Doctoral Degrees (Soil Science)

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Browsing Doctoral Degrees (Soil Science) by Subject "Digital soil mapping"

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    Mapping soil organic carbon stocks by combining NIR spectroscopy and stochastic vertical distribution models : a case study in the Mvoti River Catchment, KZN, South Africa
    (Stellenbosch : Stellenbosch University, 2019-03) Wiese, Liesl; Rozanov, Andrei Borisovich; De Clercq, W. P.; Seifert, Thomas; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
    ENGLISH ABSTRACT: The agricultural and environmental importance of maintaining and increasing soil organic carbon (SOC) has been increasingly recognized globally. To a large extent, this recognition can be attributed to soil being the largest terrestrial carbon pool, as well as to soil’s responsiveness to land use and management. Land use and land use change are major factors affecting SOC levels with changes from natural vegetation (forests, grasslands and wetlands) to croplands, for example, causing significant SOC losses. The topsoil (0-30 cm depth) is especially sensitive to changes in land use and management and the highest variation in SOC levels is observed in this zone. In this study SOC stocks in the first meter of soil were quantified and mapped under different land uses and management systems using a vertical SOC distribution model, applying near-infrared (NIR) spectroscopy for SOC analysis and estimating the uncertainty of the maps created using different approaches. The study area was chosen as a quaternary catchment of 317 km-2 south and southeast of Greytown in the Midlands area of KwaZulu-Natal, South Africa. The catchment exhibits complex topography and predominantly shale and dolerite parent material. Soils in the area have high organic carbon content ranging from 0.08 to 22.85 % (mean = 3.48 %), with clay content ranging from 3 to 49 % (mean = 14.7 % clay) and pH(H20) between 3.3 and 6.7 (mean pH(H20) = 4.5). Vertical SOC distribution functions were developed for 69 soil profiles sampled from different land uses (mainly forestry plantations, grasslands and croplands) in and around the study catchment. Bulk density samples were taken at 2.5, 7.5, 12.5, 17.5, 30, 40, 50, 75 and 100 cm depths. The aim was to reduce the number of soil observations required for SOC accounting to one point close to the soil surface by applying negative exponential vertical depth functions of SOC distribution. To achieve this, the exponential functions were normalized using the volumetric SOC content observed close to the surface and grouped as a function of land use and soil types. Normalization reduced the number of model parameters and enabled the multiplication of the exponential decline curve characteristics with the SOC content value observed at the surface to present an adequately represented value of soil carbon distribution to 1 m at that observation point. The integral of the exponential function was used to calculate the soil carbon storage to 1 m. The vertical SOC distribution functions were refined for soils under maize production systems using reduced tillage and conventional tillage. In these soils, the vertical SOC distributions are described by piecewise, but still continuous functions where the distribution within the cultivated layer (0-30 cm) is a linear decline under reduced tillage or a constant value under conventional tillage, followed by an exponential decline to 1 m (30-100 cm). The value of predicting SOC concentrations in soil samples using wet oxidation (WalkleyBlack method) and dry near-infrared (NIR) spectrometry was assessed by comparing them to the dry combustion method. NIR spectrometry is considered to be an especially promising method, since it may be used in both proximal and remote sensing applications. In addition, the effect of using paired samples with single SOC determination versus paired samples with replicated (three times) analysis by all (reference and test) methods was tested. It was shown that the use of paired tests without replication dramatically decreases the precision of SOC predictions of all methods, possibly due to high variability of SOC content in reference values analysed by dry combustion. While reasonable figures of merit were obtained for all the methods, the analysis of non-replicated paired samples has shown that the relative RMSE for the SOC NIR method only falls below 10 % for values above ~8 % SOC. For the corrected SOC Walkley Black method the relative RMSE practically never falls below 10 %, rendering this method as semi-quantitative across the range. It was concluded that for method comparison of soil analysis, it is essential that reference sample analysis be replicated for all methods (reference and test methods) to determine the “true” value of analyte as the mean value analysed using the reference method. Finally, the above elements of vertical SOC distribution models as a function of land use and soil type, predicting SOC stocks to 1 m using only a surface (0-5 cm) sample, and the use of NIR spectroscopy as SOC analysis method were combined to assess the changes in SOC stock prediction errors through mapping. Results indicated a dramatic improvement in precision of SOC stock predictions with increasing detail in the input parameters using vertical SOC distribution functions differentiated by land use and soil grouping. Still, the relative error mostly exceeded 20 % which may be seen as unacceptably high for carbon accounting, trade and tax purposes, and the SOC stock accuracy decreased in terms of map R 2 and RMSE. The results were generally positive in terms of the progressive increase in complexity associated with SOC stock predictions and showed the need for a substantial increase in sampling density to maintain or increase map accuracy while increasing precision. This would include an increase both in surface samples for the prediction of SOC stocks using the vertical SOC distribution models, as well as an increase in the sampling of profiles to include more soil types and increase the profile density per land use to improve the vertical SOC prediction models.
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    Using remote sensing and geographical information systems to classify local landforms using a pattern recognition approach for improved soil mapping
    (Stellenbosch : Stellenbosch University, 2022-05) Atkinson, Jonathan Tom; De Clercq, W. P.; Rozanov, Andrei Borisovich; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
    ENGLISH ABSTRACT: Presently, a major focus of digital soil mapping (DSM) in South Africa is unlocking the soil-landscape relationships of legacy soil data by disaggregating the only source of contiguous soil information for South Africa, the National Land Type Survey (LTS) (ARC, 2003). Each land type is best defined as a homogenous mapping unit with a unique combination of terrain type, soil pattern and macroclimate properties (Paterson et al., 2015). One of the prevailing reasons for the LTS longevity and continual temporal-interoperability is that terrain description is expressly related to a suite of catenary soil property descriptions (Milne, 1936). These terrain types are further divided into terrain morphological units (TMUs) representing a sequence of patterns based on a 5-unit landscape model of 1-crest, 2-scarp, 3-midslope, 4-footslope and 5-valley bottom. Importantly, dominant soil distribution patterns are defined by terrain units relying on an elementary terrain topo-sequence pattern approach, with much of the work done on modelling soil variation related to variation in terrain (van Zijl, 2019). Whilst the LTS remains a source of national interest, there is immense opportunity to build on the existing soil inventory data rather than only focus on “breaking it down” (disaggregation). However, what is needed is a standard operating procedure that not only leverages the ability of digital elevation models (DEM) to explicate soil-landscape associations beyond the limited 5-unit landscape model but allows better refinement of soil descriptions with landscape features. Only once the nuances of optimal DEM parametrisation under controlled conditions are fully understood can the complete scope of DSM and digital geomorphological mapping (DGM) applications be explored. This dissertation attempts to synthesise knowledge on theory, methods, and applications of using remote sensing (RS) and geographical information systems (GIS) to classify local landforms using a pattern recognition approach for improved soil mapping in the context of multiscale problems of digital terrain analysis in KwaZulu-Natal. The dissertation is divided into three parts. Part one (Chapter 2) represents the DEM pre- processing and generalisation method and establishes the protocols for soil-landscape covariate application derived from various sensor platforms and spatial scales. Part two (Chapter 3) introduces the concept of improved terrain unit mapping through the geomorphon approach and describes DEM optimisation for standardised geomorphon representation for uniformly describing soil-landscape properties for inputs to DSM applications. Finally, part three (Chapters 4 & 5) looks at applications of DEM sources and geomorphons first from a holistic landscape context by linking digital terrain and soil-landscape analysis to geodiversity. Finally, the benefit of improved RS and GIS combined with quantitative modelling approaches on improving natural resource predictions are explored by modelling soil-ecotope and soil type mapping units and proposing improvements to an existing DSS designed for KwaZulu-Natal Natal. Specifically, this research is organised into four (4) research chapters with an overview of each chapter’s contribution outlined hereafter. Chapter 2 accounts for the recognition and requirements of DEM generalisation from high to medium resolution RS platforms and the influence these pre-processing approaches have on the extraction of a wide range of terrain attributes. Digital elevation data are elemental in deriving primary topographic attributes that are input variables to various regional soil-landscape models. DEMs' utility to extract different topographic indices as primary inputs to DSM allows the generalised soil-formative relationship between topography and soil characteristics to be measured quantitatively. Traditional landscape-scale approaches to extracting and analysing soils remain subjective and an expensive last resort for large-scale regional soil distribution and variability prediction. Selecting the right DEMs is a critical step in the development of any soil-landscape model. Therefore, the ability to represent soil-landscape relationships rapidly and objectively between soil properties and landscape position using emerging technologies and elevation data in a digital environment and at varying scales is fundamental for using soil-landscape mapping as a regional planning tool. There is, however, still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape and geomorphic characterisation within South Africa. However, Atkinson et al. (2017) have shown that topographic variable extraction is highly dependent on the DEM source and generalisation approach. However, while higher resolution DEMs may represent the “true” landscape surface more accurately, they do not necessarily offer the best results for all extracted terrain variables for modelling soil-landscape outputs. Given the convenience of a wide range of open-source elevation data for South Africa, there is a need to quantify the impact that DEM generalisation approaches have on simplifying detailed DEMs and compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localised topographic variables as a primer for soil-landscape or digital soil models. Chapter 3 explores the harmonisation of geomorphons derived from various RS platforms to define the landscape character in central KwaZulu-Natal. Robust DGM approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units and a range of DEM spatial scales offer great potential for improved topographic and landscape analysis and must have their utility investigated. Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality DEMs. Therefore, a thorough assessment of the effects that different pixel resolution (grain size) and DEM sources have on replicating observed geomorphic spatial patterns and representing selected terrain parameters using advanced automated geomorphometric mapping approaches is necessary. Specifically, it would be valuable to interrogate the self-adapting ability of these automated mapping approaches under regional conditions to quantitatively analyse how the choice of terrain model and scale influences the extraction, generalisation, and representation of digitally derived terrain attributes such as slope gradient, elevation and terrain unit feature extent. Equally important is understanding how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities that ultimately influence the extraction and representation of elementary soil properties. Chapter 4 is a shift from the technical aspects of digital terrain preprocessing and modelling and instead attempts to explore the contribution of gridded soil-landscape products to the abiotic landscape development agenda. It would be worthwhile to contextualise and decode these technical aspects of terrain and soil analyses to a holistic landscape development agenda. It is argued that current global environmental problems and questions demand exploration into new scientific perspectives and improved related paradigms and methodologies. Geodiversity (abiotic complexity) has not received the same level of attention as biodiversity (biotic complexity) despite its intrinsic and indivisible linkages to ecosystem and landscape richness characterisation. The ability to better describe the substrate in which biological and human activities occur is of top standing and must have its potential explored. To date, only one landmark study has successfully investigated the influence of environmental factors on geodiversity mapping in South Africa (Kori et al., 2019). Using an array of multimodal environmental covariates, including hydrographic, lithostratigraphic, pedological, climatic, topographic, solar morphometric and geomorphic variables, I aim to provide further confirmation to regional and international geodiversity research agendas. Chapter 5 culminates in applying quantitative DSM methods, with improved terrain representation, to classify productive soil units (ecotopes) as a proposed methodology to improve the current Bioresource Report Writer (BRW) soil-landscape recommendations. In KwaZulu-Natal, it has been accepted that detailed natural resource information based on scientifically accurate and relevant criteria is required to develop spatial layers that planners, developers, local government, and other stakeholders can use to guide future development. At present, the KwaZulu-Natal Department of Agriculture and Rural Development (KZNDARD) can provide high-level crop production approximations for various crops based on BioResource Units (BRU). However, the BRW has not seen a significant revision for over two decades. Still, the natural resource information it contains provides land managers, policymakers and farmers with invaluable access to regional and farm level qualitative estimations of agricultural productivity. There is a need to preserve this information while simultaneously providing modern measures of land management recommendation at multiple scales to the end-user. Against this backdrop, access to readily interpretable soil and crop information is increasingly being prioritised by provincial planning commissions as critical inputs to DSS for sustainable land management within KwaZulu-Natal.