Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition
This thesis considers various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.The use of remote sensing technologies for land condition mon...
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| Format: | Thesis |
| Language: | English |
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Curtin University
1997
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| Online Access: | http://hdl.handle.net/20.500.11937/868 |
| _version_ | 1848743504156557312 |
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| author | Caccetta, Peter A. |
| author_facet | Caccetta, Peter A. |
| author_sort | Caccetta, Peter A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis considers various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.The use of remote sensing technologies for land condition monitoring in Western Australia had previously been established by other researchers, although significant limitations in the accuracy of the results remain. From a monitoring perspective, this thesis considers approaches for improving the accuracy of land condition monitoring by incorporating other data into the interpretation process.Digital elevation data provide one potentially useful source of information. The use of digital elevation data are extensively considered here. In particular, various methods for deriving variables relating to landform from digital elevation data and remotely sensed data are reviewed and new techniques derived.Given that data from a number of sources may need to be combined in order to produce accurate interpretations of land use/condition, methods for combining data are reviewed. Of the many different approaches available, a Bayesian approach is adopted.The approach adopted is based on relatively new developments in probabilistic expert systems. This thesis demonstrates how these new developments provide a unified framework for uniting traditional classification methods and methods for integrating information from other spatial data sets, including data derived from digital elevation models, remotely sensed imagery and human experts.Two applications of the techniques are primarily considered. Firstly, the techniques are applied to the task of salinity mapping/ monitoring and compared to existing techniques. Large improvements are apparent. Secondly, the techniques are applied to salinity prediction, an application not previously considered by other researchers in this domain. The results are encouraging. Finally limitations of the approach are discussed. |
| first_indexed | 2025-11-14T05:46:37Z |
| format | Thesis |
| id | curtin-20.500.11937-868 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:46:37Z |
| publishDate | 1997 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-8682017-02-20T06:40:31Z Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition Caccetta, Peter A. geographic information systems land condition Bayesian knowledge-based system remote sensing This thesis considers various aspects of the use of remote sensing, geographical information systems and Bayesian knowledge-based expert system technologies for broad-scale monitoring of land condition in the Western Australian wheat belt.The use of remote sensing technologies for land condition monitoring in Western Australia had previously been established by other researchers, although significant limitations in the accuracy of the results remain. From a monitoring perspective, this thesis considers approaches for improving the accuracy of land condition monitoring by incorporating other data into the interpretation process.Digital elevation data provide one potentially useful source of information. The use of digital elevation data are extensively considered here. In particular, various methods for deriving variables relating to landform from digital elevation data and remotely sensed data are reviewed and new techniques derived.Given that data from a number of sources may need to be combined in order to produce accurate interpretations of land use/condition, methods for combining data are reviewed. Of the many different approaches available, a Bayesian approach is adopted.The approach adopted is based on relatively new developments in probabilistic expert systems. This thesis demonstrates how these new developments provide a unified framework for uniting traditional classification methods and methods for integrating information from other spatial data sets, including data derived from digital elevation models, remotely sensed imagery and human experts.Two applications of the techniques are primarily considered. Firstly, the techniques are applied to the task of salinity mapping/ monitoring and compared to existing techniques. Large improvements are apparent. Secondly, the techniques are applied to salinity prediction, an application not previously considered by other researchers in this domain. The results are encouraging. Finally limitations of the approach are discussed. 1997 Thesis http://hdl.handle.net/20.500.11937/868 en Curtin University fulltext |
| spellingShingle | geographic information systems land condition Bayesian knowledge-based system remote sensing Caccetta, Peter A. Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title | Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title_full | Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title_fullStr | Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title_full_unstemmed | Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title_short | Remote sensing, geographic information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition |
| title_sort | remote sensing, geographic information systems (gis) and bayesian knowledge-based methods for monitoring land condition |
| topic | geographic information systems land condition Bayesian knowledge-based system remote sensing |
| url | http://hdl.handle.net/20.500.11937/868 |