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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/79809 |
| Date | 03 1900 |
| Creators | Mashimbye, Zama Eric |
| Contributors | De Clercq, Willem Petrus, Van Niekerk, Adriaan, Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | xxiii, 151 p. : ill., maps |
| Rights | Stellenbosch University |
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