Mapping a specific tree species at individual tree level across landscapes using remote sensing is challenging, especially in forests where co-occurring tree species exhibit similar characteristics. In Central European mixed forests, silver fir and Norway spruce have been identified as a pair of coniferous tree species with similar spectral and structural characteristics, typically leading to a major misclassification error in mapping studies. Here, we aimed to accurately map individual silver fir trees in a spruce-dominated natural forest in the Bavarian Forest National Park using integrated airborne hyperspectral and LiDAR data. To accomplish this goal, we extracted a set of relevant spectral and structural features from the hyperspectral and LiDAR data and used them to build machine learning classification models. Specifically, we compared the performance of three one-class classification algorithms (i.e. one-class support vector machine, biased support vector machine, and maximum entropy) for mapping individual silver fir trees. Our results showed that the biased support vector machine classifier yielded the highest mapping accuracy, with the area under the curve for positive and unlabeled samples (puAUC) achieving 0.95 (kappa 0.90). We found that the intensity value of 95th percentile of normalized tree height and the percentage of first returns above 2 m high were the most influential structural features, capturing the main morphological difference between silver fir and Norway spruce at the top tree crown. We also found that the wavebands at 700.1 nm, 714.5 nm, and 1201.6 nm were the most robust spectral bands, which are strongly affected by chlorophyll and foliar water content. Our study suggests that discovering links between spectral and structural features captured by different remotely sensed data and species-specific traits can significantly improve the mapping accuracy of a focal species at the individual tree level.

使用遥感方法在景观中的单个树级别上绘制特定树种具有挑战性，尤其是在同时存在的树种表现出相似特征的森林中。在中欧混交林中，银杉和挪威云杉被鉴定为一对具有相似光谱和结构特征的针叶树种，通常在制图研究中导致严重的分类错误。在这里，我们的目标是使用整合的航空高光谱和LiDAR数据，准确绘制出巴伐利亚森林国家公园内以云杉为主的天然森林中的单个银杉树。为了实现这一目标，我们从高光谱和LiDAR数据中提取了一组相关的光谱和结构特征，并用它们来构建机器学习分类模型。具体来说，我们比较了三种一类分类算法（即一类支持向量机，有偏支持向量机和最大熵）用于映射单个银杉树的性能。我们的结果表明，偏向支持向量机分类器的映射精度最高，阳性和未标记样品的曲线下面积（pu AUC）达到0.95（kappa 0.90）。我们发现归一化树高的第95个百分位数的强度值和高于2 m高的首次返回的百分比是最有影响力的结构特征，捕捉了冷杉木和挪威云杉在树冠顶部的主要形态差异。我们还发现，在700.1 nm，714.5 nm和1201.6 nm处的波段是最健壮的光谱带，受叶绿素和叶面水分的强烈影响。我们的研究表明，发现由不同遥感数据捕获的光谱和结构特征与特定物种特征之间的联系可以显着提高单个树级别上重点物种的制图准确性。