Deriving shorelines from SAR images to assess coastal vulnerability in data poor regions.

This study presents a robust framework for deriving shoreline position and change over time from SAR images which are then used with other open-access products to assess coastal vulnerability in the Niger Delta region. It commences by introducing the challenges of assessing coastal vulnerability, before reviewing methods used to assess coastal vulnerability, problems of parameter scarcity in data poor countries and the benefits of using SAR data to derive such parameters. Three analytical chapters then describe the methods used to derive the relevant shoreline parameters, shoreline change rate and assess vulnerability along the eastern coastline of the Niger Delta. Firstly, this study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons to take account of seasonal differences. Shoreline positions were obtained by identifying the land-water boundary via segmentation using histogram-minimum thresholding; vectorizing and smoothing that boundary; and averaging its position over multiple waterlines. Accuracy was assessed against reference waterlines derived manually from Sentinel-2 Multispectral Instrument optical imagery. The land-water segmentation had an overall accuracy of 95-99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43m (approximately four pixels), and often less than 10m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tide. The notable exception was the low tide line in areas with extensive inter-tidal flats. In those cases, the processing method picked up apparently subtle variations, which led to it identifying shorelines 70 to 370m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance, thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Overall, despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide. Secondly, this study demonstrates the capability of Sentinel-1 SAR data for assessing multiannual shoreline position change. On the basis of the criterion that the Sentinel-1 data were acquired at a high tidal state for Bonny in the Niger delta region, 36 images were chosen from 255 images recorded between 2015 and 2020 to represent 6 for each year. The study developed a simple and systematic GIS-based approach which provides an accessible way forward for coastal change assessment in data-poor countries. The shoreline was split spatially using sediment cells as a convenient, well-established approach to assess the spatial heterogeneity of its change processes, which will aid in identifying their causes and mechanisms. The result of the SAR imagery over the full 5-year timescale shows that the coastline is eroding more than it is accreting, but that there are areas of both net erosion (cells II, IV, VI, VII, and VIII) and accretion (cells I, III, and V) over this timescale. The result of the analysis Sentinel-1 imageries for both the multi-annual scale and seasonal change indicates the areas of coastlines nearer river mouths are more prone to change compared to those further from river mouths and on uninterrupted strand coasts. Also, the result of shoreline orientation and shoreline position change gives some localised suggestions of a relationship, which could be taken to imply the influence of wave action on shoreline position change. This approach has the potential to provide an effective coastal change monitoring capability for coastal modellers, scientists and government agencies that would enable them to develop a coastal zone framework for effective planning and management. Thirdly, this study utilised a GIS approach to derive the shoreline position, coastal elevation, and calculate CVI. The shoreline parameters are derived from Sentinel-1 SAR imagery (2015–2020), while the coastal elevation and other parameters are derived from different open-access and commercial DEMs, and literature, respectively. In addition, the parameters are ranked on a scale from 1 to 5, with 1 representing "very low" vulnerability and 5 representing "very high" vulnerability based on the existing ranking approach. These rankings are used to calculate the sensitive for the specified coastal vulnerability index (CVI5). The result of CVI indicates spatial variation that is dependent on the shoreline change method and coastal elevation derived from different DEMs since other parameters were assigned constant values across the coastline. The analysis of CVI using the EPR and LRR methods to derive shoreline change rates reveals modest spatial variations in vulnerability. Further on, coastal elevation derived from different DEMs has been shown to have more influence on the spatial variation of the CVI. The MERIT product results in the largest proportion of the coastline being assigned to the high and very high vulnerability categories compared to other open-access DEMs. The findings provide a valuable tool for stakeholders and decision-makers to develop strategies for sustainable coastal zone management and coastal protection and adaptation to climate change-related risks in the Niger delta coastal environment (ICZM). Despite these limitations, this study has demonstrated that the open-source data can be effectively used for assessing coastal vulnerability across the Niger Delta region. Overall, this study demonstrates how open-source products can be used to assess coastal vulnerability across data poor regions such as the Niger Delta. It has highlighted spatial variations in coastal vulnerability which are important given the social, economic and environmental characteristics of this low-lying landscape and threat of climate-change induced sea level rise