Catálogo de publicaciones - libros

Natural disasters are an age-old problem that occur regularly in alpine regions, posing a major threat to the safety of settlements and transport routes. Within the project “Safety of Alpine Routes — Application of Earth Observation Combined with GIS (Hannibal)” information has been extracted from satellite remote sensing and integrated into a newly developed GIS based Decision Support System (DSS). Some of the required map information were inferred from ERS- and from SPOT5- and QUICKBIRD satellites. Forest and land cover parameters have been derived from the satellite data. Change detection techniques have been applied to monitor e.g. windfall and clear-cut areas. Methods for interferometric processing of radar data have been optimized to detect landslides and to generate maps of motion fields along the slope surfaces. The results show that this information integrated into the DSS is an efficient tool for focusing the attention to potentially endangered areas.

To mitigate the property and life losses at the İ.T.U. Ayazağa Campus, after a probable disaster that may occur in İstanbul, it is strongly necessary to constitute an emergency management based object model and database in order to apply the Turkey Disaster Information System (TABİS) which was developed within the scope of “Constitution of the National RSS-GIS Based Database and Emergency Management Focused Decision Support System Project”.

Spatial data and related technologies, particularly Geographical Information System (GIS) with the capability of display, retrieval, analysis and management of spatial data, have proven crucial for disaster management. However, there are currently different problems with availability, access and usage of spatial data for disaster management. The problems with spatial data become more serious during disaster response in which reliable and up-to-date spatial data describing current emergency situation are required for planning, decision-making, and coordination of activities. It is suggested that by having an effective and efficient spatial data framework and institutional arrangements and through cooperative efforts of involved organizations in disaster response for spatial data production and then sharing these data, it is possible to have required information, always available and accessible for use. In this respect, Spatial Data Infrastructure (SDI) can provide an appropriate environment for influencing participation of organizations in spatial data production process and sharing. Meanwhile, web-based GIS can provide appropriate tool for data entry and sharing as well as data analysis to support decision-making.

This paper aims to describe development of a web-based GIS using SDI framework for disaster response. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a web-based GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities. The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response.

Emergency events can create situations, which exclude presence of people in hazardous places or at least create conditions hostile or dangerous for people. Using special equipment for assessment of a situation can help to do some operations without direct presence of people in place. There are some stationary or mobile means, mostly remotely controlled, which can be directed or navigated to risky place to do some visual investigation. These tools are usually equipped by simple camera system, which permits to do some visual investigations but they provide no metric information about observed scene. Presented paper describes modular system which permits 3D metric measurements in dangerous or inaccessible places.

The spatial mapping of risk is critical in planning for disaster preparedness. An application from a study area affected by mass movements is used as an example to portray the desirable relations between hazard prediction and disaster management. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i) construction of a hazard prediction map of “future” hazardous events; (ii) validation/reliability of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of socio-economic factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with socio-economic data. Three-dimensional dynamic display techniques can be used to obtain the contextual setting of the risk space/time/level distribution and to plan measures for risk avoidance or mitigation, or for disaster preparedness and risk management. A software approach provides the analytical structure and modeling power as a fundamental tool for decision making.

The paper describes a new method for extracting objects from high resolution color remote sensing images. This method is based on the fuzzy segmentation algorithm which has been developed in our previous works. The proposed object extraction method is following three steps. (1) Segmenting color images, (2) Detecting objects from segmented images, and (3) Postprocessing of extracted object. The paper also gives experimental results from using the proposed method to extract centerlines of road networks and roofs of building from QuickBird and Ikonos Images.

The Office of Science of the National Aeronautics and Space Administration (NASA) is focused using the vantage point of space to improve our knowledge of the Earth system, space systems, and exploration. NASA spaceborne satellites provide measurements that are used in science research associated with the water and energy cycle, the carbon cycle, weather and climate, atmospheric chemistry, and the solid Earth and natural hazards. The NASA science mission has a focus on improving the prediction capacity in the areas of weather, climate, and natural and technological hazards.

The data and knowledge resulting from the Sun-Earth observing systems and science models of the Sun and the Earth are available for assimilation into decision support systems to serve society. Through partnerships with national and international agencies and organizations, NASA contributes to benchmarking practical uses of observations from remote sensing systems and predictions from Sun-Earth science research. This objective is to establish innovative solutions using Sun-Earth science information to provide decision support that can be adapted in applications of national and international priority. A common modeling framework is followed as well as utilization of an enterprise architecture.

Space-based data acquired by NASA contribute to Sun-Earth science models that enable understanding and forecasting of weather, climate, and to disaster management to serve in primary applications including the related applications in wildfire management, food security, aviation safety, homeland security, tropical weather, human health, invasive species management, and water and air quality management. Common systems engineering approaches are employed, including dependence on geospatial standards and interoperability, verification and validation, benchmarking, visualization, and workforce development.

This paper describes the Sun-Earth observing missions, the science models, and the decision support systems in the context of a systems engineering approach to enhancing decision support systems for disaster management relevant to decision makers. The architecture for systematically delivering results from research to operations is described. Recent results and solutions are highlighted specific to disaster management as related to U. S. National and International efforts.

Within the OASIS project, intelligent decision support tools shall improve situation analysis and assessment, provide assistance in finding appropriate recovery actions and scenarios, and help in handling multiple decision criteria. This functionality will be developed in two major directions: 1) knowledge-based decision support, and 2) advanced methods for handling decision complexity. Knowledge-based modules include data analysis support tool, event recognition and prediction, intelligent checklist, information prioritization, and knowledge base on relevant norms and regulations. Modules for handling specific decision complexities include data mining, data aggregation, argumentation and collaboration support, and multi-criteria decision analysis tools.

This paper describes new methods used for mapping news events gathered from around the world. Web based graphical map displays are used to monitor both the real time situation, and longer term historical trends. The results are derived from a synthesis of world events based on 20,000 news reports collected from the Internet each day by the Europe Media Monitor (EMM) (http://emm.jrc.org). EMM has invented a powerful method of rapidly classifying multilingual articles by matching weighted combinations of phrase and word patterns. The articles are cross-classified according to the countries mentioned and to general themes like “Conflict” “FoodSecurity” “Natural Disaster” “Ecology” and so on. The Alert system runs 24 hours per day keeping continuous hourly statistics on article populations. This then allows to generate real-time graphical presentations of “news maps”, time series of Indicators, and animations of crisis developments.

In a second step, a more detailed geolocation of news articles based on town names identification in text and a reference gazetteer is performed. This has been coupled with a daily “Top News” clustering algorithm and results in 24 hourly News distributions for each EMM alert topic. This technique can also link clusters through different language versions and track stories in time. Geospatial representation of news developments enables decision makers to better assess a complex situation in a fast and objective fashion.

This paper describes a novel modeling strategy that couples a systems engineering design approach borrowed from industry that allocates spatial features in a GIS to a design-driven hierarchical functional model. We show that establishing a design goal such as ‘evacuation’ can be decomposed into a hierarchy of related functions that enable evacuation (e.g., provide shelter, provide mobility) and that these functions can be mapped to a requirements perspective and used to develop a functional feature attribution scheme that is more risk-relevant than traditionally-attributed geo-information features in the disaster modeling context. We present a riverine flooding example of this methodology applied to assessing the vulnerability of lifeline systems, and describe how this modeling approach and the systems perspective may bring efficiencies to geo-information management and provide new insights to the modeling of risk in complex interdependent systems.