Digital twins for understanding forest disturbances and recovery from space

Introduction

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).

Project Focus

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.

Key Innovations

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.
6. The embedding of forest structure in the global observation process to understand the uncertainties in monitoring disturbances.

Research Opportunities

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.