NFEPA Atlas 5.3_20111025_FINAL.indd - Biodiversity GIS - SANBI

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4.5 LandformsLandforms describe the topography of a land surface in the context within which it occurs, identifyinga range of landform classes such as valley floors, slopes and benches. Landforms provide a frameworkfor the role the landscape plays in processes related to geology, hydrology and ecology. Landformswere developed by NFEPA for classifying wetlands according to the landscape setting within which theyfall, and provided one of the primary determinants for classifying wetland ecosystem types (Section4.3). Four landform classes recommended by the national wetland classification system (SANBI 2009)were identified: benches, plains, slopes and valley floors (Figure 4.10).Valley floorsSlopesPlainsBenchesThese landform classes are useful to a wide range of planning and management applications beyondbiodiversity planning, such as assessments of soil, geology, hydrology, fire and flood risk, hazardprediction, forestry, agricultural and forestry potential, climate change as well as numerous engineeringand military applications.50 kmBENCHseepSLOPEdepressionBENCHVALLEY FLOORchannelledvalley-bottom wetlandunchannelledvalley-bottom wetlandWilma StrydomPLAINriver channelfloodplain wetlandflatdepression500 kmFigure 4.10: Schematic showing the different landscape settings of each landform class (afterSANBI 2009), which inform classification of the different wetland ecosystemtypes. Landform classes are shown in upper case, and the wetland ecosystemtypes that they potentially contain are shown in lower case.Landforms for the country were generated using a 50 m resolution digital elevation model generatedfrom contours at 20 m intervals and spot height data per 1:50 000 data sheets. The ArcGIS 9.3Landform Tool was used, which calculates the standard deviation from the average elevation ina specified search area to identify landform classes. This tool offers the option of using a smallneighbourhood to consider the landform within a local context, and a large neighbourhood toreflect the regional context within which the landform occurs. The maximum valley widths pergeomorphic province (as calculated by Partridge et al. 2010) were used to inform selection of the smallneighbourhood across the country, and the maximum width tertiary catchments (Midgley et al. 1994)informed selection of the large neighbourhood (van Deventer et al. in review). The results (Figure 4.11)were compared to 260 random points that were classified manually using Google Earth imagery andtopographical maps, and showed a 50-60% overall accuracy. The Landform Tool tended to overestimatebenches and valleys, and underestimate slopes. This level of congruency suggests that this landformlayer may be suitable for coarse-scale national application, but will have to be further refined for useat the local scale.Figure 4.11: Landforms of wetland vegetation groups (Section 4.6) to classify wetland ecosystem types.Input data layers- 54 -
4.6 Wetland vegetation groupsWetland vegetation groups were used to characterise the regional context within whichwetlands occur based on the premise that wetlands in a particular vegetation group arelikely to be more similar to one another than to wetlands in other vegetation groups.Broad vegetation groupings reflect differences in geology, soils and climate, which in turnaffect the ecological characteristics and functionality of wetlands.Wetland vegetation groups were derived by grouping the 438 vegetation types of SouthAfrica, Lesotho and Swaziland (Mucina and Rutherford 2004) into groups thought toreflect the turnover of wetland biodiversity at a national level. The expert knowledgeof regional wetland ecologists was used to do the grouping, resulting in a map of 133wetland vegetation groups (Figure 4.12). This was then used, in combination with thelandform map (Section 4.5), to classify wetland ecosystem types. Future research shouldfocus on improving these groupings using cluster analysis of representative surveys ofwetlands across the country.(a)(b)800 kmFigure 4.12: (a) Vegetation Wetland vegetation groups were used to characterise the regional context within which wetlands occur (after SANBI 2009).Input data layers - 55 -
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Front Cover: Clanwilliam and Fiery
Page 4 and 5:
AcknowledgementsThe development of
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Table of ContentsPart 1:Introductio
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Part 1: IntroductionThis section de
Page 10 and 11: 1.2 Guiding principles for managing
Page 12 and 13: 1.3.3 Destruction or degradation of
Page 15 and 16: 1.5 History of freshwater biodivers
Page 17 and 18: Part 2: FreshwaterEcosystem Priorit
Page 19 and 20: Box 2.1. Policy mechanisms supporte
Page 21 and 22: WMA 1LegendLIMPOPOTownSub-quaternar
Page 23 and 24: WMA 3CROCODILE(WEST) AND MARICOLege
Page 25 and 26: WMA 5INKOMATILegendSabieTownSub-qua
Page 27 and 28: WMA 7THUKELALegendBuffaloTownSub-qu
Page 29 and 30: WMA 9MIDDLE VAALMiddle VaalRhenoste
Page 31 and 32: WMA 11MVOTI TO UMZIMKULUMvotiLegend
Page 33 and 34: WMA 13UPPER ORANGERiet/ModderLesoth
Page 35 and 36: WMA 15FISH TO TSITSIKAMMALegendFish
Page 37 and 38: WMA 17OLIFANTS/DOORNSandveldKnersvl
Page 39 and 40: WMA 19BERGLegendTownSub-quaternary
Page 41 and 42: % Area of WMA selected as FEPA10213
Page 43 and 44: 3.3 Free-flowing riversA free-flowi
Page 45 and 46: The Mtentu River: An ecotourism ass
Page 47 and 48: The Elands River: A unique biodiver
Page 49 and 50: For species and species lineages co
Page 51 and 52: The map of high water yield areas w
Page 53 and 54: 0 10 20 30 40 50BERGBREEDECROCODILE
Page 55 and 56: 4.1.2 Flow variability categoriesTh
Page 57 and 58: % River length0 20 40 60 80 1004.2
Page 59: 4.4 Wetland conditionWetland condit
Page 64 and 65: Nel, J.L., Roux, D.J., Cowling, R.M
Page 66 and 67: Fish Support Area: Fish sanctuaries
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NFEPA Atlas 5.3_20111025_FINAL.indd - Biodiversity GIS - SANBI

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