Mapping potential soil salinization using rule based object-oriented image analysis
| dc.contributor.advisor | Zietsman, H. L. | |
| dc.contributor.author | Stals, Jacobus Petrus | en_ZA |
| dc.contributor.other | University of Stellenbosch. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. | |
| dc.date.accessioned | 2008-04-14T09:50:42Z | en_ZA |
| dc.date.accessioned | 2010-06-01T08:47:15Z | |
| dc.date.available | 2008-04-14T09:50:42Z | en_ZA |
| dc.date.available | 2010-06-01T08:47:15Z | |
| dc.date.issued | 2007-12 | en_ZA |
| dc.description | Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2007. | |
| dc.description.abstract | Soil salinization is a world wide environmental problem affecting plant growth and agricultural yields. Remote sensing has been used as a tool to detect and/or manage soil salinity. Object-oriented image analysis is a relatively new image analysis technique which allows analysis at different hierarchical scales, the use of relationships between objects and contextual information in the classification process, and the ability to create a rule based classification procedure. The Lower Orange River in South Africa is a region of successful irrigation farming along the river floodplain but also with the potential risk of soil salinization. This research attempted to detect and map areas of potential high soil salinity using digital aerial photography and digital elevation models. Image orthorectification was conducted on the digital aerial photographs. The radiometric variances between photographs made radiometric calibration of the photographs necessary. Radiometric calibration on the photographs was conducted using Landsat 7 satellite images as radiometric correction values, and image segmentation as the correction units for the photographs. After radiometric calibration, object-oriented analysis could be conducted on one analysis region and the developed rule bases applied to the other regions without the need for adjusting parameters. A rule based hierarchical classification was developed to detect vegetation stress from the photographs as well as salinity potential terrain features from the digital elevation models. These rule bases were applied to all analysis blocks. The detected potential high salinity indicators were analyzed spatially with field collected soil data in order to assess the capability of the classifications to detect actual salinization, as well as to assess which indicators were the best indicators of salinity potential. Vegetation stress was not a good indicator of salinity as many other indicators could also cause vegetation stress. Terrain indicators such as depressions in the landscape at a micro scale were the best indicators of potential soil salinization. | en_ZA |
| dc.format.extent | 37999973 bytes | en_ZA |
| dc.format.mimetype | application/pdf | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/2371 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | Stellenbosch : University of Stellenbosch | |
| dc.rights.holder | University of Stellenbosch | |
| dc.subject | Dissertations -- Geography and environmental studies | en |
| dc.subject | Theses -- Geography and environmental studies | en |
| dc.subject | Soil salinization -- South Africa -- Orange River Region | en |
| dc.subject | Soil mapping -- South Africa -- Orange River Region | en |
| dc.subject | Image analysis | en |
| dc.subject | Object-oriented methods (Computer science) | en |
| dc.title | Mapping potential soil salinization using rule based object-oriented image analysis | en_ZA |
| dc.type | Thesis | en_ZA |