Expansion of woody vegetation results in the transformation of a grass-dominated ecosystem to a tree-dominated ecosystem causing land degradation in most semi-arid areas. The imbalance in the natural ecosystem between herbaceous plants and woody vegetation poses a threat to the natural environment. Such changes alter the flow, availability and quality of nutrient resources in the biogeochemical cycle. Most of the dominating woody plants are often unpalatable to domestic livestock. Therefore, the objective is to assess the spatial extent of woody vegetation over time. Knowledge of the spatial and temporal characteristics of woody vegetation dynamics will enable the development of management plans. These characteristics can be derived using remote sensing techniques which have become efficient in such studies. This study aimed to characterize woody vegetation dynamics along the route between Alice and King Williams’s town in Eastern Cape Province South Africa using Landsat data. This aim was achieved by focussing on three specific objectives. The first objective was to compare the performance of multispectral data and Normalized Difference Vegetation Index (NDVI) data of Landsat imagery in mapping woody vegetation cover. The second objective was to investigate the effect of the spatial resolution of remotely-sensed data on discrimination of woody vegetation from other land cover types. The third objective characterised woody vegetation dynamics between 1986 and 2013/2014 using the results from the first objective. The study used Landsat imagery acquired in November or February of 1986, 1994/1995, 2002/2003 and 2013/2014. Due to lack of data which covered the study area two separate dates (November and February) where used for the study resulting in naming the study area western and eastern parts. Unsupervised classification was performed on the multispectral, NDVI and pan-sharpened images to generate four generic land cover classes, namely water, bare land, grassland and woodland. Accuracy assessments of the classified images was done using error matrix. The results showed that the classification based on NDVI images yielded a better overall accuracy than the classification based on multispectral images for the western (83 percent and 75 percent, respectively) and eastern (82 percent and 76 percent, respectively) parts of the study area. Similarly, pan-sharpening resulted in better overall classification accuracy than multispectral, but comparable to the classification of the NDVI images for both the western (82 percent) and eastern (83 percent) parts of the study area. Remote sensing is an effective tool in assessing changes in the physical environment. Landsat imagery is suitable in assessing land cover dynamics given the long-term and free availability of the image. In addition, the large spatial coverage it provides, enables Landsat data to be used on studies that have wide spatial coverage. Classification for the purpose of time-series analysis was then performed on the NDVI images of each date (1986, 1994/1995, 2002/2003 and 2013/2014). Both woody vegetation and grassland experienced changes from 1986 to 2013/2014 with grassland occupying (75 percent) compared to woodland (17 percent) in 1986. In the year 2013/14 grassland occupied 32 percent and woodland occupied 51 percent of the study area. The increase in woody vegetation in the study area can be attributed to livestock rearing and migration of people from the rural to urban areas post-Apartheid. The study output will aid in the development of a database on land cover distribution of the area between King William’s town and Alice town, providing useful information to decision-making and further studies on woody vegetation.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ufh/vital:27564 |
| Date | January 2016 |
| Creators | Fundisi, Emmanuel |
| Publisher | University of Fort Hare, Faculty of Science & Agriculture |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Masters, MSc |
| Format | 106 leaves, pdf |
| Rights | University of Fort Hare |
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