List of Tables
5. METHODOLOGY
5.4 IMAGE TRANSFORMATION
bright targets differed slightly from image to image. This was due mainly to the differing state of the tides on these beach targets, with some images having smaller areas of exposed beach sand than others.
Table 5.2: Details of the invariant targets used to generate regression formulas for the image normalisation process.
Target 1 2 3 4 5
Name Dark Target #1 Dark Target #2 Dark Target #3 Bright Target #1 Bright Target #2
Target Description Open sea
Lake Bhangazi
Northern part of Lake St Lucia Beach south
Beach north
Size (pixels) 484 484 506 175-198 228-275
Only one of the dark targets was used for the final normalisation of each band. This ensured that the number of pixels from the bright targets was approximately the same as the number of dark target pixels. The dark targets were selected by examining the impact of each one on the R2 values obtained when comparing normalisation target pixels from the target images against those from the reference image. Dark Target #2 was found most suitable for normalising band 1, Dark Target #1 was used for bands 2, 3 and 4 while Dark Target #3 was used to normalise bands 5 and 7. Mean pixel values for the selected normalisation target areas on both the reference and target images were calculated and used to generate regression (normalisation) equations. One equation was generated for each of the six reflective bands of the six target images, giving a total of 36 equations (see Appendix 1). Each of the image bands was then transformed using the relevant normalisation equation, resulting in all the target images being normalised relative to the reference image from 7 May 2001.
73
Kilometres
• Site Locations
WoE= Woody vegetation endmember site NE= Non-woody vegetation endmember site WaE= Water endmember site
BT= Bright target area DT= Dark target area WaT= Water training site WeT= Wetland training site
GrT= Grassland training site
Figure 5.4: Location of bright and dark target areas, training sites used in the supervised classification, and training sites used to define endmembers.
to investigate whether they improved classification accuracy. Secondly, a set of exclusion masks were used to exclude areas on the Eastern Shores that would not be analysed in the study. This not only reduced the volume of data to be processed but also eliminated areas in which surface water would not be found. The following areas were excluded:
1. Areas of land and water surrounding the study area were excluded from further processing. These areas comprised Lake St Lucia and the ocean as well as parts of the Western Shores of Lake St Lucia.
2. All forested areas were excluded. Most of the forest on the Eastern Shores was quite dense and it would not be possible to view surface water through the canopy.
3. Areas with slopes exceeding 5% were excluded as surface water was unlikely to collect in areas as steep as this.
4. All areas of bare sand. Most of these areas were beach sand.
5.4.1 Principal components analysis (PCA)
A principal components analysis was carried out before image masking took place, thereby ensuring that all the spectral data was included in the covariance matrix calculations of the PCA. Producing a covariance matrix based on the masked data would have resulted in unrealistically high covariances due to the fact that the zero values of the masked areas would have caused inter-band correlations to appear higher than they actually were. All six reflective bands were used in the analysis (i.e. bands 1-5 and 7) and those bands with a greater variance were given a greater weighting in the analysis. Six components were extracted for each image with components being offset, where necessary, to fit within the range 0-255. This was done in order to facilitate further processing by the Idrisi software.
Table 5.3 shows how virtually all of the variance present in the original spectral data was contained in the first three components. Together, these three components explained over 99% of the variance for each of the study dates. The remaining three components contained mostly noise and were not used during the image classification stage of this study. From here onwards, the PCA dataset will be referred to as the component data while the original, untransformed spectral dataset will be referred to as the spectral data.
Table 5.3: Percentage of variance explained by the first three components of the PCA.
Date 1991/07/23 2001/03/20 2001/05/07 2002/04/24 2002/07/13 2002/09/15 2002/10/17
Variance explained by each component (%) Comp 1
81.6 81.6 81.4 83.5 85.1 80.8 85.1
Comp 2 11.8 11.1 11.2 10.8 10.7 12.2 9.5
Comp 3 5.8 6.7 6.6 5.0 3.5 6.2 5.0
Total (%) 99.1 99.4 99.3 99.3 99,3 99.2 99.7
5.4.2 Preparation of exclusion masks
Four sets of exclusion masks were created: a study area mask, a forest mask, a slope mask and a sand mask. These masks were prepared as binary images in which areas to be excluded were given a value of zero while areas to be retained were assigned values of one.
5.4.2.1 Study area mask
On-screen digitising was used to delineate an outline of Lake St Lucia and the sea shore so that the areas of water surrounding the study area could be excluded from further processing. A 43-metre buffer generated inland of these shorelines was used to define the
study area mask. There were two reasons for using this buffer. In the first place it was used to account for fluctuations in the position of the water's edge, which varied according to the state of the tides and the level of the water in the lake. Secondly, it was used to compensate for possible misregistration of the images during the rectification process. A distance of 43 metres represented the diagonal distance across one pixel meaning that a 43-metre buffer defined a buffer zone of at least one pixel around the whole study area.
5.4.2.2 Forest mask
Coastal dune forests, swamp forests and pine plantations represent the main types of forest found on the Eastern Shores. These were mapped using the normalised difference vegetation index (NDVI), which, as an indicator of biomass, was used to distinguish forested from non-forested areas. One NDVI image was created for each of the seven study dates. A large patch of dune forest and another of plantation forest were identified on these NDVI images by means of visual inspection. Minimum NDVI values within these two patches for each of the study dates were then used as threshold values to delineate all forested areas for each of the images. The seven delineated forest areas were merged into a single image, producing a forest mask showing all areas that had been forested at some time during the study period. This compensated for the effects of afforestation and deforestation while at the same time ensuring that the size of the area being analysed was the same across all seven dates.
5.4.2.3 Slope mask
An ancillary data set was required in order to exclude steep slopes. This took the form of a digital elevation model (DEM) that was generated from spot heights and height contours obtained from 1:50000 topographical maps. The DEM was constructed with a 30-metre resolution which matched the spatial specifications of the Landsat images. Using the DEM,
a map of slope steepness was created for the whole study area. From this steepness map, all areas with slopes steeper than 5% were identified and combined into a single slope mask.
5.4.2.4 Sand mask
The sand mask was used to exclude areas of sand from further processing. Sand is characterised by high reflectance values in all six of the Landsat reflective bands and was thus fairly easy to delineate. This was in contrast to soil which contains varying amounts of organic material and has a very different spectral signature to that of sand. This was particularly apparent in the visible bands of the electromagnetic spectrum where soil reflectance values tended to be lower than those of sand. Spectral band 3 was used to delineate areas of sand for each date and these were combined into a single sand exclusion mask.
5.4.3 Application of the exclusion mask
The study area, forest, slope and sand masks were combined into a single exclusion mask in which all areas to be excluded from further study were assigned a value of zero. All other areas were given a value of one. In a series of overlay operations the exclusion mask was multiplied with each of the bands from the seven study dates resulting in all the masked areas being set to zero in the image data. The exclusion mask was also applied to all the component images resulting from the principal components analysis.