Hyperspectral indices for characterizing upland peat composition
Hyperspectral indices for characterizing upland peat composition
复制标题
用于表征高地泥炭成分的高光谱指数
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
A. Alroichdi
中科院分区：
文献类型：
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作者：
J. McMorrow;Mark Cutler;Martin Evans;A. Alroichdi
泥炭层的侵蚀是英国的一个主要环境问题。侵蚀程度和泥炭组成，特别是腐殖化和水分含量的地图，将有助于我们了解侵蚀过程，并提供信息的管理决策。HyMap图像，收购的SAR和高光谱机载运动（SHAC）的一部分，被用来测试候选指标的泥炭成分侵蚀毯泥炭在南部奔宁山脉。泥炭的物理性质，包括含水量和腐殖化程度（通过透射测量），是在实验室中推导出来的，并与遥感数据相关。HyMap SWIR反射率和透射率之间存在较强的相关性，但泥炭的其他物理性质之间没有显着相关性。计算了表征纤维素、木质素和吸水深度特征的光谱指数。传输和调整后的纤维素吸收指数（CAI），r 0.71，其肩膀之间的梯度2020和2200 nm，r 0.89之间的强正相关性。其他指数也表现良好。归一化指数表现更好，因为它们允许亮度的差异。腐殖化程度低的泥炭中较高的含水量可能加强了深层木质纤维素吸附的影响，但需要进一步取样进行测试。结果表明，高光谱遥感提供信息的表面泥炭组成在大面积的潜力。
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.