GNSS-based monitoring and mapping of shoreline position in support of planning and management of Matinhos/PR (Brazil)

Monitoring and mapping variations in shoreline location is an activity that can be undertaken using several different techniques of data collection, e.g., photogrammetric restitution, satellite images, LiDAR (Light Detection and Ranging) or classical topographical surveys to support coastal environmental protection such as identifying flood risk areas. The global navigation satellite system (GNSS) has been employed by the Federal University of Parana (UFPR) as part of their research into the application of geodetic survey methods for shoreline mapping in coastal environments since 1996. The advantages of using GNSS are accuracy and productivity, given that a great number of points can be determined within a short period of time at decimeter-level accuracy. In this work, GNSS relative kinematic positioning approach was applied to monitor Matinhos coastal district of Brazil. Other important data, such as the high- and low-tide marks, all obtained using GNSS, and thematic maps have also been incorporated.Through the reanalysis of historical surveys, it is possible to make some conclusions about the shoreline dynamics and to use this information as material in support of the planning and management of the coastal environment, for example, when planning engineering works that set out to minimize coastal erosion and for urban planning. The results achieved in this work include defining the position of the shoreline for 2008, developing the thematic map of the shoreline, the quantification of the advance and retreat of the shoreline between 2001 and 2008, and a map showing those critical areas where the shoreline position is equal to the high-tide water line. GNSS-based method offers quicker, all-weather, highly accurate and continuously updatable shoreline positional time series relevant for monitoring, thus enabling quicker management decisions to be undertaken, which may be of benefit to coastal engineering applications

Using optical satellite and aerial imagery for automatic coastline mapping

The continuous availability and rapid accessibility to multispectral data from satellite platforms within the Copernicus Programme represents a great opportunity for users in different fields of applications as: Agriculture, observation of coastal zones, monitoring land cover change. The aim of this paper is to identify a suitable method to map coastline using Sentinel-2 optical satellite image. The method provides the use of two indexes developed in remote sensing field for water environment: NDWI (Normalized difference water index) and MNDWI (Modified Normalized difference water index). Starting from the construction of maps of these indexes and, identifying appropriate threshold values, it has been possible to extrapolate the coastline. The coastlines derived from the use of the NDWI and MNDWI index were compared with a coastline obtained from the photointerpretation of a very high resolution orthophoto obtained through photogrammetric techniques. The results show that it is possible to map the coastline automatically and quickly with an intrinsic accuracy close to the geometric resolution bands of Sentinel-2 satellite images

Spatio-temporal analysis of coastal sediment erosion in Cape Town through remote sensing and geoinformation science

Coastal erosion can be described as the landward or seaward propagation of coastlines. Coastal processes occur over various space and time scales, limiting in-situ approaches of monitoring change. As such it is imperative to take advantage of multisensory, multi-scale and multi-temporal modern spatial technologies for multi-dimensional coastline change monitoring. The research presented here intends to showcase the synergy amongst remote sensing techniques by showcasing the use of coastal indicators towards shoreline assessment over the Kommetjie and Milnerton areas along the Cape Town coastline. There has been little progress in coastal studies in the Western Cape that encompass the diverse and dynamic aspects of coastal environments and in particular, sediment movement. Cape Town, in particular; is socioeconomically diverse and spatially segregated, with heavy dependence on its 240km of coastline. It faces sea level rise intensified by real-estate development close to the high-water mark and on reclaimed land. Spectral indices and classification techniques are explored to accommodate the complex bio-optical properties of coastal zones. This allows for the segmentation of land and ocean components to extract shorelines from multispectral Landsat imagery for a long term (1991-2021) shoreline assessment. The DSAS tool used these extracted shorelines to quantify shoreline change and was able to determine an overall averaged erosional rate of 2.56m/yr. for Kommetjie and 2.35m/yr. for Milnerton. Beach elevation modelling was also included to evaluate short term (2016-2021) sediment volumetric changes by applying Differential Interferometry to Sentinel-1 SLC data and the Waterline method through a combination of Sentinel -1 GRD and tide gauge data. The accuracy, validation and correction of these elevation models was conducted at the pixel level by comparison to an in-field RTK GPS survey used to capture the current state of the beaches. The results depict a sediment deficit in Kommetjie whilst accretion is prevalent along the Milnerton coastline. Shoreline propagation and coastal erosion quantification leads to a better understanding of geomorphology, hydrodynamic and land use influences on coastlines. This further informs climate adaptation strategies, urban planning and can support further development of interactive coastal information systems

GIS-based Analysis and Modelling with Empirical and Remotely-Sensed Data on Coastline Advance and Retreat

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941û2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941û2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in GuyanaÆs coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline