- Novel approaches for measuring resilience and detecting early warning of critical slowing down in forest and grassland habitats from satellite data
- Engagement with the National Trust to provide science based evidence to help mitigate the effects of climate change
- Multi-disciplinary research across ecology, earth observation, and policy.
The UK Government has set out a vision for a more biodiverse, connected, and resilient landscape in its 25 Year Environment Plan to help reverse land degradation and mitigate the effects of climate change. However, fundamental questions remain as to how we measure the ecological function of ecosystems and how they transition between degraded and recovered states. Recent work has focused on how these systems function and to measure their emergent properties as a proxy for health. One hypothesis is that the state of an ecosystem is related to its resilience, defined as the rate of return to equilibrium of a complex system after experiencing a perturbation. For example, vegetation collapse following drought leads to a shift to shrub or grassland in forests and, in extreme cases, to desertification.
The long-term records on land surface reflectance from remote sensing data offer an opportunity to quantify resilience and identify signals related to changes in state, or critical slowing down, of ecosystems and turning them into a tool for targeting resources and/or modifying management regimes and approaches. Preliminary data from the NERC Highlight Restoring Resilient Ecosystems (RestREco) project suggests that signals of critical slowing down are detectable.
The aim of this project is to develop novel methods for measuring resilience and detecting early warning of critical slowing down in forest and grassland habitats from satellite data. The studentship will focus on the development of image analysis methods for isolating vegetation response (e.g. Normalised Difference Vegetation Index) and tracking the autocorrelation in response to drought episodes as a measure of resilience. The work will be undertaken in partnership with the National Trust.
Figure 1: Vegetation vigour represented by a) daily filtered Normalised Difference Vegetation Index (NDVI) time series from satellite data for grass field with Early warning signal (EWS) of sate change calculated from a threshold (dotted line) of b) mean and uncertainty of lag-1 autocorrelation. Abnormally low NDVI (ALN) and periods of drought highlighted (shaded areas).
- Climate and Environmental Sustainability
- Organisms and Ecosystems
Dr Daniel Simms, Cranfield University ([email protected])
Prof. Ron Corstanje, Cranfield University ([email protected])
How to apply
- Each host has a slightly different application process.
Find out how to apply for this studentship.
- All applications must include the CENTA application form. Choose your application route
The student will achieve the objectives through critical review of the current literature on remote sensing, signal and image processing, machine learning, and restoration ecology. Experiments will be undertaken on Google’s cloud processing service for satellite imagery datasets, Earth Engine, at locations of grassland and forest restoration sites. Modelling will be undertaken (e.g. Dynamic Linear Model, empirical autocorrelation function, drift in the one-dimensional Langevin) to understand ecological resilience. Field data (access to site and experimental data) will be available from project partners National Trust alongside other collaboration with restoration ecologists through the RestREco project (https://restreco.com/).
Training and skills
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
Specific training in will be provided for remote sensing (incl. Google Earth Engine), image processing and machine learning through taught short courses and MSc modules delivered at Cranfield University (e.g. Scientific Python, Image Processing and Analysis). Further training will be available through placements with the National Trust, where the student can engage with a wide range of activities at their sites and across disciplines (from remote sensing to policy) and develop the soft skills required for a successful career. The student will also gain invaluable experience of working in an interdisciplinary team (RestREco), and attend national and international conferences.
Partners and collaboration
The research will be carried out in partnership with the National Trust (NT) to help them understand how management approaches could lead to stability in systems for differing land management uses and desired end points, such as Priority Habitats, in the context of the UK Government’s three principal Environmental Land Management strands. NT will contribute data, expertise, and training, with opportunities for the student to work closely alongside their staff.
If you wish to apply to the project, applications should include:
- A CENTA application form, downloadable from: CENTA application
- A CV with the names of at least two referees (preferably three and who can comment on your academic abilities)
- Submit your application and complete the host institution application process via:: https://www.cranfield.ac.uk/research/phd/measuring-resilience-and-early-warning-signals Please quote CENTA23_C3 when completing the application form.
Applications to be received by the end of the day on Wednesday 11th January 2023.
Review of current methodologies, monitoring requirements, satellite data sources, and methods for estimating complexity and resilience. Field site data exploratory analysis.
Critical investigation of current approaches for measuring the effect of known perturbations, such as drought, and development of novel technologies for detection of critical signals in time-series satellite data.
Understand the determinants leading to these signals, tied in with conventional approaches to metrics and measurements for restoration projects.
The standard model at Cranfield University is the production of peer-reviewed papers during the course of the studentship. Placements will be for periods of between 1 week to 3.
- Dakos, V., Carpenter, S. R., Brock, W. A., Ellison, A. M., Guttal, V., Ives, A. R., Kéfi, S., Livina, V., Seekell, D. A., van Nes, E. H., & Scheffer, M. (2012). Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE, 7(7). https://doi.org/10.1371/journal.pone.0041010
- Liu, Y., Kumar, M., Katul, G. G., & Porporato, A. (2019). Reduced resilience as an early warning signal of forest mortality. Nature Climate Change, 9(11), 880–885. https://doi.org/10.1038/s41558-019-0583-9
The fieldwork and site-specific activities may be affected by future changes in government policy relating to outbreaks of contact infection pandemics. This will be mitigated through use of alternative datasets already collected as part of RestREco or other datasets collected by the National Trust. Change or shift in study areas would also be considered where alternative datasets could be identified. Desk based activities would be undertaken online using remote access to specialist resources such as high-performance computing (if required). Meetings and informal communication that would normally be face to face would be arranged and facilitated using online platforms (e.g. Teams or Zoom) for video calls and instant messaging. Specialist training would be provided through Cranfield’s VLE and online teaching facilities.