Next generation forest dynamics modelling using remote sensing data

Ecosystems are threatened globally by climate change and biodiversity loss. Many countries plan to use forests for climate change mitigation, but many forest ecosystems are threatened by climate change itself. For example, climate-induced species' shifts may halve the value of European forests by 2100. How forests will absorb and store carbon in the future depends critically on individual species' responses to climate change, so predicting the impact of climate change on forests is a priority. Existing models are not suitable because they are constructed around small-scale plot data, so cannot predict the future of forests at national, continental or global scales.

Accurate predictions of the future of European forests require large monitoring networks, optimal use of existing data, cutting-edge measurement techniques, and predictive, data-driven models. This fellowship will develop wholly novel approaches to measuring and modelling forest dynamics by integrating existing information with data from new, cutting-edge remote sensing technologies using techniques drawn from machine learning and artificial intelligence.

Forest models that include diversity and ecological detail capture long-term succession dynamics and diversity shifts, and can predict changes in carbon storage, but are spatially limited because they require detailed ground data not widely available. National forest inventories contain information on diversity and demographic rates from spatially extensive plot networks. However, they are labour intensive to survey so are often only carried out once per decade, and contain only simple ground measurements of structure (such as trunk diameter and height). Earth Observation satellite data are available at large spatial and over long temporal scales, and provide information on forest function. However, these data are underused by ecologists due to challenges in interpretation, low spatial resolution, and mismatches between what is measurable from space (primarily canopy properties) and what is represented in models based on ground measurements (primarily of individual trunks).

New technologies such as Terrestrial Laser Scanning and drone remote sensing can capture 3D information on individual trees and whole-forest canopies in unprecedented detail, offering a link between ground and Earth Observation data. They can measure tree and crown shape, leaf area and arrangement, and crown tessellation in canopies, which are known drivers of productivity and dynamics, opening up the possibility of new approaches to forest modelling. Additionally, new Earth Observation satellites such as the European Space Agency's Sentinels provide global data at high spatial resolution, creating new opportunities for monitoring.

This Fellowship will create a new conceptual framework for modelling forest dynamics, parameterised and tested with forest data from across Europe. The model will link Terrestrial Laser Scanning and drone data to plot information on diversity and dynamics, and will predict forest responses to climate change robustly by additionally assimilating Earth Observation data. This will improve model spatial coverage as well as accuracy, with calibration and validation possible at monthly rather than decadal time-steps. New 3D measurements will give novel insights into how canopy structure influences dynamics. Machine learning and artificial intelligence approaches will be used to automate species detection from drone data, allowing ecological monitoring across large spatial areas.

The Fellowship will create new knowledge of how European forests function and how they will respond to climate change, with a fully data-driven model that incorporates cutting-edge monitoring. The approach will enable robust and updatable predictions of climate change impacts on forest diversity and dynamics, with flexibility to incorporate future data streams, that could inform climate change mitigation policy across the continent.

This Fellowship will produce outputs of benefit to a wide range of stakeholders. Beneficiaries of the work include the forestry and supporting sectors, government departments responsible for natural resource management, and policymakers and land managers engaged with rewilding and conservation activities.
The Fellowship will produce new methods and tools to survey forests at large spatial scales using UAVs, which will benefit the commercial forestry operations and companies within the wider forestry sector, forest managers, and government agencies responsible for monitoring national timber stocks. These organisations use ground surveys for mapping stems and monitoring growth in order to estimate timber stocks and to calculate yields, which can be time-consuming, expensive and spatially limited. The potential for UAVs to map and monitor individual trees in forests will be demonstrated, and the sampling methods, protocols designed, analysis tools developed, and lessons learned will be of interest to these different groups. The extensive field campaign surveys will determine best choice of resolution, flight line density and other surveying techniques for different species and desired measurements, and the processing of these data will create new unsupervised analysis workflows to extract structural information.
Use of UAVs in forest monitoring creates new opportunities for large-scale surveying of remote and difficult-to-access areas, including tropical forests and forests in areas with challenging topography. UAV methods developed will open new areas for monitoring, improving understanding of forest structure and dynamics, and ecosystem services such as carbon storage, as well as detecting early signs of dieback to enable effective management decisions. This Fellowship will develop methods to identify individual trees' structural properties (height, crown shape, trunk) and auto-detect species using two UAV methods. The experience of collecting these data during the Fellowship will inform best practice guidance for monitoring in planted and natural forests, demonstrating the accuracy of the UAV approach in practice, which will be shared with stakeholders through targeted dissemination events.
This Fellowship will produce data-driven predictions for the future of forests across Europe. The findings of this work will add to evidence on designing and restoring forests to ensure longevity of forests and future provision of forest ecosystem services under changing human pressures. The issue of managing forests for future resilience is high on the UK's national agenda; Forest Research, Defra and NERC all have current projects or areas of research that are directly relevant, as does the EU.
This Fellowship aims to produce predictions for the future of forests across Europe that will be of interest to those designing natural mitigation solutions for climate change, and those managing forests through planting and restoration to ensure long-term resilience and biodiversity protection. The findings of the Fellowship will provide evidence to inform UK and EU policy priority areas to understand how forests are changing, and recommendations for individual countries' plans to use forests for climate change mitigation. Recommendations from the Fellowship will inform the design and management of forest ecosystems that will persist in the future as well as identifying those at risk. The Fellowship will produce both detailed recommendations for future-proofing European forests, and a pathway to tailored recommendations in other locations, in order to protect existing carbon stores and biodiversity, and to design forests for long-term effective climate change mitigation.