Browsing by Author "Vermeulen, Divan"

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    Efficacy of machine learning, earth observation and geomorphometry for mapping salt-affected soils in irrigation fields
    (Stellenbosch : Stellenbosch University, 2018-12) Vermeulen, Divan; Van Niekerk, Adriaan; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: There is a need to monitor salt accumulation throughout agricultural irrigation schemes as it can have a major negative impact on crop yields and subsequently result in a lower food production. Salt accumulation can result from natural processes, human interference or prolonged waterlogging. Most irrigation schemes are large and therefore difficult to monitor via conventional methods (e.g. regular field visits). More cost-effective, less time-consuming approaches in identifying salt-affected and salt-prone areas in large irrigation schemes are therefore needed. Remote sensing has been proposed as an alternative approach due to its ability to cover a large region on a timely basis. The approach is also more cost-effective because less field visits are required. A literature review on salt accumulation and remote sensing identified several direct and indirect methods for identifying salt-affected or salt-prone areas. Direct methods focus on the delineation of salt crusts visible on the bare soil in multispectral satellite imagery, whereas indirect methods, which include vegetation stress monitoring and geomorphometry (terrain analysis), attempt to take subsurface conditions into account. A disadvantage of the direct approach is that it does not take subsurface conditions into account, while vegetation stress monitoring (an indirect method) can produce inaccurate results because the vegetation stress can be a result of other factors (e.g. poor farming practices). Geomorphometry offers an alternative (modelling) approach that can either replace or augment direct and other indirect methods. Two experiments were carried out in this study, both of which focussed on machine learning (ML) algorithms (namely k-nearest neighbour (kNN), support vector machine (SVM), decision tree (DT) and random forest (RF)) and statistical analyses (regression or geostatistics) to identify salt-affected soils. The first experiment made use of very high resolution WorldView-2 (WV2) imagery. A number of texture measures and salinity indices were derived from the WV2 bands and considered as predictor variables. In addition to the ML and statistical analyses, a classification and regression tree (CART) model and Jeffries-Matusita (JM) distance thresholds were also produced from the predictors. The CART model was the most accurate in differentiating salt-affected and unaffected soils, but the accuracy of kNN and RF classifications were only marginally lower. The normalized difference salinity index showed the most promise among the predictors as it featured in the best JM, regression and CART models. The second experiment applied geomorphometry approaches to two South African irrigation schemes. Elevation sources include the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) and a digital surface model (DSM) produced from stereoscopic aerial photography. A number of morphological (e.g. slope gradient) and hydrological (e.g. flow direction) terrain parameters were derived from the SRTM DEM and the DSM and used as predictors. In addition to the algorithms used for the first experiment, the geostatistical method Kriging with external drift (KED) was also evaluated in this experiment. The source of elevation had an insignificant impact on the accuracies, although the DSM did show promise when combined with ML. KED outperformed regression modelling and ML in most cases, but ML produced similar results for one of the study areas. The experiments showed that direct and geomorphometry approaches hold much potential for mapping salt-affected soil. ML also proved to be a viable option for identifying salt-affected or salt-prone soil. It is recommended that a combination of direct and indirect (e.g. vegetation stress monitoring) approaches are considered in future research. Making use of alternative data sources such as hyperspectral imagery or higher spatial resolution DEMs may also prove useful. Clearly, more research is needed before such approaches can be operationalized for detecting, monitoring and mapping salt accumulation in irrigated areas.