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Remote Sensing


Admission requirements

There are no formal course prerequisites, but successful completion of Global Challenges 2: Sustainability – Energy and Global Challenges 4: Sustainability – Earth is highly desirable.


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 Geographic Information Systems (GIS).
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 this phase of the course we also intensify attention to spatial analysis, the role of other geo-spatial GIS data, and spatial modelling. In the final leg of the course, all methods will be integrated in a small 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 or the results of spatial analyses

After completion of the course students will know:

  • The basics of remote sensing, spatial analysis and spatial modelling

  • Which remote sensing data and spatial analysis methods are available

  • For which problems they can be used

  • What the strengths and weaknesses of the methods are in practice


Please see the LUC website:

Mode of instruction

During the lectures the main physical basics and mathematical principles of remote sensing, and spatial analysis techniques will be revealed. We will also visit the Space Expo of the European Space Research and Technology Centre (ESTEC) in Noordwijk to experience the technology behind remote sensing. In the plenary sessions many examples of applications in environmental studies will be provided. These will be further elaborated in self-study, as case-based study assignments (for which literature and eLearning tools are provided). Computer exercises have been developed to stimulate analytical treatment of spatial data. Part of these exercises were developed for BEAM, another part uses ArcGIS (evaluation licences from ESRI). To foster practical understanding of spatial analysis, the study ends with an integration of methods in a small project in which the students take the role of an environmental advisor.

Assessment method

At the end of the course we will test both the theoretical and practical skills of the students with an exam on Tuesday, and a report on Friday.

  1. Interactive engagement with course material: assessed through In-class participation in discussions and practicals (20% of final grade): ongoing weeks 1-7
  2. Individual engagement with course readings: assessed through weekly oral presentations in small groups (5 powerpoint slides: 20% of final grade): weeks 1-7, Tuesdays
  3. Understanding of course content: assessed in exam (30% of final grade): week 8, Friday 9:00-11:00
  4. Expression of holistic understanding of the course: assessed in final research essay, simulated project report (3,000 words,: 30% of final grade): week 8 (Tuesday at 16:00)


a link to the blackboard page may be entered here

Reading list

Literature (available through blackboard and the Internet)
Boersema, J.J., Reijnders, L. (eds.), 2009. Principles of Environmental Sciences. Section 10.5.4 and 10.5.5 GIS and Remote Sensing. Springer, Heidelberg, pp. 163-166.
De Jeu, R.A.M., 2011. Digital Spatial Data: Introduction to Remote Sensing. VU, Amsterdam.
Delaney, J., Van Niel, K., 2007. Geographical Information Systems: An Introduction (Second Edition). Chapters 1 & 2, Oxford University Press, Oxford.
Eleveld, M.A., Wagtendonk, A.J., Pasterkamp, R. & De Reus, N. (2007). WATeRS: An open Web Map Service with near-real time MODIS Level-2 standard chlorophyll products of the North Sea. International Journal of Remote Sensing, 28(16), 3693–3699. doi:10.1080/01431160701253204
Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W., 2005. Geographic Information Systems and Science. Section 2.3.5, Environment. John Wiley, New York, pp. 55-60.
SEOS: Science Education through Earth Observation for High Schools. Internet-based eLearning tutorials (change language for English version of the tutorials) Last accessed 8 Dec. 2012.
Smith, R.B., 2006. Introduction to Remote Sensing of Environment (RSE). documentation/Tutorials/introrse.pdf (Accessed 13/12/2011).
VU-IVM, 2007. Introduction to ArcGIS 9.2/9.3

Registration This course is only open for LUC The Hague students.

Contact information

Weekly Overview

Week 1. Introduction
Week 2. Physical system: Radiative transfer and climate change
Week 3. Biogeochemical cycle: Optics of the Oceans and marine stewardship
Week 4. Hydrologic cycle: Soil moisture and the Quality of inland waters
Week 5. Land cover, agriculture and spatial planning 1
Week 6. Land cover, agriculture and spatial planning 2
Week 7. Project Integral Case
Week 8. Project work, exam, report

Preparation for first session

First reading and thinking task:
Smith, R.B., 2006. Introduction to Remote Sensing of Environment (RSE)
De Jeu 2011. Digital Spatial Data: Introduction to Remote Sensing. (on blackboard)
SEOS Module World of Images
SEOS Module World heritage