Practical application

The two methods discussed above are applied to the case study of BLK PNR and the area of vegetation and mapped features is calculated.

3.8.2.1 Activating the Jenness method and TIN

Both methods were used in ArcGIS to calculate the true surface area of polygons of the case study area. Both use a DEM to determine surface area data, however they differ slightly in their calculation method and results output. The TIN is a vector-based calculation and the Jenness is raster-based. The experiments with the two methods should inform prospective users in their choice of method. The results and their suitability for determining true surface area in mountainous landscapes were compared because the methods are not always equally accessible depending on the availability of various GIS platforms (software programs).

Activating the Jenness method entailed five steps, namely:

1. Download Jenness extension from: http://www.Jennessnt.com/ArcGIS/surface_area.htm. 2. Activate the Jenness extension in ArcGIS (follow instructions provided by extension). 3. Open ArcGIS and add DEM data for study area (add data).

4. Create surface area or surface ratios in the DEM tools extension (DEM tools extension/create surface area).

5. Activate the ArcGIS tool ʻZonal Statisticsʼ as Table (the command sequence is:

ArcToolbox/Spatial Analyst Tools/Zonal/Zonal Statistics as Table). This procedure calculates the sum of all the real three-dimensional surface area raster cells contained inside the target polygons as explained in Section 3.8.1.2.

Activating the TIN method requires the creation of a TIN data structure from the available DEM and the carrying out of the following five analytical steps:

1. Open ArcGIS and add DEM data for study area.

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2. Activate the tabs Tools/Extensions and turn 3D Analyst on. 3. Convert the DEM (raster) to a TIN.

4. Run the actions interpolate polygon/multipatch tool in 3D Analyst.

5. Open the attribute table of the output data set to access the calculated planimetric and surface area figures.

3.8.2.2 Calculation of areas of vegetation and mapped features in BLK PNR

The application of the Jenness extension resulted in the production of a raster image similar to an ordinary 3D topographical DEM, but each 10x10m cell carried an area and not a height value. This area value was colour coded for display purposes so that larger areas appear darker in Figure 3.40. The topographical height display in Figure 3.40 shows darker coloured cells (larger surface area indicative of steeper slope) along valley and mountain sides, thereby mimicking the topographical displays. Experimentation was conducted using different tolerances for creating a TIN. The first TIN database created for this study area using a tolerance suggested by the software did not allow a similarly fine-scaled image to be created. As seen in Figure 3.41, the Delaunay triangles created are much more coarsely delimited, some triangles are clearly over 5 km long, but this should be more finely delimited as a 10-m DEM was used. A second TIN database was created (seen in Figure 3.42) using a z tolerance of .05 and a z unit of 1. This data set was far more comparable to the data

Figure 3.40 Raster-based surface area display for BLK PNR from Jenness extension application

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Figure 3.41 TIN model for BLK PNR with z tolerance equal to the default suggested by the software

Figure 3.42 TIN model for BLK PNR with z tolerance of 0.5

set created by using the Jenness extension and to the 10-m DEM which was used to create it. In ArcGIS the relevant vegetation polygons inside the BLK PNR study area were clipped from this TIN and surface area grid and the areas calculated by vegetation type.

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Comparison of the two methods led to several useful conclusions, both concerning the difference in results and the strategic considerations relevant when deciding which method to employ. The results marshalled in Table 3.1 show that the refined calculations improved area by almost 10% Table 3.1 Area covered by different biomes in BLK PNR

Vegetation community Planimetric area in ha (%)1

TIN area in ha (%)2

Jenness area in ha (%)2

North Swartberg Sandstone Fynbos 1432.7 (100) 1586.7 (110.7) 1545.6 (107.9) South Swartberg Sandstone Fynbos 2039.9 (100) 2160.4 (105.9) 2163.2 (106.0) Matjiesfontein Quartzite Fynbos 472.2 (100) 490.2 (103.8) 529.8 (112.2) Matjiesfontein Shale Renosterveld 2914.7 (100) 3086.9 (105.9) 3241.6 (111.2) Prince Albert Succulent Karoo 4541.9 (100) 4737.2 (104.3) 4900.4 (107.9) Gamka Thicket: SANBI map 3598.1 (100) 3813.5 (106.0) 4008.6 (111.4)

Total 15513.0 (100) 16392.7 (105.7) 16909.9 (109.0)

Spekboom: Automated classification 1017.9 (100) 1073.2 (105.4) 1044.8 (102.6) Spekboom: Manual classification 900.1 (100) 951.0 (105.7 918.8 (102.1)

Southern Karoo Riviere 513.5 (100) 517.9 (100.9) 520.6 (101.4)

1 _{The planimetric dimension as originally mapped is taken as the benchmark calculation (100%).} 2 _{The three-dimensional calculated area figures expressed as a percentage of the benchmark} planimetric original.

(larger) on average in the case of the more accurate Jenness method, versus 6% for TIN. The total area of BLK PNR was calculated as just smaller than 17 000 ha – almost 1500 ha more than the planimetric total. For the Gamka Thicket alone the same calculation is more than 11% up on the original – although this is greatly reduced when calculated for the more patchy manual demarcation of spekboom alone. Note how significant the more accurate demarcation of spekboom alone is for CT calculation (it covers only 25% of the Gamka Thicket Biome under which it falls). Both methods show little difference between automated and manual classification of the area for spekboom. The differences between the area calculation methods are clearly related to the type of terrain  as can be expected the Southern Karoo Riviere which occupies the near-flat river valley yields an almost 100% correspondence with planimetric values. Contrariwise, vegetation groups occurring on the steep mountain sides, yield up to 10% greater areas than the planimetric measurement.

3.8.3 Advantages of 3D data structures to measure true surface area and recommendations