548-0004/12 – Remote Sensing (DPZ)

The aim of the course is to acquire knowledge of the principles and methods of remote sensing and their application in solving practical problems. Students develop the ability to select and apply appropriate methods of image data processing, evaluate and interpret the information obtained, and critically assess the quality of input data and the results achieved.

The lessons are focused on an introduction to the study of remote sensing methods. The first part deals with physical principles required to understand the fundament of these methods. The second part is focused on technical conditions and tools for data acquisiton. Further part is dedicated to the methods of processing data from remote sensing especially methods and procedures of digital processing, including pre-processing of image data, its enhancement and classification methods. The final part describes interpretation and applications. Professional knowledge: The student explains the physical principles of remote sensing, describes the basic methods of acquiring remotely sensed imagery, and distinguishes between the main methods of image preprocessing, enhancement, classification, and interpretation. The student describes the potential applications of remote sensing methods in solving spatial and environmental problems. Professional skills: The student applies basic methods of digital processing of remotely sensed imagery, including preprocessing, enhancement, and classification. The student selects an appropriate processing workflow with respect to the characteristics of the data and the problem being addressed, evaluates the results obtained, and interprets them in a professional context. General competences: The student independently selects and professionally justifies an appropriate approach to solving problems using remote sensing data, critically evaluates the quality of input data and the reliability of the results, and professionally communicates the conclusions of the analysis.

De Lange, N. Geoinformatics in Theory and Practice: An Integrated Approach to Geoinformation Systems, Remote Sensing and Digital Image Processing. Berlin/Heidelberg: Springer, 2023. ISBN 978-3-662-65757-7. Petrelli, M. Machine Learning for Earth Sciences: Using Python to Solve Geological Problems. Cham: Springer, 2023. ISBN 978-3-031-35113-6. Andronache, C. (Ed.), 2018. Remote Sensing of Clouds and Precipitation, Springer Remote Sensing/Photogrammetry. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-72583-3 Clevers, J: Overview RS-Basics Digital Lectures. Wageningen, 1999. On-line: http://www.geo-informatie.nl/courses/grs20306/lectures/overview.htm

Students are asked about knowledge from areas that they should have already known from previous lectures. Students also work on individual tasks. Tasks are frequently based on understanding of previous, simpler tasks. Students must pass written and oral exam.