857002 Remote sensing time series analysis (in Eng.)

Vorlesung und Übung
Vortragende/r (Mitwirkende/r)
Klisch, Anja , Atzberger, Clement
Angeboten im Semester
Sommersemester 2021
Unterrichts-/ Lehrsprachen


Time series and their analysis play an important role in present-day remote sensing. Remote sensing time series represent regularly collected (e.g. daily) images of the earth surface. Due to their high temporal resolution, they allow for observing and documenting valuable data about the dynamics of land surfaces. Currently, remote sensing time series become more important due to the European Earth Observation Programme Copernicus and its new Sentinel satellites. Gaining knowledge in this field of remote sensing, offers valuable skills to the students. In addition, the knowledge obtained in the course on hand can be easily applied to other scientific disciplines.

The course “Remote Sensing Time series analysis” is structured in theoretical (50%) and practical (50%) sections (VU) of 4 SWS. It addresses advanced students or doctoral students at BOKU that are particularly interested in analysing remote sensing time series in the context of environmental applications.

Inhaltliche Voraussetzungen (erwartete Kenntnisse)

Basic knowledge in the fields of remote sensing, GIS and digital image processing is pre-requisite.
Basic knowledge in programming (e.g. R, Python, Octave, Scilab, Matlab or IDL) is required.
Attendees should have a particular interest in the (mathematical) analysis of spatial data.


• The students will be able to identify and evaluate the suitability of remote sensing datasets for environmental tasks considering their temporal, spatial and spectral properties.
• The students will know freely available global satellite data that are suitable for time series analysis; they will be able to acquire these data.
• The students will know methods of data preparation, pre-processing and processing of remote sensing time series for environmental applications.
• The students will know methods for visualising image time series and will be able to apply them.
• The students will be able to calculate phenological metrics and vegetation anomalies (e.g. caused by droughts and other climatic processes) from freely available satellite data by means of open source software.
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