Examples of our Courses
Various courses are offered on different levels of expertise and focusing on different fields of applications.
Please find a selection below:
Introduction into Remote Sensing with Google Earth Engine
This training addresses advanced persons interested in the analysis of satellite imagery and spatial datasets and is dedicated to the provision of the principles of Google’s cloud infrastructure Earth Engine for earth science data and analysis. The training aims at enhancing the knowledge for developing and implementing a workbench (e.g., download of data, pre-processing, derivation of indicators and phenological indices, analysis, spatial prediction, mapping, and interpretation) for spatially analysing and visualizing remote sensing data.
Introduction into Remote Sensing with QGIS
This training addresses beginners to intermediated users and provides basics of the open-source geographic information system Quantum GIS (QGIS) for developing and implementing a workbench (e.g., download of data, pre-processing, derivation of indicators and phenological indices, analysis, spatial prediction, mapping, and interpretation) for spatially analyzing and visualizing remote sensing data. Monitoring and Evaluation (M&E) in the sense of derivation, analysis, and interpretation of time series is part of this training.
Introduction to Earth Observation
Introduction of Earth Observation theory and applications. Overview of fundamental principles, potentials and limits.
Remote Sensing Applications
Overview of Remote Sensing applications ranging from polar to tropical examples. This courses provides insights into the potential of Remote Sensing for specific research fields.
Python is a powerful language for analyzing large amounts of data. The first steps up to complex implementations into other software systems are covered.
Application in Agriculture
Agricultural monitoring using remote sensing data is an established process. Providing an overview of methods and real-world examples, as well as hands-on exercises are covered in this course.
Integrating Remote Sensing in International Development Work
This training addresses beginners and is dedicated to the basics, analysis, quality assessment, and visualization of satellite remote sensing data using QGIS and Google Earth Engine. Monitoring and Evaluation (M&E) in the sense of derivation, analysis, and interpretation of time series are part of this training. The nexus climate change – adaptation to climate change – land cover and land use – will be addressed in targeted examples and taken up in an exchange with the participants and the GeoSens team.
R is the most powerful language for data analysis and statistics. Daily data handling up to automated processing chaings executing complex statistical models are covered.
Intro to Coding
Coding or programming is a powerful tool once it has been mastered. This course covers the first steps in a coding environment up to writing a first script for a specific application.
Application in Ecology and Biodiversity
Spatio-temporal analysis of landscape properties that are relevant for ecological research are covered in theory and practice. From converting field data to a spatial format up to fragmentation analysis are covered.
Application in Conservation
Remote Sensing is a powerful tool for conservation research and application. Content ranges from fundamental analysis using Open-Source software such as QGIS up to more complex analysis using QGIS or other software solutions.
Graphics and Presentations
Graphics outlining the results of the remote sensing research and presenting them is of importance for many projects. Discussing and creating graphs and presentations for a scientific audience is content of this course.
Creating informative and visually appealing maps of the results is key for a successful communication. Designing field work as well as presentation-ready maps is covered in this course
All course examples mentioned above are just a small fraction of potential topics. We are happy to discuss specific topics and level of expertise.
Size of Courses
The size of courses or rather the lecturer-attendee ratio is a key factor for a successfull learning outcome. Hence, we aim for max. 10 attendees per lecturer.
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