Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Water scarcity and climate change impact and vulnerability in irrigated agriculture in Mediterranean river basins

Actualmente, la escasez de agua constituye un importante problema en muchos lugares del mundo. El crecimiento de la población, la creciente necesidad de alimentos, el desarrollo socio-económico y el cambio climático ejercen una importante y cada vez mayor presión sobre los recursos hídricos, a la que muchos países van a tener que enfrentarse en los próximos anos. La región Mediterránea es una de las regiones del mundo de mayor escasez de recursos hídricos, y es además una de las zonas más vulnerables al cambio climático. La mayoría de estudios sobre cambio climático prevén mayores temperaturas y una disminución de las precipitaciones, y una creciente escasez de agua debida a la disminución de recursos disponibles y al aumento de las demandas de riego. En el contexto actual de desarrollo de políticas se demanda cada vez más una mayor consideración del cambio climático en el marco de las políticas sectoriales. Sin embargo, los estudios enfocados a un solo sector no reflejan las múltiples dimensiones del los efectos del cambio climático. Numerosos estudios científicos han demostrado que el cambio climático es un fenómeno de naturaleza multi-dimensional y cuyos efectos se transmiten a múltiples escalas. Por tanto, es necesaria la producción de estudios y herramientas de análisis capaces de reflejar todas estas dimensiones y que contribuyan a la elaboración de políticas robustas en un contexto de cambio climático. Esta investigación pretende aportar una visión global de la problemática de la escasez de agua y los impactos, la vulnerabilidad y la adaptación al cambio climático en el contexto de la región mediterránea. La investigación presenta un marco integrado de modelización que se va ampliando progresivamente en un proceso secuencial y multi-escalar en el que en cada etapa se incorpora una nueva dimensión. La investigación consta de cuatro etapas que se abordan a lo largo de cuatro capítulos. En primer lugar, se estudia la vulnerabilidad económica de las explotaciones de regadío del Medio Guadiana, en España. Para ello, se utiliza un modelo de programación matemática en combinación con un modelo econométrico. A continuación, en la segunda etapa, se utiliza un modelo hidro-económico que incluye un modelo de cultivo para analizar los procesos que tienen lugar a escala de cultivo, explotación y cuenca teniendo en cuenta distintas escalas geográficas y de toma de decisiones. Esta herramienta permite el análisis de escenarios de cambio climático y la evaluación de posibles medidas de adaptación. La tercera fase consiste en el análisis de las barreras que dificultan la aplicación de procesos de adaptación para lo cual se analizan las redes socio-institucionales en la cuenca. Finalmente, la cuarta etapa aporta una visión sobre la escasez de agua y el cambio climático a escala nacional y regional mediante el estudio de distintos escenarios de futuro plausibles y los posibles efectos de las políticas en la escasez de agua. Para este análisis se utiliza un modelo econométrico de datos de panel para la región mediterránea y un modelo hidro-económico que se aplica a los casos de estudio de España y Jordania. Los resultados del estudio ponen de relieve la importancia de considerar múltiples escalas y múltiples dimensiones en el estudio de la gestión de los recursos hídricos y la adaptación al cambio climático en los contextos mediterráneos de escasez de agua estudiados. Los resultados muestran que los impactos del cambio climático en la cuenca del Guadiana y en el conjunto de España pueden comprometer la sostenibilidad del regadío y de los ecosistemas. El análisis a escala de cuenca hidrográfica resalta la importancia de las interacciones entre los distintos usuarios del agua y en concreto entre distintas comunidades de regantes, así como la necesidad de fortalecer el papel de las instituciones y de fomentar la creación de una visión común en la cuenca para facilitar la aplicación de los procesos de adaptación. Asimismo, los resultados de este trabajo evidencian también la capacidad y el papel fundamental de las políticas para lograr un desarrollo sostenible y la adaptación al cambio climático es regiones de escasez de agua tales como la región mediterránea. Especialmente, este trabajo pone de manifiesto el potencial de la Directiva Marco del Agua de la Unión Europea para lograr una efectiva adaptación al cambio climático. Sin embargo, en Jordania, además de la adaptación al cambio climático, es preciso diseñar estrategias de desarrollo sostenible más ambiciosas que contribuyan a reducir el riesgo futuro de escasez de agua. ABSTRACT Water scarcity is becoming a major concern in many parts of the world. Population growth, increasing needs for food production, socio-economic development and climate change represent pressures on water resources that many countries around the world will have to deal in the coming years. The Mediterranean region is one of the most water scarce regions of the world and is considered a climate change hotspot. Most projections of climate change envisage an increase in temperatures and a decrease in precipitation and a resulting reduction in water resources availability as a consequence of both reduced water availability and increased irrigation demands. Current policy development processes require the integration of climate change concerns into sectoral policies. However, sector-oriented studies often fail to address all the dimensions of climate change implications. Climate change research in the last years has evidenced the need for more integrated studies and methodologies that are capable of addressing the multi-scale and multi-dimensional nature of climate change. This research attempts to provide a comprehensive view of water scarcity and climate change impacts, vulnerability and adaptation in Mediterranean contexts. It presents an integrated modelling framework that is progressively enlarged in a sequential multi-scale process in which a new dimension of climate change and water resources is addressed at every stage. It is comprised of four stages, each one explained in a different chapter. The first stage explores farm-level economic vulnerability in the Spanish Guadiana basin using a mathematical programming model in combination with an econometric model. Then, in a second stage, the use of a hydro-economic modelling framework that includes a crop growth model allows for the analysis of crop, farm and basin level processes taking into account different geographical and decision-making scales. This integrated tool is used for the analysis of climate change scenarios and for the assessment of potential adaptation options. The third stage includes the analysis of barriers to the effective implementation of adaptation processes based on socioinstitutional network analysis. Finally, a regional and country level perspective of water scarcity and climate change is provided focusing on different possible socio-economic development pathways and the effect of policies on future water scarcity. For this analysis, a panel-data econometric model and a hydro-economic model are applied for the analysis of the Mediterranean region and country level case studies in Spain and Jordan. The overall results of the study demonstrate the value of considering multiple scales and multiple dimensions in water management and climate change adaptation in the Mediterranean water scarce contexts analysed. Results show that climate change impacts in the Guadiana basin and in Spain may compromise the sustainability of irrigation systems and ecosystems. The analysis at the basin level highlights the prominent role of interactions between different water users and irrigation districts and the need to strengthen institutional capacity and common understanding in the basin to enhance the implementation of adaptation processes. The results of this research also illustrate the relevance of water policies in achieving sustainable development and climate change adaptation in water scarce areas such as the Mediterranean region. Specifically, the EU Water Framework Directive emerges as a powerful trigger for climate change adaptation. However, in Jordan, outreaching sustainable development strategies are required in addition to climate change adaptation to reduce future risk of water scarcity.

Will improved access to capital dampen the need for more agricultural land? A CGE analysis of agricultural capital markets and world-wide biofuel policies. Factor Markets Working Paper No. 48, May 2013

This paper analyses the consequences of enhanced biofuel production in regions and countries of the world that have announced plans to implement or expand on biofuel policies. The analysis considers biofuel policies implemented as binding blending targets for transportation fuels. The chosen quantitative modelling approach is two-fold: it combines the analysis of biofuel policies in a multi-sectoral economic model (MAGNET) with systematic variation of the functioning of capital and labour markets. This paper adds to existing research by considering biofuel policies in the EU, the US and various other countries with considerable agricultural production and trade, such as Brazil, India and China. Moreover, the application multi-sectoral modelling system with different assumptions on the mobility of factor markets allows for the observation of changes in economic indicators under different conditions of how factor markets work. Systematic variation of factor mobility indicates that the ‘burden’ of global biofuel policies is not equally distributed across different factors within agricultural production. Agricultural land, as the pre-dominant and sector-specific factor, is, regardless of different degrees of inter-sectoral or intra-sectoral factor mobility, the most important factor limiting the expansion of agricultural production. More capital and higher employment in agriculture will ease the pressure on additional land use – but only partly. To expand agricultural production at global scale requires both land and mobile factors adapted to increase total factor productivity in agriculture in the most efficient way.

Large scale experiments on gravel and mixed beaches: Experimental procedure, data documentation and initial results

This paper provides information on the experimental set-up, data collection methods and results to date for the project Large scale modelling of coarse grained beaches, undertaken at the Large Wave Channel (GWK) of FZK in Hannover by an international group of researchers in Spring 2002. The main objective of the experiments was to provide full scale measurements of cross-shore processes on gravel and mixed beaches for the verification and further development of cross-shore numerical models of gravel and mixed sediment beaches. Identical random and regular wave tests were undertaken for a gravel beach and a mixed sand/gravel beach set up in the flume. Measurements included profile development, water surface elevation along the flume, internal pressures in the swash zone, piezometric head levels within the beach, run-up, flow velocities in the surf-zone and sediment size distributions. The purpose of the paper is to present to the scientific community the experimental procedure, a summary of the data collected, some initial results, as well as a brief outline of the on-going research being carried out with the data by different research groups. The experimental data is available to all the scientific community following submission of a statement of objectives, specification of data requirements and an agreement to abide with the GWK and EU protocols. (C) 2005 Elsevier B.V. All rights reserved.

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.

The application of computerised modelling techniques in manufacturing system design

The absence of a definitive approach to the design of manufacturing systems signifies the importance of a control mechanism to ensure the timely application of relevant design techniques. To provide effective control, design development needs to be continually assessed in relation to the required system performance, which can only be achieved analytically through computer simulation. The technique providing the only method of accurately replicating the highly complex and dynamic interrelationships inherent within manufacturing facilities and realistically predicting system behaviour. Owing to the unique capabilities of computer simulation, its application should support and encourage a thorough investigation of all alternative designs. Allowing attention to focus specifically on critical design areas and enabling continuous assessment of system evolution. To achieve this system analysis needs to efficient, in terms of data requirements and both speed and accuracy of evaluation. To provide an effective control mechanism a hierarchical or multi-level modelling procedure has therefore been developed, specifying the appropriate degree of evaluation support necessary at each phase of design. An underlying assumption of the proposal being that evaluation is quick, easy and allows models to expand in line with design developments. However, current approaches to computer simulation are totally inappropriate to support the hierarchical evaluation. Implementation of computer simulation through traditional approaches is typically characterized by a requirement for very specialist expertise, a lengthy model development phase, and a correspondingly high expenditure. Resulting in very little and rather inappropriate use of the technique. Simulation, when used, is generally only applied to check or verify a final design proposal. Rarely is the full potential of computer simulation utilized to aid, support or complement the manufacturing system design procedure. To implement the proposed modelling procedure therefore the concept of a generic simulator was adopted, as such systems require no specialist expertise, instead facilitating quick and easy model creation, execution and modification, through simple data inputs. Previously generic simulators have tended to be too restricted, lacking the necessary flexibility to be generally applicable to manufacturing systems. Development of the ATOMS manufacturing simulator, however, has proven that such systems can be relevant to a wide range of applications, besides verifying the benefits of multi-level modelling.

Toward an atomistic understanding of the immune synapse:large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex

T-cell activation requires interaction of T-cell receptors (TCR) with peptide epitopes bound by major histocompatibility complex (MHC) proteins. This interaction occurs at a special cell-cell junction known as the immune or immunological synapse. Fluorescence microscopy has shown that the interplay among one agonist peptide-MHC (pMHC), one TCR and one CD4 provides the minimum complexity needed to trigger transient calcium signalling. We describe a computational approach to the study of the immune synapse. Using molecular dynamics simulation, we report here on a study of the smallest viable model, a TCR-pMHC-CD4 complex in a membrane environment. The computed structural and thermodynamic properties are in fair agreement with experiment. A number of biomolecules participate in the formation of the immunological synapse. Multi-scale molecular dynamics simulations may be the best opportunity we have to reach a full understanding of this remarkable supra-macromolecular event at a cell-cell junction.

Across-scale patterning of plant-soil-water interactions surrounding tree islands in Southern Everglades landscapes

The freshwater Everglades is a complex system containing thousands of tree islands embedded within a marsh-grassland matrix. The tree island-marsh mosaic is shaped and maintained by hydrologic, edaphic and biological mechanisms that interact across multiple scales. Preserving tree islands requires a more integrated understanding of how scale-dependent phenomena interact in the larger freshwater system. The hierarchical patch dynamics paradigm provides a conceptual framework for exploring multi-scale interactions within complex systems. We used a three-tiered approach to examine the spatial variability and patterning of nutrients in relation to site parameters within and between two hydrologically defined Everglades landscapes: the freshwater Marl Prairie and the Ridge and Slough. Results were scale-dependent and complexly interrelated. Total carbon and nitrogen patterning were correlated with organic matter accumulation, driven by hydrologic conditions at the system scale. Total and bioavailable phosphorus were most strongly related to woody plant patterning within landscapes, and were found to be 3 to 11 times more concentrated in tree island soils compared to surrounding marshes. Below canopy resource islands in the slough were elongated in a downstream direction, indicating soil resource directional drift. Combined multi-scale results suggest that hydrology plays a significant role in landscape patterning and also the development and maintenance of tree islands. Once developed, tree islands appear to exert influence over the spatial distribution of nutrients, which can reciprocally affect other ecological processes.

RNA Virus Evolution: a Cross-scale, Disease Dynamic Perspective

RNA viruses are an important cause of global morbidity and mortality. The rapid evolutionary rates of RNA virus pathogens, caused by high replication rates and error-prone polymerases, can make the pathogens difficult to control. RNA viruses can undergo immune escape within their hosts and develop resistance to the treatment and vaccines we design to fight them. Understanding the spread and evolution of RNA pathogens is essential for reducing human suffering. In this dissertation, I make use of the rapid evolutionary rate of viral pathogens to answer several questions about how RNA viruses spread and evolve. To address each of the questions, I link mathematical techniques for modeling viral population dynamics with phylogenetic and coalescent techniques for analyzing and modeling viral genetic sequences and evolution. The first project uses multi-scale mechanistic modeling to show that decreases in viral substitution rates over the course of an acute infection, combined with the timing of infectious hosts transmitting new infections to susceptible individuals, can account for discrepancies in viral substitution rates in different host populations. The second project combines coalescent models with within-host mathematical models to identify driving evolutionary forces in chronic hepatitis C virus infection. The third project compares the effects of intrinsic and extrinsic viral transmission rate variation on viral phylogenies.

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.

Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape

Overrecentdecades,remotesensinghasemergedasaneffectivetoolforimprov- ing agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi- resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchi- cal segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra- variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a sin- gle image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.