Browsing by Author "Taalab, Khaled Paul"

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    Modeling soil bulk density at the landscape scale and its contributions to C stock uncertainty
    (Copernicus Publications, 2012-12-19T00:00:00Z) Taalab, Khaled Paul; Corstanje, Ronald; Creamer, Rachel E.; Whelan, Michael J.
    Soil bulk density (Db) is a major contributor to uncertainties in landscape-scale carbon and nutrient stock estimation. However, it is time consuming to measure and is, therefore, frequently predicted using surrogate variables, such as soil texture. Using this approach is of limited value for estimating landscape scale inventories, as its accuracy beyond the sampling point at which texture is measured becomes highly uncertain. In this paper, we explore the ability of soil landscape models to predict soil Db using a suite of landscape attributes and derivatives for both topsoil and subsoil. The models were constructed using random forests and artificial neural networks. Using these statistical methods, we have produced a spatially distributed prediction of Db on a 100m × 100m grid which was shown to significantly improve topsoil carbon stock estimation. In comparison to using mean values from point measurements, the error associated with predictions was over three times lower using the gridded prediction. Within our study area of the Midlands, UK, we found that the gridded prediction of Db produced a stock inventory of nearly 8 million tonnes of carbon less than the mean method. Furthermore, the gridded approach was particularly useful in improving organic carbon (OC) stock estimation for fine-scale landscape units at which many landscape-atmosphere interaction models operate.
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    Modelling Soil Bulk Density using Data-Mining and Expert Knowledge
    (Cranfield University, 2013-04) Taalab, Khaled Paul; Corstanje, Ronald; Whelan, Michael J.; Creamer, Rachel E.
    Data about the spatial variation of soil attributes is required to address a great number of environmental issues, such as improving water quality, flood mitigation, and determining the effects of the terrestrial carbon cycle. The need for a continuum of soils data is problematic, as it is only possible to observe soil attributes at a limited number of locations, beyond which, prediction is required. There is, however, disparity between the way in which much of the existing information about soil is recorded and the format in which the data is required. There are two primary methods of representing the variation in soil properties, as a set of distinct classes or as a continuum. The former is how the variation in soils has been recorded historically by the soil survey, whereas the latter is how soils data is typically required. One solution to this issue is to use a soil-landscape modelling approach which relates the soil to the wider landscape (including topography, land-use, geology and climatic conditions) using a statistical model. In this study, the soil-landscape modelling approach has been applied to the prediction of soil bulk density (Db). The original contribution to knowledge of the study is demonstrating that producing a continuous surface of Db using a soil-landscape modelling approach is that a viable alternative to the ‘classification’ approach which is most frequently used. The benefit of this method is shown in relation to the prediction of soil carbon stocks, which can be predicted more accurately and with less uncertainty. The second part of this study concerns the inclusion of expert knowledge within the soil-landscape modelling approach. The statistical modelling approaches used to predict Db are data driven, hence it is difficult to interpret the processes which the model represents. In this study, expert knowledge is used to predict Db within a Bayesian network modelling framework, which structures knowledge in terms of probability.This approach creates models which can be more easily interpreted and consequently facilitate knowledge discovery, it also provides a method for expert knowledge to be used as a proxy for empirical data. The contribution to knowledge of this section of the study is twofold, firstly, that Bayesian networks can be used as tools for data-mining to predict a continuous soil attribute such as Db and that in lieu of data, expert knowledge can be used to accurately predict landscape-scale trends in the variation of Db using a Bayesian modelling approach.