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Proceedings Paper

Methods and potentials for using satellite image classification in school lessons
Author(s): Kerstin Voss; Roland Goetzke; Henryk Hodam
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Paper Abstract

The FIS project - FIS stands for Fernerkundung in Schulen (Remote Sensing in Schools) - aims at a better integration of the topic "satellite remote sensing" in school lessons. According to this, the overarching objective is to teach pupils basic knowledge and fields of application of remote sensing. Despite the growing significance of digital geomedia, the topic "remote sensing" is not broadly supported in schools. Often, the topic is reduced to a short reflection on satellite images and used only for additional illustration of issues relevant for the curriculum. Without addressing the issue of image data, this can hardly contribute to the improvement of the pupils' methodical competences. Because remote sensing covers more than simple, visual interpretation of satellite images, it is necessary to integrate remote sensing methods like preprocessing, classification and change detection. Dealing with these topics often fails because of confusing background information and the lack of easy-to-use software. Based on these insights, the FIS project created different simple analysis tools for remote sensing in school lessons, which enable teachers as well as pupils to be introduced to the topic in a structured way. This functionality as well as the fields of application of these analysis tools will be presented in detail with the help of three different classification tools for satellite image classification.

Paper Details

Date Published: 6 October 2011
PDF: 8 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81740K (6 October 2011); doi: 10.1117/12.898123
Show Author Affiliations
Kerstin Voss, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Roland Goetzke, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Henryk Hodam, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)

Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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