A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2016. / This dissertation examines the role of remote sensing on rock art survey and is motivated
by two key objectives: to determine if remote sensing has any value to rock art survey,
furthermore if remote sensing is successful to determine if these individual remote sensing
components can contribute to a predictive (site locating) model for rock art survey. Previous
research effectively applied remote sensing techniques to alternate environmental studies
which could be replicated in such a study. The successful application of google earth
imagery to rock art survey (Pugin 2012) demonstrated the potential for a more expansive
automated procedure and this dissertation looks to build on that success. The key objectives
were tested using three different research areas to determine remote sensing potential
across different terrain.
Owing to the nature of the study, the initial predictions were formulated using the MARA
database – a database of known rock art sites in the surrounds of Matatiele, Eastern Cape
– and were then applied to surrounding areas to expand this database further. Upon adding
more sites to this database, the predictions were applied to Sehlabathebe National Park,
Lesotho and then 31 rock art sites in the areas adjacent to Underberg. The findings of this
research support the use of predictive models provided that the predictive model is
formulated and tested using a substantial dataset. In conclusion, remote sensing is capable
of contributing to rock art surveys and to the production of successful predictive models for
rock art survey or alternate archaeological procedures focusing on specific environmental
features. / LG2017
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/21686 |
| Date | January 2016 |
| Creators | Pugin, James Malcolm |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (xiii, 186 leaves), application/pdf, application/pdf |
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