Conclusions & Outlook

  • Christian Julian BödingerEmail author
Part of the BestMasters book series (BEST)


The focal objective of this study was to provide an assessment of state and dynamics of Chilean vegetation inside 4 sites along a latitudinal gradient. This was accomplished by producing extensive LULC maps using machine learning algorithms (RF & SVM) and calculating NDVI time series for a 4- year period between 2013 and 2017. The BFAST algorithm (Verbesselt et al., 2010) was applied to statistically analyze the time series catchment-wide, vegetation category specific and inside altitude belts to investigate the impact of climatic forcing. Furthermore, the performance of the recently released 12 m resolution TanDEM-X DEM in topographic correction and as an input in the classification variable framework was tested and compared to a Lidar DEM and the established SRTM DEM.

Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Science – GeographyEberhard Karls University of TübingenTübingenGermany

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