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|Investigator||Wolf, Nils - Pädagogische Hochschule Heidelberg (University of Education Heidelberg), Geography|
|Summary||„Learning to understand the earth – Application of modern satellite imagery for earth observation in geography classes” (Space4Geography)
Previous studies at the University of Education Heidelberg have already demonstrated the great potential remote sensing imagery has for teaching in schools, in particular but not exclusively for geography lessons. International and national education standards explicitly suggest the use of remote sensing imagery in the classroom; the competent dealing with digital remote sensing data and their self-employed interpretation is playing a major and increasing role. The actual embedding of remote sensing imagery into school teaching, however, is often difficult due to the lack of technical infrastructure and know-how. Against this background, the project Space4Geography aims at developing a web-based learning platform which enables the application of remote sensing methods inschool teaching from primary up to high school graduation.
The learning platform will feature didactically prepared educational material addressing relevanttopics of international and national curricula and educational standards. Moreover, it provides material to teach basic principles of remote sensing, including active microwave sensors. Pupils will be introduced into the capabilities of space-borneearth observation and geospatial technologies and work actively with optical (e.g.RapidEye and Landsat) and radar (TerraSAR-X) imagery in order to approach topicssuch as land use change, renewable energy, deforestation, climate change andnatural disasters. For instance, the topics of climate change and naturaldisaster (response) could be approached using TerraSAR-X multitemporal imagery inorder to map and characterize flooded areas. Using the integrated, web-basedremote sensing software BLIF (developed in our research group), pupils will beenabled to conduct basic image processing and interpretation, such as contrastenhancement and image classification. In order to support more effectivelearning, an adaptive learning environment will be implemented which allows individualizedlearning paths, adapting the learning module's content and difficulty to thecapabilities and personal preferences of the learner. For the realization of learning modules (step 3),we intend to use about 15 TerraSAR-X images (archive imagery, ~3m resolution StripMapmode will be favorable in most cases). Which scenes to use (AOI, time frames,mode) will be defined after completion of the analysis of education standardsand curricula.
The project should encourage young people to enter careers in earth observation, naturalscience and related fields. They will get an understanding of remote sensingtechnology, and TerraSAR-X in particular, and its various fields of application.
The projectis funded by the German Aerospace Center (DLR); project duration is 10/2013 to09/2016
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