This project will determine how new satellite products can generate accurate and precise estimates of forest biomass change and support better predictions across tropical rainforests.
Tropical moist forests the largest and most important forest biome on earth in terms of productivity, biomass stocks and biodiversity. They are also threatened with global change arising from land use, climate change, and fires. Understanding the biomass dynamics of these forests is crucial for climate change predictions, and for managing the region sustainably. Long-term plots in the Amazon and elsewhere have provided unprecedented information on the climate sensitivity of forest growth and mortality. Satellites, including lidar, are now providing more insights into biomass stocks and their variability across the whole region, including the impacts of land use change. Connecting plot data to satellite products is critical for ensuring robust assessments of the state of the rainforest. A new approach is to introduce ecological carbon cycle modelling into analyses of plot and satellite data. With novel machine learning approaches, the model can be calibrated to match the observations. Key challenges remain in how to use plot and satellite data to calibrate the model. Plot data are far richer, but sparser – plots measure fractions of km2, whereas satellites measure millions of km2. Satellite data can provide useful context for forest plots, which modelling can then draw together, for instance to determine the local pattern of natural forest disturbance. Calibrated models provide a means to make forecasts about the future of the rainforest under likely climate and land use scenarios, and therefore to determine the global impacts. ESA and NASA have satellites in operation or planned that aim to measure the biomass of equatorial forests. There are still key questions about how to determine from annual biomass maps the net fluxes of C from forests, and how to attribute these to climate and disturbance effects.
This PhD will investigate the carbon cycling of tropical moist forests, addressing the question: How and on what timescales will tropical forest biomass respond to global change? The focus will be on resolving the climate sensitivity of C uptake, and the residence time of C in plants and soils across the rainforest biome. A process model of carbon cycling, including theorised plant optimisation for growth and allocation, will be combined with a range of experimental and observational data from field sites, airfcraft and satellites to address the key question. The modelling analysis will make use of data assimilation methods that optimally combine simulations with observations. Working at site scale the model-data analysis will investigate how to model rates of forest growth and mortality, and their interactive effects on biomass stocks. The project will generate regional biomass analyses by making use of airborne and satellite data on fire burned areas, biomass distribution, leaf trait maps, and canopy phenology. The modelling will diagnose and map forest growth and turnover, to understand C cycle linkages to climate, soils and biodiversity.
Using data collected from forest plots, and airborne satellite data across the region on leaf area and biomass (e.g. NASA GEDI lidar mission, ESA Biomass airborne preparatory missions), the student will calibrate and evaluate the ecosystem model. The student will investigate, and attempt to explain, observed patterns of ecosystem structure, and test theories of climate sensitivity and disturbance effects across the biome, including over precipitation and altitudinal gradients. After appropriate training, the critical steps will involve:
(i) Accumulating key data. The student will derive key C cycle estimates for forest plots across the Amazon and sites in other tropical locations. Working with NASA, ESA and other collaborators, the student will draw together key earth observation datasets (radar, lidar, optical) describing variation in biomass and leaf area index around forest plots. Dr Hancock, part of the GEDI science team, will support the analysis of lidar estimates of forest structure. Prof Williams, part of the Biomass science team, will provide access to radar data, lidar data and forest plot data from locations in Gabon. Access to joint ESA/NASA online tools for biomass estimation from earth observation data will support estimates of forest structure across study regions (year 1).
(ii) Quantifying residence time of carbon. The process model will be calibrated and applied across forest sites to evaluate how C is stored and processed by tropical ecosystems. Alternate model structures will be investigated to test alternate theories of C processing and interactions (year 1).
(iii) Investigate climate-ecosystem interactions. Use the model to determine how observed patterns of woody biomass are determined by climate forcing. What alterations to model structure and/or parameters are required to match observed spatial patterns in satellite data around forest plots? What is the likely natural patterns of disturbance? (year 2).
(iv) What is the likely transient response of forests to climate change in the next 50 years? This activity will involve model forecasts using alternate viable structures and parameter ensembles to account for model structural and parameter uncertainty (year 3). How can forest data in the future test these predictions? How can the GEDI ESA Biomass mission maximise the information content from in situ data and link to modelling?
A series of scientific papers will result from these activities and form the basis of the thesis.