PhD project offered by the IMPRS-gBGC in July 2021


Monitoring structural changes in three different ecophysiological biomes of the central Amazon basin to support carbon cycle modelling

Christiane Schmullius , Susan Trumbore , Alison Hoyt , Laura Hess

Project description


To characterize carbon storage relevant surface parameters
To quantify aboveground carbon stocks and fluxes
To estimate carbon turnover (C-storage/C-sequestration)

Methods and Data

Interpretation and comparison with LiDAR data from ATTO to look at structural changes (diameter and height increments) and/with topography.
Generation of carbon cycle relevant parameters (e.g. AGB, NPP, plant traits) from spaceborne remote sensing data synergies over time.
Interface development to carbon stocks and fluxes modelling.
Development of up- and down-scaling models from TLS to airborne to spaceborne sensors, exploitation of radar-optical synergies (including SAR interferometry), application and investigation of AI and DL approaches to massive time series, DEM and surface model generation, validation exercises (TLS, airborne lidar and photogrammetry).
Exploration of new data sources: Solar Induced Fluorescence (FLEX mission) for carbon stock estimation, upcoming NISAR mission.
Time frame of research: since start of Sentinels in 2016 with possible extension of time series to earlier published results (e.g. Landsat archives).

Working group & planned collaborations

MPI-BGC Department Biogeochemical Processes, FSU Jena Department for Earth Observation

Planned project collaborations and research sites

ATTO: pristine rainforest with near-pristine atmosphere in the wet season and an atmosphere notably influenced by human activity in the dry season
BONDS plus follow-on project until 2024 (jointly with Laura Hess from UCSB): tropical floodplains with two major white-water rivers (Amazon mainstem and Juruá)
Stanford University Project: inundation mapping for peatlands/wetland classification (jointly with Alison Hoyt)


Applications to the IMPRS-gBGC are open to well-motivated and highly-qualified students from all countries. Prerequisites for this PhD project are:
  • a Master’s degree environmental science, geosciences, geography with a strong remote sensing background
  • Computational skills:
      programming skills (such as IDL, Matlab, R, Python or Julia)
      processing and analyzing large data sets
      machine learning techniques
      remote sensing data handling
  • Interest in field work in Brazil
  • Excellent oral and written communication skills in English, knowledge of German and Portugese is an asset
The Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals.

Photograph taken from top of ATTO illustrating the surface structure to be measured by the LiDAR system.
Photograph taken from top of ATTO illustrating the surface structure to be measured by the LiDAR system.

>> more information about the IMPRS-gBGC + application