Scottish Alliance for Geoscience, Environment and Society

Nina Fischer

Research interests:

Carbon cycle modelling, decision support, satellite imaging

Career history:

since 09/2021: PhD in Statistics at the University of Edinburgh
09/2020-08/2021: MSc in Statistics, University of Edinburgh. Graduated with distinction
09/2016-06/2021: MA (Social Sciences) in Mathematics and Economics, University of Glasgow. Graduated with 1st Class Honours

Active research projects:

Shaping the future of the UK’s landscapes – building a decision support system for sustainability

This project will develop a decision-support system to guide UK land use to a sustainable future by scaling a model of
ecosystem processes and earth observation data into a national integrated system.

The landscapes of the UK serve varied functions – e.g. supporting farmers’ yields, forestry, biodiversity and
recreation. For the future, there are several major questions. What should determine the nature of our landscapes in
coming decades? How can landscapes be resilient to climate change and be supported sustainably? There are major
challenges to building a system that can inform us in detail about these questions but there are also major
opportunities with new data infrastructure, high resolution earth observation data, and powerful analytical tools and
Currently, we can analyse and model land use either at national scale at coarse resolution, or at high resolution on a
small scale. Fine-scale pattern is important in driving land use decisions, but a national system is critical for policy
makers. The challenge here is to develop tools and algorithms that can scale between field and UK, to bridge the
scale/resolution trade-off that currently limits decision support.

The PhD project uses a range of data sets, statistical methods and machine learning approaches to address the
following questions:
1. What is the potential to simplify models and representations of landscapes? Can these simplifications be
used to link models of landscapes at different scales? What extra uncertainty is introduced into the decision making process by these simplifications?
2. How can information be transferred across scales? Methods will be investigated for using multiple datasets
at different scales to inform the modelling through calibration and validation.
3. For exemplar regions across the UK, what is the current balance of agricultural yield, forest biomass
accumulation, soil carbon storage and soil moisture? What are the sensitivities of these key outputs to global
change (climate, CO2) and how can decisions be taken to optimise these outputs? How should the
techniques developed be used to support policy decisions in practice?

Recent publications:


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