Prerequisite:

No special Prerequisite.

Content (Syllabus outline):

• Definition of remote sensing
• A brief history of remote sensing
• Electromagnetic waves
• Electromagnetic spectrum
• Interactions with the atmosphere
  • Scattering
  • Absorption
• Interacting with the surface
  • Spectral signature
• Resolution of imaging systems
  • Spatial resolution
  • Spectral resolution
  • Radiometric resolution
  • Temporal resolution
• Optical sensors
• Radar
• Lidar
• Satellite systems for Earth Observation
  • Weather satellites
  • Satellites for observing the Earth’s land surface
  • Satellites for observing the sea
• Image interpretation
• Visual interpretation
• Image pre-processing
• Image Enhancement
• Image Transformations
• Classification
• Time-series analysis
• Data fusion and integration
• Examples of use (in geology, biology, archaeology, forestry, agronomy, environmental studies of land and sea, natural and man-made hazards)

Readings:

• Campbell, James B. and Randolph H. Wynne. 2011. Introduction to Remote Sensing. The Guilford Press. Mather, Paul and Magaly Koch. 2011. Computer Processing of Remotely-Sensed Images: An Introduction. Wiley.
• Oštir, Krištof. 2006. Daljinsko zaznavanje. Ljubljana: Založba ZRC.
• Remote Sensing
• Remote Sensing of Environment
• ISPRS Journal of Photogrammetry and Remote Sensing

Objectives and competences:

Students acquire knowledge about remote sensing as an important source of information about the space, learn the techniques of capturing data from various sensors on aircraft and satellites, methods of electromagnetic radiation interaction with the atmosphere and the surface. They learn about the data transmission. They learn about satellite image hubs and catalogs (search), download, data processing and interpretation. They get acquainted with examples of the use of remote sensing.

Intended learning outcomes:

Knowledge and understanding of the remote sensing and scientific terminology. Understand the remote sensing procedure in all stages. Knowledge of sources of remote sensing and their characteristics. Students learn to use techniques of image processing. All theoretical chapters are closely associated with practical examples. Students learn to apply theory in practice, they are able to make decisions and choose appropriate methods and data sources for a specific application.

Assessment:

Written exam (theoretical part) and/or a seminar project (60 %), exercises and diagnostic assessment (40 %).