Infrastructure Engineering - Theses

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    Evaluation of soil parameters using dielectric permittivity
    Orangi, Amir ( 2019)
    Site characterisation plays an important role in a diverse range of engineering and non– engineering projects. Engineering projects may include geotechnical and geoenvironmental investigations while non–engineering applications can be found in agricultural and archaeological surveys. Regardless of the nature of the project, a significant part of the site characterisation process is devoted to the characterisation of the soil upon which projects will take place. Within soil characterisation, soil fundamental parameters such as water content and density are usually required to be measured and monitored frequently to ensure successful delivery and operation of a project. These parameters are often measured using conventional techniques presenting major drawbacks, for example, being destructive, invasive and/or radioactive–based. One relatively recent innovation in soil investigations is utilising electromagnetic geophysical methods to characterise the soil and subsurface. The key to the successful application of these methods, however, is a robust understanding of the interactions and physical processes which underpin the relationships between soil electrical parameters and geoenvironmental properties. Further, to enhance the quality and speed of data acquisition as well as increasing the spatiotemporal availability of the data without compromising costs, the development of new alternative ways to measure soil parameters is crucial. Thus, through an experimental investigation, this thesis studies the relationship between geotechnical and other soils physical parameters and their dielectric properties. Moreover, an essential counterpart of this thesis is the development of an alternative way of measuring soil water content and density non invasively. Therefore, within the context of this thesis, the following studies have been undertaken and novel findings and conclusions have been drawn, these are of paramount significance to the geotechnical, environmental, agricultural and archaeological disciplines: • As part of a unique tri–nation study to commemorate the centenary of the iconic First World War ANZAC landing, physical characterisation of the soils recovered from the battlefield in this region was carried out in a frequency range of 200 MHz to 6 GHz. Subsequently, empirical models for geophysical site investigations were developed encapsulating soils water content, dielectric properties as well as the frequency pertinent to certain in–situ geophysical applications and satellite surveys. Moreover, attenuation coefficients related to Ground Penetrating Radar applications were also estimated for a range of water contents at 200 MHz. Given the historical and archaeological significance of the ANZAC battlefield, these results can be used as a database for future feasibility studies, planning, result analysis and modelling of geophysical investigations, including choosing the most appropriate time of the year and geophysical prospection tool in this restricted–access study area. • Dielectric characteristics of soils from the ANZAC battlefield and Melbourne geological formations were evaluated against different water content and dry density levels. The data were utilised in the development of a new physically–based mixture model encompassing 500 MHz, 1 GHz and 6 GHz frequencies, pertinent to insertion probes such as Time Domain Reflectometry, Ground Penetrating Radar and remote sensing applications. This model uses soil specific surface area as a more fundamental physical input variable which has shown to be a better representation than particle size distribution in defining the response of soils to electromagnetic waves. Moreover, based on the performance of the newly developed mixture model and in order to provide simple but robust site–specific calibrations for these Australian soils, new empirical relationships have been developed by taking into account the specific surface area of the samples. The new models can be specifically used for soils from these regions as well as for soils with similar characteristics when planning and conducting geophysical prospections utilising electromagnetic waves. • The effects of dry density or degree of compaction on the dielectric constant of different soil types ranging from sand to bentonitic clay were experimentally investigated and evaluated based on the use of two simple mixture models (De Loor and Birchak) at 1 GHz frequency. It was found that the effects of dry density on the soil dielectric constant depends on the soil type. Through a parametric study on the experimental data, this soil type dependent behaviour was shown to be defined by the changes in the free water, bound water, and solid particle volume fractions, ultimately controlled by the soil specific surface area. Subsequently, a threshold for specific surface area was estimated theoretically and corroborated experimentally. In addition, supported by the experimental observations, ranges for the dielectric constant of bound water and geometrical parameter in the Birchak model were identified. • The effectiveness of a low–cost non–invasive method to estimate water content of different soil types ranging from sand to clay was investigated. Firstly, three sensors were developed and tested against water content variation. Subsequently, the best geometrical and electronic features of these sensors were incorporated into the design of a new sensor. This sensor demonstrated superior performance in estimating near surface soil water content non–invasively. The sensor can be potentially utilised in agricultural applications, satellite imaging and remote sensing applications of soil water content data; quantifying the risk of bushfire and generating warnings to the pertinent authorities as well as a fast way of estimating the water content of sub–base and stockpile materials in the road construction industry.
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    Assessing the utility of remote sensing in estimating groundwater discharge along the southwest margin of the Great Artesian Basin, South Australia
    Matic, Vjekoslav ( 2018)
    The Great Artesian Basin (GAB) sits under approximately one fifth of the Australian continent and is one of the largest aquifers in the world. In arid areas, groundwater is often the only reliable source of water. Yet the sustainable management of these important groundwater resources is challenging due to the difficulty of adequately estimating the water balance over the spatial and temporal scales governing these systems. In the southwestern margin of the GAB, in South Australia, water lost to vertical leakage is currently a poorly constrained, but large component of the aquifer’s water balance. This thesis proposes a framework relating surface expressions of vertical leakage (increased salt and soil moisture) to subsurface vertical leakage rates. The framework facilitates mapping these surface expressions over the GAB margin with the aim to improve estimates of vertical leakage. This framework features four discharge classes, representing roughly order of magnitude differences in leakage rates. The thesis then investigates a series of four methods to map these classes using remotely sensed data. These methods are 1) landform mapping (utilising digital elevation data and a base map); 2) a supervised classification using multispectral satellite imagery; 3) an automated evapo-transpiration algorithm (using multispectral imagery data), and; 4) modelling the depth to watertable (using digital elevation model and available depth to watertable data) to estimate discharge in absence of surface expressions. The results found that the landform mapping and supervised multispectral classification methods provided useful “upper” and “lower” estimates of probable discharge class extents. The evapo-transpiration algorithm suffered from problems with model assumptions and structure, which proved limiting in this specific application. The modelling of the depth to watertable method provided estimates of areas with very low discharge rates, though with extremely high uncertainty, due to a current lack of conceptual understanding of the unconfined watertable processes adjacent to high discharge zones and limited borehole data to adequately support this method. Field estimates of vertical leakage using a number of techniques were made to obtain ranges of discharge rates for each discharge class. Based on the field measurements, broad ranges of evaporative discharge (0.5-10, 10-100 and 100-300 mm y-1) were applied to discharge classes, providing annual discharge volumes for each. The results were compared to publicly available outputs from a numerical model estimating the regional vertical leakage discharge. The landform mapping results were the most extensive and estimated 27-113% of the total vertical leakage component as estimated by the numerical model. The supervised classification estimated 19 – 71% of the total vertical leakage. The GAB in South Australia has two major basins (eastern and western) and a mixing zone. The numerical model presents discharge rates for each sub-basin, and the validation in this thesis is interpreted at the sub-basin level. The results indicated that most of the estimated recharge from the western margin of the GAB can be accounted for by high discharge zone evaporative discharge in the western sub-basin. This implies vertical leakage away from the margin is likely to be low. The mixing zone and eastern sub-basin, however, have a much smaller portion of their total vertical leakage occurring at, and adjacent to the high discharge zones. This implies the majority of their discharge is occurring away from the margins, with more complex pathways for vertical leakage.
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    Estimating root-zone soil moisture by assimilating remotely sensed biophysical states into modelling
    Hashemian Rahaghi, Mahboobeh Sadat ( 2016)
    In this research, the soil water processes of a simple land surface model are improved to establish a more appropriate soil-biophysical linkage between root-zone moisture content and above-ground states. In the next step, this thesis examines how this modified model coupling affects updating root-zone soil moisture using surface information through assimilation.
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    Do locusts seek greener pastures?: an evaluation of MODIS vegetation indices to predict presence, abundance and impact of the Australian plague locust in southeastern Australia
    Weiss, John Edward Ritchie ( 2016)
    The management of pests is complex and difficult. Biosecurity programs aim to reduce the risk of entry, establishment and spread of pests that affect social, agricultural and environmental values. Predicting the dispersal of pest insects or pathogens is critically important in managing and restricting their impact. Insects disperse to resources or hosts that are often distributed patchily and with variable quality. Host selection involves not only choosing the right species of plant, but also selecting an individual plant within that species that is, or will be, suitable for feeding, survival and development. Insects need to detect their host from a distance usually utilising visual or olfactory cues or both. Greens and yellows attract many phytophagous insects, although other wavelengths can also be attractive. In theory, by combining the daily environmental and climatic parameters (soil moisture, soil type, temperature, light exposure, aspect, etc.) with the host’s biology, one can then predict the host’s photosynthetic rate (in terms of gC uptake/m2/day). The pest’s presence or abundance often relies upon the host’s plant photosynthetic rate. One approach to modelling or predicting the presence/abundance of a pest is then to use the photosynthetic rate or similar measure of growth of the plant as a surrogate for its suitability to a pest. By then combining this surrogate measure of suitability with a pest’s biology, climate-based simulations could predict pest outbreaks and help identify feasible and effective containment or management options. NASA’s Terrestrial Observation Prediction System (TOPS) models daily global photosynthetic rates at a spatial resolution of 1 km. In this study, I examined the feasibility of utilising Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (as a potential component of TOPS) to model photosynthetic rates and primary productivity values of vegetation types for south-eastern Australia. I assessed the ability of these vegetation indices to measure the suitability of vegetation to particular pests – specifically the Australian plague locust. Locusts are known to differentiate between yellow and greens, and anecdotal information indicates that green colours and pastures attract the Australian plague locust. Initially, I investigated annual and seasonal variation of MODIS’s vegetation indices across eastern Australia at three different scales (tiles, regions and local areas). I showed that the values of the vegetation indices have high spatial and temporal variability. I determined that any prediction based on the condition of the vegetation has to be at both fine spatial and temporal scales. The locust incursion into Victoria during summer and autumn of 2010 – 2011 provided an ideal opportunity to test the suitability of MODIS vegetation indices as predictors of locust presence and abundance. From December to April, I surveyed 19 locations weekly that had pasture sites of various levels of green, for presence and abundance of two generations of adult locusts. I found strong spatial auto-correlation at two spatial scales (locations and sites), indicating that the distribution of adult locusts was spatially clustered. However, the values of the vegetation indices at the one-kilometer scale did not correlate with the density of adult locusts. To examine correlates of distribution further and to have a larger data set spread over a greater area and a longer time, I examined 10 years of historical locust survey data for eastern Australia. With the larger set of data, I was able to assess how the vegetation indices averaged over scales of one, ten, and fifty kilometers correlated with adult locust occurrence and abundance. While the MODIS vegetation indices measured at a 50-km spatial scale showed the strongest relationship with the abundance of locusts, locust presence or abundance was not strongly related to any of the MODIS vegetation indices. When utilising the vegetation indices as a predictive tool for the 2008-2009 locust records in NSW, I found spatial accuracy varied widely from month to month and generally underpredicted locust abundance. Finally, I investigated another potential use of MODIS vegetation indices in locust management, the capacity to estimate the level of damage of larval feeding on primary production. Although aerial observers could easily discern the visual impact, neither of my remote sensing approaches were able to identify change in vegetation indices due to larval feeding. In summary, I aimed to evaluate MODIS vegetation indices, as a component of NASA’s Terrestrial Observation Prediction System, to model photosynthetic rates or primary productivity values of vegetation types for south-eastern Australia. I hoped to measure their suitability to particular pests, in my case the Australian plague locust. However, I found that all the vegetation indices examined correlated too weakly and variably over time to be useful in predicting the distribution and abundance of a major plant pest. I briefly explore other information sources that could support better predictions for locust management. These include community reporting of locust presence and abundance, relationships with major climatic events such as El Niño and optimal surveillance. Of these options, optimal surveillance has the greatest potential to support pest management, with an empirical test demonstrating good performance of optimal surveillance methods.
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    Improving flood prediction in sparsely gauged catchments by the assimilation of satellite soil moisture into a rainfall-runoff model
    ALVAREZ, CAMILA ( 2015)
    This thesis explores the assimilation of remotely-sensed soil moisture (SM-DA) into a rainfall-runoff model for improving flood prediction within data scarce regions. Satellite soil moisture (SM) observations are used to correct the two main controlling factors of the streamflow generation: the wetness condition of the catchment (state correction scheme) and the magnitude of rainfall events (forcing correction scheme). The core part of the research focuses on the state correction scheme. A simple rainfall runoff model (the probability distributed model, PDM) is used for this. The soil water state of PDM is corrected by assimilating active and passive satellite SM observations using an ensemble Kalman filter. Within this framework, the efficacy of different existing tools for setting up the state correction scheme are evaluated, and new techniques to address some of the key challenges in the assimilation of surface satellite SM observations into hydrological models are introduced. Various options for the state correction scheme were implemented and enhanced through- out the thesis. The proposed schemes consistently led to improved streamflow ensemble predictions for a case study. In the final state correction scheme, the ensemble root mean square error was reduced by 24% at the catchment outlet, the false alarm ratio was reduced by a 9%, and the skill and reliability of the streamflow ensemble were improved after SM-DA. The state correction scheme was also effective at improving the streamflow ensemble prediction within ungauged inner locations, which demonstrates the advantages of incorporating spatially distributed SM information within large and poorly instrumented catchments. I showed that since stochastic SM-DA is formulated to reduce the random component of the SM error (and therefore does not address systematic biases in the model), the efficacy of the state correction schemes was restricted by the model quality before assimilation. This is critical within a data scarce context, where streamflow predictions suffer from large errors coming from the poor quality data used to force and calibrate the model. Additionally, due to the higher control that SM exerts in the catchment runoff mechanisms during minor and moderate floods, the state correction scheme had more skill when the low flows were evaluated. Consequently, SM-DA improved mainly the quality of the streamflow ensemble prediction (skill, reliability and average statistics of the ensemble) but did not significantly reduced the existing biases in the peak flows prediction. These results reveal one key limitation of the proposed approach: improving flood prediction by reducing random (and not systematic) errors in the SM state of a rainfall-runoff model, while SM is probably not the main controlling factor in the runoff generation during major floods within the study catchment. Addressing the above limitation, I set up a forcing correction scheme that aimed at reducing the errors in the rainfall data (the rainfall input, in addition to the infiltration estimates from the model, are probably the main factors controlling the accuracy of flood predictions). I adopted for this the soil moisture analysis rainfall tool (SMART) proposed by Crow et al., (2009). In SMART, active and passive satellite SM were assimilated into the Antecedent Precipitation Index model to correct a near real-time satellite rainfall, which was subsequently used to force PDM (without state correction). The results showed that remotely sensed SM was effective at improving mean-to-high daily satellite rainfall accumulations, which in turn led to a consistent improvement of the streamflow prediction, especially during high flows. The efficacy of the state correction and the forcing correction schemes were compared within 4 catchments. For most cases, the reduction of model SM error by the assimilation of satellite SM led to improved streamflow prediction compared with the correction of the forcing data. This was true for both the low flows and high flows. The outper- formance of the state correction scheme during high flows is counterintuitive with the stronger influence that rainfall probably has during floods, and differs from previous studies. I interpreted these different results by various factors including the methodological configuration (rainfall-runoff model, model error quantification, etc.), the quality of the satellite rainfall data and the quality of the satellite SM retrievals. In agreement with the literature, the combination of the forcing and the state correction schemes further improved flood predictions. The significance of this thesis is in providing novel evidence (based on real data experiments) of the value of satellite soil moisture for improving both an operational satellite rainfall product and the streamflow prediction within data scarce regions. Additionally, I highlighted a number of challenges and limitations within the forcing and state correction schemes. I introduced new techniques to overcome some of these challenges and proposed future strategies to further address them. This contributes to advancing towards a reliable data assimilation framework for improving operational flood prediction within data scarce regions.
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    Crop monitoring with Unmanned Aerial Vehicles (UAV)
    Nolan, Andrew Peter ( 2015)
    Remote sensing is one of the tools used in precision agriculture to map the spatial variability of crops at critical grow stages. Remote sensing using Unmanned Aerial Vehicles (UAV) enables the collection of high resolution imagery to aid in the precision agriculture decision management process and has demonstrated the detection of crop water stress, crop yield estimation, phenotyping and disease detection. However, the commercial adoption of UAV in precision agriculture to date has been limited due to a number of factors including technical and data interpretation challenges, UAV regulations and economic factors. To aid in the adoption and ease of use of UAV for Precision Agriculture, this work addresses two fundamental technical challenges i) reliable remote sensing image acquisition and ii) automated image processing. Accurate forward and side overlap between images ensures robust geometry is captured for image feature matching and stitching. To ensure reliable image acquisition, we implemented an adaptive intervalometer UAV photogrammetric payload system that adjusts image acquisition in real-time to accommodate ground speed and altitude variations. The adaptive intervalometer, evaluated on a multirotor and fixed-wing platform, achieved an image forward overlap RMSE of 0.84% and 2.26% respectively, which is a significant improvement in accuracy compared to the commonly used time-based intervalometer. To illustrate the effectiveness of the UAV payload, the adaptive intervalometer was used to simultaneously collect high resolution thermal and multispectral imagery to detect crop water stress status in an orchard. Several studies have demonstrated that high-resolution visual/near-infrared (VNIR) vineyard maps can be used as a PA tool for multi-temporal monitoring of vineyard spatial variability, shape and vigor to aid in the application of variable-rate treatments and irrigation scheduling. Generating vineyard maps requires separating vine pixels from non-vine pixels in order to accurately determine vine spectral and spatial information. However, manual segmentation of high resolution aerial images is time consuming and costly. Previously, several image texture and frequency analysis methods have been applied to vineyard map generation; however these approaches require manual preliminary delineation of the vine fields. To assist in the automation of vine row classification, we developed an algorithm that uses skeletonisation techniques to reduce the complexity of agricultural scenes into a collection of skeletal descriptors. The algorithm was evaluated on a high resolution aerial orthomosaic and proved its efficiency in unsupervised detection and delineation of vine rows (precision = 0.97) in a commercial vineyard. UAV present new and exciting remote sensing possibilities for environmental monitoring and agricultural applications. By simplify the skill set required to collect and analyse remotely sensed data, we can start to realise the potential UAV have in precision agriculture and environmental monitoring. Our research focuses on horticulture and viticulture UAV applications; however our image acquisition and automated segmentation techniques could be applied to a wide variety of areas including search and rescue, infrastructure management and archeology.
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    The spatio-temporal distribution of honey bees and floral resources in Australia
    Arundel, Jonathan Paul ( 2015)
    The Western honey bee, Apis mellifera, is the most economically important pollinator worldwide. In Australia today there are more than half a million hives managed by beekeepers around the country. In addition, there is a large population of feral A. mellifera providing free pollination services to native and disturbed environments. The sustainability of pollinator populations globally, including both managed and feral A. mellifera, is under considerable pressure from four key threats; pests and pathogens, alien species, land-use intensification and climate change. Understanding what impact these threats will have on pollination in Australia requires better knowledge of the spatio-temporal distribution of honey bee colonies (both managed and feral) and the resources on which these colonies depend. In Australia, the life of honey bees is inextricably linked to the flowering of eucalypts. The majority of honey in Australia is produced from eucalypts, and beekeepers frequently move their hives across distances of hundreds of kilometres in pursuit of flowering events. Little is known about how this erratic flowering effects the distribution and abundance of feral colonies. This thesis demonstrates how agent-based models can be used in conjunction with genetics-based field survey methods to make inferences about the density of feral honey bee colonies in south eastern Australia. Colony densities vary with number of colonies observed in the sample in a log-linear relationship, rather than a linear relationship as previously thought. As a consequence, previous field surveys have overestimated densities by an order of magnitude. The surveys described in this thesis have shown that densities of feral honey bees are remarkably uniform across different types of environments, with some evidence that densities are marginally higher in undisturbed environments. Regardless of the type of environment, densities of feral honey bees are not high enough to provide pollination of most horticultural or agricultural crops. The spatial-temporal distribution of managed honey bees is determined by the aggregate behaviour of individual beekeepers. This thesis shows that in planning the movement of their hives from one set of flowering events to the next beekeepers need to solve complex optimisation problems. Where a beekeeper is able to use foresight to predict the location of future flowering events, the routes they choose can be up to 2-6% shorter than those obtained from using current flowering information only. To better understand the spatio-temporal distribution of floral resources, this thesis outlines the development of a new software application that processes remote-sensing data and makes the output available to beekeepers and researchers. This application is used to highlight agreement between patterns of eucalypt growth, flowering, and honey production at landscape scale. Further work is still needed to fully understand the relationships between climate, growth, eucalypt flowering, and the effects of flowering on feral colony densities and beekeeper movements. An agenda for future research outlines how threats to pollination security in the future can be better managed.
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    Mapping riparian vegetation functions using remote sensing and terrain analysis
    LYMBURNER, LEO ( 2005)
    Land use practices over the last 200 years have dramatically altered the distribution and amount of riparian vegetation throughout many catchments in Australia. This has lead to a number of negative impacts including a decrease in water quality, an increase in sediment transport and a decrease in the quality of terrestrial and aquatic habitats. The task of restoring the functions of riparian zones is an enormous one and requires spatial and temporal prioritisation. An analysis of the existing and historical functions of riparian zones and their spatial distribution is a major aid to this process and will enable efficient use of remediation resources. The approach developed in this thesis combines remote sensing, field measurement and terrain analysis to describe the distribution of five riparian zone functions: sediment trapping, bank stabilization, denitrification, stream shading and large woody debris production throughout a large semi-arid catchment in central Queensland.
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    The investigation of land cover change and trends through the reconstruction of historical landscapes
    Maphanyane, Joyce Gosata ( 2012)
    The thesis presented on a new and novel method of reconstruction of historical landscapes for investigation of land cover change and trends. It also discusses and accentuate the importance of land cover change thus the reasons it should be known. Remote sensing and geographical information systems (GIS) method is the only available method for the land cover change investigations at present. This modern techniques method of remote sensing and GIS is reviewed and its inadequacies established. Its major problem is that it has only been available since 1972 therefore for periods that spans more than 40 years there is no satellite data. Then, since land cover change is a slow process, it is necessary to access evidence about the land cover conditions of past eras, data from 40, 50, 60 years ago and beyond. Therefore, it is imperative that a method is found that can be used to assess land cover change in the historical times. The major question is whether reconstruction of historical landscape can be used successfully for the land cover change investigation to fill up the gap where remote sensing is unavailable or inadquent. The proposal of this new method to counteract the inadequacies of remote sensing and GIS method is set forward. Its blue print (Table 3-2 and Figure 3-1) is submitted and tested against the well used, tested and trusted land cover change investigation method of remote sensing and GIS. The new method uses the elements of traditional ecological knowledge of oral history, songs, poems, praise singing and relics; archival data of maps, photographs and aerial photographs; written records and archeological research results. Since this approach does not rely on technology in its original data capture phase, it can be applied anywhere and long time after land cover changes has taken place. For the development and evaluation of the alternative method, test areas in Botswana were used because in these areas, it was possible to draw on the strong African tradition of oral history. The study was made between three time periods of – 1970-1979 – Sir Seretse Khama‟ Era, 1980-1990 – Sir Ketumile Masire‟ Era, and 1991-2003 – Festus Mogae‟s Era. Besides that, on the local setting of Botswana, three minor questions were asked: one was on the political landscape - how did Botswana come into being? And what were the political historical landscapes governing the southern African region? And whether these had any effect on shaping land use patterns and development as seen today? The other question was on population - What are the variability of population changes of Botswana within this period of study and their impact on its land use and therefore land cover changes? And the third question was on climate - What is the extent and variability of climatic changes in Botswana within this period of study and what assessment can be made by correlation of temperature/precipitation data with the years of drought and those of floods? The creation of land cover change maps by the new method followed some procedures. First, the informal data from the traditional ecological knowledge voice data recorded from interviews; the archival maps and photographs graphical data; the written records and the archaeological research findings text and tabular data were deciphered. Second, the epicentres of land cover change point of reference data of the areas were deduced from these data, which were then extracted, and tabulated. Third, the field visits to these points of reference were made and coordinates of their exact locations were measured using a global positioning system (GPS). Fourth, these single items, large scale field data was categorised into land cover classes similar to those seen from smaller scale satellite images of the same area. After these steps it was only then that these informal data were used to create databases which were then transformed and translated into land cover maps for different periods. The land cover maps of consecutive periods were then compared and from that change were detected land cover change map was formed. Other important findings were that for the two methods, that of remote sensing and that of the reconstruction of historical landscape sees the world differently. First it is about remote sensing and GIS intrinsic characteristics. Remote sensing sees the land cover surface from the above, a bird‟s eye view. Although it captures everything and is unbiased some details can be obscured by those above them and are missed out. It is also sees the Earth‟s phenomena from great distances above. Geostationary Operational Environmental Satellites (GOES) 8 orbit the Earth at 35 790 kilometres altitude, the latest of Landsat Earth resources group of satellites system, Landsat 7‟s orbiting altitude is 705 kilometres and the very high resolution IKONOS orbiting altitude is 681 kilometres. Consequently, the satellite data is always viewed as land cover class categories at small scale like built-up-area town/village, or field/forest/vegetation or river/lake/swamp not as single land cover items like a building or a tree or water. The view can change in incidence that depends on factors acting on the land surface; land change by the effects of seasons. The areas which can be viewed at one time are enormous. Good in showing the land cover change and what the land class has really changed to become and this is recorded and archived. Remote sensing views the world far beyond human eye; they utilize the visible electromagnetic spectrum as well as the infra red – they can even see heat; the microwave – RADAR satellites. But need trained people to interpret and analyze. Second, the natural way human beings see the world, the reconstruction of historical landscape method. The world is viewed piecemeal – large scale single item and in oblique, sideways fashion. Also what is viewed is the understory only. The items remain the same to the human mind, it does not change by season or by the way it looks. The world is categorized artificially into names, land uses, ownerships and governed by rules which sometimes has nothing to do with what is on the Earth‟s surface, the land cover its self. People also have the ability to know why something happed and also the ability to draw from their experience, rightly or wrongly. Therefore, as a consequence to that, if these two methods have to be used interchangeably a land cover class specification commensurate to the two methods has to be created. Third, besides all that, the remote sensing platforms and sensors have improved tremendously over the years. Consequently, land cover change from consecutive time period studied from data of satellite from different generation could be more pronounced not only on account of the changed landscape but also on account that better equipment are being employed. So, if a trans-generation satellite data has to be employed some form of standardization of the different generation data has to be made. Various elements of reconstruction of historical landscape subscribe to the land cover change investigation endevours at different levels which complement each other. The traditional ecological knowledge is the most effective giving information which could nearly complete the land cover change. Archaeology although it has brought life to the historical landscape is sporadic and falls apart by age and by space and it also depends on preservations. Others like the songs and the written records depend on whether they are available and also, the information they include is at the author‟s discretion. This thesis has sufficiently proved that the method of reconstruction of historical landscape for the investigation of land cover change can be applied to fill in the gap where modern technologyis inadequate or unavailable. And that indeed climatic change, population density and historical, political and economical landscape are factors that affect land cover change. This is, without any doubt, a welcome accomplishment for the field of engineering and will greatly facilitate further national and regional as well as global scientific undertakings of this nature.
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    The channels of Mars and the Victorian basalt plains: an investigation of channel chronology
    Tan, Qi Jing Kenny ( 2012)
    Current models of Martian channel formation postulate a sudden change in the environment during the Hesperian period. To investigate the chronology of this change, Martian channels with extensive dendritic valley networks where active volcanism was present during the late Noachian to the early Hesperian period of Mars were chosen. Using stereo-derived digital terrain model (DTM) data from the High Resolution Stereo Camera (HRSC) on the orbiter, selected ancient drainage channels on Mars were identified and compared with those on the basaltic plains in Western Victoria, identified on Victorian government digital elevation data. The elevation data from Mars and Victoria were used for stream analysis and to produce longitudinal profiles of streams. An analysis of the maturity of the Victorian stream systems was undertaken using the DEM and the concavity indices of the streams was also calculated. Observation on the maturity of selected stream systems in Victoria was undertaken and the respective sites geologically dated using K-Ar dating of the underlying basaltic plains. The Victorian stream systems are relatively immature by normal terrestrial standards having been formed after flood basalts covered the prior drainage systems. The same stream analysis was then applied to the Martian channels, where there is limited dating control. To better understand the chronology of their formation, the Martian channel concavity indices were calculated and compared to the terrestrial results. Results reveal streams dating up to 1.4 million years ago, narrowing down the time when channel formation ceased to approximately the same amount of time (1.4 Ma) after the volcanic plains were formed in the selected Martian regions.