EGU2020-13453
https://doi.org/10.5194/egusphere-egu2020-13453
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Forecasting forest dynamics with the individual-based model LAVESI across the Siberian treeline: from UAV surveys to simulations

Stefan Kruse1, Iuliia Shevtsova1, Frederic Brieger1,2, Mareike Wieczorek1, Luidmila A. Pestryakova3, and Ulrike Herzschuh1,2,4
Stefan Kruse et al.
  • 1Alfred-Wegener-Institute Helmholtz-Centre for Marine- and Polar Research, Polar Terrestrial Environmental Systems, Potsdam, Germany (stefan.kruse@awi.de)
  • 2Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
  • 3Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia
  • 4Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

Boreal forests in Siberia store huge amounts of aboveground carbon. Global warming potentially threatens this carbon storage due to more frequent droughts or other disturbances such as fires. These disturbances can change recruitment patterns, and thus may have long-lasting impacts on population dynamics. Assessing high-resolution forest stand structures and forecasting their response for the upcoming decades with detailed models is needed to understand the involved key processes and consequences of global change.

We present forest stand inventories derived from UAV imagery and a developed processing chain including Individual Tree Detection (ITD) and species determination for 56 sites on a bioclimatic gradient at the Tundra-Taiga-Ecotone in Northeastern Siberia. We will use these and further 58 traditional count and measurement data as starting points for the detailed individual-based spatially explicit forest model LAVESI to predict future forest dynamics covering multiple sites across the Siberian treeline.

In our analyses, we will focus on assessing future structural changes of the forests and their aboveground biomass dynamics. For our discussion, we will evaluate the reliability of UAV-derived forest inventories by measuring the impact strength of error sources introduced in the methodology on the forecasts.

How to cite: Kruse, S., Shevtsova, I., Brieger, F., Wieczorek, M., Pestryakova, L. A., and Herzschuh, U.: Forecasting forest dynamics with the individual-based model LAVESI across the Siberian treeline: from UAV surveys to simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13453, https://doi.org/10.5194/egusphere-egu2020-13453, 2020

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