Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.632194
Title: High resolution remote sensing for landscape scale restoration of peatland
Author: Cole, Elizabeth
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2013
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Abstract:
Upland peatlands provide vital ecosystem services, especially carbon storage and biodiversity. However, large areas of peatland are heavily degraded in the UK. When peat becomes exposed the potential for it to actively sequester carbon is greatly reduced and carbon stores are rapidly lost through erosion. Peatland restoration is a tool that addresses the government public service agreement targets for biodiversity, and soil and water protection in uplands. Blanket bogs are a UK Biodiversity Action Plan priority habitat. Many areas fall under designations for sites of protection under the EU habitats directive which is aimed at bringing the areas into ‘favourable condition’.The Moors for the Future Partnership is restoring large areas of badly eroded peat in the Peak District National Park to stabilise the surface and re-establish ecosystem functions. Monitoring is of pivotal importance to judge the success of the restoration work. This project assesses the suitability of high resolution remote sensing as an alternative monitoring tool to traditional field based plot surveys which are both time consuming and expensive. Remote sensing has been seen as a potential tool for mapping and monitoring peatlands, but to date the application of high spatial and spectral resolution remote sensing to monitoring peatland restoration has not been fully investigated. A floristic restoration trajectory has been established using a statistical classification (TWINSPAN) of vegetation cover data combined with expert knowledge of previous restoration, and autecology of the moorland species. Hyperspectral classification techniques were applied, including: Spectral Angle Mapping (SAM); Support Vector Machines (SVM); and maximum likelihood classification using both Minimum Noise Fraction (MNF), and narrow band vegetation indices. A successful classification of the restoration succession has been achieved. A predictive model for vegetation cover of plant functional types has been produced using a Partial Least Squares Regression and applied to the whole restoration site at the landscape-scale. RMSEs of between 10 and 16% indicate that the models can be used as a useful operational tool. A spectral library of key moorland species and their phenological response has been established using field spectroscopy in parallel to the image analysis. This has enabled the suggestion that the species are most separable from one another in July and it is recommended that this is the optimal month for remote sensing monitoring. This has facilitated the development of a set of recommendations for the most appropriate vegetation indices to use throughout the year depending species to be differentiated. High spatial and spectral resolution remote sensing data is needed to successfully characterise the vegetation response to restoration management in the upland peatland environment.
Supervisor: Mcmorrow, Julia; Evans, Martin Sponsor: Natural England
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.632194  DOI: Not available
Keywords: Hyperspectral Remote Sensing ; Peatland ; Restoration Monitoring ; Upland Vegetation ; Field Specroscopy
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