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Environmental Remote Sensing



[BSc] S, M:ID, PSc

Admission Requirements

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.

Course Objectives

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
Percentage: 20%
Deadline: Ongoing Course Weeks 1 – 6

Assessment: Weekly oral presentations (small groups, 5 PowerPoint slides)
Learning aim: Individual engagement with course readings
Percentage: 20%
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
Percentage: 30%
Deadline: Week 7 (Tue. 10 Dec. 09:00-10:50; Thursday, 12 Dec. 15:00-16:50) Deadline/hand-in: 12 Dec 16:50

Assessment: Exam
Learning aim: Theoretical understanding of course content
Percentage: 30%
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 (change language for English version of the tutorials) Last accessed 12 July 2013.

Smith, R.B., 2006. Introduction to Remote Sensing of Environment (RSE). documentation/Tutorials/introrse.pdf (Accessed 12 July 2013).

Contact Information

Weekly Overview

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)

  • Student presentation

  • 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

  • Practicals

WEEK 3 Land-Atmosphere: Soil & Vegetation
Session 1 (Tue. 12 Nov. 09:00-10:50)

  • Student presentation

  • Lecture on Soil & Vegetation

  • Practical
    Session 2 (Thu. 14 Nov. 15:00-16:50)

  • Lecture Land cover/use

  • Practical

WEEK 4 Inland lakes: Aquatic ecoloy
Session 1 Tue. (Tue. 19 Nov. 09:00-10:50)

  • Student presentation

  • Lecture on Inland lakes
    Session 2 ((Thu. 21 Nov. 15:00-16:50)

  • Practical

WEEK 5 Soil moisture
Session 1 (26 Nov. 2013 09:00-10:50)

  • Student presentation

  • Lecture Soil moisture
    Session 2 (Thu. 28 Nov. 15:00-16:50)

  • Practical

WEEK 6 Marine optics / Optical oceanography
Session 1 (Tue. 02 Dec. 09:00-10:50)

  • Student presentation

  • Lecture on Marine Optics
    Session 2 (Thu. 05 Dec. 15:00-16:50)

  • Video presentation

  • Practical

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
    Deadline 16:50

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)
    Deadline 16:50

Preparation for first session

Optional SEOS Module World of Images