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Conclusions & Outlook

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

Abstract

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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