Teodor Stefanut

Grid based satellite image processing platform for Earth observation application development

Satellite images supply important information on earth surface, weather, clime, geographic areas, vegetation, and natural phenomena. Processing of satellite data requires high computation resources and flexible tools in order to search, discover, and reveal the main information, to experiment new algorithms, and to include them into new Earth Observation applications. This paper describes the features and the architectures of the ESIP and gProcess platforms, supporting the Grid based satellite imagery processing. The development methodology of Earth Observation applications is highlighted as well in order to hide the Grid complexity to the user.

Grid Based Training Environment for Earth Observation

The content of the satellite images supplies information on the earth surface, weather, clime, geographic areas, pollution, and natural phenomena. Unfortunately, the real time supervision and the teaching related techniques require high computation and massive spatial data storage resources. This paper presents the Grid oriented GiSHEO eLearning Environment (eGLE) supporting the training and experimental practice in Earth Observation (EO). eGLE facilitates specific access for the academic and scientific community to on-demand services related to specific EO applications. The teachers have the ability to easily create lessons for different topics, supporting both the knowledge presentation and Grid based processing. The eGLE environment is based on the gProcess platform developed through the MedioGrid national project. The gProcess platform consists of a set of tools supporting the flexible description, instantiation, scheduling and execution of the workflow based Grid processing.

Grid based architectural components for SWAT model calibration

Different scenarios based on environmental changes and water quality models could be used to assess the sustainability and vulnerability of a particular geographical region. The gSWAT application allows the calibration of SWAT models and the execution of different scenarios based on calibrated SWAT model over the Grid infrastructure. Based on the requirements for the SWAT models, lots of input and output data, the high number of simulations that must be performed in the calibration process, the Grid could offer both the storing and execution solutions. This paper presents the gSWAT system highlighting the architectural components and presenting some test cases to assess the system performances.

Grid based environment application development methodology

The satellite image oriented processing in the Grid based environment applications needs flexible tools, process description and execution in order to reveal significant information The main processing consists of imagery classification which is actually a search of information through combinations of multispectral bands of satellite data The Grid based application development process should assist the developer to implement required functionality, but making transparent to the user the Grid complexity and the mapping of the conceptual application over the physical Grid resources The paper exemplifies the gProcess based Grid application development methodology throughout the development phases of the GreenView environment application, and analyzes the efficiency of such an approach The primary aim of the GreenView application is the refinement of surface and vegetation parameters in SEE and CE regions based on satellite images The GreenView application is based on ESIP platform, which lies on the gProcess toolset specific satellite image processing operators and services.

Earth Observation application development based on the Grid oriented ESIP satellite image processing platform

Satellite images supply important information on earth surface, weather, clime, geographic areas, vegetation, and natural phenomena. Processing of satellite data requires high computation resources and flexible tools in order to search, discover, and reveal the main information, to experiment new algorithms, and to include them into new Earth Observation applications. This paper describes the features and the architectures of the ESIP and gProcess platforms, supporting the Grid based satellite imagery processing. The development methodology of Earth Observation applications is highlighted as well in order to hide the Grid complexity to the user.

Software Platform Interoperability Throughout EnviroGRIDS Portal

The Black Sea Catchment area is well known for subjects such as ecologically unsustainable development or inadequate resource management. The EnviroGRIDS project addresses these issues by using emerging information technologies. The enviroGRIDS Web Portal allows the users to access the geospatial functionality given by Web infrastructure, and to high power computation resources given by Grid infrastructure. The Black Sea Catchment Observation System portal provides a single point of access to the enviroGRIDS applications and tools. Both the vertical and horizontal interoperability are available between the platforms and applications throughout the portal. The horizontal interoperability is accomplished through services, meaning the applications are working together by the exposed services. The vertical interoperability is supported by the communication between the layers of end user applications, Web infrastructure, and Grid infrastructure. The basic solution of interoperability is accomplished by services, messages, and data. The paper highlights the solutions developed by the Black Sea Catchment Observation System portal to support various types of interoperability between the modules of geospatial data management, hydrological model calibration and running, satellite image processing, spatial data visualization, and virtual training center.

gSWAT Platform for Grid Based Hydrological Model Calibration and Execution

The assessment of the sustainability and vulnerability of a particular geographical region could be performed by simulating different scenarios based on environmental changes and water quality models. The gSWAT application allows the calibration of the SWAT models and the execution of different scenarios based on calibrated SWAT model over the Grid infrastructure. The requirements for running the SWAT models involves lots of input and output data, a high number of simulations that must be performed in the calibration process, recommend the Grid infrastructure for storing and execution SWAT models. This paper presents the gSWAT platform highlighting the architectural components and presenting some test cases to assess the system performances.

Semantic Annotation and Linking of Medical Educational Resources

Educational content is often shared among different educators and is enriched, adapted and in general repurposed so that it can be re-used in different contexts. This paper presents the MetaMorphosis+ environment for publishing, sharing and repurposing educational content in medical education. The environment meshes the paradigms of social Web and semantic Web to publish richly annotated educational resources that are further semantically enriched and exposed in the Linked Open Data cloud. The goal is to enable more relevant searching and retrieval of medical educational resources, as well as linking to other related resources in the medical domain, including scientific publications and clinical data.

Grid based environment application development — GreenView application

This paper presents the development phases of the GreenView application which is based on ESIP Platform (Environment Oriented Satellite Image Processing) and uses gProcess Platforms operators and services. The gProcess Platform is an interactive toolset supporting the flexible description, instantiation, scheduling and execution of the Grid processing. GreenView application is used for refining surface and vegetation parameters in Central and Southern European regions based on satellite images processing. In the first stage, this application aims to monitor the Carpathian Basin vegetation distribution and climate, but can be extended to other regions as well

Remote Sensed Image Processing on Grids for Training in Earth Observation

Remote sensing involves techniques that use sensors to detect and record signals emanating from target of interest not in direct contact with the sensors. Remote sensing systems integrate cameras, scanners, radiometers, radar and other devices, and deal with the collection, processing, and distribution of large amounts of data. They often require massive computing resources to generate the data of interest for their users. Nowadays, remote sensing is mainly applied to satellite imagery. Satellites have proven in the last two decades their powerful capabilities to allow the Earth observation on a global scale. This observation is currently used in strategic planning and management of natural resources. The applications based on satellite data are often encountered in at least six disciplines: (1) agriculture, forestry and range resources in vegetation type, vigor and stress, biomass, soil conditions, or forest fire detection; (2) land use and mapping for classification, cartographic mapping, urban areas, or transportation networks; (3) geology for rock types, delineation, landforms, or regional structures detection; (4) water resources for water boundaries, surface, depth, volume, floods, snow areal, sediments, or irrigated fields detection; (5) oceanography and marine resources for marine organisms, turbidity patterns, or shoreline changes detection; (6) environment for surface mining, water pollution, air pollution, natural disasters, or defoliation detection. Current applications involving satellite data needs huge computational power and storage capacities. Grid computing technologies that have evolved in the last decade promise to make feasible the creation of an environment, for these kinds of applications, which can to handle hundreds of distributed databases, heterogeneous computing resources, and simultaneous users. Grid-based experimental platforms were developed already at this century’s beginning with a strong support from NASA and ESA. In this context, the chapter presents for the beginners an overview of the technological challenges and user requirements in remote sensed image processing, as well as the solutions provided by the Grid-based platforms built in the last decade. Section 2 starts with a short description of the basic principles of the satellite imagery, the technical problems and state of the art in solving them. It points also the fact that the training activities in Earth observation are not following the intensity of the research activities and there is a clear gap between the request for specialists and the labor market offer.