Next generation forest dynamics modelling using remote sensing data

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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.