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Title: Measuring peatland carbon uptake by remote sensing
Author: Lees, Kirsten
ISNI:       0000 0004 7971 9076
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2019
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Peatlands are an important ecosystem for carbon storage, due to their semi-permanent water saturated condition which inhibits decomposition. Many peatlands in the UK have been degraded through human land use to the point where they are releasing carbon, and restoration is now a priority to protect these landscapes and the carbon held within them. Most methods of monitoring peatland restoration are small-scale and expensive. Remote sensing methods, however, are large-scale and often freely available to the end user. This project considers the potential benefits of using remote sensing to estimate peatland carbon uptake, and describes experiments which answer research questions in this area. Much of the work was done within the Forsinard Flows RSPB reserve, which has a chronosequence of blanket bog sites at different stages of restoration. A laboratory experiment on the effects of drought stress on the carbon flux and spectral reflectance of Sphagnum moss was first completed. This was followed by a field experiment to assess factors affecting peatland GPP and whether these could be detected by remote sensing data. The final part of this project involved the development of a Temperature and Greenness (TG) model using remote sensing to estimate GPP across blanket bog ecosystems. The project used both flux chamber and eddy covariance techniques to measure carbon uptake and compared the results to spectral reflectance at small-scale using a hand-held spectrometer, and large-scale using satellite data from MODIS. The results from these experiments suggest that spectral indices, and models using them, can give information about Sphagnum drought stress, seasonal change in peatland vegetation, and restoration progress, and are functional at scales from a few centimetres up to one kilometre. Next steps could include calibrating the developed model for a range of sites to broaden its applicability, and further work into monitoring water table depth using remote sensing.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral