Time Series Data Analysis


Andreas Dietz





Within this course remote sensing time series potentials and limitations will be discussed and practically evaluated. Analyzing time series data and discussing their meaning, further required analysis and potential for the applications.



Time series of remote sensing data are valuable to reveal short and long term processes occurring on the Earth’s surface. Impacts of climate change on land cover, start and end of the growing season, the dynamic behavior of snow covered or glaciated areas, or even extreme events such as forest fires, floods, and droughts are possible applications for time series data. In order to be able to analyze such time series accordingly, the data need to be preprocessed before applying techniques to extract the desired information. In this seminar, necessary preprocessing measures as well as techniques to analyze time series of remote sensing data will be discussed. Water body, snow cover, and vegetation dynamics will be extracted from MODIS and Sentinel data using routines developed and prepared together in Python (or IDL). After learning the basic techniques the participants of the seminar will choose a topic of their own choice as their final project.


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