. . "45" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research and thesis writing"@en . . "MSc research and thesis writing"@en . "MSc research and thesis writing"@en . . "30" . "5.0" . "140.0" . "10.0" . . . . . . "online" . . . . . . . . . . . . . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "Geospatial problem solving for addressing societal challenges employs a wide variety of theories, methods, and tools, each applicable to a specific type or aspect of the problem solving process. All approaches, however, involve the acquisition, processing, and dissemination of data in one form or another. The Geoinformation and Earth Observation specialist must, therefore, be equipped with the necessary skills to find, use, preserve, and disseminate geospatial data. This course introduces conceptual models, analysis tools, and infrastructure for representing and analysing geographic phenomena in computer systems. The course covers both spatial and temporal aspects of the observed phenomena. Fundamental concepts of spatial representation including geometric primitives, topology, multidimensionality, spatial autocorrelation, graphs and networks, will be introduced in the context spatial data management. By the end of the course students should be able to interact with local or remote data resources using a variety of technologies including SQL and common web service APIs (e.g OGC WMS, WFS, WCS, REST). The student should therefore become familiar with common of data formats used in GIS and EO. Students will also learn to apply elementary data transformations (analysis) to obtain data in the appropriate structure for dissemination and presentation in both static and dynamic spatiotemporal visualizations. Applications in urban and land futures planning will be used in examples and exercises throughout this course. Learning units are organized so that concepts and methods from various knowledge categories are combined into a wholistic skill set that a student can use to solve a specific geoapstial problem."@en . "Fundamental Spatial Data Engineering and Innovations"@en . . "Fundamental Spatial Data Engineering and Innovations"@en . "Fundamental Spatial Data Engineering and Innovations"@en . . "41" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "MGEO - foundation course"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "5.0" . "4.0" . "Fieldwork is often an essential component to acquire reference data for calibration and validation of the remotely sensed observations. This course provides skills and techniques to plan, execute and report on field observations. The course starts with an introduction to in-situ field measurement devices and lab equipment, and demonstrates standard operational procedures when analysing samples in the laboratory. Subsequently, students have to design their own field data collection based on a self-defined objective, e.g. which parameters are required and how to conduct sampling, which instruments are required, how to measure, sampling procedures and storing of samples. Considering focus group interviews how to prepare the questionnaires and review of ethical considerations. Another element would be the timing related to eventual satellite overpass or image acquisition in the terrain and collection of available information from installed in-situ measuring devices. \n\nBeing well prepared, a 3 day fieldwork is envisaged for practical collecting data in a fieldwork area with participants from multiple disciplines: water, natural and earth resources. \n\nOnce back, the data collected has to be analysed in the lab or subject to further processing. In the end, students are required to present their results obtained and have to report on the procedures applied, reflect on the quality of obtained results, and describe their analysis conducted into more detail. "@en . "Lab & Field Work Skills "@en . "Lab & Field Work Skills"@en . . "Lab & Field Work Skills"@en . "Lab & Field Work Skills "@en . "Lab & Field Work Skills"@en . . "11" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "Foundation, CORE Book"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "2.0" . "Remote sensing is a unique tool to observe the Earth system, and to quantitatively monitor a variety of key atmospheric, land and ocean variables by measuring radiation reflected or emitted by the earth or atmosphere. With the availability of more and more remote sensing data from various types of instruments with different spectral characteristics, temporal and spatial resolutions, the field of quantitative land remote sensing is advancing rapidly. This course provides an overview of Earth Observation from Space by describing basic concepts of orbits and viewing from space, instrument characteristics as well as exploring the electromagnetic radiation ranges used by remote sensing devices, like in the VIS, NIR, SWIR, TIR atmospheric windows and active and passive Microwave regions, but also within atmospheric absorption bands. Radiative transfer equation and atmospheric correction for signal correction are discussed and practised. \n\nAttention is given to space and ground segments, operational (meteorological) satellite programmes within the ocean and sea ice, land and atmospheric domains and the retrieval of various space based observations of geophysical variables and their availability in cloud repositories and online processing platforms, and their retrieval.\n\nAlso attention is given to calibration and validation, related to instrument calibration (before launch, on board and vicarious calibration) but also to bias adjustment of long term data records and the need of validation when using the geophysical variables obtained through space based observations. "@en . "Quantitative Remote Sensing 5 EC Resource Security"@en . . "Quantitative Remote Sensing 5 EC Resources Security"@en . "Quantitative Remote Sensing 5 EC Resource Security"@en . . "17" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . "CORE MODULE"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . "4.0" . "2.0" . "Water and energy are fundamental for life on Earth, their variations, trends, and extremes are sources for drought extremes, heat waves, heavy rains, floods, and intensive storms that are increasingly threatening our society to cause havoc as the climate changes. Better observations and analysis of these phenomena will help improve our ability to understand their physical processes and to model and predict them. Earth Observation technology is a unique tool to provide a global understanding of essential water and energy variables and monitor their evolution from global to basin scales. The focus of this course is on the physical principles of how electromagnetic signals are applied to monitor these essential variables by spaceborne sensors, and learn tools and methods to collect, process, and visualize Earth observation data of surface solar radiation, evapotranspiration, precipitation, soil moisture, and terrestrial water storage. Furthermore, students will learn how to retrieve the essential water/climate variable – soil moisture from Earth observation data, applying the radiative transfer theory."@en . "Water Cyle in the anthropocene"@en . . "Water Cyle in the anthropocene"@en . "Water Cyle in the anthropocene"@en . . "31" . "5.0" . "140.0" . "10.0" . . . . . "online" . . . . . . . . . . . . "Open for students in the Master of Science degree programme in Geo-Information Science and Earth Observation. As first Foundational course, the pre-requisites align to those of acceptance to the M-GEO program, consequently there are no other specifics needed. For other cases, the candidates will be assessed on an individual basis."@en . "Open for students in the Master of Science degree programme in Geo-Information Science and Earth Observation. As first Foundational course, the pre-requisites align to those of acceptance to the M-GEO program, consequently there are no other specifcs needed. For other cases, the candidates will be assessed on an individual basis."@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . . . "This 5 ECTS course is designed for students aiming to acquire foundational knowledge and skills in these critical geospatial technologies. The course provides a comprehensive introduction to the principles, techniques, and applications of remote sensing (RS) and Geographic Information Systems (GIS), emphasizing their integration to solve real-world problems.\nCourse Structure and Content\n1.\tIntroduction to RS and GIS: The course begins with an overview of RS and GIS, exploring their core concepts, and significance in various fields such as environmental monitoring, urban planning, agriculture, and disaster management.\n2.\tRS Fundamentals: Students will learn about different types of RS systems, focusing on optical passive sensors included in satellite and airborne platforms, as well as the electromagnetic spectrum's role in data acquisition. Participants are exposed to key preprocessing steps such as radiometric and geometric corrections ensuring data quality.\n3.\tGIS Basics and Data Handling: Interlinked with RS, comes the introduction of GIS principles, spatial data models, data acquisition and visualization. Students will engage in hands-on exercises to collect, input, and manage and visualize spatial data, learning essential techniques like digitizing, GPS, and attribute data collection.\n4.\tImage Interpretation and Classification: Students will gain skills in interpreting RS imagery, performing both visual analysis and supervised image classification, and assessing classification accuracy.\n5.\tSpatial Analysis and Integration: The course integrates RS data with GIS to enhance spatial analysis capabilities. Students will practice GIS techniques in combination with RS data in different processing flows.\n6.\tPractical Applications: Along the course students are exposed to RS and GIS techniques linked to different thematic lines. "@en . "This 5 ECTS course is designed for students aiming to acquire foundational knowledge and skills in these critical geospatial technologies. The course provides a comprehensive introduction to the principles, techniques, and applications of remote sensing (RS) and Geographic Information Systems (GIS), emphasizing their integration to solve real-world problems.\nCourse Structure and Content\n1. Introduction to RS and GIS: The course begins with an overview of RS and GIS, exploring their, core concepts, and significance in various fields such as environmental monitoring, urban planning, agriculture, and disaster management.\n2. RS Fundamentals: Students will learn about different types of RS systems, focusing on optical passive sensors included in satellite and airborne platforms, as well as the electromagnetic spectrum's role in data acquisition. Participants are exposed to key preprocessing steps such as radiometric and geometric corrections ensuring data quality.\n3. GIS Basics and Data Handling: Interlinked with RS, comes the introduction of GIS principles, spatial data models, and database management. Students will engage in hands-on exercises to collect, input, and manage spatial data, learning essential techniques like digitizing, GPS, and attribute data collection.\n4. Image Interpretation and Classification: Students will gain skills in interpreting RS imagery, performing both visual analysis and supervised image classification, and assessing classification accuracy.\n5. Spatial Analysis and Integration: The course integrates RS data with GIS to enhance spatial analysis capabilities. Students will practice GIS analytical techniques, such as buffer and overlay analysis, combining data acquire from geoportals and processing flows.\n6. Practical Applications and Project Work: Along the course there is a “cumulative” project-based learning of choice, where students are exposed to RS and GIS techniques of at least one learning path, while practicing the instructed methodology. Collaborative projects and specific case studies will reinforce theoretical knowledge and practical skills, preparing students for professional applications."@en . "Fundamentals of GIS and Earth Observation"@en . . "Fundamentals of GIS and Earth Observation"@en . "Fundamentals of GIS and Earth Observation"@en . . "5" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "3.0" . "[CONCEPT] Monitoring of physical and chemical atmospheric, land and ocean variables with remote sensing requires an understanding of Earth’s ecosystems. Understanding of systems can be based on expert knowledge, experimental relations or physical relations, and this understanding can be captured in a descriptive model. Models are to understand, detect, predict, and describe interactions within and between ecosystems and the atmosphere across scales that range from local to global.\n\nRemote sensing can be used for parameter input in models, but also for spatial and temporal interpolation or extrapolation. This course provides an introduction to knowledge-driven, data-driven and physical modelling, starting with appropriate model selection given a specific problem or data availability. The course therefore deals with basic concepts and boundary conditions. Much emphasis is on integration of remote sensing observations into models, and selecting optimal object / pixel / time based mapping method for a given problem "@en . "Modeling & Mapping"@en . . "Modeling & Mapping"@en . "Modeling & Mapping"@en . . "20" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Academic Research Skills"@en . . "Academic Research Skills"@en . "Academic Research Skills"@en . . "3" . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . "Q2 (QRS) and preferably Q3 - Modelling and Mapping / open for second year as elective"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "No"@en . "No"@en . . . . . . . . . . . . . "4.0" . "3.0" . "[CONCEPT] The earth surface is a dynamic environment that constantly undergoes change. Various process interact at various time scales, ranging from minutes in atmospheric processes to days in land processes and even millions of years in geological processes. Monitoring of natural resources therefore deals with monitoring of a changing earth surface cover. Even when observing geological processes, the observational environment still changes by the minute. \n\nIn this course, remote sensing is applied for monitoring changes in land cover and land use, covering both system drivers (e.g., changes in land use) and response variables. Attention is given to linking the physical world with ethical and social considerations, environment and social aspects of technology, consulting different stakeholders in the management of the resources. "@en . "Impact monitoring and management"@en . . "Impact monitoring and management"@en . "Impact monitoring and management"@en . . "46" . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . "MSc research proposal writing"@en . . "MSc research proposal writing"@en . "MSc research proposal writing"@en . . "29" . "5.0" . "140.0" . "10.0" . . . . . . "online" . . . . . . . . . . . "No"@en . "Yes"@en . . . . . . . . . . . . . . . . . . . . . . . . . . "1.0" . "In an increasingly data-driven world, proficiency in data analysis and geospatial data processing has become a crucial skill set. The course “From Statistics to Programming and Machine Learning” is designed to equip students with the fundamental tools and techniques required to navigate the complex landscapes of data analysis, descriptive statistics, geospatial data handling and foundational machine learning techniques using Python. Python, with its extensive libraries and versatility, is the go-to language for professionals in data science, geography, environmental science, urban planning, and beyond.\n\nThis 5 EC course offers a comprehensive introduction to Python, starting with the basics of programming and gradually progressing to more advanced topics like descriptive statistics, data visualisation, and geospatial data processing and Machine Learning. With Jupyter Notebooks, an interactive environment widely used by data professionals, students will gain hands-on experience in writing Python code, analysing data, and visualising results.\n\nThe course is structured into five modules, each focusing on a critical aspect of Python for data analysis. Beginning with Python programming fundamentals, students will build a strong foundation before moving on to descriptive statistics and data visualisation techniques. The course then delves into geospatial data, where students will learn to handle and analyse vector and raster data, followed by a module on advanced manipulation and visualisation of tabular data. The course culminates in an integrated project where students will apply their knowledge to solve a real-world problem, combining geospatial and tabular data for a comprehensive analysis.\n\nBy the end of this course, students will not only have mastered the essential skills in Python programming and data analysis but also be prepared to tackle complex data-driven challenges in GIS and Earth observation."@en . "Fundamental Programming for Geospatial Data Analysis"@en . . "Fundamental Programming for Geospatial Data Analysis"@en . "Fundamental Programming for Geospatial Data Analysis"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Resources Security"@en . . . "Water and vegetation modelling and management (W