L’Arganier [Argania spinosa (L.) Skeels] est un arbre de la famille des Sapotacées, endémique du sud-ouest marocain. C’est un arbre multi-usages, qui constitue une ressource primordiale pour les populations de cet espace semi-aride et aride du Maroc. Il constitue la clef-de-voûte de l’agro-écosystème traditionnel de l’arganeraie reposant sur un équilibre entre ressources et exploitation humaine, et joue également un rôle important dans la lutte contre la désertification et l’érosion. Actuellement, la menace de dégradation de l’arganeraie est une préoccupation majeure aussi bien pour la population que pour les scientifiques. On assiste en effet depuis plusieurs décennies à une diminution du couvert arboré, à la fois en surface occupée et en densité d’arbres. Face à cette préoccupation, nous avons étudié l’espace multidimensionnel de l’arganeraie en cherchant à identifier les principales caractéristiques de cet espace, ainsi que les facteurs responsables de sa dégradation. Ensuite, nous avons dévoilé l’originalité de cet espace à partir de son organisation sociale et spatiale, ainsi que le mode de fonctionnement et de gestion de ce territoire. Dans la deuxième partie nous avons montré l’apport de la télédétection spatiale et des systèmes d’information géographique pour la caractérisation de l’occupation du sol et l’identification des changements à partir d’un suivi diachronique, en utilisant une série d’images SPOT, Landsat, Google Earth, Ikonos. Nous avons également testé la possibilité d'évaluer la densité des arganiers à partir des images à haute résolution spatiale Ikonos et Google Earth. Nous concluons à la nécessité d’un suivi de ce territoire afin de pouvoir évaluer les changements et prendre les mesures d’aménagement et de protection nécessaires / The Argan [Argania spinosa (L.) Skeels] is a species of tree endemic to the calcareous semi-desert Sous valley of southwestern Morocco. It is the sole species in the genus Argania (family of Sapotaceae). It is a multi-purpose tree, and the main resource provider for the population of this semi-arid and arid area (source of forage, oil, timber and fuel). Argan is the keystone species of the traditional agro-ecosystem of the Berber society, ensuring a meta-stable equilibrium between resource availability and anthropic use; it plays a major role in preventing erosion and desertification damages.Currently, in spite of the Biosphere Reserve label attributed by UNESCO in 1998, the threat of degradation of the sparse Argan forest is a main concern for both local population and scientists. Since several decades, a decrease of extension area of the species and of tree density has been observed. According to this preoccupation, we have studied the multidimensional space of the Argan forest, in view of identifying its main features and the potential drivers of degradation processes. Then the originality of this area has been demonstrated through the assessment of its social and spatial organization, and of land-use and management practices.In the second part, we have shown the possible use of remotely sensed data and of Geographic Information Systems for surveying land-use/land-cover and for monitoring changes through a multi-temporal analysis of satellite images: SPOT, Landsat, Ikonos and Google Earth imagery. The evaluation of tree density has been performed through object-oriented classification of high spatial resolution satellite imagery (Ikonos, Google Earth). In conclusion, we recommend the effective use of a monitoring system to follow environmental changes in the Argan tree area, and to produce the detailed information needed for implementation of management and conservation strategies ensuring a sustainable development of the area.

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\''s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\''s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.