Modelling forest dynamics
Fusing ground, aerial and satellite remote sensing data with long-term inventories to predict the future of forests at large scales.
Artificial Intelligence for ecology
Applying modern data science techniques to high-resolution remote sensing data of forests to improve the extraction of ecologically meaningful information.
Forests in three dimensions
Rethinking how we understand forests using three dimensional information from high resolution ground and drone-based remote sensing.
Data fusion for improved monitoring
Combining Earth Observation, aerial remote sensing and ground data to improve our understanding of environmental change.
Our fieldwork
Terrestrial Laser Scanning
We use terrestrial laser scanning to understand the shape and structure of individual trees, how crowns interact and how canopies fill space to maximise light capture.
UAV photogrammetry
We use drones to take photographs of the canopy to understand individual crown shape, and to monitor the effects of disturbances such as pests, pathogens and drought.
UAV Lidar
We use our UAV Lidar system to monitor large and inaccessible areas quickly, to understand forest structure and dynamics.
Forest surveying
We survey forests with traditional approaches and use existing plot networks and national inventories.
External projects and activities
Cambridge Centre for Earth Observation
Wet Woodlands Research Network
NERC Treescapes Voices of the Future
COST Action CA20118 3DForEcoTech
Alan Turing Institute Environmental monitoring: blending satellite and surface data
Our work is possible thanks to our funders, including:
UKRI Future Leaders Fellowship
The University of Cambridge
The Alan Turing Institute
The Natural Environment Research Council (NERC)
European Cooperation in Science and Technology (COST)
NERC London Doctoral Training Partnership
AI for the study of Environmental Risks (AI4ER) UKRI Centre for Doctoral Training
The Cambridge NERC Doctoral Training Partnership
The Woodland Trust