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A manually classified map was created for comparing the accuracy of the automated segmented and classified map in eCognition. In the first approach the vegetation was demarcated and mapped through onscreen heads-up digitizing. Heads-up digitizing entailed mouse-clicking the cursor around the visible edges of vegetation patches and fully tracing their extent. With so many individual stands of spekboom and the large size of the study area, this exercise was time consuming and for this reason the strategy is inefficient for large tasks. The second approach used the already segmented map (at a scale of 50) draped over the image and the editor toolbar in ArcGIS was used to manually select the polygons (segments) that contained spekboom. By displaying the polygons as hollow without outline, visual identification of the vegetation was made easier with the naked eye. To highlight the desired segments to be classified as spekboom, the shift/click action allowed fast selection of polygons with spekboom occurrences. When a substantial number of segments had been selected, they were classified in the attribute table.

In areas of uncertain spekboom occurrence, a number of tools aided the identification and selection process. First Jenness's (2010) method was used to create an aspect layer for the terrain from the DEM. Earlier, the strict preference was pointed for spekboom growth on sunbathed north- facing slopes and on those with easterly and westerly tending aspects, where they receive sunlight on winter mornings and afternoons. Consequently, this aspect map allowed the user to determine whether vegetation in doubt adhered to the right spectrum of aspect criterion values to be defined as spekboom or not.

Second, physical fieldwork mapping was undertaken. This entailed traversing the west-east stretching Gamka Thicket vegetation occurrence in the landscape along three north-south observation spokes. These spokes followed major drainage lines and spekboom stands were photographed in the field at locations recorded by hand-held GPS co-ordinates as waypoints. Photographs were taken in various directions at these locations to form a visual reference database

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of the target vegetation to help identify it during image analysis. This data set was structured to represent all the various aspect directions, slope and height occurrences of spekboom in the mountainous terrain. The waypoints were loaded into ArcGIS (depicted in Figure 3.19) where they were used in conjunction with the photographs to help identify uncertain patches of vegetation. For

Figure 3.19 Waypoint sets along traverses for accuracy assessment of spekboom stands in BLK PNR

example, Figure 3.20 shows the landscape location of waypoints 273 and 274. From these vantage points the green patches photographed in Figures 3.21 and 3.22 were unmistakably confirmed as spekboom capping the mountain ridge.

The Jenness (2010) DEM extension allowed the creation of an aspect layer, as shown in Figure 3.23, for the area shown in Figure 3.20 and in which waypoints 273 and 274 are indicated for orientation. The mapped aspect classes each represent an aspect direction of 45 degrees centred on the eight major compass directions. In Figure 3.23 the vegetation clearly occurs on the north-facing sunbathed slopes and the type is plainly identifiable in Figure 3.22.

The third method of accuracy assessment entailed field demarcation of very distinctive spekboom stands by GPS-delimited waypoints. Stands were manually delimited by GPS waypoints at 5-m intervals around stands to create polygons importable to ArcGIS. These waypoint polygons were superposed on the imagery for verification of stand appearances on the imagery with other

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Figure 3.20 Landscape location of waypoints 273 and 274 for accuracy assessment of vegetation about which uncertainty existed in BLK PNR

Figure 3.21 View of spekboom stands in a southerly direction from waypoint 273 in BKL PNR 275 274 273 0 75 150 300Meters

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Figure 3.22 View of spekboom stands in a south-south-westerly direction from waypoint 274 in BLK PNR

Figure 3.23 Aspect map used to identify spekboom growing on north-facing slopes in BLK PNR

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stands and with the photographs to facilitate the accurate inclusion of uncertain patches of spekboom vegetation. Figure 3.24 shows the patch of spekboom that was manually delineated with waypoints and subsequently plotted on the image in Figure 3.25.

Figure 3.24 Example of a spekboom stand manually delineated by waypoint series in BLK PNR

Figure 3.25 Spekboom stand shown in Figure 3.24 plotted on satellite image

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When superposed on the segmented image (Figure 3.26), the high level of accuracy with which the combination of methods succeeded in identifying and delimiting spekboom stands in the study area is illustrated, although a measure of generalization is also evident.

Figure 3.26 Superposed waypoint demarcation of spekboom on segmented stand image in BLK PNR

The final manually classified map for the larger part of the study area is shown in Figure 3.27 and its accuracy is compared to that of the automated classification in the next section.Note in Figure 3.27 the unexpected occurrence of smaller isolated stands of spekboom on some north facing slopes in the northerly part of the Gamka Thicket. A noteworthy feature is the sharp transition boundaries between spekboom and other vegetation types – due mainly to the deterministic role of landscape form and terrain aspect. Significantly, the area of spekboom demarcated by this manual method showed that about 900 ha are covered by this vegetation type  almost 25% of the total area demarcated as Gamka Thicket in the SANBI map.

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Figure 3.27 Final manually classified spekboom stand demarcated in the Gamka Thicket in BLK PNR

The automatically classified vegetation map (created in Section 3.3) and the manually classified vegetation map (created in this section) will be used in the accuracy assessment in Section 3.9. The Acacia karroo will be mapped next.