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C439: Why are rainforests greenest when it's driest? Tropical forest canopy dynamics unveiled by airborne imaging (Lead Supervisor: David Coomes, Plant Sciences)

Supervisors: David Coomes (Plant Sciences), Grégoire Vincent (IRD) and Géraldine Derroire (CIRAD)  

Importance of the area of research:

Remote sensing studies have reported strong seasonal variation in the greenness of Amazonian rainforests with - perhaps counterintuitively - increased greenness during dry periods. If dry-season greening is occurring, it would have important implications for forest productivity, and the coupling of vegetation with the atmosphere in climate change models. However, measurements of greenness made by spaceborne optical sensors are difficult to interpret. They may result from changes in leaf area, changes in leaf optical properties related to leaf ageing, or measurement problems related to atmospheric moisture. To resolve these problems, we plan to use emerging technologies to re-evaluate the greening effect and its significance. We will track leaf phenology in the forests of French Guiana using optical sensors and a laser scanner mounted on a drone. Laser scanners emit pulses of light and measure return times off reflecting surfaces to construct 3D images. This active sensing is better at detecting changes in canopy structure than optical remote sensing. By taking a time series of measurements, linked to flux-tower and litterfall measurements, the project will gain novel insights into the phenology of rainforests.

Project summary:

The project will involve regular laser scanning and/or optically sensing forests over 1-2 years and interpreting the data produced. The project benefits from working at a long-established field site in French Guiana, namely the Paracou field station managed by co-supervisor Géraldine Derroire. The site has a flux tower that continuously measures CO2 and water fluxes, permanent plots, and long-term litterfall measurements, which will all aid in the interpretation of airborne remote sensing data. We are particularly interested in looking at species phenological patterns and leaf lifespan in relation to other functional traits and demographic traits, and also in studying the environmental and climatic drivers of intra-specific variations in phenology, to see whether this helps understand the sensitivity of species and communities to climate change.

What the student will do:

The student would regularly conduct airborne remote sensing surveys of 20 hectares of forest in French Guiana. These surveys will be conducted every month for a year, 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 and 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, and carbon fluxes measured from a flux tower.  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,  by Landsat and/or the Copernicus 2 satellites.  This project will improve understanding of phenological processes in a tropical forest and evaluate critically the contribution that optical sensing from satellites can play in monitoring phenology.  The student will spend a substantial period of time in the field,  and will also write code to process and interpret the laser scanning (lidar) and optical datasets.  They will collaborating with established researchers to interpret findings in the wider context of flux tower, litterfall and tree growth measurements.

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.


Saleska, S. R., Didan, K. , Huete, A. R., da Rocha, H. R. (2007) Amazon forests green-up during 2005 drought. Science 318, 612.

Vincent Grégoire, Antin C., Laurans M., Heurtebize J., Durrieu S., Lavalley C., Dauzat J. (2017). Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor. Remote Sensing of Environment, 198, 254-266. ISSN 0034-4257

Coomes, D.A. et al. (2017) Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning dataRemote Sensing of Environment194, 77-88.

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

Other projects available from the Lead Supervisor can be viewed here.

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