Please use this identifier to cite or link to this item:
Title: Empirical modelling of vegetation abundance from airborne hyperspectral data for upland peatland restoration monitoring
Authors: Cole, Beth
McMorrow, Julia
Evans, Martin
First Published: 1-Jan-2014
Publisher: MDPI
Citation: Remote Sensing, 2014, 6 (1), pp. 716-739
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.
DOI Link: 10.3390/rs6010716
ISSN: 2072-4292
eISSN: 2072-4292
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2014 the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (
Appears in Collections:Published Articles, Dept. of Geography

Files in This Item:
File Description SizeFormat 
remotesensing-06-00716-v2.pdfPublished (publisher PDF)6.3 MBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.