Classification of peatland vegetation types using in situ hyperspectral measurements
Classification of peatland vegetation types using in situ hyperspectral measurements
复制标题
使用原位高光谱测量对泥炭地植被类型进行分类
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Houet
中科院分区：
文献类型：
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作者：
Thierry Erudel;S. Fabre;X. Briottet;T. Houet
本研究的目的是评估两类方法来区分13泥炭地植被类型使用反射率数据从高光谱原位测量。这些植被类型是根据其组成，地层和生物多样性丰富度经验定义的。我们认为，特定的生物物理特性/组件可能有助于区分植被类型应用监督分类，如随机森林（RF），支持向量机（SVM），正则化逻辑回归（RLR），偏最小二乘判别分析（PLS-DA）。考虑到植被光谱指数，生物物理部分可以局部方式使用，或考虑到表征具体生物物理特性的光谱范围，以全球方式使用。以及增强吸收特征的变换的光谱特征。研究结果表明，尽管植被异质性和混合性，RLR分类器仍有希望绘制具有高生态价值的不同植被类型。
This study aims at evaluating two classes of methods to discriminate 13 peatland vegetation types using reflectance data from hyperspectral in situ measurements. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. We suppose that specific biophysical properties/components may help discriminating vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges which characterize specific biophysical properties. and transformed spectral signatures enhancing absorption features. The results of this study suggest that RLR classifier is promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture.