857002 Remote sensing time series analysis (in Eng.)
Diese Seite ist erreichbar unter:
- Vorlesung und Übung
- Vortragende/r (Mitwirkende/r)
- Klisch, Anja , Atzberger, Clement
- Angeboten im Semester
- Sommersemester 2022
- 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 evaluate the suitability and quality of freely available global satellite data for time series analysis; they are able to search for and acquire these data.
• The students compare, decide on and apply methods of data preparation, pre-processing and processing of remote sensing time series.
• The students apply methods for visualising (e.g. boxplot, heatmap, hovmöller diagram) and analysing (e.g. land surface phenology, vegetation anomalies) image time series.
• The students utilize R packages related to spatial data and data science (e.g. reading, manipulating, visualisation).
• The students utilize QGIS for visualising image time series (e.g. assess quality of image time series).
• The students utilize TIMESAT for processing and analysing (e.g. land surface phenology).
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