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Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles.

Abstract : The evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
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Submitted on : Wednesday, April 7, 2021 - 3:05:10 PM
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Kévin Jacq. Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles.. Chimie analytique. Université Grenoble Alpes, 2019. Français. ⟨NNT : 2019GREAA024⟩. ⟨tel-03191841⟩



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