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An incoherent CO₂ laser radar, or lidar, system using a tunable CO₂ TEA laser has been developed, along with analytical techniques to permit the determination of atmospheric transmittance and aerosol backscatter from multi-angle lidar returns. This work has been motivated by the need for a more complete knowledge of the optical properties of the atmosphere in the 9 to 11 μm spectral region. Results of preliminary observations are discussed. CO₂ lidar systems have been used before to measure backscatter and transmittance. Here, a new analytic method is developed, applicable to the 8-12 μm window region in conditions of high visibility, when the aerosol component of extinction is negligible compared to the molecular component. In such cases the backscatter sensed by the system is due to the atmospheric aerosol while atmospheric transmittance is determined by molecular species such as carbon dioxide and water vapor. It is not possible to assume a functional relationship between backscatter and extinction, as required by many previous analytic techniques. Therefore, a new solution technique based on a weighted, non-linear least squares fit applied to multi-zenith angle lidar returns has been developed. It is shown how constraints may be applied to rule out solutions which are unlikely on a priori grounds. An error analysis and a discussion of proper weighting techniques are presented. A CO₂ lidar system capable of acquiring multi-angle returns was developed, which included a gain-switching amplifier to compress the dynamic range of the return signal. The entire system is operated under computer control and data acquisition and storage are fully automated. A laser pulse energy monitor allows sequential returns to be averaged to reduce signal fluctuations. Preliminary observations with the system have demonstrated the capability of acquiring and averaging hundreds of returns on a routine basis. The return signal was observed to have fluctuations of 20 to 50% from shot to shot, due to atmospheric fluctuations. This result indicates signal averaging will be necessary to reduce signal fluctuations to levels where the multi-angle solution method may be applied.

Globally, water turbidity remains a crucial parameter in determining water quality. South Africa is largely regarded as arid and is often characterised by limited but high intensity rainfall. This characteristic renders most of the country’s water bodies turbid. Consequently, the use of turbidity as a measure of water quality is of great relevance in a South African context. Generally, turbidity alters biological and ecological characteristics of water bodies by inducing changes in among others temperature, oxygen levels and light penetration. These changes may affect aquatic life, ecosystem functioning and available water for industrial and domestic use. Siltation, a direct function of turbidity also impacts on the physical storage of dams and shortens their useful life. To date, determination of water turbidity relies on the tradition laboratory based methods that are often time consuming, expensive and labour intensive. This has increased the need for more cost effective means of determining water turbidity. In the recent past, the use of remote sensing techniques has emerged as a viable option in water quality assessment. Hyperspectral remote sensing characterizes numerous contiguous narrow bands that have great potential in water turbidity measurement. This study explored the applicability of hyperspectral data in water turbidity detection. It explored the visible and near-infrared region to select the optimal bands and indices for turbidity measurement. Using the Analytical Spectral Device (ASD) field spectroradiometer and a 2100Q portable turbidimeter, spectral reflectance and laboratory based turbidity measurements were taken from prepared turbid solutions of predetermined concentrations (i.e. 10g/l to 150g/l), respectively. The Pearson’s coefficient of correlation and R2 values were employed to select optimal spectral bands and indices. The findings showed a positive linear relationship between reflectance, the amount of soil in water and turbidity values. The strongest relationships came from bands 528, 489, 657, 1000 and 983, reporting adjusted R2 values of 0.7062, 0.7004, 0.6864, 0.7120 and 0.6961, respectively. The highest coefficient came from band 1000nm. The strongest indices were 625/440 and (770-1000)/(770+1000), with adjusted R2 values of 0.6822 and 0.6973 respectively. The use of hyperspectral data in turbidity detection is ideal for optimal band interrogation. Although good results were generated from this study, further investigations are needed in the near-infrared region. / Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.