Equation 9 Pixel Height Mean Difference
2.2 APPLICATION OF REMOTE SENSING AND GIS IN COASTAL STUDIES
2.2.4 COASTAL OBSERVATION INDICATORS
Differential Interferometry Synthetic Aperture Radar (DInSAR) is now the preferred approach with radar data in coastal erosion studies. The phase difference between two images taken from two different points of view as a satellite traverses the same orbit, is used to obtain an interference pattern to extract terrain change, deformation patterns and elevation (Ferretti et al., 2007; Mason et al., 1999). The difference between these two views is the baseline. If the baseline is too short, signal phase difference can be undetectable whilst too long of a baseline may introduce additional noise in an image (Prats-Iraola et al., 2015).
DInSAR has been used to map coastal zones as well as generate DEM’s and has the advantages of being able to penetrate shallow waters to capture bathymetric features, as well as being operational in all weather and day or night conditions (Horritt et al., 2001). The temporal lag between the two acquisitions with different angles is still an issue (i.e., low coherence due to long baseline, decorrelation caused by incidence angles impacting the backscattering, and changes in surface roughness from a tidal cycle to another). This limits the use of this method to single-pass interferometry systems for which there is no temporal decorrelation.
Remote sensing is clearly an appropriate tool for coastal monitoring. The variety in technology and data accessibility determines study limitations. The theory associated with beach systems and remotely sensed data is combined below to determine appropriate measurable coastal indicators.
Table 1. Coastal indicators
Indicator Category VISIBLE TIDAL-DATUM EXTRACTION Definition Physical
morphological features that can be seen in imagery.
Combination of beach profile with a specific vertical elevation through tidal parameters.
Extracting shoreline features using image processing
techniques.
Examples High tide wet/dry boundary,
vegetation, dune line
Mean Sea Level (ML), Mean Lower Low Water
(MLLW)
Elevation modelling
Potential data sources
Aerial photographs, Coastal maps and charts, Historical photographs, Multispectral satellite remote sensing, Microwave sensors
Tide data, Coastal maps, and charts
LIDAR, GPS shorelines, stereo- optical images, SAR, Beach surveys, Microwave sensors
Advantages Relatively easy, more indicators to work with
Objective and numerically definite.
Good and easily adjustable resolution during field surveys Data collection easily adjustable to tide levels for optimum coverage Disadvantages The manual
approach means visual interpretation is very subjective.
Affected by wind, tide, meteorological factors
Datum shifts and conversions are an intricate concept to execute.
Inconsistent tide data collection.
Field surveys can be tedious
2.2.4.1.1 THE WATERLINE METHOD
Whilst Interferometry is the first choice for elevation modelling in satellite remote sensing, Mason et al. (1995), introduced a more precise technique that combined proxy and datum- based coastal indicators called the waterline method, which incorporated the use of both images and hydrodynamic models (Mason et al., 1995). Image processing techniques were used to determine the position of the water’s edge and thereafter a hydrodynamic tide-surge model coinciding with the acquisition time of the images was used to predict water elevations that were then superimposed along the waterline. The use of multiple images depicting various tidal conditions meant a cluster of waterlines could be generated and used to build a gridded DEM with both spatial and temporal dimensions and thus occurrences such as erosion could be measured (Mason et al., 1995). The hydrodynamic models introduce a more well-rounded approach because the tide momentum and wind stress are also influencers of coastal erosion. Only the incorporation of bathymetric data would strengthen this technique.
Beach profiles may have the same general structure, but the slight differences caused by sedimentary evolution and water movement means that there is a need to identify more definite indicators to properly define individual study sites (Toure et al., 2019). The detection of these indicators allows for the understanding of the profile’s environmental modifications.
The assortment of these indicators is usually the reason an array of sensors and geoprocessing tools is recommended to account for the dynamic spatial, temporal, and reflective properties of littoral surfaces (Gens, 2010; Toure et al., 2019). Although the waterline is the most accessible and widely used indicator, its subjectivity is why datum-based indicators are introduced by means of sea level data which is not a visually detectable indicator and as such image extraction techniques must be applied to the data for digital model generation (Amaro et al., 2014; Gens, 2010)
2.2.4.1.2 RELATING SOUTH AFRICA’S LAND AND HYDROGRAPHIC DATUMS FOR COASTAL MAPPING
South Africa uses a 2D+1D coordinate system. This is to say horizontal and vertical coordinates occur on different datums (Department of Rural Development and Land Reform, 2013). The Surveyor General has specified the national land leveling datum as measured
from the Hartebeesthoek94 Datum, referenced from the WGS84 Ellipsoid. The South African Navy Hydrographic Office (SANHO) also has its own chart datum reference for its tide measurements being the Lowest Astronomical Tide (LAT) which is the lowest predictable tide over a period of 19years (Ocean Rhythm, 2021). As shown in Table 2 on page 32, SANHO determined the offset between the land and tide datums to be -0.98m in 2018 which is to say SANHO’s soundings are measured 0.98m below the WGS84 ellipsoid. This is highlighted in red in Table 2 and Figure 5 on page 31, which shows how these datums relate based on a typical beach profile. Because the mean sea level (ML) fluctuates, a general tidal range is established between the Mean High Water (MHW) and Mean Low Water (MLW).
The influence of neap and spring tides is also depicted in Figure 5 along with their specific chart datum levels in Table 2.
Figure 5 Comparison of land and hydrographic datums.
Table 2. Cape Town tidal levels (Source: SANHO-2,2018)
DESCRIPTION LEVEL IN m
RELATIVE TO MEAN LEVEL
RELATIVE TO CHART DATUM
Highest Astronomical Tide HAT +1.04 +2.02
Mean High Water of Spring
Tide MHWS +0.76 +1.74
Mean High Water of Neap
Tide MHWN +0.28 +1.26
Mean Level ML 0 +0.98
Mean Low Water of Neap
Tide MLWN -0.28 +0.70
Mean Low Water of Spring
Tide MLWS -0.73 +0.25
Lowest Astronomical Tide LAT -0.98 0.00
Chart Datum CD -0.98 0.00