Les images satellites représentent de nos jours une source d’information incontournable. Elles sont exploitées dans diverses applications, telles que : la gestion des risques, l’aménagent des territoires, la cartographie du sol ainsi qu’une multitude d’autre taches. Nous exploitons dans cette thèse les Séries Temporelles d’Images Satellites (STIS) pour le suivi des évolutions des habitats naturels et semi-naturels. L’objectif est d’identifier, organiser et mettre en évidence des patrons d’évolution caractéristiques de ces zones.Nous proposons des méthodes d’analyse de STIS orientée objets, en opposition aux approches par pixel, qui exploitent des images satellites segmentées. Nous identifions d’abord les profils d’évolution des objets de la série. Ensuite, nous analysons ces profils en utilisant des méthodes d’apprentissage automatique. Afin d’identifier les profils d’évolution, nous explorons les objets de la série pour déterminer un sous-ensemble d’objets d’intérêt (entités spatio-temporelles/objets de référence). L’évolution de ces entités spatio-temporelles est ensuite illustrée en utilisant des graphes d’évolution.Afin d’analyser les graphes d’évolution, nous avons proposé trois contributions. La première contribution explore des STIS annuelles. Elle permet d’analyser les graphes d’évolution en utilisant des algorithmes de clustering, afin de regrouper les entités spatio-temporelles évoluant similairement. Dans la deuxième contribution, nous proposons une méthode d’analyse pluri-annuelle et multi-site. Nous explorons plusieurs sites d’étude qui sont décrits par des STIS pluri-annuelles. Nous utilisons des algorithmes de clustering afin d’identifier des similarités intra et inter-site. Dans la troisième contribution, nous introduisons une méthode d’analyse semi-supervisée basée sur du clustering par contraintes. Nous proposons une méthode de sélection de contraintes. Ces contraintes sont utilisées pour guider le processus de clustering et adapter le partitionnement aux besoins de l’utilisateur.Nous avons évalué nos travaux sur différents sites d’étude. Les résultats obtenus ont permis d’identifier des profils d’évolution types sur chaque site d’étude. En outre, nous avons aussi identifié des évolutions caractéristiques communes à plusieurs sites. Par ailleurs, la sélection de contraintes pour l’apprentissage semi-supervisé a permis d’identifier des entités profitables à l’algorithme de clustering. Ainsi, les partitionnements obtenus en utilisant l’apprentissage non supervisé ont été améliorés et adaptés aux besoins de l’utilisateur. / Nowadays, remotely sensed images constitute a rich source of information that can be leveraged to support several applications including risk prevention, land use planning, land cover classification and many other several tasks. In this thesis, Satellite Image Time Series (SITS) are analysed to depict the dynamic of natural and semi-natural habitats. The objective is to identify, organize and highlight the evolution patterns of these areas.We introduce an object-oriented method to analyse SITS that consider segmented satellites images. Firstly, we identify the evolution profiles of the objects in the time series. Then, we analyse these profiles using machine learning methods. To identify the evolution profiles, we explore all the objects to select a subset of objects (spatio-temporal entities/reference objects) to be tracked. The evolution of the selected spatio-temporal entities is described using evolution graphs.To analyse these evolution graphs, we introduced three contributions. The first contribution explores annual SITS. It analyses the evolution graphs using clustering algorithms, to identify similar evolutions among the spatio-temporal entities. In the second contribution, we perform a multi-annual cross-site analysis. We consider several study areas described by multi-annual SITS. We use the clustering algorithms to identify intra and inter-site similarities. In the third contribution, we introduce à semi-supervised method based on constrained clustering. We propose a method to select the constraints that will be used to guide the clustering and adapt the results to the user needs.Our contributions were evaluated on several study areas. The experimental results allow to pinpoint relevant landscape evolutions in each study sites. We also identify the common evolutions among the different sites. In addition, the constraint selection method proposed in the constrained clustering allows to identify relevant entities. Thus, the results obtained using the unsupervised learning were improved and adapted to meet the user needs.

Geographic data is a hallowed element within the Geographic Information Systems (GIS) discipline. As geographic data faces increased usage in distributed and mobile environments, the ability to access and maintain that data can become challenging. Traditional methods of data management through the use of file storage, databases, and data catalog software are valuable in their ability to organize data, but provide little information about how the data was collected, how often the data is updated, and what value the data holds for an organization. By defining geographic data as an asset it becomes a valuable resource that requires acquisition, maintenance and sometimes retirement during its lifetime. To further understand why geographic data is different than other types of data, we must look at the many components of geographic data and specifically how that data is gathered and organized. To best align geographic data to the asset management discipline, this thesis will focus on six key dimensions, established through the work of Vanier (2000, 2001), which seek to evaluate asset management systems. Using a conceptual narrative linked to an environmental analysis case study, this research seeks to inform as to the strategies for efficiently managing geospatial data resources. These resources gain value through the context applied by the inclusion of a standard structure and methodologies from the asset management field. The result of this thesis is the determination of the extent to which geographic data can be considered an asset, what asset management strategies are applicable to geographic data, and what are the requirements for geographic data asset management systems.

With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques become essential tools for successful analysis of large spatial datasets. Spatial clustering is fundamental and central to geographical data mining. It partitions a dataset into smaller homogeneous groups due to spatial proximity. Resulting groups represent geographically interesting patterns of concentrations for which further investigations should be undertaken to find possible causal factors. In this thesis, we propose a spatial-dominant generalization approach that mines multivariate causal associations among geographical data layers using clustering analysis. First, we propose a generic framework of multi-purpose exploratory spatial clustering in the form of the Template-Method Pattern. Based on an object-oriented framework, we design and implement an automatic multi-purpose exploratory spatial clustering tool. The first instance of this framework uses the Delaunay diagram as an underlying proximity graph. Our spatial clustering incorporates the peculiar characteristics of spatial data that make space special. Thus, our method is able to identify high-quality spatial clusters including clusters of arbitrary shapes, clusters of heterogeneous densities, clusters of different sizes, closely located high-density clusters, clusters connected by multiple chains, sparse clusters near to high-density clusters and clusters containing clusters within O(n log n) time. It derives values for parameters from data and thus maximizes user-friendliness. Therefore, our approach minimizes user-oriented bias and constraints that hinder exploratory data analysis and geographical data mining. Sheer volume of spatial data stored in spatial databases is not the only concern. The heterogeneity of datasets is a common issue in data-rich environments, but left open by exploratory tools. Our spatial clustering extends to the Minkowski metric in the absence or presence of obstacles to deal with situations where interactions between spatial objects are not adequately modeled by the Euclidean distance. The genericity is such that our clustering methodology extends to various spatial proximity graphs beyond the default Delaunay diagram. We also investigate an extension of our clustering to higher-dimensional datasets that robustly identify higher-dimensional clusters within O(n log n) time. The versatility of our clustering is further illustrated with its deployment to multi-level clustering. We develop a multi-level clustering method that reveals hierarchical structures hidden in complex datasets within O(n log n) time. We also introduce weighted dendrograms to effectively visualize the cluster hierarchies. Interpretability and usability of clustering results are of great importance. We propose an automatic pattern spotter that reveals high level description of clusters. We develop an effective and efficient cluster polygonization process towards mining causal associations. It automatically approximates shapes of clusters and robustly reveals asymmetric causal associations among data layers. Since it does not require domain-specific concept hierarchies, its applicability is enhanced. / PhD Doctorate

Native vegetation condition provides an indication of the state of vegetation health or function relative to a stated objective or benchmark. Measures of vegetation condition provide an indication of the vegetation's capacity to provide habitat for a range of species and ecosystem functions through the assessment of selected vegetation attributes. Subsets of vegetation attributes are often combined into vegetation condition indices or metrics, which are used to provide information for natural resource management. Despite their value as surrogates of biota and ecosystem function, measures of vegetation condition are rarely used to inform biodiversity assessments at scales beyond individual stands. The extension of vegetation condition information across landscapes, and approaches for achieving this, using remote sensing technologies, is a key focus of the work presented in this thesis. The aim of this research is to assess the utility of multi-spectral remotely sensed data for the recovery of stand-level attributes of native vegetation condition at landscape scales. The use of remotely sensed data for the assessment of vegetation condition attributes in fragmented landscapes is a focus of this study. The influence of a number of practical issues, such as spatial scale and ground data sampling methodology, are also explored. This study sets limitations on the use of this technology for vegetation condition assessment and also demonstrates the practical impact of data quality issues that are frequently encountered in these types of applied integrated approaches. The work presented in this thesis demonstrates that while some measures of vegetation condition, such as vegetation cover and stem density, are readily recoverable from multi-spectral remotely sensed data, others, such as hollow-bearing trees and log length, are not easily derived from this type of data. The types of information derived from remotely sensed data, such as texture measures and vegetation indices, that are useful for vegetation condition assessments of this nature are also highlighted. The utility of multi-spectral remotely sensed data for the assessment of stand-level vegetation condition attributes is highly dependent on a number of factors including the type of attribute being measured, the characteristics of the vegetation, the sensor characteristics (i.e. the spatial, spectral, temporal, and radiometric resolution), and other spatial data quality considerations, such as site homogeneity and spatial scale. A series of case studies are presented in this thesis that explores the effects of these factors. These case studies demonstrate the importance of different aspects of spatial data and how data manipulation can greatly affect the derived relationships between vegetation attributes and remotely sensed data. The work documented in this thesis provides an assessment of what can be achieved from two sources of multi-spectral imagery in terms of recovery of individual vegetation attributes from remotely sensed data. Potential surrogate measures of vegetation condition that can be derived across broad scales are identified. This information could provide a basis for the development of landscape scale multi-spectral remotely sensed based vegetation condition assessment approaches, supplementing information provided by established site-based vegetation condition assessment approaches.

No description available.

Aquesta tesi tracta la jerarquia i l'heterogeneïtat dels sistemes fluvials que afecten l'estructura de les comunitats bentòniques de diatomees. A nivell regional, es van buscar diferents grups de punts i les seves espècies indicadores, es va estudiar la resposta de les comunitats de diatomees als gradients ambientals, es va avaluar la utilitat de diferents índexs de diatomees i es va buscar el millor sistema de classificació per a condicions de referència. A nivell de conca, es volien definir els factors que determinen la distribució longitudinal de la diversitat de les comunitats de diatomees. Finalment, a nivell d'hàbitat es van determinar quins factors afecten les algues i els cianobacteris a aquesta escala i es va examinar la contribució relativa de l'ambient i l'espai en la distribució de la biomassa i composició d'algues i cianobacteris. Per tant, els diferents capítols d'aquesta tesi han estat desenvolupats seguint aquest esquema. / This thesis deals with the hierarchy and heterogeneity of stream systems affecting the structure of benthic diatom communities. At a regional level, I search for different groups of sites and their indicator taxa, I studied the responses of the diatom communities to the gradients of environmental variables, I tested the usefulness of diatom indices and I searched for the best classification system for reference conditions. At a watershed level my interest was to define the factors that determined the longitudinal distribution of diversity of diatom communities. Finally, at a habitat level it was interesting to determine the factors affecting algae and cyanobacteria at this scale and examine the relative effects of environmental factors and space on the distribution of biomass and composition of benthic algae and cyanobacteria. Thus, the different chapters of the thesis had been approached following this scheme.

Spatial Data Analysis (SDA) is relatively narrower and constitutes one of the areas of Spatial Analysis. Geographic Information System (GIS) offers a potentially valuable platform for supporting SDA techniques. Integration of SDA with GIS helps SDA to benefit from the data input, storage, retrieval, data manipulation and display capabilities of GIS. Also, GIS can benefit from SDA techniques in which the integration of these techniques can increase the analysis capabilities of GIS. This integration serves for disseminating and facilitating improved understanding of spatial phenomena. How SDA techniques should be integrated with GIS arise the coupling problem. The complete integration of SDA techniques in GIS can be applied without the support of GIS vendor when the free/libre and open source software (FLOSS) development methodology is properly followed. This approach causes to interpret the coupling problem in a new way. This thesis aims to develop a fully coupled SDA with GIS in FLOSS environment. A fully coupled SDA in free GIS software as FLOSS system is developed by writing nearly 13,000 line Python code in 2.5 years. Usage of this system has reached to nearly 1600 unique visitors, 3000 visits and 8600 page views in two years. As the current status of development in GIS is considered, it is unlikely in commercial market to have full coupled SDA techniques in GIS software. However, it is expected to have more SDA developments in proprietary GIS software in the near future as there is an increasing trend for requesting more sophisticated SDA tools.

Recently, millions of databases have been used and we need a new technique that can automatically transform the processed data into useful information and knowledge. Data mining is the technique of analyzing data to discover previously unknown information and spatial data mining is the branch of data mining that deals with spatial data. In spatial data mining, clustering is one of useful techniques for discovering interesting data in the underlying data objects. The problem of clustering is that give n data points in a d-dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in different clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformatics, map regions and GIS, etc. In recent years, many researchers have focused on finding efficient methods to the clustering problem. In general, we can classify these clustering algorithms into four approaches: partition, hierarchical, density-based, and grid-based approaches. The k-means algorithm which is based on the partitioning approach is probably the most widely applied clustering method. But a major drawback of k-means algorithm is that it is difficult to determine the parameter k to represent ``natural' cluster, and it is only suitable for concave spherical clusters. The k-means algorithm has high computational complexity and is unable to handle large databases. Therefore, in this thesis, we present an efficient clustering algorithm for large spatial databases. It combines the hierarchical approach with the grid-based approach structure. We apply the grid-based approach, because it is efficient for large spatial databases. Moreover, we apply the hierarchical approach to find the genuine clusters by repeatedly combining together these blocks. Basically, we make use of the Hilbert curve to provide a way to linearly order the points of a grid. Note that the Hilbert curve is a kind of space-filling curves, where a space-filling curve is a continuous path which passes through every point in a space once to form a one-one correspondence between the coordinates of the points and the one-dimensional sequence numbers of the points on the curve. The goal of using space-filling curve is to preserve the distance that points which are close in 2-D space and represent similar data should be stored close together in the linear order. This kind of mapping also can minimize the disk access effort and provide high speed for clustering. This new algorithm requires only one input parameter and supports the user in determining an appropriate value for it. In our simulation, we have shown that our proposed clustering algorithm can have shorter execution time than other algorithms for the large databases. Since the number of data points is increased, the execution time of our algorithm is increased slowly. Moreover, our algorithm can deal with clusters with arbitrary shapes in which the k-means algorithm can not discover.

In the future, more data intensive applications, such as P2P auction networks, P2P job--search networks, P2P multi--player games, will require the capability to respond to more complex queries such as the nearest neighbor queries involving numerous data types. For the problem of answering nearest neighbor queries (NN query) for spatial region data in the P2P environment, a quadtree-based structure probably is a good choice. However, the quadtree stores the data in the leaf nodes, resulting in the load unbalance and expensive cost of any query. The MX--CIF quadtree can solve this problem. The MX--CIF quadtree has three properties: controlling efficiently the height of the tree, reducing load unbalance, and reducing the NNquery scope with controlling the value of the radius. Although the P2P MX--CIF quadtree can do the NN query efficiently, it still has some problems as follows: low accuracy of the nearest neighbor query, the expensive cost of the tree construction, the high search cost of the NN query, and load unbalance. In fact, the index structures for the region data can also work for the point data which can be considered as the degenerated case of the region data. Therefore, the KDB--tree which is a well-known algorithm for the point data can be used to reduce load unbalance, but it has the same problem as the quadtree. The data is stored only in the leaf nodes of the KDB--tree. In this thesis, we propose an Adjustable KDB--tree (AKDB--tree) to improve this situation for the P2P system. The AKDB--tree has five properties: reducing load unbalance, low cost of the tree construction, storing the data in the internal nodes and leaf nodes, high accuracy and low search cost of the NN query. The Chord system is a well--known structured P2P system in which the data search is performed by a hash function, instead of flooding used in most of the unstructured P2P system. Since the Chord system is a hash approach, it is easy to deal with peers joining/exiting. Besides, in order to combine AKDB--tree with the Chord system, we design the IDs of the nodes in the AKDB--tree. Each node is hashed to the Chord system by the ID. The IDs can be used to differentiate the edge node in the AKDB-tree is a vertical edge or a horizontal edge and the relative position of two nodes in the 2D space. And, we can calculate the related edge of a region in the 2D space according to the ID of the region. As discussed above, we make use of the property of IDs to reduce the search cost of the NN query by a wide margin. In our simulation study, we compare our method with the P2P MX--CIF quadtree by considering five performance measures under four different situations of the P2P MX--CIF quadtree. From our simulation results, for the NN query, our AKDB-tree can provide the higher accuracy and lower search cost than the P2P MX--CIF quadtree. For the problem of load, our AKDB-tree is more balance than the P2P MX--CIF quadtree. For the time of the tree construction, our AKDB-tree needs shorter time than the P2P MX--CIF quadtree.

Die Zusammenführung von Geodaten auf Basis homologer Objekte ist ein wichtiger Teilprozess zur Wissensgenerierung aus verfügbaren Geoinformationen. Forschungen im Bereich der digitalen Geodatenfusion gibt es bereits seit Anfang der 80er Jahre. Das Aufgabenspektrum umfasst dabei die Aktualisierung, Veränderungsdetektion, Informationsanreicherung und Integration verfügbarer Datensätze. Gleichzeitig vollzieht sich seit Ende der 90er Jahre ein Paradigmenwechsel hin zum Aufbau dienstebasierter Geoinformationslandschaften auf Basis serviceorientierter Architekturen (SOA). Dieser wird insbesondere durch die Entwicklung einer Geodateninfrastruktur (GDI) im öffentlichen Sektor forciert und bildet einen Schwerpunkt der aktuellen Forschung im Bereich Geoinformatik. Innerhalb dieser interoperablen Strukturen kann ein entscheidender Informationsmehrwert durch die Kombination thematisch verwandter Ressourcen geschaffen werden. Die Fusion von Daten wird daher einen zentralen Bestandteil zukünftiger Entwicklungen im Bereich Web-basierter Anwendungen darstellen. Zur Bereitstellung von Geodaten in einer GDI hat das Open Geospatial Consortium (OGC) bereits zahlreiche Standards veröffentlicht. Darüber hinaus eröffnet die Entwicklung des Web 2.0 weitere, oftmals Community-gestützte, Möglichkeiten zur Bereitstellung von Geodaten außerhalb standardisierter GDI. Die Verarbeitung dieser Geodaten kann durch die Einführung des OGC Web Processing Service (WPS) realisiert werden. Diese Schnittstellenspezifikation ermöglicht die Verlagerung von Geoprozessierungsfunktionalitäten in eine GDI und trägt somit zur Ablösung monolithischer Geoinformationssysteme (GIS) durch verteilte dienstebasierte Strukturen bei. Für die Umsetzung komplexer Prozesse wie einer Geodatenfusion ist die Verfügbarkeit, Interoperabilität und Verkettung beteiligter Dienste von entscheidender Bedeutung. Nach der Einführung in Grundlagen von GDI und Geodatenfusion werden in dieser Arbeit Systemarchitektur und Bestandteile einer dienstebasierten Geodatenfusion konzipiert. Im Anschluss erfolgt die Beschreibung einer proof-ofconcept Implementierung wesentlicher Bestandteile unter Nutzung des 52°North WPS-Framework. Gegenstand der Implementierung ist die Fusion von Straßendaten der Modelle ATKIS (Amtliches Topographisch-Kartographisches Informationssystem) und OSM (OpenStreetMap) durch einen Feature- und Attributtransfer. Die Metadatenverarbeitung, Generalisierung und Evaluierung im Kontext einer dienstebasierten Geodatenfusion stellen weitere Teilaspekte dieser Arbeit dar. / The conflation of spatial data is one important task concerning the generation of knowledge from available geo-information. Research in this domain has been carried out since the early 80s and incorporates updating, change detection, enhancement and integration of spatial data. At the same time a paradigm shift leads towards service-oriented Architectures (SOA) in the field of geoinformation science. In the public sector this change is promoted by the developement of spatial data infrastructures (SDI). Especially whithin these interoperable structures, the combination of thematically comparable ressources can be used to enhance available spatial information. The conflation of data in general represents a core component of future research on web-based applications. The Open Geospatial Consortium (OGC) has already published various standards for spatial data dissemination. In addition, the Web 2.0 developement offers the possibility of user-generated spatial data beyond standardized SDI. The conflation of institutional- and community-provided datasets can be realized by the introduction of the OGC Web Processing Service (WPS). The WPS interface offers geoprocessing capabilities within SDI and thus helps distributed serviceoriented environments to replace monolithic Geographic Information Systems. Availibility, interoperability and chaining of services are crucial for implementing complex processes, such as conflation. After an introduction to the fundamentals of SDI and conflation, a servicebased architecture for geodata conflation will be developed within this thesis. The proof-of-concept implementation is realized using the 52°North WPS and exercises the conflation of street data. For this purpose, the data models ATKIS (Authoritative Topographic Cartographic Information System) and OSM (OpenStreetMap) were applied to perform a transfer of attributes and features. Other important aspects of this thesis related to service-based conflation include the processing of metadata, generalization and evaluation.