Duration
30h Th, 30h Pr
Number of credits
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Remote sensing is a multidisciplinary field at the interface of physics, engineering, computer science, and environmental sciences. It relies on the physics of radiation and sensors, digital processing and artificial intelligence methods, as well as Earth sciences to interpret and leverage the extracted information. Its integrative nature makes it an essential domain for studying ecosystems, managing resources, preventing natural hazards, and monitoring environmental and climate changes.
The aim of this course is to provide students with the theoretical and practical foundations necessary to understand the physical principles of Earth observation, master the main data analysis techniques from remote sensing, and apply this knowledge to environmental and societal challenges.
Theoretical topics covered include:
- General concepts in remote sensing
- Optical remote sensing
- Thermal infrared remote sensing
- Passive and active microwave remote sensing (radar)
- Very high-resolution remote sensing (drones, nanosatellites) and hyperspectral imaging
- Applications of Earth observation for monitoring terrestrial ecosystems
- Extraction of time series of optical vegetation indices (Google Earth Engine)
- Time series analysis for monitoring terrestrial ecosystems (Python Notebook)
- Radar data processing for flood mapping
- Optical data processing for forest fire detection
- Analysis of LiDAR and drone photogrammetric data
- Estimation of biophysical variables from hyperspectral data (Radiative Transfer Models)
Learning outcomes of the learning unit
By the end of the course, students will be able to:
Knowledge
- Explain the physical and technical principles underlying the main remote sensing methods (optical, thermal, radar, hyperspectral, LiDAR).
- Describe the characteristics of Earth observation sensors and platforms.
- Identify the major applications of remote sensing for the study and monitoring of terrestrial and environmental systems.
- Handle and preprocess different types of remote sensing data (optical, radar, hyperspectral, LiDAR, drones).
- Extract and analyze time series of vegetation indices and other biophysical variables.
- Implement processing tools (Google Earth Engine, Python Notebooks, etc.) for detecting and monitoring environmental events (floods, forest fires, vegetation dynamics).
- Integrate multi-source data to address complex environmental challenges.
- Develop a rigorous scientific approach by defining a research question and selecting appropriate methods.
- Work in teams to design, carry out, and present an applied remote sensing project.
- Critically interpret results and evaluate their limitations.
- Communicate analysis results effectively, both in writing and orally, to a scientific or professional audience.
Prerequisite knowledge and skills
A solid understanding of fundamental concepts in physics (optics, electromagnetic radiation) and mathematics (linear algebra, statistics, data analysis) is required to facilitate comprehension of the signal acquisition mechanisms and the analysis methods used in remote sensing.
Planned learning activities and teaching methods
The course unit combines theoretical lectures, practical exercises, and a capstone project in a balanced way to promote a deep understanding of concepts and mastery of remote sensing tools. The teaching approach is active and progressive, combining interactive presentations, case studies, hands-on learning, and personalized guidance during the project.
- Lectures: Instructor-led presentations supported by concrete examples enable students to acquire the conceptual and methodological foundations of Earth observation.
- Supervised practical exercises: Conducted in computer labs with the aim of directly applying the concepts covered, through the manipulation of real datasets (optical, radar, hyperspectral, LiDAR, drones) and the use of specialized platforms and software (Google Earth Engine, Python, etc.).
- Project: Conducted in small groups (usually three students) with the goal of enabling students to integrate theoretical and practical knowledge around an applied problem.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Face-to-face lectures and practical exercises.
A project is to be carried out independently in groups, usually consisting of three students.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
The PowerPoint slides for the lectures are available on eCampus. At the end of each presentation (chapter), links to additional resources (videos, reference books, scientific articles) are also provided.
The slides and supplementary materials (videos, reference books, scientific articles, etc.) are in English, but the course is taught entirely in French.
Please note that the slides presented during the lectures serve as a concise course aid. They cover the essential points but do not include the full oral explanations; taking personal notes is strongly recommended.
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Written work / report
Further information:
The evaluation is based on a written exam (60%) and the project (40% ? contingent on passing the theoretical part of the exam). For the written exam, the exam form is in English, but students are free to answer in either French or English.
For each project, a report must be submitted by each group in the form of a Notebook, along with the Python code, within the deadlines set during the first theoretical class session. Each group must present and defend their project in front of the class (in French or English). The project is mandatory; students who have not completed and presented the project will not be allowed to take the exam.
The purpose of the exam is to assess students' understanding of the concepts covered in the lectures. Students will be required to answer questions encompassing the entire course content.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Prof. François Jonard, francois.jonard@uliege.be
Earth Observation and Ecosystem Modelling Lab - EOSystM - www.eosystm.uliege.be
Office 2/48, Building B5a, Quartier Agora, Allée du six Août 19, 4000 Liège