Digital twins for understanding forest disturbances and recovery from space

Project Information

SPACETWIN

Grant agreement ID: 101039795

DOI

10.3030/101039795

EC signature date 20 March 2022

Start date 1 November 2022

End date 31 October 2027

Funded under

• European Research Council (ERC)

Total cost

€ 1 498 859,00

EU contribution

€ 1 498 859,00

Coordinated by

UNIVERSITEIT GENT
Belgium

Periodic Reporting for period 1 - SPACETWIN (Digital twins for understanding forest disturbances and recovery from space)

Reporting period: 2022-11-01 to 2025-04-30

Summary of the context and overall objectives of the 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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

24 months into the project, the key achievements are:
• Multiple successful (international) field campaigns, collecting data with terrestrial laser scanning (TLS), field spectroscopy and wood penetrating radar (WPR)
• Benchmarking of state-of-the-art tree instance segmentation algorithms based on a novel manually annotated dataset of TLS forest point clouds
• Training and benchmarking of deep learning-based algorithms for leaf-wood segmentation of tree point clouds, based on a novel manually annotated dataset of tropical tree TLS point clouds.
• Comparison and validation of multiple quantitative structure model (QSM) algorithms
• Preliminary results for optical radiative transfer (RT) simulations for disturbed forest sites.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

24 months into the project, the most important results and how they are communicated (or planned to be communicated):
1. Perspective paper: Calders et al. (in review) Digital twins for understanding forest disturbances and recovery from space. ISPRS Journal of Photogrammetry and Remote Sensing. // Here we have further developed the overall idea of the ERC project in a perspective paper.
2. Scientific paper: Van den Broeck, W. A. J., et al. (in review). Pointwise Deep Learning for Leaf-Wood Segmentation of Tropical Tree Point Clouds from Terrestrial Laser Scanning, ISPRS journal of photogrammetry and remote sensing. // Development of a new method for semantic segmentation of forest point clouds.
3. Scientific paper: Cherlet et al. (in preparation). Benchmarking Instance Segmentation in Terrestrial Laser Scanning Forest Point Clouds. // Intended submission in January 2025. Here we have created a new dataset to benchmark instance segmentation of forest point clouds.
4. Scientific paper: Cooper et al. (in preparation). Validation of Quantitative Structure Models Against Destructively Sampled Trees. // Intended submission March 2025. Crucial evaluation of algorithms to convert point clouds into volumetric models.
5. StrucNet. Paper led by prof. Calders and establishment of new global network (https://strucnet.org/) co-chaired by prof. Calders. and GFZ Potsdam.

Furtheremore, we are working together with Studio KNETS (https://www.studioknets.be/) for the website and visuals for this project. The website can be visited at https://spacetwin.ugent.be/. One important tool in our communication is the creation of travel-poster inspired posters. They have been used on social media (e.g. X, LinkedIn) in the communication around the field campaigns. We received several positive feedback on these.

SPACETWIN project poster