Thesis (M.Sc. (Geography)) -- University of Limpopo, 2020 / Wetland vegetation provides a variety of goods and services such as carbon sequestration, flood control, climate regulation, filtering contamination, improve and maintain water quality, ecological functioning. However, changes in land cover and uses, overgrazing and environmental changes have resulted in the transformation of the wetland ecosystem. So far, a lot of focus has been biased towards large wetlands neglecting wetlands at a local scale. Smaller wetlands continue to receive massive degradation by the surrounding communities.Therefore, this study seeks to assess and map wetland vegetation as an indicator of ecological productivity on a small scale. The Sentinel-2 MSI image was used to map wetland plant species diversity and above-ground biomass (AGB). Four key diversity indices; the Shannon Wiener (H), Simpson (D), Pielou (J), and Species richness (S) were used to measure species diversity. A multilinear regression technique was applied to establish the relationship between remotely sensed data and diversity indices and AGB. The results indicated that Simpson (D) has a high relationship with combined vegetation indices and spectral band, yielding the highest accuracy when compared to other diversity indices. For example, an R² of 0.75, and the RMSE of 0.08 and AIC of -191.6 were observed. Further, vegetation AGB was estimated with high accuracy of an R² of 0.65, the RMSE 29.02, and AIC of 280.21. These results indicate that Maungani wetland has high species abundance largely dominated by one species (Cyperus latifidius) and highly productive. The findings of this work underscore the relevance of remotely sensed to estimate and monitor wetland plant species
diversity with high accuracy.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3478 |
| Date | January 2020 |
| Creators | Mashala, Makgabo Johanna |
| Contributors | Dube, T., Dhau, I. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xi, 66 leaves |
| Relation |
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