Savannas and dry forests are in flux, with both increasing and decreasing woody biomass in many areas. Why this is happening is unknown, but it is thought to be linked to both local land-use (e.g. tree harvesting for energy and timber, the use of fire), and global changes in climate and CO2 concentration (which favours the growth of trees over grasses). The result is hugely variable patterns of change depending on the type of savanna and dry forest and the nature of human use (Fig 1). This matters because of the linkages between ecosystem services derived from these ecosystems and human livelihoods: for example, in southern Africa, hundreds of millions of the rural and urban poor, use products from these savannas to mitigate the effects of poverty1. This project aims to understand the patterns and causes of vegetation change across the entire dry tropics, building on recent work in Africa (ref2).
What are the patterns and rates of woody biomass change in the dry tropics? [to be answered with radar remote sensing]
Can we disentangle the drivers of woody biomass change in the dry tropics? [answered with field data + spatial analysis]
This PhD will mix ecological fieldwork with radar remote sensing and spatial analysis. Changes in woody biomass will be mapped using state of the art radar remote sensing, calibrated with field data from across the dry tropics (method in2). To understand the causes of the observed changes, we will use spatial analysis and field data. The spatial analysis will analyse how change is related to the spatial patterns of climate change, land use and vegetation type. Data from long term plots3,recording how tree populations and floristic composition have changed will be used to complement the spatial analysis because this ecological information can “fingerprint” the causes of change. This will involve collecting new data at several sites across the dry tropics and working with partners to collate existing data.
The student will gain state of the art skills much in demand across the environmental sector, including remote sensing, image analysis, spatial modelling, and ecological fieldwork. You will join a research group that uses R, Python and Google Earth Engine for most of its analysis, and will work closely with post docs and other PhD students conducing similar work. Full training will be provided in all the required methods.
The successful student will need to have enthusiasm for learning new methods and be comfortable with quantitative analysis. As such this project would suit a student with a background in most natural sciences, and is also suitable for people with a physics, informatics or maths background, as long as you have an interest in ecosystem science. Fieldwork duration is flexible but is likely to involve 2-3 months a year away from Edinburgh.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,500 for 2019/20), tax-free stipend at the UK research council rate (£15,009 for 2019/20), and a research training and support grant to support national and international conference travel. www.eo-cdt.org/apply-now
 Ryan et al (2016) Ecosystem services from southern African woodlands and their future under global change. Phil. Trans. Royal Society B, 371, 20150312;  McNicol, Ryan, Mitchard. (2018) Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands. Nature Comm.,9, 3045. : https://seosaw.github.io; http://www.forestplots.net;
How good is research at University of Edinburgh in Earth Systems and Environmental Sciences?
FTE Category A staff submitted: 104.98
Research output data provided by the Research Excellence Framework (REF)
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