Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2005. / Salinisation is a major environmental hazard that reduces agricultural yields and
degrades arable land. Two main categories of salinisation are: primary and secondary
soil salinisation. While primary soil salinisation is caused by natural processes,
secondary soil salinisation is caused by human factors. Incorrect irrigation practices
are the major contributor to secondary soil salinisation. Because of low costs and less
time that is associated with the use of remote sensing techniques, remote sensing data
is used in this study to identify and map salinised irrigated land between Upington and
Keimoes, Northern Cape Province, in South Africa.
The aim of this study is to evaluate the potential of digital aerial imagery in
identifying salinised cultivated land. Two methods were used to realize this aim. The
first method involved visually identifying salinised areas on NIR, and NDVI images
and then digitizing them onscreen. In the second method, digital RGB mosaicked,
stacked, and NDVI images were subjected to unsupervised image classification to
identify salinised land. Soil samples randomly selected and analyzed for salinity were
used to validate the results obtained from the analysis of aerial photographs.
Both techniques had difficulties in identifying salinised land because of their inability
to differentiate salt induced stress from other forms of stress. Visual image analysis
was relatively successful in identifying salinised land than unsupervised image
classification. Visual image analysis correctly identified about 55% of salinised land
while only about 25% was identified by unsupervised classification. The two
techniques predict that an average of about 10% of irrigated land is affected by
salinisation in the study area.
This study found that although visual analysis was time consuming and cannot
differentiate salt induced stress from other forms; it is fairly possible to identify areas
of crop stress using digital aerial imagery. Unsupervised classification was not
successful in identifying areas of crop stress.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2645 |
| Date | 04 1900 |
| Creators | Mashimbye, Zama Eric |
| Contributors | Zietsman, H. L., University of Stellenbosch. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
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