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How resilient are tropical rainforest to anthropogenic climate change?

Supervisors: David Coomes (Plant Sciences) with Grégoire Vincent, France's Research Institute for Sustainable Development

Reference Code: CE011

Importance of the area of research:

How resilient will tropical rainforests be to anthropogenic climate change in the decades ahead? This key question is the subject of much debate. Inventory plots indicate that tree mortality across Amazonia has risen steadily over the past 40 years, reducing the basin's carbon sink. Although global warming may have contributed to this trend, tropical forests have experienced El Niño events for over 100,000 years so must be somewhat tolerant of drought. Indeed, optical remote sensing shows that Amazonian forest canopies become greener in dry periods, suggesting that photosynthesis is stimulated not curtailed by reduced rainfall. The physiological explanation for this paradoxical finding remains poorly understood. By tracking leaf production and loss in rainforest canopies over two successive years using high-resolution airborne laser scanning, this project will greatly improve understanding of canopy dynamics in response to environmental change. These findings are fundamental to predicting forest resilience to future climate change.

Project summary:

Understanding the physiological response of canopies to drought is key to modelling the resilience of Amazon rainforests to climate change. By regularly laser scanning forest over two years, a clear picture of canopy dynamics will emerge. By linking this remotely sensed data with eddy covariance measurements of productivity and other field-based measurements, scientific understanding of forest resilience will be advanced.

What the student will do:

The student will regularly conduct airborne remote sensing surveys of 20 hectares of forest in French Guiana. These surveys will be conducted every month for two years, using a combination of optical and LiDAR sensors mounted on a drone. The student will process these datasets to produce 3-D images of the forest from which forest structure and leaf area distributions will be mapped using algorithms written in R or C. Tracking leaf area over the course of a year will provide one estimate of leaf phenology. This will be compared with estimates of phenology measured on the ground using littertraps, leaf labelling and Eddy Covariance measurements. The forests have been mapped on the ground already, and many papers written on their dynamics and functioning. The advantage for the student of working at such a well-studied site is that they can drill down into more detail with the data, for example by delineating individual tree species from the imagery and tracking their phenology. Finally, the fine-scale imagery obtained from drones will be compared with greenness patterns observed from space (using ESA's Copernicus 2 data) to evaluate the research's broader implication


  • Saleska, S. R., Didan, K., Huete, A. R., da Rocha, H. R. (2007) Amazon forests green-up during 2005 drought. Science 318, 612.
  • Vincent G. et al., (2012) Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sensing of Environment 125, 23.
  • Coomes, D.A. et al. (2017) Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sensing of Environment, 194, 77-88.

Follow this link to find out about applying for this project.

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.