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The capability to monitor bushfires on a large scale from space has long been identified as an important contribution to climate and atmospheric research as well as a tool an aid in natural hazard response. Since the work by Dozier (1981), fire monitoring from space has relied on the principles he described. His method of identifying fires within a pixel significantly larger than the fire by utilizing the different responses of the 3 μm and 11 μm channels has been applied to a number of sensors. Over the last decade a lot of work has been invested to refine and validate fire detections based on this approach. So far, the application of the method proposed by Dozier (1981) reached its peak with the launch of the MODIS instrument on board the Terra satellite. In contrast to earlier sensors, MODIS was equipped with spectral channels specifically designed for the detection of fires with algorithms based on the work by Dozier (1981). These channels were designed to overcome problems experienced with other platforms, the biggest of which is the saturation of the 3 μm channel caused by big, hot fires. Since its launch, MODIS has proven itself to be a capable platform to provide worldwide fire detection at a moderate resolution of 1 km on a daily basis. / It is the intention of this work to open up new opportunities in remote sensing of fires from satellites by showing capabilities and limitations in the application of other spectral channels, in particular the 2.1 μm channel of MODIS, than the ones currently used. This channel is chosen for investigation as fires are expected to emit a significant amount of energy in this bandwidth and as it is available at a native resolution of 500 m on MODIS; double the resolution of the 3 μm and 11 μm channels. The modelling of blackbodies of typical bushfire temperatures shows that a fire detection method based on the 2.1 μm channel will not be able to replace the current methods. Blackbodies of temperatures around 600 to 700 K, that are common for smoldering fires, do not emit a great amount of energy at 2.1 μm. It would be hardly possible to detect those fires by utilizing the 2.1 μm channel. The established methods based on the 3 μm and 11 μm channels are expected to work better in these cases. Blackbodies of typically flaming fires (above 800 K) however show a very high emission around 2.1 μm that should make their detection using the 2.1 μm channel possible. / In order to develop a fire detection method based on the 2.1 μm channel, it is necessary to differentiate between the radiance caused by a fire of sub pixel size and the radiance of a pixel caused by the reflection of sunlight. This is attempted by using time series of past observations to model a reflectance value for a given pixel expected in absence of a fire. A fire detection algorithm exploiting the difference between the expected and observed reflectance is implemented and its detection results are compared to high resolution ASTER fire maps, the standard MODIS fire detection algorithm (MOD14) and burnt area maps. The detections of the method based on the 2.1 μm channel are found to correspond very well with the other three datasets. However, the comparison showed detections that do not align with MOD14 active fire detections but are generally aligned with burn areas. This phenomena has to be investigated in the future.

Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less than ideal optical conditions. It also demonstrates that the collection of inherent and apparent optical properties is important for validating remotely sensed imagery.

Hyperspectral remote sensing image data, such as that recorded by AVIRIS with 224 spectral bands, provides rich information on ground cover types. However, it presents new problems in machine assisted interpretation, mainly in long processing times and the difficulties of class training due to the low ratio of number of training samples to the number of bands. This thesis investigates feasible and efficient feature reduction and image classification techniques which are appropriate for hyperspectral image data. The study is reported in three parts. The first concerns a deterministic approach for hyperspectral data interpretation. Multigroup and multiple threshold spectral coding procedures, and associated techniques for spectral matching and classification, are proposed and tested. By coding on subgroups of bands using one or three thresholds, spectral searching and matching becomes simple, fast and free of the need for radiometric correction. Modifications of existing statistical techniques are proposed in the second part of the investigation A block-based maximum likelihood classification technique is developed. Several subgroups are formed from the complete set of spectral bands in the data, based on the properties of global correlation among the bands. Subgroups which are poorly correlated with each other are treated independently using conventional maximum likelihood classification. Experimental results demonstrate that, when using appropriate subgroup sizes, the new method provides a compromise among classification accuracy, processing time and available training pixels. Furthermore, a segmented, and possibly multi-layer, principal components transformation is proposed as a possible feature reduction technique prior to classification, and for effective colour display. The transformation is performed efficiently on each of the highly correlated subgroups of bands independently. Selected features from each transformed subgroup can be then transformed again to achieve a satisfactory data reduction ratio and to generate the three most significant components for colour display. Classification accuracy is improved and high quality colour image display is achieved in experiments using two AVIRIS data sets.

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.

[Truncated abstract] Spatially explicit marine habitat data is required for effective resource planning and management across large areas, although mapped boundaries typically lack rigour in explaining what factors influence habitat distributions. Accurate, quantitative methods are needed. In this thesis I aimed to assess the utility of ecological models to determine what factors limit the spatial extent of marine habitats. I assessed what types of modeling methods were able to produce the most accurate predictions and what influenced model results. To achieve this, initially a broad scale marine habitat survey was undertaken in the Recherche Archipelago, on the south coast of Western Australia using video and sidescan sonar. Broad and more detailed functional habitats types were mapped for 1054km2 of the Archipelago. Broad habitats included high and low profile reefs, sand, seagrass and extensive rhodolith beds, although considerable variation could be identified from video within these broad types. Different densities of seagrass were identified and reefs were dominated by macroalgae, filter feeder communities, or a combination of both. Geophysical characteristics (depth, substrate, relief) and dominant benthic biota were recorded and then modelled using decision trees and a combination of generalised additive models (GAMs) and generalised linear models (GLMs) to determine the factors influencing broad and functional habitat variation. Models were developed for the entire Archipelago (n=2769) and a subset of data in Esperance Bay (n=797), which included exposure to wave conditions (mean maximum wave height and mean maximum shear stress) calculated from oceanographic models. Additional distance variables from the mainland and islands were also derived and used as model inputs for both datasets. Model performance varied across habitats, with no one method better than the other in terms of overall model accuracy for each habitat type, although prevalent classes (>20%) such as high profile reefs with macroalgae and dense seagrass were the most reliable (Area Under the Curve >0.7). ... This highlighted not only issues of data prevalence, but also how ecological models can be used to test the reliability of classification schemes. Care should be taken when mapping predicted habitat occurrence with broad habitat models. It should not be assumed that all habitats within the type will be defined spatially, as this may result in the distribution of distinctive and unique habitats such as filterfeeders being underestimated or not identified at all. More data is needed to improve prediction of these habitats. Despite the limitations identified, the results provide direction for future field sampling to ensure appropriate variables are sampled and classification schemes are carefully designed to improve descriptions of habitat distributions. Reliable habitat models that make ecological sense will assist future assessments of biodiversity within habitats as well as provide improved data on the probability of habitat occurrence. This data and the methods developed will be a valuable resource for reserve selection models that prioritise sites for management and planning of marine protected areas.

This thesis examines a method for estimating the daytime fluxes of heat, water vapour and carbon dioxide at regional scales by using simple models to combine spatially resolved surface properties with bulk meteorological quantities measured at a central location. The central themes of this thesis are that the spatial and temporal variability of regional scale fluxes are contained in the surface properties and meteorology respectively and that the surface properties can be interpolated across a heterogeneous landscape using remotely sensed data. The regional scale fluxes estimated using this technique are compared to the values from three other methods and this allows some conclusions to be made regarding the relative strengths and weaknesses of each method. The surface property approach yields robust estimates of the fluxes that will be useful in researching exchange processes at regional scales, providing input parameters for, and validation of, the biosphere components of General Circulation Models and testing inventory estimates of CO2 budgets. The surface properties are derived using data from 33 aircraft flights and eight ground-based sites along a 96 km transect established during the 1995 Observations At Several Interacting Scales experiment held near Wagga Wagga, New South Wales, Australia. Surface properties examined are the evaporative fraction (ratio of evapotranspiration to available energy), the Bowen ratio (ratio of sensible heat flux to evapotranspiration), the maximum stomatal conductance (maximum stomatal opening under optimal conditions) and the water-use efficiency (ratio of CO2 flux to evapotranspiration). Maximum stomatal conductance is calculated using a simple model of the stomatal response to light and water vapour deficit assuming soil evaporation occurs at the equilibrium rate. The diurnal trend and day-to-day variability in the surface properties is found to be significantly less than the spatial variability. All of the surface properties examined show some sensitivity to the synoptic conditions. The relationships between the surface properties and the Normalised Difference Vegetation Index (NDVI) are examined using a 130 km by 50 km sub-scene from a Landsat 5 Thematic Mapper (TM) image obtained five days before the start of the experiment period. The ground-based and aircraft observations are used to calculate the source-area influencing each measurement and this is combined with the Landsat 5 TM data to produce an average, source-area weighted NDVI for each ground-based site and each aircraft location. The source-area model is important because it provides the link between the observations and the remotely sensed data by identifying the surface patch that influences the measurements. Linear relationships are found between the source-area weighted NDVI and the surface properties. The observed relationships are used to interpolate the surface properties over the region covered by the satellite image and spatial variations in water loss and CO2 uptake by the surface vegetation are identified that are not resolved by the ground-based network. Analysis of the ground-based data showed that the spatial variability of the bulk meteorological quantities used in the surface property approach was much less than the diurnal trend in these data. With the small temporal variation in the surface properties noted before, this confirms the utility of assigning the spatial and temporal variability of the fluxes to the surface properties and the meteorology respectively. The combination of surface properties derived from the aircraft data and meteorology measured at a single location at the centre of the transect shows good skill in predicting the observed fluxes. Furthermore, the discrepancies between the predictions and the observations are explained by the different source-areas of the aircraft and ground-based data and much of the bias is removed when the surface properties are scaled from the NDVI of the aircraft source-area to the NDVI of the ground-based sites. Regional scale fluxes of heat and water vapour calculated using the surface property approach agree with averages of the ground-based data and this indicates that the ground-based network was representative of the OASIS region. Estimates of regional scale CO2 fluxes are not available from the ground-based network due to the lack of measurements at the driest ground-based site but the surface property approach yields plausible values. The results demonstrate the utility of extrapolating surface properties across heterogeneous landscapes using remotely sensed data.

Since the launch of the first earth resources monitoring satellite, remote sensing imagery has been used to provide information on the progress of urbanization, and land cover and land use change. The launch of the first SPOT satellite marked a significant improvement in spatial and spectral resolution for discriminating individual targets and increased the potential to acquire more information regarding land cover and land use. This study aims to investigate the capability of using SPOT digital imagery for monitoring land use change in the urbanised catchment of Tuggeranong Creek in the Australia Capital Territory, and assess its effects on catchment hydrology. SPOT multispectral and panchromatic imagery was acquired over the study area for January 1987 and September 1990. This imagery was digitally processed and analysed using microBRIAN (MB) V3.01 software to derive information on land cover and land use within the catchment. Multi-temporal imagery was co-registered to a base map with sub-output pixel accuracy. In order to improve spatial resolution, the multispectral imagery was merged with panchromatic imagery acquired on the same day using HIS and HPF techniques. The HPF technique retained more integrity of the original multispectral data than did the HIS technique. Both HPF merged and unmerged (original) image sets were used to assess the possibility of using higher spatial resolution imagery in subsequent classification and change detection analysis. On the basis of statistical calculation, non-vegetation classification results were found to be consistent between merged and un-merged imagery, but not consistent for vegetation classes. The inconsistency was found to be the result of seasonal differences in phenology and sun angle. However more small sub-pixel sized features such as houses and lawns were identified using merged imagery. Regression differencing and post classification comparisons were performed on both merged and unmerged image sets to detect temporal changes which had occurred between both image dates. As expected, merged imagery led to more sub-pixel sized examples of change being highlighted using both the HPF and HIS techniques. However, errors associated with multi-temporal image registration, compounded by classification errors arising viI from seasonal differences, meant that the reliability of all identified incidences of change could not be validated. Nevertheless, post classification change detection was found to be the most useful approach for identifying the nature of change from one type of land use to another. The results of classification and change detection techniques were used to diagnose likely changes in catchment hydrology attributable to changes in land use. Preliminary hydrologic analyses found that catchment yield is more sensitive to changes in land use than runoff volume or peak flood discharge. This study confirms that SPOT imagery can be used for mapping and monitoring land use change in urban areas. SPOT imagery was found to be suitable for providing information on land use and land cover changes and assessing the likely hydrologic consequences of such change. The use of imagery from anniversary dates would further improve the reliability of hydrologic assessments based on remote sensing of land use change.

The objective of the research reported in this thesis was to develop a technique to monitor the dynamics of sediments and nutrients entering the coastal ocean with river plumes associated with high intensity low frequency events (e.g. floods), using ocean colour remote sensing. To achieve this objective, an inverse bio-optical model was developed, based on analytical and empirical relationships between concentrations of optically significant substances and remote sensing of water-leaving radiance. The model determines concentrations of water-colouring substances such as chlorophyll, suspended sediments, and coloured dissolved organic matter, as well as the values of optical parameters using water-leaving radiances derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). To solve atmospheric correction in coastal waters, the aerosol type over clear waters is transferred to adjacent turbid water pixels. The vicinity of the Herbert River, central Great Barrier Reef zone, Australia, was used as a case study for the application of the algorithm developed. The satellite ocean colour technique was successfully validated using sea-truth measurements of water-colouring constituents acquired in the area during various seasons throughout 2002-2004. A high correlation between chlorophyll and dissolved organic matter was found in the coastal waters of the region, and when the bio-optical model was constrained to make chlorophyll a function of dissolved organic matter, the relationship between in situ and satellite-derived data was substantially improved. With reliable retrieval of the major water-colouring constituents, the technique was subsequently applied to study fluxes of particulate and dissolved organic and inorganic matter following a flood event in the Herbert River during the austral summer of 1999. Extensive field observations covering a seasonal flood in the Herbert River in February 2004 revealed high sediment and nutrient exports from the river to the adjacent coastal waters during the flood event. Due to rapid settling, the bulk of the sediment-rich influx was deposited close inshore, while the majority of nutrients exported from the river were consumed by phytoplankton in a relatively small area of the coastal ocean. With the help of ocean colour remote sensing, it was demonstrated that river-borne sediments and nutrients discharged by a typical flood in the Herbert River are mostly precipitated or consumed within the first 20 km from the coast and therefore are unlikely to reach and possibly affect the midshelf coral reefs of this section of the Great Barrier Reef lagoon.

Remote sensing is the collection of information about an object from a distance without physically being in contact with it. The type of remote sensing of interest here is in the form of digital images of water bodies acquired by satellite. The advantage over traditional sampling techniques is that data can be gathered quickly over large ranges, and be available for immediate analysis. Remote sensing is a powerful technique for the monitoring of water bodies. To interpret the remotely sensed data, however, knowledge of the optical properties of the water constituents is needed. One of the most important of these is the volume scattering function, which describes the angular distribution of light scattered by a sample. This thesis presents the first measurements of volume scattering functions for Australian waters. Measurements were made on around 40 different samples taken from several locations in the Gippsland lakes and the Great Barrier Reef. The measurements were made by modifying an existing static light scattering spectrometer in order to accurately measure the volume scattering functions. The development of the apparatus, its calibration and automation, and the application of a complex series of post-acquisition data corrections, are all discussed. In order to extrapolate the data over the full angular range, the data was analysed using theoretical curves calculated for multi-modal size distributions using Mie light scattering theory applied to each data set. From the Mie fits the scattering and backscattering coefficients were calculated. These were compared with scattering coefficients measured using in situ sensors ac-9 and Hydroscat-6, and with values from the literature. The effect of chlorophyll a concentrations on the scattering coefficients was examined, and a brief investigation of the polarisation properties of the samples was also undertaken. Finally the angular effects on the relationship between the backscattering coefficient and the volume scattering function were investigated. This is important as in situ backscattering sensors often assume that measuring at a single fixed-angle is a good approximation for calculating the backscattering coefficient. This assumption is tested, and the optimal measurement angle determined.