Equation 9 Pixel Height Mean Difference
5.1 SPECTRAL ANALYSIS
5.1.2 RADAR BAND SELECTION FOR THRESHOLDING
Figure 28 Spectral plot for Intensity VH band of Sentinel-1 data with change in land cover shown by arrows
The land cover changes are not as easy to note from the intensity of the VV Band because there are several fluctuations over both land and water in Figure 29, however again, the transition from Wildevoelvlei Dam to land begins at pixel 10 and the land portion occurs from pixel 18.166 until 115 where the beach occurs to pixel 123 and transitions over the ocean. These land cover transitions are indicated by the red arrows.
Figure 29 Spectral plot for Intensity VH band of Sentinel-1 data with change in land cover shown using arrows
Figure 28 and Figure 29 reiterate the theoretical concepts behind Radar-based remote sensing discussed in section 2.2.3.3 on page 27. The general expectation is for water bodies to have a lower intensity because they are flat surfaces as opposed to land components that are mixed and include volume scatterers (vegetation, buildings, soil, and roads among others.) (Schmidt et al., 2020). The fluctuations in Figure 29 over the ocean may be caused by the presence of rocks, seaweed, or wave peaks. The VV Band was clearly more sensitive than the VH band in detecting surface backscatter changes, which is more suitable for the complexity of the bio- optical properties of coastal zones to determine the most precise shoreline position, however, the VH band has a more clearly defined water-land transition. This contradiction could be better refined by combining radar and optical images.
5.1.2.2 RELATING CALIBRATION TO LAND COVER BACKSCATTER
Calibration was observed to further determine the most appropriate band. Radar data can be calibrated to either Sigma, Beta or Gamma based on the project expectations. Gamma calibration is simply a flattened, terrain corrected derivative of either Sigma or Beta calibrated data (European Space Agency, 2021).
The graphs in Figure 30 below depict the combined effect of polarization and calibration on land cover response measured in decibels. The same transect over the Kommetjie coastal area was used. All 4 variations were able to detect general land cover changes with landcover transactions indicated by the red arrows. Again, the graphs associated with the VV band were more sensitive and detected more backscatter over waterbodies. They all detected the transition from the Wildevoelvlei dam at pixel 10 and the graphs fluctuate over the urban land area before transitioning to the beach. The peaks that occur between pixels 120 and 135 can be attributed to the beach areas and there after the lower backscatter references the ocean.
These graphs did not offer conclusive evidence on which calibration was applicable and so their histograms were observed as shown below. The choice to ultimately use Sigma calibration stems from literature, which emphasizes that Sigma is the backscatter coefficient whereas Beta is the Brightness coefficient, and we are interested in backscatter response in this instance (European Space Agency, 2021).
The histograms show that VH polarization was able to clearly differentiate better between land and water components regardless of whether sigma or beta calibration was used. Whilst the VV bands only had one intensity peak, the VH histograms had two peaks. The higher peaks signified the land components which had more pixels whilst the lower peaks were the waterbodies, again highlighting the expected lower backscatter from waterbodies (Salameh et al., 2020).
The ability to pinpoint the transition as indicated by the red markers between landcovers led to the initial assumption that the Sigma VH band was more suitable for thresholding because its histogram shows two clearly distinguished peaks for land and water components respectively. A closer visual inspection of the images concluded that the Sigma VV band was most appropriate.
Figure SEQ Figure \* ARABIC19 Intensity Histograms for Calibrated Spectral Response
Figure SEQ Figure \* ARABIC19 Intensity Histograms for Calibrated Spectral Response
Figure 30 Comparing the combined effect of calibration and polarization on coastal area spectral plots
Figure 31 Comparing combined effect of calibration and polarization on spectral histograms
Figure 32 on page 97 shows an example of Sigma VH and VV backscatter images over the general Kommetjie area. Whilst they both have a clear delineation between the land and ocean, the Sigma VH band image on the left segments natural and urbanized land covers. It detects beach sand, rocks, and the ocean as one land cover leading to a false shoreline position. The Sigma VV band on the other hand is sensitive enough to detect the semi- submerged rock outcrops and the ocean wave run-up interaction with the beach leading to a more accurate shoreline position. This observation ultimately shows the value of using the VV band as opposed to VH and the discussion here reiterates the sentiments of subsection 2.2.3.3 on page 25 about the factors that determine backscatter detection in Radar imagery (Braun, 2021; Filipponi, 2019; Veci, 2015).
Figure 32 Comparing influence of polarization on coastal radar imagery
SUMMARY
The spectral analysis ultimately led to identifying MNDWI as an appropriate spectral index for optical image manipulation whilst applying thresholding to the Sigma VV band seemed appropriate for Radar data. The underlying theory of image segmentation is applicable for both datasets. As the earth surface undergoes both natural and anthropogenic changes, it is also reflected in satellite images. Based on pixel changes we can deduce and quantify land use land cover changes (Orlikova &Horak, 2019).