Thesis (PhD)--Stellenbosch University, 2013. / 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. / AFRIKAANSE OPSOMMING: Konkrete bewyse van droëland sout is waargeneem in die Bergrivier opvanggebied in die Wes- Kaap van Suid-Afrika. Verbrakking van grond is 'n wêreldwye probleem wat ‘n negatiewe invloed op die produktiwiteit van grond kan hê. Tydige en akkurate herkenning van verandering in grond soutgehalte is ‘n noodsaaklike aksie vir voorkoming. Dit sou beperkend wees in terme van koste om konvensionele nat chemiese metodes te gebruik vir die opsporing en monitering daarvan in die hele Bergrivier opvanggebied. Die doel van hierdie studie was om ondersoek in te stel na minder tydsame, akkurate en koste-effektiewe tegnieke vir beter monitering. Eerstens, is hiperspektrale afstandswaarnemings (HRS) tegnieke wat die beste in staat is elektriese geleidingsvermoë (EG) in die grond te kan voorspel deur gebruik te maak van individuele bande, 'n unieke genormaliseerde grond soutindeks verskil (NDSI), parsiële kleinste kwadratiese regressie (PLSR) en afwyking in PLSR, is ondersoek. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van 'n spektrale analitiese toestel: FieldSpec spektrometer in 'n donkerkamer. Voorspellings modelle vir grond soutgehalte is bereken met behulp van 'n toets datastel (n = 63). 'n onafhanklike validasie datastel (n = 32) is gebruik om die modelle te evalueer. Daarbenewens is veld-gebaseerde regressie voorspellings modelle vir EG, pH oplosbare Ca, Mg, Na, Cl and SO4 ontwikkel deur gebruik te maak van grondmonsters (n = 23) versamel in the Sandpruit opvangsgebied. Hierdie grondmonsters is nie gemaal of gesif nie en die spectra is gemeet deur gebruik te maak van die son as ‘n bron van energie om veld toestande na te boots. Tweedens, is die waarde van NIR spektroskopie vir die voorspelling van die EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 met behulp van 49 grondmonsters geëvalueer. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van die Bruker NIR veeldoelige analiseerder . Kruisvalidering (LOOCV) is gebruik om PLSR voorspellings modelle vir EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 te kalibreer. Hierdie modelle is gevalideer: R2, wortel-gemiddelde-kwadraat fout kruisvalidering (RMSECV), verhouding van voorspellings afwyking (RPD) en die verhouding van die voorspelling se inter-kwartiel afstand (RPIQ). Derdens is land komponente gekarteer vanweë die nut daat van tov grondeienskappe, en die waarde van DEMs is ondersoek om akkurate land komponente af te baken. Land komponente uit die tweede weergawe van die 30 m gevorderde ruimte termiese emissie en refleksie radio globale DEM (ASTER GDEM2), die 90-m ruimtetuig radar topografie sending DEM (SRTM DEM), twee weergawes van die 5 m Universiteit van Stellenbosch DEMs (SUDEM L1 en L2) en 'n 5 m DEM (GEOEYE DEM) afgelei van GeoEye stereo-beelde, is vergelyk. Land komponente is afgebaken met behulp van helling, gradiënt en aspek afgeleides van elke DEM. Die land komponente is visueel geïnspekteer en kwantitatief ontleed met behulp van die helling gradiënt standaardafwyking te meet en die gemiddelde helling-gradiënt-plaaslike variansie verhouding vir akkuraatheid. Vierdens, is die ruimtelike akkuraatheid van hidrologiese parameters (stroomlyn en opvanggebied grense) geëvalueer soos afgelei vanaf die 5 m resolusie SUDEM (L1 en L2), die 30 m ASTER GDEM2 en die 90 m SRTM . Die verwysings opvanggebied grens en stroomlyn is gegenereer vanaf die 1,5-m GEOEYE DEM. Opvanggebied grense en stroomlyn uit die DEMs is bepaal deur gebruik te maak van die Arc Hydro module in ArcGIS. Visuele inspeksie, korrektheid indeks, 'n nuwe Euklidiese afstand indeks en die indikasie-van-meriete indeks is gebruik om die resultate te valideer. Laastens is die waarde van die terrein eienskappe om grond southalte te modeleer ondersoek, gebaseer op die EG van die grond en grondwater. Grond soutgehalte regressie voorspellings modelle is ontwikkel met behulp van CurveExpert sagteware. Verder, stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings modelle gebaseer op jaarlikse evapotranspirasie, die dorheids indeks en terrein eienskappe is ontwikkel met behulp van Statgraphics sagteware. Die modelle is gevalideer deur gebruik te maak van R2, standaardfout en korrelasiekoëffisiënte. Die modelle is ook onafhanklik bekragtig deur die gebruik van grondwater hidro-sensus-data wat die Sandspruit opvanggebied insluit. Hierdie studie het bevind dat 'n goeie voorspelling van grond soutgehalte gebaseer op uitsak PLSR met behulp van eerste orde afgeleide reflektansie (R2 = 0,85), PLSR deur gebruik te maak van ongetransformeerde reflektansie (R2 = 0,70), 'n unieke NDSI (R2 = 0,65) en die ongetransformeerde individuele band op 2257 nm (R2 = 0,60) voorspellings modelle verkry is. Verder is vasgestel dat betroubare voorspellings van die EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 in die veld moontlik is met behulp van eerste afgeleide reflektansie. Die R2 van EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 is 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 en 0.58 onderskeidelik. Ten opsigte van NIR spektroskopie het die validasie van R2 vir al die PLSR voorspellings modelle gewissel tussen 0,62-0,87. Die RPD waardes was groter as 1,5 vir al die modelle en RMSECV het gewissel tussen 0,22-0,51. Hierdie studie het bevestig dat NIR spektroskopie die potensiaal het om gebruik te word as 'n vinnige, betroubare en goedkoper metode vir die analise van soutgeaffekteerde gronde. T.o.v. hidrologiese parameters, het die studie tot die gevolgtrekking gekom dat waardevolle hidrologiese parameters afgelei kan word uit DEMs. 'n nuwe Euklidiese afstand verhouding is bevestig as 'n betroubare hulpmiddel om raster datastelle te vergelyk. Ten opsigte van grond komponente, is daar tot die gevolgtrekking gekom dat hoër resolusie DEMs nodig is vir die bepaling van sinvolle land komponente. Dit lyk waarskynlik dat die land komponent soutgehalte modellering hidrologiese modellering verbeter en dat hulle geïntegreer kan word met ander datastelle vir meer akkurate kaarte op opvangsgebied skaal. In die geval van die terrein eienskappe het, die studie vasgestel dat belowende grond soutgehalte voorspellings gemaak kan word gebaseer op helling, elevasie, evapotranspirasie en terrein natheid indeks (TWI). 'n stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings model wat gebaseer is op elevasie, evapotranspirasie en TWI het effens meer akkurate voorspellings van die grond soutgehalte gelewer. In geheel gesien, het die studie getoon dat dit moontlik is om grond soutgehalte monitering te verbeter met behulp van HRS, NIR spektroskopie, land komponente, hidrologiese parameters en terrein eienskappe. / The Agricultural Research Council (ARC), Water Research Commission and the National Research Foundation for funding.