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Lecturer(s)
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Course content
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The theoretical foundations are covered through lectures, which are supplemented by exercises in the computer lab. Lecture topics: 1) History of Remote Sensing 2) Physical Fundamentals of Remote Sensing 3) Specifications and operating principles of remote sensing instruments (radiometers, spectrometers, mechanical and electronic scanners, SAR and SLAR radars, lidars, etc.). 4) Remote sensing platforms (ground-based, airborne, and satellite-based). Major satellite-based remote sensing systems. 5) Digital remote sensing data processing: radiometric and geometric corrections, data visualization, image enhancement, classification and interpretation, spectral indices. 6) Software tools for remote sensing data processing 7) Applications of remote sensing data (physical geography, urban planning, landscape changes, agriculture and forestry, environmental issues). Exercise Topics: 1) Available Remote Sensing Data (web applications, web data sources) 2) Landsat Data (ArcGIS PRO) 3) Sentinel data (ArcGIS PRO, ArcGIS Online, SNAP) 4) Image enhancement and highlighting 5) Indicesspectral image processing 6) Image classification (supervised and unsupervised classification)
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Laboratory Work
- Preparation for the Exam
- 25 hours per semester
- Attendace
- 100 hours per semester
- Homework for Teaching
- 25 hours per semester
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Learning outcomes
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This course introduces students to the fundamentals and principles of remote sensing, data processing methods, and their applications in geographical disciplines. The goal is to acquire basic skills in the interpretation, visualization, and classification of multispectral data. Students will learn to work with remote sensing data using both proprietary software (ArcGIS PRO) and open-source platforms (QGIS, SNAP, web applications).
The student is familiar with the basic principles of remote sensing. The student has an overview of available remote sensing data sources and archives and their effective use (data filtering, limitations of use). The student is able to utilize and process available multispectral data using both proprietary software tools (ArcGIS PRO) and open-source solutions (QGIS, SNAP, etc.). They are proficient in and understand the principles of image enhancement and classification methods.
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Prerequisites
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Knowledge covering the general geographical disciplines of a bachelor's degree program in geography. GIS skills.
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Assessment methods and criteria
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Written exam, Student performance, Analysis of Activities ( Technical works)
Knowledge covering the course lectures and assigned reading. Skills in acquiring, interpreting, processing, and analyzing remote sensing data using specific software solutions (ArcGIS PRO, QGIS, SNAP, web applications).
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Recommended literature
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Halounová L. (2018). Dálkový průzkum Země a GIS pro sledování časových změn na Zemi = Remote sensing and GIS for change detection on the Earth. Praha.
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Kropáček, J. et al. (2025). Dálkový průzkum Země: družicové systémy. Praha.
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Lillesand T.M., Kiefer, R.W., Chipman, J.W. (2015). Remote Sensing and Image Interpretation. 7th ed.. Hoboken, NJ, USA.
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Richards, J. A., Xiuping JIA. (2013). Remote Sensing Digital Image Analysis: An Introduction. Berlin.
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Tarolli P., Mudd S. (Eds.). (2020). Remote Sensing of Geomorphology.. Elsevier.
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