and classification methods. The research utilises mid-resolution multispectral datasets from Landsat-8 OLI, Sentinel-2 MSI, and PlanetScope to compare shoreline positions derived from different water indices (NDVI and NDWI) and threshold values. The methodology involves preprocessing optical imagery, applying water indices for shoreline delineation, and employing unsupervised classification techniques. The accuracy of shoreline positions is assessed using metrics such as mean error position (MEP) and root mean square error (RMSE), revealing significant discrepancies between datasets, particularly between high-resolution PlanetScope and coarser Landsat-8 imagery. Results indicate that the highest positional accuracy was achieved between PlanetScope and Sentinel-2, while Landsat-8 showed higher errors due to its coarser resolution. This study indicates that higher-resolution sensors provide more precise shoreline mapping, essential for effective coastal management. The study underscores the necessity of sensor-specific calibration and highlights the potential of integrating multiple satellite datasets to enhance the accuracy of shoreline monitoring and environmental assessments.
Nigeria coastline extends approximately 800km alone the Gulf of Guinea. The shoreline is assumed to be one of the curved river deltas global with about 21 major river inlet and intersect the coast which breaks it up into series barrier islands. Identification of shoreline position is imperative to understand the coastal area dynamics and a vital parameter for coastal vulnerability assessment (Sexton W.J. and Murday, 1994). Change in the shoreline position is in response to climate change-induced sea level rise and morphological changes due to the response of coastal processes (Pardo-Pascual et al., 2012). Shoreline position has been posing a significant hazard to socioeconomics of coastal communities (Lane et al., 2013), coastal land cover (Hadley, 2009; Lo and Gunasiri, 2014), coastal ecosystems, and cultural heritage site (Mazurczyk et al., 2018). In order to determine change in shoreline position, it is important to understand the underpinning indicators for definition shoreline. Because the nature of this position and definition chosen must be considered both spatial and temporal knowledge and the dependence of this position at a given time. (Boak and Turner, 2005) outline several indicators of ascertaining shoreline positions in terms of the vertical/horizontal sense to the physical water boundary. The challenge is the ability to develop a robust and repeatable technique to extract shoreline position is needed. Shoreline extraction technique in the low-lying region dependences on the data source and shoreline indictors used. The physical indictors for extraction of shoreline position considering the dynamic nature of coastline comprises (i) instant tidal level such as high water line (Pajak and Leatherman, 2002) Water line (Valderrama-Landeros et al., 2019), high tide wrack line (Thieler et al., 2013), Mean high water line (Chang et al., 2005) (ii) wetting limit (Zarillo and Synder, 2001), (iii) tidal datums such as Mean sea level (Aagaard et al., 2004), Mean water level (Morton & Miller, 2005); (Miller and Dean, 2007), Mean higher high water line (Allan et al., 2015), Mean low water line (Reeve and Fleming, 1997) and Lowest astronomical images (iv) vegetation limits such as Permanent vegetation line (Priest, 1999), Seaware edge of dune vegetation (Pajak and Leatherman, 2002), Upper limit of algae and marine lichen (Morton and Paine, 1985) (v) beach contour, (vi) storm lines such as Storm-surge penetration boundary and crest of geowash-over terrace and (vii) geomorphological reference lines (Boak and Turner, 2005); (Toure et al., 2019). While Waug (1995) described other indicators such as the movement of earth crust such as tectonic, geology, weathering, deposition and biotic factors. However, the optimal utilisation of the indicator depends on the material quality, operator experience and working conditions. Nevertheless, changes in shoreline position mapping have been determined through a number of data acquisition methods which includes Argus video camera, traditional field survey, aerial photography, and remote sensing. Remote sensing techniques are deemed to be particularly useful given data availability, geographic coverage, low cost, appropriateness, technological advancement in the satellite imagery and wide range of image-processing technology (Duarte et al., 2018). Although aerial photography and some satellite imageries provide high-resolution images for analysing shoreline position, this only capture the instant position at a given time. There are several factors that can potentially influence shoreline position, this are divided into primary and secondary factors. The primary factors includes; tidal stage, beach slope and groundwater position. Meanwhile, at short temporal scale, runup maxima/minima have the abilty to influence the shoreline position of instantenaous water level by tens of meters in low-lying beach area. The secondary factors are sediment grain size, mineralogy, solar zenith angle and geometery of the sensor. The positional accuracy of delineating the shoreline position is a difficult task, especially in the low-lying delta regions. Several research have been adopted different technique to assess the accuracy of change in shoreline position. Although most research have adopted confusion matrix method for the assessment of shoreline position. For instant, (Kelly and Gontz, 2018) adopt compared two GPS-surveyed intertidal zone with seven generic water indices to evaluate the degree of shoreline extraction performance. In their research High water or wet/dry sediment was chosen as an indicator for extraction of shoreline position using Landsat-8 OLI image. (Acharya et al., 2018), employ confusion matrix to evaluate the performance of four water indices (i.e. NDVI, NDWI, MNDWI and AWEI) for extraction of shoreline position using Landsat-8 OLI image in Nepal. Elsahabi et al. (2016), exploit similar method to detect better technique for the extraction of surface water using eight technique and Landsat ETM+ in the Aswan Dam Lake. Other assessment adopted includes, (Liu et al., 2017), exploits downscaling the pansharpening of Landsat-8 OLI images improve the accuracy. (Sekovski et al., 2014), employed zone-based technique to statistically compare reference shoreline and three supervised and one unsupervised classification technique using high-resolution multispectral Worldview-2 imagery. (Zhang et al., 2016) used Modified Histogram Bimodal to assess the accuracy of automated dynamic threshold to compare different water indexes (NDWI, MNDWI and AWEI) using Landsat-8 OLI. (Liu & Jezek, 2004), integrated Levenberg and Marquardt and Canny edge detector to speed up the convergence of iterative Gaussian curve fitting process and improve the accuracy of the bimodal Gaussian parameter for extraction of shoreline position using SAR and Landsat image. Rishikeshan and Ramesh (2017) exploit mathematics morphology-based algorithm for extraction shoreline position. The accuracy of the analysis was evaluated using computed-based technique as such F-scoring equation, accuracy equation and Matthew’s Correlation coefficient equation. (Pardo-Pascual et al., 2018) evaluated the accuracy of shoreline position using multisource infrared (IR) data (Landsat-7 ETM+, Landsat-8 OLI and Sentinel-2 MSI) and Polynomial Radiometric Correction (PCR) technique on a natural beach. In their study, a comparative analysis of shoreline position using multisource data of same date, indicates that Landsat have a bias of 2.17±3.38m. It was observation that surface brightness variation is the factor affecting shoreline position. Although, the data have similarity in environmental disturbance factor, Land zone brightness and wavelength of the incident wave. In recent years, there has been a growing number of open-access optical satellite imageries. Historically, Landsat missions are the most used optical satellite images for delineation of shoreline position due to large data coverage, historic record, and methodology. Other open-access multispectral imageries are the Sentinel-2 MSI, which was launched in June 2015 by the European Space Agency, while the PlanetScope is a multiple launch of a group of individual multispectral satellites (DOVEs), launched on 22 June 2016. In order to determine the potential of combining this data for coastal management and assessment, shoreline positions from multi-sensor imagery acquired at different spatial resolutions on the same day with slightly higher tides are required to be compared to ascertain the positional accuracy. Therefore, this study will compare the shoreline positions derived using threshold and unsupervised classification methods from two indices (NDVI and NDWI).
MATERIALS AND METHODOLOGY
Study Area
The study area is located between Andoni and Imo River, and it is a typical coastal state in the southern part of the Eastern Niger Delta region of Nigeria (figure 1). In this study, a portion of the state was selected which covers approximately 27.90 kilometres of the coastline. The seasonal climate in the region is tropical, dry and wet. The coastal environment is characterised by wetlands, low-lying tidal flats, beaches, estuaries and sandy barrier islands with an elevation of 0-45metres (Dike et al., 2024; Ochege et al., 2017; Sexton and Murday, 1994). Also, the mean tidal range increases from 1.9 to 3.0 metres from Bonny River and Ibo River, respectively (Abam, 2016). The average temperature in the region is 27°C, the annual average rainfall is 1626 mm/yr. The annual temperature of the area varies from 20 ?C to 35 ?C with high cloud cover and relative humidity ranging from 70 to 80%
Emmanuel Chigozie Dike*, Bright Godfrey Ameme, Evangeline Nkiruka Le-ol Anthony, Comparative Analysis of Multi-Spectral Shoreline Delineation Using Landsat-8, Sentinel-2, and PlanetScope Imageries in Coastal Environments of Nigeria, Int. J. Sci. R. Tech., 2025, 2 (2), 159-174. https://doi.org/10.5281/zenodo.14907334
10.5281/zenodo.14907334
