Browsing by Author "Mayr, T."
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Item Open Access 3D advance mapping of soil properties(Cranfield University, 2012-07) Veronesi, Fabio; Mayr, T.; Corstanje, RonaldSoil is extremely important for providing food, biomass and raw materials, water and nutrient storage; supporting biodiversity and providing foundations for man-made structures. However, its health is threatened by human activities, which can greatly affect the potential of soils to fulfil their functions and, consequently, result in environmental, economic and social damage. These issues require the characterisation of the impact and spatial extent of the problems. This can be achieved through the creation of detailed and comprehensive soil maps that describe both the spatial and vertical variability of key soil properties. Detailed three-dimensional (3D) digital soil maps can be readily used and embedded into environmental models. Three-dimensional soil mapping is not a new concept. However, only with the recent development of more powerful computers has it become feasible to undertake such data processing. Common techniques to estimate soil properties in the three-dimensional space include geostatistical interpolation, or a combination of depth functions and geostatistics. However, these two methods are both partially flawed. Geostatistical interpolation and kriging in particular, estimate soil properties in unsampled locations using a weighted average of the nearby observations. In order to produce the best possible estimate, this form of interpolation minimises the variance of each weighted average, thus decreasing the standard deviation of the estimates, compared to the soil observations. This appears as a smoothing effect on the data and, as a consequence, kriging interpolation is not reliable when the dataset is not sampled with a sampling designs optimised for geostatistics. Depth function approaches, as they are generally applied in literature, implement a spline regression of the soil profile data that aims to better describe the changes of the soil properties with depth. Subsequently, the spline is resampled at determined depths and, for each of these depths, a bi-dimensional (2D) geostatistical interpolation is performed. Consequently, the 3D soil model is a combination of a series of bi-dimensional slices. This approach can effectively decrease or eliminate any smoothing issues, but the way in which the model is created, by combining several 2D horizontal slices, can potentially lead to erroneous estimations. The fact that the geostatistical interpolation is performed in 2D implies that an unsampled location is estimated only by considering values at the same depth, thus excluding the vertical variability from the mapping, and potentially undermining the accuracy of the method. For these reasons, the literature review identified a clear need for developing, a new method for accurately estimating soil properties in 3D – the target of this research, The method studied in this thesis explores the concept of soil specific depth functions, which are simple mathematical equations, chosen for their ability to describe the general profile pattern of a soil dataset. This way, fitting the depth function to a particular sample becomes a diagnostic tool. If the pattern shown in a particular soil profile is dissimilar to the average pattern described by the depth function, it means that in that region there are localised changes in the soil profiles, and these can be identified from the goodness of fit of the function. This way, areas where soil properties have a homogeneous profile pattern can be easily identified and the depth function can be changed accordingly. The application of this new mapping technique is based on the geostatistical interpolation of the depth function coefficients across the study area. Subsequently, the equation is solved for each interpolated location to create a 3D lattice of soil properties estimations. For this way of mapping, this new methodology was denoted as top-down mapping method. The methodology was assessed through three case studies, where the top-down mapping method was developed, tested, and validated. Three datasets of diverse soil properties and at different spatial extents were selected. The results were validated primarily using cross-validation and, when possible, by comparing the estimates with independently sampled datasets (independent validation). In addition, the results were compared with estimates obtained using established literature methods, such as 3D kriging interpolation and the spline approach, in order to define some basic rule of application. The results indicate that the top-down mapping method can be used in circumstances where the soil profiles present a pattern that can be described by a function with maximum three coefficients. If this condition is met, as it was with key soil properties during the research, the top-down mapping method can be used for obtaining reliable estimates at different spatial extents.Item Open Access Innovative methods for soil parent material mapping(Cranfield University, 2010-01) Farewell, Timothy S.; Mayr, T.Soil parent material exerts a fundamental control on many environmental processes. Nevertheless, resulting from the separate mapping programmes of the geological and soil surveys, parent material is currently poorly mapped in the United Kingdom. This research develops and tests four methods of predicting soil parent material using three study areas in England. The qualities of desirable parent material maps were stated, and then used to create new map value metrics to assess the success of the four methodologies. Firstly, translations of surface and bedrock geology maps to parent material maps were tested, using international and national parent material classifications. Secondly, qualitative expert knowledge of parent material, captured from published literature, was formalised into inputs for a corrected probability model. Parent material likelihood was predicted using three map evidence layers: geology, slope and soil. Thirdly, extensive data mining was used to create fully quantitative inputs for the same probability model, and the results were compared. The final method provided a quantitative framework for the expert knowledge model inputs by the incorporation of sparse data sampling. The expert knowledge method created parent material maps of higher value than those created by the translation of geological maps. However, the inputs derived from qualitative expert knowledge were demonstrated to benefit from the addition of quantitative sample data. The resulting maps achieved overall accuracies between 60% and 90% and contained numerous detailed classes with explicit probabilities of prediction. Extensive parent materials were shown to be predicted well, and physically and chemically distinctive parent materials could be effectively predicted irrespective of their extent. Parent material class confusion arose between units where the evidence datasets were unable to provide the sufficient geographic or descriptive detail necessary for differentiation. In such cases, class amalgamation was used to overcome consistent misclassification. Recommendations are provided for the application of this research.Item Open Access Modelling the drivers of soil moisture in the landscape in order to apply the STAMINA model at a regional level(2007-01) Baggaley, Nicola J.; Mayr, T.The STAMINA (Stability And MItigatioN of Arable systems in hilly landscapes) model is a new crop yield model than takes into account the impact of terrain on crop growth. The ability to model yield variations as a function of terrain can help policy makers plan for potential changes in climate. In its original form the STAMINA model is too complex to be run at a regional extent. The literature pointed to the key drivers of crop growth being linked to water availability in the Iandscape. The research was therefore split into two sections. The first section outlines the investigation of soil moisture in the landscape. Two field experiments were undertaken. The first measured surface soil moisture at over 100 locations, using a Delta-T ThetaProbe on eight occasions in three different fields. The results from regression models showed that up to 80% of the variation in surface soil moisture can be explained using 1:10,000 taxonomic soil units. Radiation and wetness indices combined explained a maximum of 41% of the variation. These variables also explained significant additional variation when combined with 1:25,000 soil units. The second experiment measured soil moisture at six depths from 100 mm to 1 m at 38 locations in two fields using a Delta-T profile probe. Regression models showed that 1:10,000 taxonomic soil units combined with depth explain up to 67% of the variance in soil moisture. These results highlight the importance of the drainage characteristics of the soil profile in determining soil moisture content. The second section focuses on the development of an index approach to apply the STAMINA model at a regional extent. This method and STAMINA version 1.8 were tested on three representative catchments in a 10 km2 area of Bedfordshire. They succeeded in highlighting similar areas of the landscape that are at risk from low yields. Grid size analysis suggested that a grid size of 100 m was sufficient for running the index based version of the STAMINA model. This still maintained an accurate representation ❑f the topographic features that control the modelled yield variability. Further investigation into the soil hydrology module in STAMINA version 1.8 suggested that predicted yield was very sensitive to a change in the way soil water drainage was modelled, in particular the potential for a soil to recharge over the winter months.Item Open Access Rapid measurement of polycyclic aromatic hydrocarbon contamination in soils by visible and near-infrared spectroscopy(Cranfield University, 2013-10) Okparanma, R. N.; Mouazen, A. M.; Mayr, T.Polycyclic aromatic hydrocarbons (PAHs) are widely distributed organic pollutants. At petroleum contaminated sites, PAHs are often the key risk drivers because of their carcinogenicity. Assessing the risk of PAH at contaminated sites by conventional soil sampling, solvent extraction and gas chromatography–mass spectrometry (GC–MS) analysis is expensive and time-consuming. Employing a rapid and cheap measurement technique for PAH would be beneficial to risk assessment by eliminating costs and time associated with the conventional method. The literature has shown that visible and near infrared (vis-NIR) spectroscopy is a rapid and cheap technique for acquiring information about key soil properties. In this study, models based on vis-NIR spectroscopy (350–2500 nm) were developed to predict and map PAH in contaminated soils for the ultimate aim of informing risk assessment and/or remediation. The reference chemical analytical method used was GC–MS while the multivariate analytical technique used for model development was partial least squares (PLS) regression analysis with full cross-validation. A total of 150 soil samples from the UK were used for the laboratory-scale study while 137 samples were used for the near-onsite adaptive trials at three oil spill sites in Ogoniland, Niger Delta province of Nigeria. Both laboratory- and field-scale results showed that soil diffuse reflectance decreased with increasing PAH concentration. Hydrocarbon absorption features observed around 1647 nm in the first overtone region of the NIR spectrum showed a positive link to PAH. Laboratory-scale study showed that both individual and combined effects of oil concentration, and moisture and clay contents on soil spectral characteristics and calibration models were significant (p<0.05). For the field-scale study, inverse distance weighting soil maps of PAH developed with chemically-measured and vis-NIR-predicted data were comparable with a fair to good agreement between them (Kappa coefficient = 0.19–0.56). Hazard assessment of the oil spill sites using both measurement methods showed that the impact of the contamination varied distinctly across the management zones. The type of action required for site-specific risk assessment and/or remediation also varied among the different zones. This result shows promise that vis-NIR can be a good screening tool for petroleum release sites.Item Open Access Regional and national scale calibrations of hyperspectral gamma-ray signals for soil monitoring(Cranfield University, 2013-07) Carnell, Edward; Corstanje, Ronald; Mayr, T.There is an increasing demand for accurate, timely soil information to ensure the sustainable management of our limited land resources. This information is crucial for effective environmental modelling, essential for adapting to climatic changes and ensuring global food security. Traditionally, soil information has been attained through conventional soil sampling and laboratory analyses, which are time consuming and expensive. Consequently, soil maps typically lack the fine-scale spatial and temporal resolution required for computer simulations, soil monitoring and land management. Increasingly, this fine-scale information is being attained through the use of proximal and remote sensors, which generally rely on indirect, surrogate indicators of soil variability, such as electrical conductivity. In this study, the potential of γ-ray spectroscopy as a soil-monitoring tool is assessed. The underlying principle of γ-ray spectroscopy is that long-lived terrestrial radionuclides act as environmental tracers, reflecting changes in the mineralogical and textural composition of soil. Airborne radiometric surveys proved to be valuable tools for geological mapping and have led to the development of ground-based (proximal) sensors for soil sensing. A recent study by Viscarra Rossel et al. (2007) demonstrated that robust predictions of topsoil characteristics could be made through multivariate calibrations of proximal γ-ray signals, at the within-field scale. Adopting this chemometric approach, this study assesses whether similar predictions could be made at coarser scales, using a laboratory-based spectrum analyser. The results show that at a regional scale, fair predictions of cation-exchange capacity (CEC) can be made, despite changes in parent material, land use and topography. However, more tenuous results were found at the national scale, which suggests that local relationships between γ-ray activity and soil properties (such as soil texture) may not necessarily hold at coarse scales. The findings indicate that radiometric baselines vary between soil types and host geologies, which subsequently mask localised variations in physical and chemical soil properties.