- Title
- Assessment of indigenous forest degradation and deforestation along the wild coast, near Port St John’s, Eastern Cape Province, South Africa
- Creator
- Katende, Lukyamuzi Lucky Fulgentius
- Subject
- Deforestation -- South Africa -- Eastern Cape
- Subject
- Forest degradation -- South Africa -- Eastern Cape Forest management -- South Africa -- Eastern Cape
- Date Issued
- 2018
- Date
- 2018
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10948/30649
- Identifier
- vital:31006
- Description
- Indigenous forests along the Wild Coast of the Eastern Cape Province have experienced both degradation and deforestation over the past decades. In early 2000, steps were taken to rehabilitate some of the degraded areas. Nevertheless, there is no monitoring mechanism in place, so little is known about the extent of degradation and impact of the rehabilitation efforts. The present study assesses the extent to which deforestation and degradation of the indigenous forests have occurred, and evaluates rehabilitation efforts in the study area around Port Saint John’s. Forest degradation was defined as the decrease in forest cover density while deforestation was defined as an increase in the trend of light forests and/ or a decrease in dense forests. The details for this study were obtained from multi – temporal remotely sensed data for a period between 1982 and 2013 (31 years). Multi-temporal Landsat satellite imagery for 1982, 1986, 1989, 2002, 2009 and 2013 was acquired and analysed. On the basis of prior knowledge of the area, the supervised classification approach was used. The Maximum likelihood supervised classification technique was used to extract information from satellite data. The classified images were filtered using a majority filtering procedure to reduce noise. Google Earth (Astrium) ancillary images were used to refine the classification based on expert rules. The derived changes in the degraded and rehabilitated areas were further validated through field visits. The overall image classification accuracy generated from Landsat image data ranged from 80% to 90%. It was noted that the area of dense forest almost doubled between 1986 and 1989, coinciding with a 59% decrease in the light forest. Subsequently, dense forests increased by 14,820 ha while light forests decreased by 16,690 ha between 1989 and 2002. The subsequent reduction in light forest coverage is explained by the establishment of the Participatory Forest Management (PFMA) approach by Department of Water Affairs and Forestry (DWAF) which reversed the degradation trend. However, specific degradation hotspots were identified, particularly where new settlements have been established. The emergence of the non-vegetated area increased gradually from 7% in 1986 to 23.4% in 2013. Notably, dense forest was observed to have experienced higher rates of forest degradation and deforestation than the light forest. The highest number patches were 4 recorded between 2002 and 1998, followed by between 2010 and 2013 and lastly 1986. Based on spatial connectedness of patches, the year 1986 had the highest landscape connectedness of forest vegetation (CONAT = 35.3) followed by 2002 and 1996 while the year 2010 and 2013 had the lowest landscape contiguity. Over the study period, the distribution of patches clearly shows that forest degradation and deforestation rates were lower in the years 1986, 1998 and tremendously increased in the later period of between 2010 and 2013. However, as a result of rehabilitation efforts, dense forest was seen to steadily gain more land than light forest. Finer details of degradation trends could not be easily picked from the images used in the study, given their spatial resolution limitations. That notwithstanding, the trends identified are good for overview decisions. The study has also established that de-agraianisation, forest restoration and rehabilitation greatly contributed to increased forest cover. Therefore, with more use of GIS by forest managers, and imagery of the high resolution being readily available, forests will in future be easily monitored using remote sensing.
- Format
- 116 leaves
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | KATENDE – LUKYAMUZI LUCKY FULGENTIUS.pdf | 2 MB | Adobe Acrobat PDF | View Details Download |