Across boreal forests and resource rich areas, human-induced change is rapidly occurring at various spatial scales. In the past, satellite remote sensing has provided a cost effective, reliable method of monitoring these changes over time and over relatively small areas. Those instruments offering high spatial detail, such as Landsat Thematic Mapper or Enhanced Thematic Mapper (TM or ETM+), typically have small swath widths and long repeat times that result in compositing intervals that are too large to resolve accurate time scales for many of these changes. Obtaining multiple scenes and producing maps over very large, forested areas is further restricted by high processing costs and the small window of acquisition opportunity. Coarse spatial resolution instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Very High Resolution Radiometer (AVHRR) typically have short revisit times (days rather than weeks), large swath widths (hundreds of kilometres), and in some cases, hyperspectral resolutions, making them prime candidates for multiple-scale change detection research initiatives. <p>In this thesis, the effectiveness of 250m spatial resolution MODIS data for the purpose of updating existing large-area, 30m spatial resolution Landsat TM land cover map product is tested. A land cover polygon layer was derived by segmentation of Landsat TM data using eCognition 4.0. This polygon layer was used to create a polygon-based MODIS NDVI time series consisting of imagery acquired in 2000, 2001, 2002, 2003, 2004 and 2005. These MODIS images were then differenced to produce six multiple-scale layers of change. Accuracy assessment, based on available GIS data in a subregion of the larger map area, showed an overall accuracy as high as 59% with the largest error associated with change omission (0.51). The Cramers V correlation coefficient (0.38) was calculated using the GIS data. This was compared to the results of an index-based Landsat change detection, Cramers V=0.67. This thesis research showed that areas greater than 15 hectares are adequately represented (approximately 75% accuracy) with the MODIS-based change detection technique. The resulting change information offers potential to identify areas that have been burned or extensively logged, and provides general information on those areas that have experienced greater change and are likely suitable for analysis with higher spatial resolution data.

Across boreal forests and resource rich areas, human-induced change is rapidly occurring at various spatial scales. In the past, satellite remote sensing has provided a cost effective, reliable method of monitoring these changes over time and over relatively small areas. Those instruments offering high spatial detail, such as Landsat Thematic Mapper or Enhanced Thematic Mapper (TM or ETM+), typically have small swath widths and long repeat times that result in compositing intervals that are too large to resolve accurate time scales for many of these changes. Obtaining multiple scenes and producing maps over very large, forested areas is further restricted by high processing costs and the small window of acquisition opportunity. Coarse spatial resolution instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Very High Resolution Radiometer (AVHRR) typically have short revisit times (days rather than weeks), large swath widths (hundreds of kilometres), and in some cases, hyperspectral resolutions, making them prime candidates for multiple-scale change detection research initiatives. <p>In this thesis, the effectiveness of 250m spatial resolution MODIS data for the purpose of updating existing large-area, 30m spatial resolution Landsat TM land cover map product is tested. A land cover polygon layer was derived by segmentation of Landsat TM data using eCognition 4.0. This polygon layer was used to create a polygon-based MODIS NDVI time series consisting of imagery acquired in 2000, 2001, 2002, 2003, 2004 and 2005. These MODIS images were then differenced to produce six multiple-scale layers of change. Accuracy assessment, based on available GIS data in a subregion of the larger map area, showed an overall accuracy as high as 59% with the largest error associated with change omission (0.51). The Cramers V correlation coefficient (0.38) was calculated using the GIS data. This was compared to the results of an index-based Landsat change detection, Cramers V=0.67. This thesis research showed that areas greater than 15 hectares are adequately represented (approximately 75% accuracy) with the MODIS-based change detection technique. The resulting change information offers potential to identify areas that have been burned or extensively logged, and provides general information on those areas that have experienced greater change and are likely suitable for analysis with higher spatial resolution data.

The methods of analysis with a focused ion beam, commonly termed as nuclear microscopy, include quantitative physical processes like PIXE and RBS. The element concentrations in a sample can be quantitatively mapped with a sub-micron spatial resolution and a sub-ppm sensitivity. Its fully quantitative and non-destructive nature makes it particularly suitable for analysing biological samples. The applications in biomedical research are manifold. The iron overload hypothesis in Parkinson\\\'s disease is investigated by a differential analysis of human substantia nigra. The trace element content is quantified in neuromelanin, in microglia cells, and in extraneuronal environment. A comparison of six Parkinsonian cases with six control cases revealed no significant elevation in iron level bound to neuromelanin. In fact, a decrease in the Fe/S ratio of Parkinsonian neuromelanin was measured, suggesting a modification in its iron binding properties. Drosophila melanogaster, or the fruit fly, is a widely used model organism in neurobiological experiments. The electrolyte elements are quantified in various organs associated with the olfactory signalling, namely the brain, the antenna and its sensilla hairs, the mouth parts, and the compound eye. The determination of spatially resolved element concentrations is useful in preparing the organ specific Ringer\\\'s solution, an artificial lymph that is used in disruptive neurobiological experiments. The role of trace elements in the progression of atherosclerosis is examined in a pilot study. A differential quantification of the element content in an induced murine atherosclerotic lesion reveals elevated S and Ca levels in the artery wall adjacent to the lesion and an increase in iron in the lesion. The 3D quantitative distribution of elements is reconstructed by means of stacking the 2D quantitative maps of consecutive sections of an artery. The feasibility of generating a quantitative elemental rodent brain atlas by Large Area Mapping is investigated by measuring at high beam currents. A whole coronal section of the rat brain was measured in segments in 14 h. Individual quantitative maps of the segments are pieced together to reconstruct a high-definition element distribution map of the whole section with a subcellular spatial resolution. The use of immunohistochemical staining enhanced with single elements helps in determining the cell specific element content. Its concurrent use with Large Area Mapping can give cellular element distribution maps.

The methods of analysis with a focused ion beam, commonly termed as nuclear microscopy, include quantitative physical processes like PIXE and RBS. The element concentrations in a sample can be quantitatively mapped with a sub-micron spatial resolution and a sub-ppm sensitivity. Its fully quantitative and non-destructive nature makes it particularly suitable for analysing biological samples. The applications in biomedical research are manifold. The iron overload hypothesis in Parkinson\\\''s disease is investigated by a differential analysis of human substantia nigra. The trace element content is quantified in neuromelanin, in microglia cells, and in extraneuronal environment. A comparison of six Parkinsonian cases with six control cases revealed no significant elevation in iron level bound to neuromelanin. In fact, a decrease in the Fe/S ratio of Parkinsonian neuromelanin was measured, suggesting a modification in its iron binding properties. Drosophila melanogaster, or the fruit fly, is a widely used model organism in neurobiological experiments. The electrolyte elements are quantified in various organs associated with the olfactory signalling, namely the brain, the antenna and its sensilla hairs, the mouth parts, and the compound eye. The determination of spatially resolved element concentrations is useful in preparing the organ specific Ringer\\\''s solution, an artificial lymph that is used in disruptive neurobiological experiments. The role of trace elements in the progression of atherosclerosis is examined in a pilot study. A differential quantification of the element content in an induced murine atherosclerotic lesion reveals elevated S and Ca levels in the artery wall adjacent to the lesion and an increase in iron in the lesion. The 3D quantitative distribution of elements is reconstructed by means of stacking the 2D quantitative maps of consecutive sections of an artery. The feasibility of generating a quantitative elemental rodent brain atlas by Large Area Mapping is investigated by measuring at high beam currents. A whole coronal section of the rat brain was measured in segments in 14 h. Individual quantitative maps of the segments are pieced together to reconstruct a high-definition element distribution map of the whole section with a subcellular spatial resolution. The use of immunohistochemical staining enhanced with single elements helps in determining the cell specific element content. Its concurrent use with Large Area Mapping can give cellular element distribution maps.

Les missions spatiales d'observation de la Terre de nouvelle génération telles que Sentinel-2 (préparé par l'Agence Spatiale Européenne ESA dans le cadre du programme Copernicus, auparavant appelé Global Monitoring for Environment and Security ou GMES) ou Venµs, conjointement développé par l'Agence Spatiale Française (Centre National d 'Études Spatiales CNES) et l'Agence Spatiale Israélienne (ISA), vont révolutionner la surveillance de l'environnement d' aujourd'hui avec le rendement de volumes inédits de données en termes de richesse spectrale, de revisite temporelle et de résolution spatiale. Venµs livrera des images dans 12 bandes spectrales de 412 à 910 nm, une répétitivité de 2 jours et une résolution spatiale de 10 m; les satellites jumeaux Sentinel-2 assureront une couverture dans 13 bandes spectrales de 443 à 2200 nm, avec une répétitivité de 5 jours, et des résolutions spatiales de 10 à 60m. La production efficace de cartes d'occupation des sols basée sur l'exploitation de tels volumes d'information pour grandes surfaces est un défi à la fois en termes de coûts de traitement mais aussi de variabilité des données. En général, les méthodes classiques font soit usage des approches surveillées (trop coûteux en termes de travaux manuels pour les grandes surfaces), ou soit ciblent des modèles locaux spécialisés pour des problématiques précises (ne s'appliquent pas à autres terrains ou applications), ou comprennent des modèles physiques complexes avec coûts de traitement rédhibitoires. Ces approches existantes actuelles sont donc inefficaces pour l'exploitation du nouveau type de données que les nouvelles missions fourniront, et un besoin se fait sentir pour la mise en œuvre de méthodes précises, rapides et peu supervisées qui permettent la généralisation à l'échelle de grandes zones avec des résolutions élevées. Afin de permettre l'exploitation des volumes de données précédemment décrits, l'objectif de ce travail est la conception et validation d'une approche entièrement automatique qui permet l'estimation de la couverture terrestre de grandes surfaces avec imagerie d'observation de la Terre de haute résolution spatiale, spectrale et temporelle, généralisable à des paysages différents, et offrant un temps de calcul opérationnel avec ensembles de données satellitaires simulés, en préparation des prochaines missions. Cette approche est basée sur l'intégration d'algorithmes de traitement de données, tels que les techniques d'apprentissage de modèles et de classification, et des connaissances liées à l'occupation des sols sur des questions écologiques et agricoles, telles que les variables avec un impact sur la croissance de la végétation ou les pratiques de production. Par exemple, la nouvelle introduction de température comme axe temporel pour un apprentissage des modèles ultérieurs intègre un facteur établi de la croissance de la végétation à des techniques d'apprentissage automatiques pour la caractérisation des paysages. Une attention particulière est accordée au traitement de différentes questions, telles que l'automatisation, les informations manquantes (déterminées par des passages satellitaires, des effets de réflexion des nuages, des ombres ou encore la présence de neige), l'apprentissage et les données de validation limitées, les échantillonnages temporels irréguliers (différent nombre d'images disponible pour chaque période et région, données inégalement réparties dans le temps), la variabilité des données, et enfin la possibilité de travailler avec différents ensembles de données et nomenclatures. / The new generation Earth observation missions such as Sentinel-2 (a twin-satellite initiative prepared by the European Space Agency, ESA, in the frame of the Copernicus programme, previously known as Global Monitoring for Environment and Security or GMES) and Venµs, jointly developed by the French Space Agency (Centre National d'Études Spatiales, CNES) and the Israeli Space Agency (ISA), will revolutionize present-day environmental monitoring with the yielding of unseen volumes of data in terms of spectral richness, temporal revisit and spatial resolution. Venµs will deliver images in 12 spectral bands from 412 to 910 nm, a repetitivity of 2 days, and a spatial resolution of 10 m; the twin Sentinel-2 satellites will provide coverage in 13 spectral bands from 443 to 2200 nm, with a repetitivity of 5 days, and spatial resolutions of 10 to 60m. The efficient production of land cover maps based on the exploitation of such volumes of information for large areas is challenging both in terms of processing costs and data variability. In general, conventional methods either make use of supervised approaches (too costly in terms of manual work for large areas), target specialised local models for precise problem areas (not applicable to other terrains or applications), or include complex physical models with inhibitory processing costs. These existent present-day approaches are thus inefficient for the exploitation of the new type of data that the new missions will provide, and a need arises for the implementation of accurate, fast and minimally supervised methods that allow for generalisation to large scale areas with high resolutions. In order to allow for the exploitation of the previously described volumes of data, the objective of this thesis is the conception, design, and validation of a fully automatic approach that allows the estimation of large-area land cover with high spatial, spectral and temporal resolution Earth observation imagery, being generalisable to different landscapes, and offering operational computation times with simulated satellite data sets, in preparation of the coming missions.