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  5. Hyperspectral indices for characterizing upland peat composition

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Article
English
2004

Hyperspectral indices for characterizing upland peat composition

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English
2004
International Journal of Remote Sensing
Vol 25 (2)
DOI: 10.1080/0143116031000117065

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Mark Cutler
Mark Cutler

University Of Dundee

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Julia Mcmorrow
Mark Cutler
Martin Evans
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Abstract

The erosion of blanket peat is a major environmental issue in the UK. Maps of erosion extent and peat composition, especially humification and moisture content, would aid our understanding of the erosion process and provide information for management decisions. HyMap images, acquired as part of the SAR and Hyperspectral Airborne Campaign (SHAC), were used to test candidate indices of peat composition for eroded blanket peat in the southern Pennines. Peat physical properties, including moisture content and degree of humification (measured as transmission), were derived in the laboratory and related to the remotely sensed data. Strong correlations were found between HyMap SWIR reflectance and transmission, but other peat physical properties were not significantly correlated. Spectral indices were calculated to express the depth of cellulose, lignin and water absorption features. Strong positive correlations were found between transmission and an adjusted cellulose absorption index (CAI), r 0.71, and the gradient of its shoulders between 2020 and 2200 nm, r 0.89. Other indices also performed well. Normalized indices performed better because they allowed for differences in brightness. Higher moisture content in poorly humified peats may have reinforced the effect of deeper ligno-celluloic absorptions, but further sampling is required to test this. The results suggest the potential for hyperspectral remote sensing to provide information on surface peat composition across large areas.

How to cite this publication

Julia Mcmorrow, Mark Cutler, Martin Evans, A. Alroichdi (2004). Hyperspectral indices for characterizing upland peat composition. International Journal of Remote Sensing, 25(2), pp. 313-325, DOI: 10.1080/0143116031000117065.

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Publication Details

Type

Article

Year

2004

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

International Journal of Remote Sensing

DOI

10.1080/0143116031000117065

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