Although remotely sensed data have been employed to assess various environmental problems, relatively few previous studies have focused on the impacts of mining. In Indonesia, mining activities have increasingly become one of major drivers of land cover change. The majority of remote sensing research projects on mining environments have exploited optical data which are frequently complicated by tmospheric disturbance, especially in tropical territories. Active remote sensors such as Synthetic Aperture Radar (SAR) are invaluable in this case. Monitoring by Independent SAR data has been limited due to single polarisation. Dual-polarised data have been employed considerably, although for some forestry applications the data were found insufficient to retrieve basic information. This Masters thesis is devoted to assess fully polarimetric SAR data for environmental monitoring of the tailings deposition zone of the PT Freeport Indonesia Grasberg mine in Papua, Indonesia. The main data were two granules of the AIRSAR datasets acquired during the PACRIM-II campaign. To support the interpretation and analysis, a scene of Landsat ETM February 2001) was used, juxtaposed with classified aerial photographs and a series of SPOT VEGETATION images. Both backscattering information and complex coherence matrices, as common representations of polarimetric data, were studied. Primary applications of this research were on degraded forest and environmental rehabilitation. Most parts of Indonesian forests have experienced abrupt changes as an impact of clear-cut deforestation. Gradual changes such as those due to fire or flooded tailings, however, are least studied. It was shown that the Cloude-Pottier polarimetric decomposition provided a convenient way to interpret various stages of forest disturbance. The result suggested that the Entropy parameter of the Cloude-Pottier decomposition could be used as a disturbance indicator. Using the fully polarimetric dataset combined with Support Vector Machine learning, the outcomes were generally acceptable. It was possible to improve classification accuracy by incorporating decomposition parameters, although it seemed insignificant. Land rehabilitation on tailings deposits has been a central concern of the government and the mining operator. Indigenous plant pioneers such as reeds (Phragmites) can naturally grow on dry tailings where soil structure is fairly well developed. To assist such efforts, a part of this research involved identification of dry tailings. On the first assessment, interpretation of surface scatterers was aided by polarimetric signatures. Apparently, longer wavelengths such as L- and P-band were overpenetrated; hence, growing reeds on dry tailings were less detectable. In this case, the use of C-band data was found fairly robust. Employing Mahalanobis statistics, the combination of HH and VV performed well on classification, having similar accuracy with quad polarimetric data. Extension on previous results was made through the Freeman-Durden decomposition. Interpretation using a three-component image of odd, even bounce and volume scattering showed that dry and wet tailings could be well distinguished. The application was benefited from unique responses of dielectric materials in the tailings deposit on SAR signals; hence it is possible to discriminate tailings with different moisture levels. However, further assessment of tailings moisture was not possible due to security reasons and access limitations at the study site. Fully polarimetric data were also employed to support rehabilitation of stressed mangrove forest on the southern coast. In this case, the Cloude-Pottier decomposition was employed along with textural parameters. Inclusion of textural properties was found invaluable for the classification using various statistical trees, and more important than decomposition parameters. It was concluded that incorporating polarimetric decompositions and textural parameters into coherence matrix leads to profound accuracy.
| Identifer | oai:union.ndltd.org:ADTP/240879 |
| Date | January 2008 |
| Creators | Trisasongko, Bambang, Physical, Environmental & Mathematical Sciences, Australian Defence Force Academy, UNSW |
| Publisher | Awarded by:University of New South Wales - Australian Defence Force Academy. |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright |
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