GIS - Open Source
In the QGIS Hyperspectral course, you’ll learn how to install and use the HyperCost QGIS plugin to open, view, and analyze hyperspectral datasets. The plugin supports the import and visualization of hyperspectral data from various missions, including AVIRIS, NEON, PACE, EMIT, DESIS, PRISMA, and ENMAP, as well as other datasets such as ECOSTRESS.
Would you like to learn how to use highly detailed satellite and sensor data to gain in-depth insights into materials, vegetation, and processes on Earth? In this course, you’ll learn how to use QGIS and hyperspectral data to independently analyze and interpret hundreds of spectral bands per pixel.
QGIS (Quantum GIS) is a powerful, open-source GIS program that allows you to view, analyze, and visualize geographic information. In this course, you’ll build on that foundation and discover how QGIS, in combination with specialized plugins such as HyperCoast, can be used for hyperspectral analysis. You’ll learn how to make sense of complex datasets and translate them into useful maps and analyses.
Hyperspectral data goes beyond traditional satellite imagery. Instead of just a few color bands, these datasets contain tens to hundreds of spectral bands, allowing you to detect subtle differences in materials, vegetation condition, water quality, and soil. You’ll work with data from sources such as AVIRIS, NEON, PACE, EMIT, DESIS, PRISMA, and ENMAP.
By combining QGIS with hyperspectral data, you’ll gain a unique and detailed view of the Earth. Whether you’re working in environmental analysis, ecology, agriculture, water management, or research—this course shows you how to turn rich spectral information into concrete insights.
You’ll work with open data and open-source software, take the course at your own pace, and develop a specialized skill set at the intersection of GIS and advanced remote sensing. Technically challenging, yet clearly explained and practically applicable.
A solid knowledge of QGIS is required for this course. We recommend taking the QGIS basics course if you do not already have this knowledge.
In this course, you’ll learn step by step how to open, explore, and analyze hyperspectral datasets in QGIS. You’ll start with the basics: what hyperspectral data is, where to find it, and how to import and visualize it using the HyperCoast plugin.
Next, you’ll learn how to interpret spectral information. You’ll work with band selection, spectral analysis, and visualizations to highlight differences in vegetation, water, and materials. You’ll also learn how to combine hyperspectral raster data with other geographic layers in QGIS.
You’ll be introduced to practical applications such as vegetation analysis, material recognition, and environmental monitoring. Through clear explanations and exercises, you’ll learn how to turn complex, multidimensional data into clear and actionable results.
In short: an accessible introduction to hyperspectral analysis, with practical examples and a strong focus on both understanding and application.
This course is unique because it makes hyperspectral remote sensing accessible within a familiar GIS environment. While hyperspectral analysis often seems specialized and technical, this course shows that you can get started right away using QGIS and open-source tools.
You’ll learn, among other things:
The course is designed for hands-on, self-directed learning, with clear explanations and no reliance on expensive software or proprietary data.
Whether you want to deepen your GIS knowledge, work with advanced Earth observation, or are curious about the next step beyond Sentinel data—this course will give you the knowledge and tools to use hyperspectral data effectively.
This course is intended for anyone who wants to go beyond standard satellite imagery and is interested in detailed spectral analysis. Do you work in the environment, ecology, agriculture, water management, education, or research? Then this course offers valuable in-depth knowledge.
You don’t need any prior experience with hyperspectral data, but you do need a basic understanding of QGIS. The course has a hands-on approach and focuses on learning by doing.
Have you already worked with Copernicus or other satellite data and want more detail, accuracy, and analytical capabilities? Then this course is a logical next step toward creating even richer geographic insights with QGIS.
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Contact usThe first day of the course is entirely devoted to understanding, context, and orientation. You’ll start by placing hyperspectral data within the broader field of remote sensing. What makes this data different from multispectral satellite imagery, and why is it so powerful for analyzing materials, vegetation, and environmental issues? Concepts such as spectral resolution, bands, reflectance curves, and data volumes are clearly explained, so you can develop a solid mental model of what you’ll be analyzing.
Next, you’ll make the transition to QGIS as your working environment for hyperspectral analysis. You’ll refresh your knowledge of raster data and layers and learn how hyperspectral datasets are technically structured. After that, you’ll get hands-on experience finding and using open hyperspectral data from various sensors. You’ll learn how to correctly import these datasets into QGIS and how specialized plugins like HyperCoast can assist you in this process.
Throughout the day, the focus is on exploration and visualization. You’ll examine hyperspectral rasters, learn to work with hundreds of bands, and discover how to make this data more understandable through band selection, color compositions, and simple visualizations. You’ll also learn how hyperspectral data relates to other geographic layers, such as vector files or thematic maps.
By the end of Day 1, you’ll have a solid foundation: you’ll understand what hyperspectral data is, where to get it, and how to open, view, and prepare it for analysis in QGIS.
The second day of the course focuses on analysis, interpretation, and application. You’ll build on the datasets and skills from Day 1 and delve deeper into working with spectral information. You’ll learn how to view and interpret spectra on a per-pixel or per-area basis, and how subtle differences in reflectance provide insights into vegetation condition, water quality, or material type.
Next, you’ll get to work on targeted analyses. You’ll learn how to select specific bands or band combinations for particular applications and how to use hyperspectral data to reveal patterns and differences. The focus isn’t on complex math, but on understanding what you see and why certain spectral characteristics are relevant.
In the second part of the day, you’ll work on practical applications. You’ll apply hyperspectral analysis to realistic examples, such as vegetation analysis, environmental monitoring, or material recognition. In doing so, you’ll combine hyperspectral raster data with other QGIS layers to interpret analyses spatially and translate them into maps that are understandable even to non-specialists.
The day concludes by bringing together everything you’ve learned: from raw hyperspectral data to clear maps and concrete insights. You now know how to independently explore, analyze, and utilize hyperspectral datasets within QGIS, and you have a clear understanding of how to apply these techniques in your own work or research.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
Multispectral satellite images (such as Sentinel-2) contain a limited number of broad spectral bands. Hyperspectral data consist of tens to hundreds of very narrow bands per pixel. This allows you to detect much more subtle differences in material, vegetation condition, water quality, and soil chemistry. In this course, you will learn how to put this additional spectral detail to practical use within QGIS.
You will work with open, up-to-date hyperspectral datasets from various sensors and missions. This includes aerial and satellite-based sources, allowing you to learn how to handle a wide range of resolutions, data volumes, and applications. The focus is on understanding the data structure and analyzing it practically in QGIS, not on any one specific sensor.
No, programming knowledge is not required. The course shows you how to analyze hyperspectral data within the QGIS graphical interface, using plugins such as HyperCoast. The focus is on understanding, interpretation, and practical GIS skills. However, it is important that you have basic experience with QGIS and working with raster layers.
After completing the course, you will be able to independently explore hyperspectral datasets, analyze spectra, and translate this information into clear maps and analyses. This knowledge is directly applicable in fields such as environmental analysis, ecology, agriculture, water management, and research. You will know which data are suitable for your specific research questions and how to use rich spectral information to gain better spatial insights.
Yes, to fully benefit from the program, we recommend that you have a solid grasp of the basics of QGIS. If you don’t, you can take our QGIS basics training course.
