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Student Number : 0216831W - MSc research report - School of Environmental Sciences - Faculty of Science / This report deals with the relationship between the NDVI obtained from the NOAA AVHRR sensor and that obtained from the MODIS sensor. The relationship is quantitatively assessed for distinct polygons over various land-cover types in the northeastern Kwa-Zulu Natal Province of South Africa. Spatial and temporal variations in the relationships are addressed and discussed with reference to spectral response, sunsensor- target geometries and atmospheric factors. Specifically, various methods are investigated to estimate a MODIS-equivalent NDVI from the AVHRR NDVI and in so doing create the potential to develop a self-consistent NDVI between the historically available AVHRR NDVI dataset and the relatively new MODIS NDVI dataset. NOAA-16 AVHRR NDVI data and AQUA MODIS NDVI data for the two-year period from January 2002 to December 2003 are used to develop the method. A linear relationship exists between the AVHRR and MODIS NDVI. However, spatial variations in the relationship in terms of land-cover and mean NDVI are pointed out. The potential of atmospheric corrections applied to AVHRR data through a radiative transfer atmospheric correction model to improve the relationship between the two NDVI datasets is also investigated. The importance of geo-location accuracy of the AVHRR NDVI dataset is assessed in the light of the accuracy obtainable with the proposed method to estimate a MODIS-equivalent NDVI from the AVHRR NDVI. A method to estimate the MODIS NDVI from the AVHRR NDVI that takes the mean AVHRR NDVI value into account, as opposed to a constant linear relationship over all the points, is proposed. Atmospheric correction is shown not to improve the accuracy of the method in a statistically significant way. The root-mean-square error of the proposed method is in the order of 0.05 NDVI units and varies between 0.5 and 2 standard deviations of the MODIS NDVI over an entire season.

>Magister Scientiae - MSc / The lack of monitoring of non-perennial rivers is a major problem for water resources management, despite their significance in satisfying agricultural, economic and recreational needs. Pools in non-perennial rivers are not monitored, due to their remoteness. Remote sensing offers a promising alternative for the monitoring of changes in water storage in these pools. This study aims to assess the extent to which remotely-sensed datasets can be used to monitor the spatio-temporal changes of water storage of pools along non-perennial rivers in the Western Cape. The objectives of this study are: (1) to determine a suitable image preprocessing and classification technique for detecting and monitoring surface water along nonperennial rivers, and (2) to describe the spatial and temporal changes of water availability of pools along non-perennial rivers, using remotely sensed datasets. The Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh) and non-shadowed regions (AWEInsh) and the Multi-Band Water Index (MBWI) classification techniques were investigated in this study, using the Sentinel-2 and Landsat 8 datasets. In-situ measurements were used to validate the satellite-derived datasets, while the use of high resolution aerial photography and Digital-Globe WorldView imagery were further compared to the results. The results suggested that the NDWI is the most suitable classification technique for identifying water in pools along non-perennial rivers throughout the Western Cape. The NDWI applied to the Sentinel-2 Top-of-Atmosphere (TOA) reflectance dataset had the highest overall accuracy of 85%, when compared to the Sentinel-2 Dark Object Subtraction 1 (DOS1) atmospheric correction, Sentinel-2 Sen2Cor atmospheric correction, Landsat 8 TOA reflectance and Landsat 8 DOS1 atmospheric correction datasets. The incorporation of atmospheric correction was shown to eliminate surface water pixels in many of the smaller pools.

The atmosphere distorts the spectrum of remotely sensed data, negatively affecting all forms of investigating Earth's surface. To gather reliable data, it is vital that atmospheric corrections are accurate. The current state of the field of atmospheric correction does not account well for the benefits and costs of different correction algorithms. Ground spectral data are required to evaluate these algorithms better. This dissertation explores using cameras as radiometers as a means of gathering ground spectral data. I introduce techniques to implement a camera systems for atmospheric correction using off the shelf parts. To aid the design of future camera systems for radiometric correction, methods for estimating the system error prior to construction, calibration and testing of the resulting camera system are explored. Simulations are used to investigate the relationship between the reflectance accuracy of the camera system and the quality of atmospheric correction. In the design phase, read noise and filter choice are found to be the strongest sources of system error. I explain the calibration methods for the camera system, showing the problems of pixel to angle calibration, and adapting the web camera for scientific work. The camera system is tested in the field to estimate its ability to recover directional reflectance from BRF data. I estimate the error in the system due to the experimental set up, then explore how the system error changes with different cameras, environmental set-ups and inversions. With these experiments, I learn about the importance of the dynamic range of the camera, and the input ranges used for the PROSAIL inversion. Evidence that the camera can perform within the specification set for ELM correction in this dissertation is evaluated. The analysis is concluded by simulating an ELM correction of a scene using various numbers of calibration targets, and levels of system error, to find the number of cameras needed for a full-scale implementation.

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La télédétection spatiale de la couleur de l’océan implique l’élimination de la contribution atmosphérique, appelée correction atmosphérique (CA). Au-dessus des eaux claires, cette dernière se base sur l’hypothèse que l’eau est totalement absorbante dans le Proche Infra-Rouge (PIR) pour estimer la réflectance atmosphérique et déterminer la réflectance marine. À l’opposé, au-dessus des eaux côtières turbides, la contribution du signal marin n’est pas négligeable dans le PIR. De ce fait, différentes méthodes alternatives ont été proposées. La thèse se consacre à l’évaluation des algorithmes de CA proposés et leur amélioration pour le capteur Ocean and Land Colour Instrument (OLCI) au-dessus des eaux côtières en exploitant sa synergie avec le capteur Sea and Land Surface Temperature Radiometer (SLSTR). Dans ce but, des mesures radiométriques in-situ ont été acquises dans deux zones côtières françaises contrastées : Manche orientale et Guyane Française, avec le spectro-radiomètre ASD, suivant un nouveau protocole d’acquisition et de post-traitement. Le post-traitement s’est basé sur le coefficient de variabilité et la différence relative de la médiane dans le contrôle de qualité, en plus du score d’assurance de qualité (QAS). Suivant l’analyse statistique basée en partie sur l’angle spectral moyenné (SAM), l’inter-comparaison radiométrique de l’ASD avec les autres radiomètres (TriOS-above et Radeau), a révélé la cohérence des mesures ASD. L’utilisation de ces dernières a permis l’inter-comparaison de la performance de cinq algorithmes de CA, où l’algorithme Polymer est le plus performant d’après un système à points avec une seule métrique. Cependant aucun algorithme a atteint le maximum de points soulignant la grande marge de progression à accomplir, surtout en eau côtière. Dans cette optique, trois relations spectrales de la réflectance des aérosols ont été testées sur une base de données simulées suivant la synergie OLCI/SLSTR. Une autre relation, Full Spectrum AC (FSAC) a été développée initialement en combinant deux relations existantes, après l’élimination de l’hypothèse du pixel noir dans l’ultra-violet (UV) et l’intégration d’un schéma itératif. L’inter-comparaison des relations révèle la cohérence de FSAC qui est légèrement moins performante qu’une relation publiée. L’application de FSAC sur des images OLCI/SLSTR ouvrirait des perspectives dans l’amélioration de la CA au-dessus des eaux côtières. / The ocean color remote sensing involves the removal of the atmospheric contribution, the so-called atmospheric correction (AC). Over clear waters, the latter is based on the hypothesis that the sea water is totally absorbent in the Near Infra-Red (NIR), to estimate the atmospheric reflectance and to determine the water reflectance. By contrast, over coastal turbid waters, the marine signal is not negligible in the NIR. Accordingly, different alternative methods were proposed. The thesis is committed to evaluate the proposed AC algorithms and their improvement for the Ocean and Land Colour Instrument (OLCI) sensor over coastal waters exploiting its synergy with the Sea and Land Surface Temperature Radiometer (SLSTR) sensor. For this purpose, radiometric in-situ measurements were acquired in two contrasted French coastal areas : Eastern English Channel and FrenchGuiana, with the ASD spectro-radiometer, according to a newly developed measurement and post-processing protocol. The post-processing was based on the coefficient of variability and the median relative difference, in addition to the Quality Assurance Score (QAS). Following the statistical analysis in part based on the Spectral Angle Mean (SAM), the radiometric inter-comparison of the ASD and other radiometers (TriOS-above and TriOS in-water), shows the consistency of the ASD measurements. The use of these measurement leads to carry out the performance inter-comparison of five AC algorithms, where the Polymer algorithm is the most efficient according to a unique metric scoring system. However, neither algorithm obtained the maximum score, highlighting the big room for improvement, especially for coastal waters. With this in mind; three spectral relationships of aerosols reflectance were tested with a simulated data set based on OLCI/SLSTR synergy. Another relationship, Full Spectrum AC (FSAC) was initially developed combining two existing relationships, after excluding the black pixel hypothesis in the Ultra-Violet and integrating a iterative scheme. The relationships inter-comparison shows consistency ofFSAC which is slightly less performing than one published relationship. The application of FSAC on OLCI/SLSTR images could have perspectives to improve the AC over coastal waters.

Hyperspectral data is a powerful tool for mineral explorations and lithological discriminations. EO1-Hyperion is a space borne hyperspectral system for hyperspectral imaging which is capable of 220 spectral image channels within the range of 400 to 2500 nm wavelengths. It has advantages over airborne systems such as data cost and coverage area. Although it has many advantages, much more uncertainty exists in application period, of which this uncertainty does exist in all processing stages starting from the data preparation to the end of analysis stages. The aim of this thesis is to state the potential use of Hyperion data for lithological and mineralogical discriminations to further develop new hyperspectral image processing approach, and to improve existing preprocessing method in literature. The proposed algorithm is mainly based on atmospheric corrections and cross track illumination correction of Hyperion data. In order to achieve this, two test sites were selected. Site 1 located on the Central Anatolia, (Ekecek test site) is used for lithological discrimination and Site 2 located on West Anatolia (Biga test site) is used for mineralogical discrimination. The obtained results were compared and assessed with the field verifications, spectral measurements and existing spectral libraries. In the end of the study it is found that when proposed approach is followed hyperspectral data is proven to be a useful tool for mineralogical discrimination in mono minerallic outcrops and valuable for lithological mapping in relatively homogenous un-covered outcrops.

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Determination of Atmospheric Pollution from Satellite Data Abstract The subject of this project is to determine air quality in Prague and the surrounding area based on satellite images and ground measurements data. The goal is to derive equations for calculating an amount of a specific pollutant over the entire area of the image. The first part of the thesis is devoted to the general theory and literature review related to this topic. The methodological part describes the steps of the procedure to handle the task, including e.g. converting satellite DN values to the radiometric values, atmospheric correction, regression analysis and mapping of the area of interest. Subsequently, the results are visualized, compared to the traditional interpolation methods and discussed. Finally, the contributions of this project and possible improvement of work on the topic are presented. Key words: Air pollution, Satellite images, Atmospheric correction, Regression analysis, Prague and surroundings