Southern African Landscapes

In the first week, we investigate the landscapes of Makhanda in the context of Southern African Geological Evolution. The study will involve a number of readings as well as field trips to investigate the area's Geomorphology. The deliverable is an essay on the Geomorphic Evolution of Makhanda.

Assessment:

Following the field trips and reading relevant material on the geomorphic evolution of southern Africa, a detailed synopsis on the geomorphology of the Makhanda area must be presented.

Readings:

• Lewis, C.A. (2008). Geomorphology of the Eastern Cape, South Africa. Grahamstown, NISC, 188 pp.

• Maude, R. R. (2012). Macroscale Geomorphic Evolution. In P. J. Holmes, Meadows, M.E. (Ed.), Southern African Geomorphology: recent trends and new directions (pp. 7–21). African Sun Media.

• Partridge, T. C., & Maude, R. R. (1987). Geomorphic Evolution of Southern Africa Since the Mesozoic. South African Journal of Geology, 90(2), 179-208.

In the second week, we explore how spatial technologies can be utilised to describe landscapes. Using digital elevation data, we will create terrain models and use algorithms to describe the landscape. The premise is that we will build on the work completed in Week 1 and utilise digital methodologies. A large number of tools, primarily in the field of Geomorphometrics, have been developed, enabling the landscape to be described, modelled, and analysed. These will be utilised to continue the work from the first week and further developed to incorporate spatial technologies.

Assessment:

Using spatial technologies, a document that describes the Geomorphology of the Makhanda area. The submission should be rich in maps, graphics and images. It should build on the work that was completed in the first week.

Readings:

• Atkinson, J., de Clercq, W., & Rozanov, A. (2020). Multi-resolution soil-landscape characterisation in KwaZulu Natal: Using geomorphons to classify local soilscapes for improved digital geomorphological modelling. Geoderma Regional, 22, e00291. https://doi.org/https://doi.org/10.1016/j.geodrs.2020.e00291

• De Reu, J., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., De Smedt, P., Chu, W., Antrop, M., De Maeyer, P., Finke, P., Van Meirvenne, M., Verniers, J., & Crombé, P. (2013). Application of the topographic position index to heterogeneous landscapes. Geomorphology, 186, 39–49. https://doi.org/https://doi.org/10.1016/j.geomorph.2012.12.015

• ESRI (n.d.) Geomorphon Landforms (Spatial Analyst)—ArcGIS Pro | Documentation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/geomorphon-landforms.htm Accessed: 1 August 2025.

• Guisan, A., Weiss, S.B., Weiss, A.D. (1999): GLM versus CCA spatial modelling of plant species distribution. Plant Ecology, 143, 107-122.

• Jasiewicz, J. & Stepinski, T.F. (2013). Geomorphons - a pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147–56. DOI: 10.1016/j.geomorph.2012.11.005

• Jenness, J. (2006). Topographic Position Index. (TPI). https://www.jennessent.com/downloads/tpi_poster_av3.zip

• Weiss, A.D. (2000): Topographic Position and Landforms Analysis. Poster http://www.jennessent.com/downloads/tpi-poster-tnc_18x22.pdf.

• Wilson, J.P. & Gallant, J.C. (2000). Primary Topographic Attributes. In: Wilson, J.P. & Gallant, J.C. [Eds.]: Terrain Analysis: Principles and Applications, John Wiley & Sons, p.51-85.

In the third week, spatial technologies are applied to determine the location of buildings in the built and physical landscapes. The Global Google-Microsoft Open Buildings Dataset (https://gee-community-catalog.org/projects/global_buildings/) will be utilised to determine building locations in the landscape, using TPI (Topographic Position Index) and Geomorphons to identify the preferred locations for buildings. In addition, the location of buildings relative to road infrastructure will be determined using Kernel Densities and other similar metrics, as well as determining a number of metrics related to settlement.

Assessment:

The assignment for this week is to undertake a detailed spatial analysis of buildings, using parts of the previous Transkei as a case study. Various spatial metrics will be measured, including kernel densities, Nearest Neighbour Statistics, and Clustering. Additionally, the relationships between building locations and topographic indices will be examined. A written report, with maps and diagrams, must be submitted.

Readings:

• Florczyk, A. J., Melchiorri, M., Zeidler, J., Corbane, C., Schiavina, M., Freire, S., Sabo, F., Politis, P., Esch, T., & Pesaresi, M. (2020). The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth, 13(1), 45–60. https://doi.org/10.1080/17538947.2018.1550121

• Xi, C.-B., Qian, T.-L., Chi, Y., Chen, J., & Wang, J.-C. (2018). Relationship between settlements and topographical factors: An example from Sichuan Province, China. Journal of Mountain Science, 15(9), 2043–2054. https://doi.org/10.1007/s11629-018-4863-z

• Xu, J., Zheng, L., Ma, R., & Tian, H. (2023). Correlation between Distribution of Rural Settlements and Topography in Plateau-Mountain Area: A Study of Yunnan Province, China. Sustainability, 15(4), 3458. https://doi.org/10.3390/su15043458

Week 4 involves using satellite imagery and digital elevation models to investigate the preferred locations of melting snow using digital terrain models. Satellite imagery and digital elevation models will be utilised to analyse the melting regime of winter snowfalls in South Africa and Lesotho for a particular year.

Assessment:

A report detailing the topographic preferences of late-lying snow must be presented, richly illustrated with graphs, maps and diagrams.

Readings:

• Brown, A. (2012). Accounting for snow types. Nature Climate Change, 2, 394. https://doi.org/10.1038/nclimate1571

• Grab, S. W., Mulder, N. A., & Mills, S. C. (2009). Spatial associations between the longest‐lasting winter snow cover and cold region landforms in the High Drakensberg, Southern Africa. Geografiska Annaler: Series A, Physical Geography, 91(2), 83–97. https://doi.org/10.1111/j.1468-0459.2009.00356.x

• Mulder, N., & Grab, S. W. (2002). Remote sensing for snow cover analysis along the Drakensberg escarpment. South African Journal of Science, 98(5-6), 213–217.

• Mulder, N., & Grab, S. W. (2009). Contemporary spatio-temporal patterns of snow cover over the Drakensberg. South African Journal of Science, 105(5-6), 228–233.

• Wunderle, S., Gross, T., & Hüsler, F. (2016). Snow extent variability in Lesotho derived from MODIS data (2000-2014). Remote Sensing, 8(6), Article 448. https://doi.org/10.3390/rs8060448

Assessment:

The final week of the course involves "tidying up" the previous four weeks' assignments and collating he information into a portfolio of evidence. The portfolio should be prefaced by a document that considers "digital landscapes in a southern African context". There should also be a concluding section that considers the future of digital landscape studies.