[BSc] S, M:ID, PSc
There are no specific formal prerequisites, but some affinity with natural sciences (high school level) and computers is necessary.
A basic understand of spatial data as taught in the Block 1 GIS course is useful but certainly not compulsory.
Remote sensing offers a wealth of environmental data to study environmental sustainability. We are all aware that satellite imagery is used in weather forecasts, but we also rely on these datasets to help us making many other environmental decisions. The present course fosters awareness of the possibilities and pitfalls that these remotely collected measurements present.
Earth observation data are usually collected with satellite sensors or sensors on airplanes, but data are also collected in the field. Often, image processing software is used to convert the data into formats for further analysis. Subsequently, they can be used in modelling directly, or they can be combined with other geo-spatial data.
In the course we will explore the physical principles behind remote sensing, starting with the fundamental characteristics of electromagnetic radiation and its interaction with substances in the atmosphere and water. After going through a selection of environmental problems encountered in these media, we move on to detection of direct human influence on soil, vegetation (land use) and urban environment. In the final leg of the course, all methods will be integrated in a small remote sensing project in which the students take the role of an environmental advisor.
After completion of the course students will be able to:
Select suitable remote sensing data and analysis techniques for environmental resource management
Analyse/work with remote sensing and other spatial data
Assess the quality of the datasets and analyses
After completion of the course students will know:
The basics of remote sensing
Which remote sensing data and image processing methods are available
For which problems they can be used
The strengths and weaknesses of the methods are in practice
Mode of Instruction
During the lectures the main physical basics and mathematical principles of remote sensing, will be revealed. In the plenary sessions selected examples of applications in environmental studies will be provided.
These will be further elaborated in self-study, as well-defined, case-based study assignments (for which literature and eLearning tools are provided). The cases use interactive problem-based learning (PBL), and are international in their coverage. To add depth and insight, additional peer-reviewed articles will also be proposed.
Computer exercises have been developed to stimulate further analytical interaction with the data. To foster practical understanding, the students conduct a small research project in which they take the role of an environmental advisor.
Assessment: In-class participation in discussions and practicals
Learning aim: Interactive engagement with course material
Deadline: Ongoing Course Weeks 1 – 6
Assessment: Weekly oral presentations (small groups, 5 PowerPoint slides)
Learning aim: Individual engagement with course readings
Deadline: Weeks 1 – 6 (Tuesdays (9:05-9:30)
Assessment: Final research essay, simulated project report (ca. 2500 words, Figures, Tables and References)
Learning aim: Analytical skills and practical understanding of course content
Deadline: Week 7 (Tue. 10 Dec. 09:00-10:50; Thursday, 12 Dec. 15:00-16:50) Deadline/hand-in: 12 Dec 16:50
Learning aim: Theoretical understanding of course content
Deadline: Week 8 (Thursday, 19 Dec. 15:00-16:50) Deadline/hand-in: 19 Dec 16:50
Literature* (available through blackboard and the Internet)
De Jeu, R.A.M., 2011. Digital Spatial Data: Introduction to Remote Sensing. VU, Amsterdam.
SEOS: Science Education through Earth Observation for High Schools. Internet-based eLearning tutorials http://www.seos-project.eu/home.html (change language for English version of the tutorials) Last accessed 12 July 2013.
Smith, R.B., 2006. Introduction to Remote Sensing of Environment (RSE). www.microimages.com documentation/Tutorials/introrse.pdf (Accessed 12 July 2013).
WEEK 1. Introduction Environmental Remote Sensing & Basics Remote Sensing
Session 1 (Tue. 29 Oct. 2013 09:00-10:50)
Introductory lecture on Environmental Remote Sensing
Practical introduction to and discussion about the SEOS modules
Session 2 (Thu. 31 Oct. 15:00-16:50)
Lecture on Basics RS: EM spectra
Practical introduction to SEOS module Spectra of the earth + field spectrometry
WEEK 2 Basics Remote Sensing
Session 1 (Tue. 05 Nov. 09:00-10:50)
Lecture on Basics RS: Satellite sensors (incl. orbits & resolution)
Practical SEOS Introduction to RS
Session 2 (Thu. 07 Nov. 15:00-16:50)
Lecture on Basics RS: Image processing
WEEK 3 Land-Atmosphere: Soil & Vegetation
Session 1 (Tue. 12 Nov. 09:00-10:50)
Lecture on Soil & Vegetation
Session 2 (Thu. 14 Nov. 15:00-16:50)
Lecture Land cover/use
WEEK 4 Inland lakes: Aquatic ecoloy
Session 1 Tue. (Tue. 19 Nov. 09:00-10:50)
Lecture on Inland lakes
Session 2 ((Thu. 21 Nov. 15:00-16:50)
WEEK 5 Soil moisture
Session 1 (26 Nov. 2013 09:00-10:50)
Lecture Soil moisture
Session 2 (Thu. 28 Nov. 15:00-16:50)
WEEK 6 Marine optics / Optical oceanography
Session 1 (Tue. 02 Dec. 09:00-10:50)
Lecture on Marine Optics
Session 2 (Thu. 05 Dec. 15:00-16:50)
WEEK 7. Project Integral Case
Session1 (Tue. 10 Dec. 2013 09:00-10:50)
Developing and working on the Remote Sensing Project
Session 2 (Thu. 12 Dec. 15:00-16:50)
Working on, and finalising the Remote Sensing Project report
WEEK 8. (Prepare for the) Exam
Self study for the exam (Tue. 17 Dec. 09:00-10:50)
Exam (Thu. 19 Dec. 15:00-16:50)
Preparation for first session
Optional SEOS Module World of Images