Doctoral Degrees (Soil Science)

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Browsing Doctoral Degrees (Soil Science) by Author "Mashimbye, Zama Eric"

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    Remote sensing of salt-affected soils
    (Stellenbosch : Stellenbosch University, 2013-03) Mashimbye, Zama Eric; De Clercq, W. P.; Van Niekerk, Adriaan; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
    ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western Cape Province of South Africa. Soil salinization is a global land degradation hazard that negatively affects the productivity of soils. Timely and accurate detection of soil salinity is crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River catchment. The goal of this study was to investigate less tedious, accurate and cost effective techniques for better monitoring. Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated. Spectral reflectance of dry soil samples was measured using an analytical spectral device FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These soil samples were not ground or sieved and the spectra were measured using the sun as a source of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral reflectance of dry soil samples was measured using the Bruker multipurpose analyser spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land components to map soil properties, the value of digital elevation models (DEMs) to delineate accurate land components was investigated. Land components extracted from the second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components were visually inspected and quantitatively analysed using the slope gradient standard deviation measure and the mean slope gradient local variance ratio for accuracy. Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2 and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a new Euclidean distance index and figure of merit index were used to validate the results. Finally, the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater was investigated. Soil salinity regression predictive models were developed using CurveExpert software. In addition, stepwise multiple linear regression soil salinity predictive models based on annual evapotranspiration, the aridity index and terrain attributes were developed using Statgraphics software. The models were validated using R2, standard error and correlation coefficients. The models were also independently validated using groundwater hydro-census data covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive models were achieved. Furthermore, it was established that reliable predictions of EC, pH, soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2 for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the potential to be used as a quick, reliable and less expensive method for evaluating salt-affected soils. As regards hydrological parameters, the study concluded that valuable hydrological parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a reliable tool to compare raster data sets. Regarding land components, it was concluded that higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling and that they can be integrated with other data sets to map soil salinity more accurately at catchment level. In the case of terrain attributes, the study established that promising soil salinity predictions could be made based on slope, elevation, evapotranspiration and terrain wetness index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity. Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR spectroscopy, land components, hydrological parameters and terrain attributes.