The research contained within this thesis was directed at examining the spectral properties of regolith-dominated terrains using airborne and proximal hyperspectral instruments. The focus of the investigation was to identify the mineralogy of the regolith and determine if surficial materials were indicative of the underlying bedrock in the regolithdominated terrain of the eastern Olary Domain, South Australia. The research area was constrained to a 250 km2 area around the Cu-Au mineralisation of the White Dam Prosect. Integrated remote sensing, using airborne hyperspectral datasets (HyMap), Landsat imagery and gamma-ray spectroscopy data, was performed to map regolith-landforms and extract information on surficial materials. Detailed calibration of the HyMap dataset, using a modified model-based/empirical line calibration technique, was required prior to information extraction. The White Dam area was able to be divided into: alluvial regolith-dominated; in situ regolith-dominated; and bedrock-dominated terrains, based on mineralogical interpretations of the regolith, using the remotely sensed hyperspectral data. Alluvial regions were characterised by large abundances of vegetation and soils with a hematite-rich mineralogy. Highly weathered areas of in situ material were discriminated by the presence of goethite and kaolinite of various crystallinities, whereas the bedrock-dominated regions displayed white mica-/muscovite-rich mineralogy. Areas flanking bedrock exposures commonly consisted of shallow muscovite-rich soils containing regolith carbonate accumulations. Traditional mineral mapping processes were performed on the HyMap data and were able to extract endmembers of regolith and other surficial materials. The Mixture Tuned Matched Filter un-mixing process was successful at classifying regolith materials and minerals. Spectral indices performed on masked data were effective at identifying the key regolith mineralogical features of the HyMap imagery and proved less time consuming than un-mixing processes. Processed HyMap imagery was able to identify weathering halos, highlighted in mineralogical changes, around bedrock exposures. Proximal spectral measurements and XRD analyses of samples collected from the White Dam Prospect were used to create detailed mineralogical dispersion maps of the surface and costean sections. Regolith materials of the logged sections were found to correlate with the spectrally-derived mineral dispersion profiles. The HyLogger drill core scanning instrument was used to examine the mineralogy of the fresh bedrock, which contrasted with the weathering-derived near-surface regolith materials. The overall outcomes of the thesis showed that hyperspectral techniques were useful for charactering the mineralogy of surficial materials and mapping regolith-landforms. / Thesis (Ph.D.)--School of Earth & Environmental Sciences, 2004.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale.

The objective of this study was to investigate the application of multiscale satellite remote sensing data for assessment of land cover change in the rural-urban fringe. Inherent in this assessment process was the interpretation of multispectral data collected by several medium resolution satellite systems and evaluation of the quality of the resulting change information. Each dataset was acquired for a single date and classified at two levels of detail using standard classification algorithms. The optimum classification approach for each date was identified and the changes in land cover evaluated in several ways. The contribution of spatial and thematic errors and their propagation through the analysis process was investigated.Data for this research were acquired over an area approximately 4.5 km square located in the southern metropolitan area of Perth, Western Australia. At the time of the initial data acquisition in 1972 the area was predominantly rural and comprised mostly dense pine plantations, however by the final stages of data acquisition in 1991, the area was almost completely given over to urban residential land use. Changes were interpreted from classified Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM) and SPOT (System Pour l'Observation de la Terre) High Resolution Visible (HRV) multispectral data, and were compared to reference maps compiled from medium scale aerial photographs. The geometric properties of high resolution panchromatic IRS1-D data were also evaluated to test the geometric potential of high resolution satellite data.Supervised and unsupervised classification algorithms were used for derivation of land cover maps from each multispectral dataset at two levels of detail. Data were classified onto four general levels at the broadest (Level I) classification, and into nine levels at the finest (Level II) classification. The ++ / Kappa statistic and its variance were used to determine the optimum classification approach for each dataset and at each level of detail. No significant differences were observed between classification techniques at Level I, however at Level II the supervised classification approach produced significantly better results for the Landsat TM and SPOT HRV data. Classification at the more general Level I did not produce substantially higher classification rates compared to the same data at Level II. Additionally, higher spatial resolution data did not provide increased accuracy, however this was due mainly to a much greater complexity of land covers present at the time the higher resolution Landsat TM and SPOT HRV data were recorded.Land cover changes were assessed separately at Level I for all datasets, and also between Landsat TM and SPOT HRV data at Level II. Integrated multiscale assessment of land cover change was undertaken using classified Landsat MSS data at Level I and Landsat TM data at Level 11. This enabled the continuity of change to be established across classification levels and sensor systems, even though there were variations in the level of detail extracted from each image.The sources of spatial and thematic errors in the data were investigated and their effects on change assessment analysed. The evaluation of high resolution panchromatic satellite data emphasised the contribution to the analysis of spatial errors contained within the reference data. The multiscale data also indicated that combined propagation of spatial and thematic errors requires investigation using appropriate simulation modelling to establish the influence of data uncertainty on classification and change assessment results.This research provides useful results for demonstrating a process for the integration of information derived from remotely sensed data at different measurement ++ / scales. Availability of data from an increasing range of remote sensing platforms and uncertainty of long term data availability emphasises the need to develop flexible interpretation and analysis approaches. This research adds value to the existing data archive by demonstrating how historical data may be integrated regardless of the spectral and spatial characteristics of the sensors.

Thesis (Ph.D.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (p. 196-204).

Thesis (Ph.D.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 155-167).

Thesis (Ph.D.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (p. 180-185).

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 133-138).

Thesis (Ph.D.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (p. 174-180).

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv. / Härtill 8 uppsatser.