Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
30.0 h + 30.0 h
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Prerequisites : Notions of statistics, general physics course. The course has three components: 1: The presentation during lectures of the theoretical and methodological bases of remote sensing; 2: The application of image processing and interpretation methods to Landsat data on a region of Belgium, using image processing software on PC; 3: The exploration of a large range of remote sensing applications and of the methods used in each application, on the basis of CD-ROMs demonstrating case studies. Physical bases of remote sensing: - Definitions: radiant energy, radiant flux, radiant flux density, radiance; - Interactions between energy and the surface of the earth: laws of Stefan-Boltzmann and Wien. - Spectral reflectance curves ; - Atmospheric effects; - Physical interactions with thermal infra-red energy. The sensors used in remote sensing: - Landsat MSS and TM, SPOT; - AVHRR, Vegetation, MODIS; - the new high spatial resolution sensors. Image processing: - Corrections for non-systematic and systematic geometric distortions - Radiometric corrections - Extraction of statistics from images - Contrast enhancement - Spatial filtering - Supervised classification - Unsupervised classification - Classification errors estimation - Change detection methods - Multispectral transformations: Tasseled cap transformation; principal components analysis; - Notions of microwave remote sensing. Practical work: Processing of a Landsat TM image of Belgium: 1st session Introduction to image processing software 2nd session Color composites and contrast enhancement 3rd session Design of a scientific project 4th and 5th sessions Geometric correction 6th session Unsupervised classification 7th session Supervised classification 8th session Accuracy assessment of classification
At the end of this learning unit, the student is able to :
|1||Knowledge : The students will acquire a good knowledge of the different applications of remote sensing, and a capacity to decide which sensors and which image processing and interpretation methods are most appropriate for a given application. Skills : The students will gain understanding of the bases of remote sensing and will be able to process and interpret satellite data on a given region, using a image processing software on PC. Emphasis is put on optical remote sensing for terrestrial ecosystem applications.|
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Continuous evaluation based on:
- Two tests during the trimester: (i) physical basis of teledetection and (ii) image processing technique (10% of final result for each test, so 20% for both);
- two practical work reports for each processing step (30% of final result for the overall of reports);
- a work on teledetection applications: answer to several questions, based on CD-ROMs (20% of final result).
- Richards J. 1986. remote Sensing Digital Image Analysis, Springer-Verlag, 2ème édition
- matériel sur moodle
Faculty or entity
Title of the programme
Master  in Biology of Organisms and Ecology
Interdisciplinary Advanced Master in Science and Management of the Environment and Sustainable Development
Minor in Geography
Bachelor in Geography : General
Master  in Population and Development Studies
Master  in Physics
Master  in History of Art and Archaeology : General
Master  in Environmental Science and Management