. . "201800276" . "NHR_001" . "7"^^ . "196"^^ . "10"^^ . "2023-02-02T23:00:00Z"^^ . "f2f" . "The course is based on student-centered learning principles, whereby students will be enabled to cut though the complexity of natural systems, risk situations and disaster scenarios in this case, and learn to identify relevant questions to understand complex systems. In a project-based setting students will work backwards from a disaster event to discover the genesis of the event through understanding of the conceptual elements. The aim is for students not only to learn about theoretical aspects of different hazard and disaster types, but to understand the conceptual links, and to gain the ability to apply the risk concepts to different contexts and scales. A further aim is to enable the students to identify relevant questions before sourcing answers, including from other ESA staff members. There will further be emphasis on presentations (including groups to each other) and critical discussion. At critical points students will receive lectures, but the course is more strongly aimed at self-discovery of relevant facts, concepts and methods. Select RS analysis methods will be taught in a practical setting, while others will be discovered as part of the group work. In addition, skills related to the use of different data acquisition techniques will be gained during a field excursion. With courses Q2.1 and 2.2 running in parallel, the teaching of different modelling techniques will be aligned with the introduction relevant key input data, and some classes will be done in a plenary setting, involving different NHR teachers. Research skills will also be incorporated into the course where appropriate, rather than taught in parallel."@en . . . . . . . . . "Compulsory for the ‘Natural Hazards and Disaster Risk Reduction’ (NHR) specialization of the ‘Geo- information Science and Earth Observation (M-GEO) programme. Students from other specializations and programmes should have introductory level experience with GIS and Remote Sensing, and a background in earth sciences, geography, environmental science, physics, data science, or civil engineering."@en . . . "1"^^ . "2" . "1B" . . . . . . . . . "2022-11-13T23:00:00Z"^^ . "This course will provide a fundamental introduction to natural hazards and the disaster risk concept, as well as the role of geomatics, in particular remote sensing (RS). It builds on the knowledge students gained in the core courses on basic RS and GIS principles, and expands it. The course aims at creating a knowledge base for the other hazard modelling and risk management courses and electives in the NHR specialization, by enabling the students to develop a solid understanding of the main geohazard types, and all relevant conceptual aspects of disaster risk. Students will learn how geo-information and geomatics tools are uniquely suited to study, monitor and quantify each aspect of risk and disasters. Following an introduction to the main hazard types and their core properties, students will dissect past disaster events to discover the nature and properties of the underlying hazards and vulnerabilities, and learn how in particular RS provides comprehensive and specifically tailored means to gain insights into the risk components for different hazards and environmental settings. The course runs in parallel to the Statistically-based Hazard Modelling course (Q2.2), and both are closely coupled. Particular attention will be given to the generation of input data for hazard modelling, including image-based indices and topographic derivatives. Relevant background information on soils, geology and landforms as drivers of hazards will also be provided. Academic skills will be taught together with this course in an integrated manner."@en . "Introduction to Hazard and Risk"@en . . "Introduction to Hazard and Risk"@en . "Introduction to Hazard and Risk"@en . . . "Learning outcome"@en . . "Explain fundamental photogrammetric principles, and link them with topographic data quality."@en . "Explain fundamental photogrammetric principles, and link them with topographic data quality."