Duration
18h Th, 36h Pr
Number of credits
| Master in environmental bioengineering (120 ECTS) | 6 crédits | |||
| Master in forests and natural areas engineering (120 ECTS) | 6 crédits |
Lecturer
Coordinator
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
The course aims to introduce the techniques of acquisition, processing and analysis of very high resolution remote sensing data, with a specific focus on the monitoring of agricultural, forest and semi-naturals ecosystems.
This optional course follows the course ENVT0045 "Applied Geomatics and Remote Sensing".
The course is structured in 4 main parts:
- introduction to UAV imaging acquisition techniques;
- initiation to the processing and analysis of very high resolution aerial imagery (photogrammetric processing, segmentation and classification);
- introduction to LiDAR (aerial and terrestrial) data processing and analysis ;
Learning outcomes of the learning unit
At the end of this course, the student must be able to:
- Select a UAV equipment (platform and sensor) for a given objective;
- Prepare and execute a data acquisition campaign with a UAV;
- Process and analyze an image dataset produces with UAV (photogrammetric processing, analysis of the 3D model, classification);
- Process ans analyze an Aerial LiDAR dataset (process of the point cloud and the raster layers derived from it)
Prerequisite knowledge and skills
ENVT0045 "Applied Geomatics and Remote Sensing" or similar
Planned learning activities and teaching methods
Table of Contents
1. Introduction and Reminders
2. UAV
2.1. Theoretical concepts (operation, piloting, regulation, sensors)
2.2 Implementation (flight planning and execution, image acquisition)
3. LiDAR
3.1 Theoretical concepts
3.2 Data acquisition
4. Image and data processing
4.1. Theoretical concepts of photogrammetry
4.2 Photogrammetric processing of UAV imaging (Photoscan software)
4.3. Aerial image processing (introduction to GDAL and OTB tools in R environnment)
4.4. LiDAR data processing (lidR package)
5. Image processing (preprocessing, segmentaion, pixel and object based classification)
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Additional information:
The course is organized as follows:
- a series of theoretical sessions (video watching in remote mode)
- tutorial sessions (face-to-face mode)
- study cases sessions (training for tests)
- tests sessions (continuous assessment)
Recommended or required readings
The teaching materials will be available on eCAMPUS page of the course and on a dedicated server for the datasets.
Written work / report
Additional information:
Continuous assessment based on testing sessions conducted during the quarter
For the second session, the exam is organized as an open book exam
Work placement(s)
Organisational remarks and main changes to the course
Contacts
p.lejeune@ulg.ac.be