3 PhD Students to work on the ERC project SPACETWIN

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Deadline 20 August 2022


Organisation: Ghent University – Department of Environment

CAVElab is seeking three highly motivated PhD students to work on the ERC project SPACETWIN: Digital twins for understanding forest disturbances and recovery from space.

About CAVElab

UGent's CAVElab studies vegetation dynamics, carbon and water cycling of terrestrial ecosystems. CAVElab has a strong focus on tropical forests, but a broad interest in all types of terrestrial ecosystems, including projects on temperate forests, drylands and urban ecosystems. Process-based vegetation modelling and 3D forest reconstructions are our core research tools, the questions arising from the modelling work require dedicated fieldwork activities.

About the SPACETWIN project

Forests worldwide are undergoing large-scale and unprecedented changes in terms of structure and species composition due to anthropogenic disturbances, climate change and other global change drivers. Climate, disturbances and forest structure are all closely linked: changes in climate can lead directly to physical changes in forest structure and vice versa or to an anticipated increase in forest disturbances. However, it is still uncertain how forest structure is impacted by disturbances (locally) and how we can detect and monitor various levels of disturbance regimes using spaceborne satellite data (globally).

This project will focus on the impact of drought, fire and logging disturbances across a range of tropical and temperate forest ecosystems. It will lead to a step-change in our ability to observe, quantify and understand forest disturbances and recovery by using time series of the most detailed structural and radiometric 3D forest models ever built: 'digital twin' forests. The key innovations will be:

  1. the establishment of an unprecedented 4D dataset across 57 disturbed sites using terrestrial laser scanning (~11,500 individual trees);
  2. the development of next generation methods to enable big data science of forest point clouds;
  3. the identification of key axes of variation of disturbed tree and forest structure;
  4. the first ever implementation of digital twins for optical and microwave radiative transfer modelling;
  5. the near-real time inversion of remote sensing of forest disturbances using emulation; and
  6. the embedding of forest structure in the global observation process to understand the uncertainties in monitoring disturbances.

These innovations will open a realm of untapped research questions and applications that call for the most detailed 3D information on canopy structure possible. These insights are also urgently needed to reduce uncertainties and advance the forecasting of carbon stocks and dynamics within the context of the IPCC.