The southeastern Fennoscandian Shield is a specific region dominated by glacial and periglacial originated Quaternary deposits. In dense boreal vegetation zones, direct remote decoding of Quaternary sediments is impossible. Our aim is to assess the efficiency of machine learning algorithms for identification of types of Quaternary deposits based on spectral data of vegetation cover and digital elevation model data. A comparative analysis of two classification methods, discriminant analysis (DA) and classification and regression trees (CARTs), was performed. Two models, DA and CART, were constructed based on a set of spectral variables. They included principal components and spectral indices, such as the normalized difference vegetation index, the normalized difference moisture index, and the clay minerals ratio. When the absolute height and the topographic position index (DA+ and CART+ models) are added to a set of independent variables, the classification of Quaternary sediments becomes more accurate. The nonparametric CART method was shown to be more accurate in differentiating loose deposits. The correctness of the CART+ model for the reference sample of observation points was as high as 90.5%. According to the trained data, mapping of Quaternary deposits was carried out. Kappa analysis showed that the agreement with the map of Quaternary deposits for the CART+ and DA+ models was 0.5 and 0.44, respectively. For the DA and CART models, the agreement was much lower. It is noted that the sets of spectral data for the vegetation cover more clearly show the grain-size composition of loose sediments. To make the classification of Quaternary deposits more correct, morphometric indicators were added. Furthermore, the CART+ method was used to estimate the maximum height of glaciolacustrine sediments (117.5 m), which corresponds to the maximum absolute mark of the periglacial reservoir.

中文翻译：

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利用遥感绘制北方植被地区冰川和冰缘地层松散沉积物的绘图

Fennoscandian Shield东南部是一个以冰川和冰缘起源的第四纪沉积物为主的特定区域。在密集的北方植被区，第四纪沉积物的直接远程解码是不可能的。我们的目标是评估机器学习算法在基于植被覆盖光谱数据和数字高程模型数据识别第四纪沉积物类型方面的效率。对两种分类方法，判别分析 (DA) 和分类和回归树 (CART) 进行了比较分析。基于一组光谱变量构建了两个模型，DA 和 CART。它们包括主成分和光谱指数，如归一化差异植被指数、归一化差异水分指数和粘土矿物比率。当将绝对高度和地形位置指数（DA+和CART+模型）添加到一组自变量中时，第四纪沉积物的分类变得更加准确。非参数 CART 方法被证明在区分松散沉积物方面更准确。CART+模型对观测点参考样本的正确率高达90.5%。根据训练数据，进行了第四纪沉积物的测绘。Kappa 分析表明，CART+ 和 DA+ 模型与第四纪矿床图的一致性分别为 0.5 和 0.44。对于 DA 和 CART 模型，一致性要低得多。值得注意的是，植被覆盖的光谱数据集更清楚地显示了松散沉积物的粒度组成。为使第四纪矿床分类更加准确，增加了形态指标。此外，CART+方法被用来估计冰湖相沉积物的最大高度（117.5 m），这对应于冰缘水库的最大绝对标记。