Geospatial Systems - Rachel Walker
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Rachel Walker
Biography
About Rachel Walker
My name is Rachel Walker, and I joined the third cohort of the EPSRC CDT in Geospatial Systems in September 2021.
My background is in Geography; I completed a Geography BSc at the University of Leeds in 2013 with specific areas of interest including geomorphology, climate and GIS. After my degree I completed my Geography PGCE at the University of Bristol and taught Geography, mainly to 13–18-year-olds until summer 2021. Throughout the PGCE and further CPD, I completed work at master’s level, read about key world issues and started to become interested in coding.
Although studying for a PhD was always a consideration, it was not until I found the Geospatial Systems CDT that I decided to apply for one. The support on offer and the potential to develop a range of skills throughout the four-year programme really appealed, as did the within and cross cohort collaboration. The CDT enables me to develop new skills such as remote sensing, whilst also extending my GIS and Python skills.
Geospatial is a rapidly growing area and is increasingly important to tackle world issues. I am keen to be involved in researching climate change mitigation strategies, with a particular interest in using geospatial data to aid carbon sequestration to reduce the volume of carbon dioxide in the atmosphere. Following my PhD, I am open to working in industry, government or academia.
Both my MRes and PhD focus on peatlands as although these landscapes only cover 3% of the land surface, they contain over 30% of the terrestrial carbon store. As a result, it is important that they are monitored and restored to enable carbon to be sequestered rather than released (degraded peats change from a carbon sink to a source and 80% of the UK’s peat is degraded so affects our ability to meet emission reduction targets). Additionally, peatlands provide a range of other ecosystem services including water management and habitats for a range of species.
My MRes title was A Hyperspectral Approach to Understand the Association Between PSM (as measured by InSAR Data) and Vegetation Assemblage for a Scottish Peatland. Using machine learning, the hyperspectral data was able to accurately map plant functional types across four sites (one near-natural, two undergoing forest-to-bog restoration and one eroded) in the Flow Country.
My PhD studies will continue to analyse hyperspectral and PSM data to assess peatland condition, but will also integrate with other geospatial datasets including multispectral, thermal and LiDAR to create maps which assess changes in peatland condition over time primarily in the North Pennines AONB. This will also help assess which restoration techniques are most successful in this landscape enabling better use of resources in future restoration projects. Additionally, hyperspectral data from a range of sensors (satellite, airborne (aircraft and UAV) and handheld spectrometer) will be compared to determine which is better at mapping a range of peatland characteristics (such as vegetation assemblage and NDVI) which can be used as proxies for peatland condition. These images are collected at a range of spatial and temporal scales with different associated costs.
Research interest: A Hyperspectral Approach to Understand the Association Between PSM (as Measured by InSAR Data) and Vegetation Assemblage for a Scottish Peatland
