Hyperspectral indices for characterizing upland peat composition

J. M. Mcmorrow (Lead / Corresponding author), M. E. J. Cutler, M. G. Evans, A. Al-Roichdi

    Research output: Contribution to journalArticle

    20 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)313-325
    Number of pages13
    JournalInternational Journal of Remote Sensing
    Volume25
    Issue number2
    DOIs
    Publication statusPublished - 2004

    Fingerprint

    peat
    moisture content
    humification
    erosion
    cellulose
    physical property
    index
    environmental issue
    lignin
    reflectance
    synthetic aperture radar
    remote sensing
    sampling
    water

    Cite this

    Mcmorrow, J. M. ; Cutler, M. E. J. ; Evans, M. G. ; Al-Roichdi, A. / Hyperspectral indices for characterizing upland peat composition. In: International Journal of Remote Sensing. 2004 ; Vol. 25, No. 2. pp. 313-325.
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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.",
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    Hyperspectral indices for characterizing upland peat composition. / Mcmorrow, J. M. (Lead / Corresponding author); Cutler, M. E. J.; Evans, M. G.; Al-Roichdi, A.

    In: International Journal of Remote Sensing, Vol. 25, No. 2, 2004, p. 313-325.

    Research output: Contribution to journalArticle

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