- Title
- Characterising termite mound spatial patterns in the Eastern Cape Karoo : applying drone remote sensing,GIS and spatial statistics
- Creator
- Mngcele, Lizalise Sive Nqaba
- Subject
- Drone aircraft in remote sensing – Eastern Cape Karoo
- Subject
- Termites
- Subject
- Geographic information Systems – South Africa
- Date Issued
- 2022-12
- Date
- 2022-12
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/59555
- Identifier
- vital:62169
- Description
- The Eastern Cape Karoo in South Africa has been earmarked for potential Shale Gas development, which has necessitated the understanding of existing ecosystems to be quantiĄed pre-development, in order to have a baseline against which the exploration can be monitored. Termite mounds as baseline mechanisms, are known to be sensitive to ecosystem disturbance and because of their abundance in the exploration zone, have been studied as indicator species. They are both a surface and subsurface phenomena which makes them an ideal baseline monitoring mechanism. Termite mound height, basal circumference and geospatial data was collected against natural and anthropogenic factors: elevation, vegetation, water, soil, geology, human settlements and roads. Mound distributions were observed across four study sites, and seven plots, using a DJI Phantom 4 Pro drone, an aerial and ground survey. Observed mound data on the drone and aerial survey was compared to that of the ground survey. Overall, the drone survey outperformed the aerial survey in recording accurate termite mound data. This was largely attributed to the scale of the study which gave the drone a competitive advantage. It allowed for drone data to be collected at 40 m altitude with an image resolution of 2-6 cm/pixel on each plot. In addition, drone detection accuracy was improved through the ability to generate digital surface models (DSMs) through point clouds and overlaying them with orthomosaics. Considering observed mound spatial point patterns, both the drone and aerial survey were more than 50% percent consistent with the ground survey, although the drone survey detected 28.57% more accurate mound spatial point patterns than the aerial survey.
- Description
- Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2022
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (114 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Mngcele, LSN Dec 2022.pdf | 9 MB | Adobe Acrobat PDF | View Details Download |