Indirect measurement of forest leaf area index using path length model and Multispectral Canopy Imager
Indirect measurement of forest leaf area index using path length model and Multispectral Canopy Imager
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DOI：
10.1109/igarss.2015.7326179
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Ronghai Hu;Jinghui Luo;G. Yan;Jie Zou
中科院分区：
文献类型：
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作者：
Ronghai Hu;Jinghui Luo;G. Yan;Jie Zou
冠层内部的非随机性和木材成分是限制间接叶面积指数（LAI）测量精度的两个因素。本文首次将路径长度分布模型与多光谱冠层成像仪（multi - spectral Canopy Imager， MCI）相结合，提高了路径长度分布模型的精度。结果表明：在4个样地，冠层内的非随机性低估了17.1% ~ 28.2%的LAI，而木本成分高估了14.6% ~ 27.8%的LAI。虽然这两个因素有时会被抵消，但不同森林的冠层内部的非随机性程度和木质成分的比例各不相同。特别是在以树干和树枝为主的针叶林中，应更多地关注冠层内部的非随机性和木本成分的影响。
Non-randomness within canopies and woody component are two factors limiting the accuracy of indirect leaf area index (LAI) measurement. Here we combine the path length distribution model and Multispectral Canopy Imager (MCI) together for the first time to improve the accuracy. The results show that non-randomness within canopies underestimates 17.1%-28.2% LAI, while woody component overestimates 14.6%-27.8% LAI in four forest sites. Although these two factors were sometimes offset, the degree of non-randomness within canopies and the proportion of woody component vary in different forests. More attention should be paid to the impact of the non-randomness within canopies and the woody component, especially in coniferous forest dominated by tree trunks and branches.