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Remote Sens. 2014, 6(1), 716-739; doi:10.3390/rs6010716
Article

Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring

1,* , 2
 and 2
1 Centre for Landscape and Climate Research, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UK 2 School of Environment Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK
* Author to whom correspondence should be addressed.
Received: 22 November 2013 / Revised: 20 December 2013 / Accepted: 31 December 2013 / Published: 9 January 2014
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Abstract

Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.
Keywords: hyperspectral; peatland; restoration; partial least squares regression (PLSR); plant functional type (PFT); vegetation abundance hyperspectral; peatland; restoration; partial least squares regression (PLSR); plant functional type (PFT); vegetation abundance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cole, B.; McMorrow, J.; Evans, M. Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring. Remote Sens. 2014, 6, 716-739.

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