SCENARIO NERC DTP - Using Sentinel satellite remote sensing data to monitor the state of grasslands across a range of management intensities

Aim: To test the suitability of Sentinel-2 satellite optical data to spatiotemporally monitor the state of grasslands. The research makes a distinction between (1) productivity monitoring of forage swards and (2) condition monitoring of semi-natural grasslands, across a range of management intensities.

Methodology
We propose to employ a holistic approach involving statistically sound sampling designs, field campaigns, RS data processing and analyses, and predominantly mechanistic modelling.
This will involve the use of a vegetation radiative transfer model to derive vegetation structural information from Sentinel-2 data, verified by hyperspectral proximal sensing, as well as UAV multi-spectral surveys, agronomic data and simple biodiversity measures.

Training
- Placements with CEH to work with scientists at the country’s foremost research centres for vegetation biodiversity (e.g. Dr James Bullock);
- Involvement with ongoing studies at CEH semi-natural grassland field observatories – Parsonage Down - and the University of Reading’s Centre for dairy research farm as well as operational farms;
- Training in field sampling design by leading geospatial scientists such as Dr Ben Marchant.
- Training in botanical techniques, although the student will work alongside experienced botanist at Parsonage Down
- Access to training courses within CEH on relevant topics such as geospatial statistics and ArcGIS;
- On-the-job training with UoR/CEH in general remote sensing techniques and more specific grassland hyperspectral and biodiversity monitoring techniques
- The student would get in-depth hands-on training by Dr White with regards to piloting of the UAV and post-processing of data. They would obtain their UAV pilot qualification and learn to process UAV images using ‘Structure from Motion’ software.


To read more about this project please follow the link: http://www.met.reading.ac.uk/nercdtp/home/available/desc/entry2018/SC201827.pdf