Stem rot of peanut (Arachis hypogaea L.), caused by a soilborne fungus Athelia rolfsii (Curzi) C. C. Tu and Kimbr. (anamorph: Sclerotium rolfsii Sacc.), is one of the most important diseases in peanut production worldwide. Though new varieties with increased partial resistance to this disease have been developed, there is still a need to utilize fungicides for disease control during the growing season. Fungicides with activity against A. rolfsii are available, and several new products have been recently registered for control of stem rot in peanut. However, fungicides are most effective when applied before or during the early stages of infection. Current scouting methods can detect disease once signs or symptoms are present, but to optimize the timing of fungicide applications and protect crop yield, a method for early detection of soilborne diseases is needed. Previous studies have utilized optical sensors combined with machine learning analysis for the early detection of plant diseases, but these studies mainly focused on foliar diseases. Few studies have applied these technologies for the early detection of soilborne diseases in field crops, including peanut. Thus, the overall goal of this research was to integrate sensor technologies, modern data analytic tools, and properties of standard and newly registered fungicides to develop improved management strategies for stem rot control in peanuts. The specific objectives of this work were to 1) characterize the spectral and thermal responses of peanut to infection with A. rolfsii under controlled conditions, 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate the standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. For Objective 1, spectral reflectance and leaf temperature of peanut plants were measured by spectral and thermal sensors in controlled greenhouse experiments. Differences in sensor-based responses between A. rolfsii-infected and non-infected plants were detected 0 to 1 day after observation of foliar disease symptoms. In addition, spectral responses of peanut to the infection of A. rolfsii were more pronounced and consistent than thermal changes as the disease progressed. Objective 2 aimed to identify specific signatures of stem rot from reflectance data collected in Objective 1 utilizing a machine learning approach. Wavelengths around 505, 690, and 884 nm were repeatedly selected by different methods. The top 10 wavelengths identified by the recursive feature selection methods performed as well as all bands for the classification of healthy peanut plants and plants at different stages of disease development. Whereas the first two objectives focused on disease detection, Objective 3 focused on disease control and compared the properties of different fungicides that are labeled for stem rot control in peanut using a laboratory bioassay of detached peanut tissues. All of the foliar-applied fungicides evaluated provided inhibition of A. rolfsii for up to two weeks on plant tissues that received a direct application. Succinate dehydrogenase inhibitors provided less basipetal protection of stem tissues than quinone outside inhibitor or demethylation inhibitor fungicides. Overall, results of this research provide a foundation for developing sensor/drone-based methods that use disease-specific spectral indices for scouting in the field and for making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases. / Doctor of Philosophy / Plant diseases are a major constraint to crop production worldwide. Developing effective and economical management strategies for these diseases, including selection of proper fungicide chemistries and making timely fungicide application, is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Optical sensors combined with machine learning analysis are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of this research was to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies and different fungicide chemistries that can be utilized for the management of soilborne plant diseases. The specific objectives of this work were to 1) characterize the temporal progress of spectral and thermal responses of peanut to infection and colonization with Athelia rolfsii, the causal agent of peanut stem rot 2) identify optimal wavelengths to detect stem rot of peanut using hyperspectral sensor and machine learning, and 3) evaluate standard and newly registered peanut fungicides with different modes of action for stem rot control in peanuts using a laboratory bioassay. Results of this work demonstrate that spectral reflectance measurements are able to distinguish between diseased and healthy plants more consistently than thermal measurements. Several wavelengths were identified using machine learning approaches that can accurately differentiate between peanut plants with symptoms of stem rot and non-symptomatic plants. In addition, a new method was developed to select the top-ranked, non-redundant wavelengths with a custom distance. These selected wavelengths performed better than using all wavelengths, providing a basis for designing low-cost optical filters to specifically detect this disease. In the laboratory bioassay evaluation of fungicides, all of the foliar-applied fungicides provided inhibition of A. rolfsii for up to two weeks on leaf tissues that received a direct application. Percent inhibition of A. rolfsii decreased over time, and the activity of all fungicides decreased at a similar rate. Overall, the findings of this research provide a foundation for developing sensor-based methods for disease scouting and making fungicide application recommendations to manage stem rot of peanut and other soilborne diseases.

Biomedical optics is a rapidly expanding field of research focusing on the development of methods to detect, diagnose, and treat disease using light. While there are a myriad of optical systems that have been developed for biological tissue imaging, optical diagnostics, and optical therapeutics, all of these methods suffer severely limited penetration depths due to attenuation of light by tissue constituent chromophores, including cells, water, blood, and protein structures. Tissue optical clearing is a recent area of study within biomedical optics and photonics, where chemical agents have been used to alter tissue optical properties, reducing optical absorption and scattering and enabling light delivery to and collection from deeper tissue regions. However, there are concerns as to the safety and efficacy of these chemical clearing agents in vivo, especially in the skin, where the projective barrier function of the stratum corneum must be removed. Mechanical optical clearing is a recently developed technology which utilizes mechanical loading to reversibly modify light transport through soft tissues, and much of the work published on this technique has focused on applications in skin tissue. This clearing technique enables deeper light delivery into soft tissues but does not require use of exogenous chemicals, nor does it compromise the skin barrier function. While this clearing effect is thought to be resultant from interstitial water and blood transport, the underlying mechanism has not been concretely identified nor characterized. The hypothesis of this body of work was that interstitial transport of tissue chromophores (e.g. water and blood) causes intrinsic optical property changes, reduces tissue optical absorption and scattering, and improves light delivery in diagnostic applications. To test this hypothesis, we first developed a mathematical framework to simulate mechanical optical clearing, using both mechanical finite element models and optical Monte Carlo simulations. By directly simulating interstitial water transport in response to loading, data from mechanical simulations was combined with optical Monte Carlo simulations, which enabled prediction of light transmission measurements made during mechanical indentation experiments. We also investigated changes in optical properties during mechanical indentation using diffuse reflectance spectroscopy. These studies used controlled flat indentation by a fiberoptic probe to dynamically measure intrinsic optical properties as they changed over time. Finally, we apply mechanical optical clearing principles to functional near-infrared spectroscopy for neuroimaging. By building a prototypical mechanical optical clearing device for measuring cerebral hemodynamics, we demonstrated that mechanical optical clearing devices modify measured cerebral hemodynamic signals in human subjects, improving signal quality. / Ph. D.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2010. / Thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE IN ENGINEERING (MINERAL PROCESSING) in the Department of Processing Engineering at the University of Stellenbosch / ENGLISH ABSTRACT: This work evaluates the use of inverse least squares (ILS) and classical least squares (CLS) models for calibration of a diffuse reflectance spectrophotometer for on-line monitoring of the aqueous phase in a flotation cells. Both models use a Beer's law for the quantification of the metals. The formulated statistical models are compared to a proprietary Blue Cube model in terms of prediction ability to determine the potential applicability of the models. A diffuse reflectance spectrophotometry was used for simultaneous analysis of copper (Cu), cobalt (Co) and zinc (Zn) in the solutions. The laboratory set-up of Blue Cube instrument was used for the experimental analysis. The concentrations and matrix compositions of the samples are simulated according to Skorpion zinc mine plant conditions. The calibration samples were prepared using a simplex-centroid mixture design with the triplicates of the centroid run. The unknown or test samples were prepared randomly within the same concentration of the calibration samples. The effects of temperature and nickel concentration on absorption of the metals were evaluated in the following range, 20 - 80 °C and 125 - 400 ppm, respectively. The statistical models (ILS and CLS) were calibrated from visible and near infrared (VNIR) spectra data of the calibration samples. A modified Beer's method was used as a preprocessing technique to convert the raw data into absorbance values. The manual wavelength selection procedure was used to select the wavelengths to be used in both models. The quality of the models was evaluated based on Rª and % root mean squared error (RMSE) values with 0.90 and 10% used as the guideline for the respective statistical parameters. Both ILS and CLS models showed good results for all three metals (Cu, Co and Zn) during their calibration steps. It was further shown that both models give worse predictions for Zn as compared to other metals due to its low relative intensity in the mixture. The derivative orders of absorbance spectra that were used to enhance the prediction results of Zn had no positive effect but they rather lowered accuracy of predictions. An increase in temperature was found to increase the intensities of the absorption spectra of all the metals while an increase in nickel concentration decreases the prediction ability of model. The developed statistical models were compared to a Blue Cube model in terms of prediction ability using analysis of variance (ANOVA) test. The ANOVA results revealed that there is no statistical difference between the developed models and Blue Cube model since the F-values for all the metals were below the critical F-value. Furthermore, the partial least squares (PLS) model shows an increased accuracy results for prediction of zinc metal as compared to both the ILS and CLS models. Finally, good comparisons of the statistical models results with atomic absorption spectroscopy (AAS) analyses were establish for the unknown samples. The study demonstrates that chemometric models (ILS and CLS) developed here can be used for quantification of several metals in real hydrometallurgical solutions as samples were simulated according to a plant conditions. However, in order to have confidence in the results of the models, a factorial-mixture design must be used to study the effect of temperature and nickel concentration. Moreover the models must be further tested and validated on the real samples from a plant. / AFRIKAANSE OPSOMMING: Hierdie werkstuk evalueer die gebruik van inverse kleinste kwadraatmetodes (IKK) en klassieke kleinste kwadraatmetodes (KKK) vir die kalibrasie van 'n diffuse reflektansiespektrofotometer vir die aanlyn monitering van die waterige fase in flottasieselle. Beer se wet word vir die kwantifisering van metale vir albei modelle gebruik. Die omskrewe data-gebaseerde modelle is op grond van voorspellingsvermoë vergelyk met'n. Blue Cube model, sodat die moontlike toepaslikheid van hierdie modelle bepaal kan word. 'n Diffuse reflectantie spektrofotometrie is ingespan vir die gelyktydige analise van koper (Cu), kobalt (Co) en sink (Zn) in oplossing. Eksperimentele analises is met behulp van 'n laboratoriumopstelling met 'n Blue Cube instrument uitgevoer. Die konsentrasies en matriks-samestellings van monsters is gesimuleer om Skorpion sinkmyn aanlegkondisies na te boots. Kalibrasie monsters is voorberei volgens . simpleks-sentroïed mengselontwerp met drievoudige sentroïede lopies. Onbekende (toets) monsters is ewekansig voorberei binne dieselfde konsentrasie spesifikasies as die kalibrasie monsters. Die invloed van temperatuur en nikkelkonsenstrasie op die absorpsie van die metale is in die bestek van 20 - 80 °C en 125 - 400 dpm, onderskeidelik, bepaal. Die data-gebaseerde modelle (IKK en KKK) is met sigbare en naby infrarooi (SNIR) spektra data van die kalibrasie monsters gekalibreer. 'n Gewysigde Beer metode is vir data voorbereiding benut om rou data na absorbansie waardes om te skakel. Die handgolflengte-seleksieprosedure is vir beide modelle gebruik om die golflengtes te kies. Die kwaliteit van die modelle is op grond van Rª en % wortel gemiddelde kwadratiese fout (WGKF) geevalueer, met waardes van 0.90 en 10% (onderskeidelik) as riglyne vir hierdie statistiese parameters. Beide IKK en KKK modelle het vir hul kalibrasie stappe vir al drie metale (Cu, Co en Zn) goeie resultate getoon. Dit is verder getoon dat albei modelle die slegste voorspellings lewer vir Zn (vergeleke met die ander metale) as gevolg van Zn se lae relatiewe intensiteit in die mengsel. Afgeleide ordes van absorbansie spektra is gebruik om die Zn voorspellings te versterk, maar het geen positiewe effek gehad nie; inteendeel, voorspellingakkuraatheid is verlaag. ʼn Verhoging in temperatuur het die intensiteite van die absorpsie spektra van alle metale verhoog, terwyl ʼn verhoging in nikkelkonsentrasie die voorspellingakkuraatheid van die modelle verlaag het. Die ontwikkelde data-gebaseerde modelle is met ʼn Blue Cube model vergelyk in terme van voorspellingsvermoë met behulp van variansie-analise (ANOVA). Die ANOVA resultate toon dat daar geen statistiese verskil tussen die ontwikkelde modelle en die Blue Cube model is nie, aangesien die F-waardes vir al die metale onder die kritiese F-waarde is. Die gedeeltelike kleinste kwadraatmodel (GKK) toon verder verhoogde voorspellingakkuraat-heid vir sinkmetaal tenoor beide die IKK en KKK modelle. Ten slotte, goeie ooreenstemming van die data-gebaseerde modelresultate met atoomabsorpsie spektroskopie (AAS) analise is vir die onbekende monsters gevind. Hierdie werkstuk toon dat die chemometriese modelle (IKK en KKK) wat hier ontwikkel is, gebruik kan word vir die kwantifisering van verskeie metale in werklike hidrometallurgiese oplossings, aangesien monsters gesimuleer is volgens aanlegkondisies. Om egter verdere vertroue te hê in die modelresultate, sal ʼn faktoriaal-mengselontwerp toegepas moet word om die effek van temperatuur en nikkelkonsentrasie te ondersoek. Voorts moet die modelle verder getoets en gevalideer word op werklike monsters van ʼn aanleg.

Approved for public release, distribution is unlimited / ine discrimination of similar soil classes was produced by the BRDF variations in the high-spatial resolution panchromatic image. Texture analysis results depended on the directionality of the gray level co-occurrence matrix (GLCM) calculation. Combining the different modalities of analysis did not improve the overall classification, perhaps illustrating the consequences of the Hughes paradox (Hughes, 1968) / Flight Lieutenant, Royal Thai Air Force

No description available.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Methods for rapid, safe and effective detection of unmapped buried ordnance are vital to the protection of humans and environmental quality throughout the world. This study aimed to investigate the use of phytosensing and to understand the physiological response of woody plants to 2,4,6-trinitrotoluene (TNT) contamination. Baccharis halimifolia were potted in soils containing various concentrations of TNT and physiological responses were observed over a 9-week experimental period. Measurements included the collection of remotely sensed data, such as hyperspectral reflectance and chlorophyll fluorescence, and traditional plant-level physiological data. In accordance with the hypothesis, low levels of TNT improved physiological response in plants due to the slight increase in nitrogen, while high levels of TNT induced stress. Key markers in stress responses were identified, specifically with reflectance indices and derivatives, which may separate TNT-contaminated plants from naturally stressed plants and would allow for accurate detection of buried ordnance at the landscape level.

La thèse est dédiée au problème de la stéréophotométrie non-Lambertienne sans connaissance a priori sur les conditions d’illumination et son application aux images de boîte de Pétri. Pour obtenir une bonne reconstruction de surfaces non-Lambertiennes, il est proposé de traiter une séquence d’entrée en deux étapes: premièrement il faut supprimer les effets spéculaires et obtenir ainsi des images de surface ’pseudo-Lambertienne’. Ensuite dans une deuxième étape à partir de ces images une reconstruction stéréophotométrique Lambertienne sans aucune information préalable sur les directions d’illumination est effectuée. Dans ce travail nous proposons deux méthodes originales respectivement pour la suppression de spécularités et la reconstruction de surface sans information a priori. Les méthodes proposées sont appliquées pour la caractérisation des colonies microbiennes.La spécularités est un effet optique lié à la nature physique complexe des objets. Il est utile pour la perception humaine des objets 3D mais il gêne le processus de traitement automatique d’images. Pour pouvoir appliquer le modèle Lambertien à la stéréophotométrie, les spécularités doivent être supprimées des images d’entrée. Nous proposons donc une méthode originale pour la correction des zones spéculaires adaptée pour une reconstruction ultérieure. L’algorithme proposé est capable de détecter les spécularités comme des valeurs anormalement élevées d’intensité dans une image de la séquence d’entrée, et de les corriger en utilisant les informations des autres images de la séquence et une fonction de correction continue. Cette méthode permet de faire la suppression des spécularités en préservant toutes les autres particularités de distribution de lumière qui sont importantes pour la reconstruction de surface.Après nous proposons une technique de reconstruction stéréophotométrique de surface Lambertienne sans connaissance a priori sur l’illumination. Le modèle mis en œuvre consiste en quatre composantes, deux composantes (albédo et normales) permettent de d´écrire des propriétés de surface et deux autres (intensités des sources de lumière et leurs directions) décrivent illumination. L’algorithme proposé de reconstruction utilise le principe de l’optimisation alternée. Chaque composante du modèle est trouvée itérativement en fixant toutes les variables sauf une et en appliquant des contraintes de structures, valeurs et qualité pour la fonction d’optimisation. Un schéma original de résolution permet de séparer les différents types d’information inclus dans les images d’entrée. Grâce à cette factorisation de matrices, la reconstruction de surface est faite sans connaissance préalable sur les directions de lumière et les propriétés de l’objet reconstruit. L’applicabilité de l’algorithme est prouvée pour des donnés artificielles et des images de bases publiques pour lesquelles la vérité terrain sur les surfaces des objets est disponible.La dernière partie de la thèse est dédiée à l’application de la chaine complète proposée pour le traitement d’images de boîte de Pétri. Ces images sont obtenues en utilisant les sources de lumières complexes qui sont supposées être inconnues pour le processus de reconstruction. L’évaluation de surfaces de colonies microbiennes s’est révélée être une étape importante pour l'analyse visuelle et automatique des colonies. La chaine proposée est efficace pour ce type de données et permet de compléter les informations d'images par de la surface 3D. / The PhD thesis work is dedicated to the problem of uncalibrated non-Lambertian photometric stereo surface reconstruction. The proposed approach consists in two phases: first we correct images of the input sequence from specularities in order to obtain images of pseudo-Lambertian surfaces, and then realize Lambertian photometric stereo reconstruction. In this work we proposed two original methods, respectively, for specularity correction and surface reconstruction with no prior information neither on light sources nor on surface properties. We apply the novel processing to Petri dish images for microbial colonies surface reconstruction.Specularity is an optical effect of a complex physical nature. This effect is useful for human 3D objects perception but it affects automated image processing. In order to be able to apply the Lambertian photometric stereo model, specularities should be removed from the input images. We propose an original method for specular zones correction adapted to estimation of pseudo-Lambertian surface images and further reconstruction. This algorithm is able to detect specularities as abnormally elevated pixel intensity values in an image of the input sequence and to correct the found zones using information from all other images of the sequence and a specific continuous correcting function. This method allows removing specularities while still preserving all other particularities of shading important for the further surface reconstruction.We then propose an original stereo photometric method for Lambertian surface reconstruction with no prior on illuminations. The implemented photometric stereo model consists of four components, two of them (albedo and normals) describe surface properties and the others (light sources intensities and directions) describe illumination. The proposed algorithm of the photometric stereo reconstruction uses the alternating optimization principle. Each model component is found iteratively fixing all variables but one and applying value and quality constraints for the optimization function. The original scheme of resolution allows separating of different information types included in input images. Thanks to such matrix factorization, the surface reconstruction is made with no prior information on lighting directions and the reconstructed objects properties. The applicability of the algorithm is proved using artificially created and open data-sets for which the ground truth information is available.The last part of the thesis is dedicated to the application of the proposed uncalibrated non- Lambertian photometric stereo approach to the Petri dish images. Images are obtained using illuminating sources which are supposed to be unknown for the reconstruction process. Moreover, the reconstructed microbial colonies are very diverse, generally have small size, can be Lambertian or not, and their surface properties are not defined in advance. The results of reconstruction for such complex real-world data add value and importance to the developed approach.

Ph.D., Faculty of Science, University of the Witwatersrand, 2011 / Deep-level gold mining in the Witwatersrand Basin Goldfields (WBG) of central South Africa is characterised by the production of extensive unlined tailings storage facilities (TSFs) comprising large quantities of pulverised rock and water contaminated with salts and a wide range of other inorganic pollutants (Weiersbye et al., 2006). There are more than 200 such TSFs covering a total area of more than 400 km2 (Rosner et al., 2001), and significant contaminated “footprint” areas occur after removal and reprocessing of the original TSFs (Chevrel et al., 2003). It is estimated that the Witwatersrand Basin contains six billion tons of gold and uranium tailings (Chevrel et al., 2003), 430 000 tons of uranium (Council of Geoscience, 1998; Winde, 2004a; b; c) and approximately 30 million tons of sulphur (Witkowski and Weiersbye, 1998a). An estimated 105 million tons of waste per annum is generated by the gold mining industry within the WBG (Department of Tourism, Economic and Environmental Affairs, 2002; Chamber of Mines of South Africa, 2004). A major environmental problem resulting from deep level mining in the WBG is the contaminated water that seeps from TSFs into adjacent lands and groundwater. Van As (1992) reported on the significant environmental hazards resulting from the storage of highly pulverised pyrite rock waste in TSFs (Straker et al., 2007). Adjacent lands become polluted through near-surface seepage, and this is enhanced by the movement of polluted groundwater in shallow aquifers that are commonly 1-30 m below ground (Funke, 1990; Hodgson et al., 2001; Rosner et al., 2001; Naicker et al., 2003). The impact of the mines and the TSFs extends far beyond their localities (Cogho et al., 1990). The Vaal River catchment receives a large proportion of the pollutants from WBG mining activities, with consequent acidification and salinisation of surface and ground waters. Salt discharges to the Vaal River were estimated to be 170 000 t/annum (Best, 1985), whereas discharges from the Free State gold mines south of the Vaal catchment were estimated at 350 000 t/annum of salts (Cogho et al., 1990). Concern also exists over the spread of dangerous contaminants such as uranium, chromium and mercury (Coetzee et al., 2006; Winde, 2009). Engineering solutions to these problems are hindered by the large sizes and great extent of TSFs, the high and indefinite costs involved, and the typically low hydraulic conductivity in affected aquifers, which makes the “pump and treat” option impractical. An alternative phytoremediation strategy is to establish belts or blocks of trees in strategic areas surrounding the TSFs in order to reduce the seepage of contaminated water into adjacent lands and groundwater bodies. The major reasons why trees are likely to have a greater impact on seepage water than the existing grasslands that characterise the area around most TSFs in the WBG, are that some tree species have the potential to develop very deep root systems and to continue transpiring water throughout the year. This is in contrast to seasonally dormant grasslands. In addition, some tree species are known to be tolerant to salts and other pollutants. Trees are thus potentially able to reach deep water tables, take up large quantities of water, and remove some of the pollutants in this water. It is crucial for a successful implementation of this strategy to know on what sites trees are able to access mine seepage water, and consequently maintain a high year-round rate of water use. If this access is limited, then growth and water use will be curtailed during the long winter dry season, and control of seepage will be considerably below potential. A primary aim of this study was to develop methodologies to discriminate between water-stressed and non-water-stressed trees currently growing in three gold mining districts (Welkom, Vaal River, West Wits) within the WBG. This information was required to assess what site types are likely to support adequate tree growth and permit high rates of water use and seepage control. The tree species selected were those most widely occurring in these areas, and include the non-native species Eucalyptus sideroxylon A. Cunningham ex Woolls and Eucalyptus camaldulensis Dehnhardt, as well as the indigenous species Searsia lancea L.f. Various remote sensing technologies including leaf-level spectroscopy, satellite and airborne remote sensing images were evaluated for their usefulness in detecting levels of winter-time water stress. Four commonly used ground-truthing techniques (predawn leaf water potential, leaf chlorophyll fluorescence, leaf chlorophyll and carotenoid pigment content, and leaf water content) were used for localised measurements of plant water stress and for ground-truthing of remotely sensed data on 75 sample sites and 15 sample sites. This study provided a unique opportunity to test and compare the use of stress reflectance models derived from different remote sensing data acquired at different spatial and spectral resolutions (i.e. multispectral and hyperspectral) for the same geographical location. The use of remote sensing to examine the spectral responses of vegetation to plant stress has been widely described in the scientific literature. A collation of published spectral reflectance indices provided the basis for investigating the use of hand-held remote sensing technology to detect plant water stress, and was used as a stepping stone to further develop spectral plant water stress relationships for specific tree species in this study. Seventy seven spectral reflectance indices and specific individual spectral wavelengths useful for detecting plant water stress, plant pigment content, the presence of stress related pigments in vegetation, and changes in leaf cellular structure, were investigated using hand-held spectroscopy. Ground-based measurements of plant water stress were taken on 75 sample trees. In this study, the measurement of predawn leaf water potential has been identified as a key methodology for linking remotely sensed assessments of plant water stress to actual plant water stress; a reading of -0.8 MPa was used to separate stressed trees from unstressed trees in the landscape (Cleary and Zaerr, 1984). The results of the predawn leaf water potential measurements ranged from -0.56 to -0.68 MPa at unstressed sites, and from -0.93 to -1.78 MPa at stressed sites. A novel approach of using spectral reflectance indices derived from previous studies was used to identify specific indices which are applicable to South Africa and to the three species investigated in the WGB. Maximal multiple linear regression models were derived for all possible combinations of plant water stress measurements and the 77 spectral reflectance indices extracted from leaf-level spectral reflectance data, and included the interactions of district and species. The results of the multiple linear regression models indicated that the (695/690) index, DATT index (850-710)/(850-680), near infra-red index (710/760) and the water band (900/970) index performed well and accounted for more than 50% of the variance in the data. The stepwise regression model derived between chlorophyll b content and the DATT index was selected as the “best” model, having the highest adjusted R2 of 69.3%. This was shown to be the most robust model in this application, which could be used at different locations for different species to predict chlorophyll content at the leaf-level. Satellite earth observation data were acquired from two data sources for this investigation; the Hyperion hyperspectral sensor (United States Geological Survey Earth Resources Observation Systems) and the Proba Chris pseudo-hyperspectral sensor (European Space Agency). The Hyperion sensor was selected to obtain high spatial and spectral resolution data, whereas the Proba Chris sensor provided high spatial and medium spectral resolution earth observation data. Twelve vegetation indices designed to capture changes in canopy water status, plant pigment content and changes in plant cellular structure, were selected and derived from the satellite remote sensing imagery. Ground-based measurements of plant water stress undertaken during late July 2004 were used for ground-truthing the Hyperion image, while measurements undertaken during July 2005 and August 2005 were used for ground-truthing the Proba Chris images. Predawn leaf water potential measurements undertaken for the three species, ranged from -0.42 to -0.78 MPa at unstressed sites, and -0.95 to -4.66 MPa at stressed sites. Predawn leaf water potentials measured for E. camaldulensis trees sampled in species trials in Vaal River were significantly different between stressed and non stressed trees (t = 3.39, 8df, P = 0.009). In contrast, E. camaldulensis trees sampled near a pan within the Welkom mining district, which had greater access to water but were exposed to higher concentrations of salts and inorganic contaminants, displayed differences in total chlorophyll content (t = -2.20, 8df, P = 0.059), carotenoid content (t = -5.68, 8df, P < 0.001) and predawn leaf water potential (t = 4.25, 8df, P = 0.011) when compared to trees sampled on farmland. E. sideroxylon trees sampled close to a farm dam in the West Wits mining district displayed differences in predawn leaf water potential (t = 69.32, 8df, P < 0.001) and carotenoid content (t = -2.13, 8df, P = 0.066) when compared to stressed trees further upslope away from the water source. Multiple linear regressions revealed that the predawn leaf water potential greenness normalised difference vegetation index model, and the predawn leaf water potential water band index model were the “best” surrogate measures of plant water stress when using broad band multispectral satellite and narrow-band hyperspectral satellite data respectively. It was concluded from these investigations that vegetation indices designed to capture changes in plant water content/plant water status and spectral changes in the red edge region of the spectrum, performed well when applied to high spectral resolution remote sensing data. The greenness normalised difference vegetation index was considered to be a fairly robust index, which was highly correlated to chlorophyll fluorescence and predawn leaf water potential. It is recommended that this index has the potential to be used to map spatial patterns of winter-time plant stress for different genera/species and in different geographical locations. Airborne remote sensing surveys were conducted to investigate the application of high spatial resolution remote sensing data to detect plant water stress. Multispectral airborne imagery was acquired by Land Resource International (PTY) Ltd, South Africa. Ground-based measurements of plant water stress were carried out during July and August 2005.Four individual spectral bands and two vegetation spectral reflectance indices, which are sensitive to changes in plant pigment content, were derived from the processed multispectral images viz. red, green, blue and near-infrared spectral bands and the normalised difference vegetation index (NDVI) and greenness normalised difference vegetation index (GNDVI).The results of the multispectral airborne study revealed that carotenoid content together with the green spectral waveband resulted in the “best” surrogate measure of plant water stress when using broad-band multispectral airborne data. Airborne remote sensing surveys were conducted by Bar-Kal Systems Engineering Ltd, Israel, to investigate the application of hyperspectral airborne imagery to detect plant water stress. Six vegetation spectral reflectance indices designed to capture changes in plant pigment and plant water status/content, were derived from the processed hyperspectral images. When using airborne hyperspectral data, predawn leaf water potential with the normalized difference water index was selected as the most appropriate model. It was concluded, upon evaluation of the multiple linear regression models, that the airborne hyperspectral data produced several more regression models with higher adjusted R2 values (Ra2 range 6.2 - 76.2%) when compared to the airborne multispectral data (Ra2 range 6 - 50.1). Exploration of relationships between vegetation indices derived from leaf-level, satellite and airborne spectral reflectance data and ground-based measurements used as “surrogate” measures of plant water stress, revealed that several prominent and recurring spectral reflectance indices could be applied to identify species-specific plant water stress within the Welkom, Vaal River and West Wits mining districts. The models recommended for mapping and detecting spatial patterns of plant water stress when using different sources of remote sensing data are as follows: the chlorophyll b DATT spectral reflectance model when derived from leaf-level spectral reflectance data, can be applied across all three mining districts the predawn leaf water potential GNDVI spectral reflectance model and predawn leaf water potential water band index spectral reflectance model when utilising satellite multispectral and hyperspectral remote sensing data carotenoid content green band spectral reflectance model can be used for airborne multispectral resolution data predawn leaf water potential NDVI spectral reflectance model is best suited for airborne high spatial and hyperspectral resolution data. These results indicate that measurements of predawn leaf water potential and plant pigment content have been identified as key methodologies for ground-truthing of remotely sensed data and can be used as surrogate measures of plant water stress. Some preliminary research was undertaken to evaluate if wood anatomy characteristics could be used as a non-destructive and rapid low-cost survey approach for identifying trees which are experiencing long-term plant stress. Seventy two wood core samples were extracted and analysed. Predawn leaf water potential measurements were used to classify stressed and unstressed trees. Relative differences in radial vessel diameter, vessel frequency and wood density were examined. Comparison of the radial vessel diameter and vessel frequency measurements revealed significant differences in three of the five comparative sampling sites (p <0.05). The results of the density analyses were significantly different for all five comparative sampling sites (p < 0.01). In general, trees experiencing higher plant water stress displayed smaller vessel diameters, compared to less stressed or healthy trees. Sites which were influenced by high levels of contaminated water also displayed smaller vessel diameters, indicating that the uptake of contaminants could affect the wood anatomy of plants. Trees considered to be experiencing higher plant water stress displayed higher vessel frequency. This preliminary study showed that plant stress does influence the wood anatomical characteristics (radial vessel diameter, vessel frequency and wood density) in E. camaldulensis, E. sideroxylon and S. lancea in the three mining districts. Spatial patterns of trees, mapped in the three gold mining districts, Welkom (27º57´S, 26º34´E) in the Free State Province, Vaal River (26º55´S, 26º40´E) located in the North West Province, and West Wits (26º25´S, 27º21´E) located in Gauteng, which were not experiencing winter-time water stress were correlated to site characteristics such as average soil depth, percent clay in the topsoil, groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity and groundwater water level. The spectral reflectance model derived between predawn leaf water potential and the green normalised difference vegetation index using broad-band multispectral Proba Chris satellite data was used to map spatial patterns of unstressed trees across the three mining districts. Very high resolution (75 cm) multispectral airborne images acquired by LRI in 2005 were used to demarcate and classify vegetation using the maximum likelihood supervised classification technique. Interpolated surfaces of groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity, pH and groundwater table levels were created using the kriging geostatistical interpolation technique for each mining district. Random sample analyses between stressed and unstressed trees were extracted in order to determine whether site characteristics were significantly different (using t-tests). Site characteristic surfaces which were significantly different from stressed areas were spatially linked to trees which were not experiencing winter-time plant water stress for each tree species investigated in each mining district. This spatial correlation was used to make recommendations and prioritise sites for the establishment of future block plantings. Analysis of the site characteristic data and the geophysical surveys undertaken in the three mining districts which provided detailed information on groundwater saturation and an indication of the salinity conditions, confirmed the presence of relatively shallow and saline groundwater sources. This would imply that tree roots could access the relatively shallow groundwater even during the dry winter season and assist in containing contaminated groundwater seeping into surrounding lands. Keywords : airborne imagery, ground-based measurements of plant water stress, hyperspectral, leaf-level spectroscopy, multispectral, satellite imagery, spatial patterns of unstressed trees, spectral reflectance indices

Avian brood parasitism is an ideal system for the study of coevolution. Brood parasites and their hosts have developed interesting adaptations during co-evolution allowing them to maximize their fitness. The evolution of these adaptations has a character of an "arms race" where the evolution of one trait in the host is tied with the evolution of another trait in the parasite. In my doctoral thesis, I deal with two of these adaptations: recognition of parasitic eggs by hosts and mimicry of eggs in parasites. Since both these adaptations are influenced by birds' visual system, in all my studies I used an objective method to measure the colour and the modelling of avian visual system that is quite different from the human visual system. For instance, humans in contrast to birds cannot perceive ultraviolet (UV) light. However, this part of spectrum influences behaviour of birds substantially (e.g. courtship or foraging). We found that the hosts of brood parasites can use UV light when recognizing parasitic eggs. However, it seems that this part of spectrum is not the main cue in egg recognition (manuscript 1). Ambient light has also an important impact on colour perception. We determined whether the light conditions in nests influence host responses to alien eggs. The Red Bishop (Euplectes orix) was an ideal...