Applications of Geo-information Science and Earth Observation

This topic consists of two 7 EC courses

Spectral Data Processing

This course gives an introduction to scientific computing, focused on hyperspectral data acquisition, handling and visualization in remote sensing and earth sciences. The backbone is an introduction to scripting in IDL: writing own scripts allows to create custom processing solutions, and can also be used to automate the processing of large data sets.

More information can be found in our current Study Guide.

Spectral Geology

This course focuses on the use of spectroscopic methods to obtain geologic information related to mineralized and geothermal systems, soils and other natural materials. It is designed for students with a solid understanding of Earth Sciences that want to use state-of-the-art spectroscopic methods to analyse geologic samples for their mineral contents.

More information can be found in our current Study Guide.

This topic consists of two 7 EC courses

Acquisition and Exploration of Geospatial Data

One driver of today’s information society is geospatial data. Recent years have seen an increase in volume and diversity of geospatial data. In this course, you will use algorithmic thinking and programming skills to find, retrieve, store, and explore various geospatial datasets.

In scientific research significant time and effort goes into acquiring, understanding, and cleaning the data before the actual analysis begins. Maps and diagrams are not only used to present the final results but also to verify and explore the data during the whole data processing process phase. After this course, you will have a good overview of acquisition and exploration of geospatial data principles and methods.

More information about the course can be found in our current Study guide.

Scientific Geocomputing

In this course, you learn about developing algorithmic solutions to geospatial problems. Turn-key software systems for geo-information science and Earth observation are functionally powerful but have no instant solution to each geospatial problem. The ability to construct custom solutions is an essential capability of the Geoinformatics specialist, who should have competence in addressing geospatial problems by algorithmic solutions. You specifically learn about solution strategies, high-level solution descriptions in pseudo-code, and translations of these into implementation in some programming language. We will also discuss the scientific side of programming by an introduction into literate programming, which emphasizes documentation of code and the FAIR principles of scientific data management, which apply to data and code. We emphasize the role of data in geospatial algorithms, as these are often data-intensive. By reviewing and developing (pseudo-)code, you will increase your understanding of basic concepts in geo-information science and Earth observation like spatial filters, maximum likelihood classification, coordinate transforms and least-squares adjustment. The course’s programming language will be Python. 

More information about the course can be found in our current Study guide.

This topic consists of two 7 EC courses

Land Information Systems and Models

Land information systems are systems for acquiring, processing, storing, and distributing information about the land. They may contribute to secure land tenure or support land valuation, land use planning and land development. Despite contextual differences between countries, there are fundamental concepts that apply to all land information systems. The main objective of this course is to discover, apply, and assess these concepts and technologies – and inspire students to deploy them in the creation and maintenance of scalable real-world land information systems. Modelling of data and processes for land administration is a crucial part of this course.

More information about the course can be found in our current Study guide.

Responsible Land Administration

This course introduces land administration in the context of land policy and sustainable development using the land management paradigm as an initial guiding framework. The course relates state-of-the-art scientific knowledge to students' experiences, perceptions and country context.

More information about the course can be found in our current Study guide.

This topic consists of two 7 EC courses

From Data to Geo-information for Natural Resources Management

Sound management of natural resources requires adequate geo-information describing spatial and temporal dimensions of the ecosystems. This involves - in most cases - large datasets, originating from multiple sources and stakeholders from various disciplines and institutions. The internet plays an increasingly important role, not only for acquiring data but also for storing, sharing and disseminating data. An online information platform allows the sharing and exchange of data and information. This implies that data must be acquired, handled, (pre)processed, standardized and their quality and fitness for use must be assessed.

Based on information requirements for a forest, agricultural or environmental system, you will learn to implement this on an online platform. After the acquisition of data from various on- and off-line sources its fitness for use will be assessed. You will be introduced into statistical tools and techniques for exploratory data analysis and assessment of data quality.

More information about the course can be found in our current Study guide.

Systems Approach for Management of Natural Resources

This course has a multi-disciplinary focus. You learn to unravel and deal with the complexity and large variation in Natural Resources Management (NRM) issues. It challenges you to develop a common basis for the assessment of the multi-actor, multi-purpose, multi-level and multi-disciplinary nature of NRM.

Concepts of NRM are reviewed and discussed and particular attention is given to the importance of geo-spatial data, techniques and expertise in NRM.

You learn to apply systems thinking and analytical reasoning when translating complex real-world situations into conceptual diagrams. This enables you to describe and develop knowledge about how ecosystems work and how human activities make an impact on natural systems. You discover how this step is essential in identifying cause-effect relationships which exist in space and time. You are also challenged to select biophysical or socio-economic variables that need quantification. The application of remote sensing and GIS is targeted towards making claims about environmental problems and solutions. Conceptualizing real-world situations also helps students in identifying knowledge gaps and formulating research hypotheses.

Natural Resources Management is a multiple-stakeholder effort per default. Therefore, part of the assignments will involve working in multi-disciplinary teams.

More information about the course can be found in our current Study guide.

This topic consists of two 7 EC courses

Building Inclusive and Competitive Cities

Cities are unequal. Considerable parts of the urban population, especially in the Global South, are poor, whereas others are affluent. In part, poverty is associated with the influx of poor rural immigrants in need of jobs, shelter and basic services such as water, electricity, education and health care. Levels of access to these basic services can differ a lot between socio-economic groups and will also vary across urban space. To address such inequalities, contemporary urban development strategies and policies are directed towards the inclusion of socially and economically weaker groups. These groups need to benefit most from sustainable planning interventions. Here, inclusiveness and competitiveness need to be linked, only inclusive cities can be truly competitive. Successful cities offer competitive locations and are centres of innovation, where liveability and inclusiveness are important factors.

Classical economic models frequently disregard the role of geography when analysing the economic performance of an urban region, yet urban competitiveness requires an understanding of spatial relationships inside cities (e.g., variations of locational factors and clustering of economic activities). Furthermore, the role of land use (planning) and land markets is essential for understanding competitiveness in all its dimensions for building competitive and inclusive cities.

More information about the course can be found in our current Study guide.

Planning Sustainable Cities

This course aims to develop a critical understanding of spatial planning based on academic discourses, the international development agenda and your own experiences. Throughout the course, the role of spatial data and information systems in urban planning and management will be highlighted and illustrated.

You will develop a spatial understanding of specific urban issues in your home country by applying knowledge and skills in spatial information handling. The concept of Sustainability will be introduced and discussed in light of its rise as a global development paradigm since the early 90s. We will address The Sustainable City as one of the visions of desirable future cities. We make use of GIS-based and statistical methods to measure the dynamics of working towards sustainability in an urban context. Available databases and data catalogues will be retrieved to map sustainability indicators. Different sustainability frameworks will be studied and debated, including bottom-up planning processes and concepts contributing to the sustainable development of cities and regions (such as Local Agenda 21, transition towns, cradle to cradle, peak oil, eco urbanism, and degrowth).

More information about the course can be found in our current Study guide.

This topic consists of two 7 EC courses

Introduction to Hazard and Risk

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). You will develop a solid understanding of the main geohazard types, and all relevant conceptual aspects of disaster risk. In addition, you 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, you 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. Relevant background information on soils, geology and land use will also be provided. The course thus prepares you for subsequent hazard modelling courses, as well as courses on risk assessment and management. Academic skills will be taught together with this course in an integrated manner.

More information about the course can be found in our current Study guide.

Natural Hazard Modelling

The identification and assessment of natural hazards is a crucial component of disaster risk management. For given elements at risk, the hazard assessment plays a central role in the risk analysis. 

This course will focus on the modelling of natural hazards, with an emphasis on hydro-meteorological hazards (floods, landslides and erosion) and earthquakes. Starting from the relevant natural phenomena and their causes, the main methods and tools to assess the susceptibility and hazard at different scales will be explained. Emphasis will be given to empirical modelling, including both data-driven – statistical approaches, and knowledge-driven - expert-based - approaches. The collection of data from historical events involving their triggers and their use as input sources for the empirical modelling and forward prediction of natural hazards will be discussed.

Empirical modelling approaches relevant in the context of risk assessment, with reference to the classification of elements at risk and their physical vulnerability will also be considered.

More information about the course can be found in our current Study guide.

This topic consists of two 7 EC courses

Earth Observation of Water Resources

Space agencies use earth observations to provide a wealth of spatial information on the present-day water resources, in terms of quantity as well as quality. In this course, students will learn the physical principles of how water affects electromagnetic signals recorded by Earth observing sensors.

You will learn tools and methods to collect, process, and visualize Earth observation data that either includes or can be used to derive rainfall, soil moisture, evapotranspiration and water quality. In addition, students will be taught to deduce spatially distributed hydrological state variables from earth observation data and assess its reliability.

More information about the course can be found in our current Study guide.

Hydrological and Environmental Cycles

The purpose of this course is to have a physical process-based understanding of hydrological and environmental cycles. It deals with the occurrence, distribution, circulation, and properties of water, energy and carbon in the Earth system.

The hydrological and environmental processes include the physical (e.g. water & energy) and biogeochemical (e.g. carbon) processes through the atmosphere, to the Earth, over and beneath the land surface, to the ocean, and back to the atmosphere (e.g. known as the water cycle, energy cycle and carbon cycle). These three cycles play the central role in processes regulating the Earth system, where human activity is now inseparable from natural events.

The introduction to the concept of these cycles and their main components forms the framework of this course. 

More information about the course can be found in our current Study guide.