Abstract Remote sensing can play a key role in understanding the make-up of urban forests. This study analyzes how high-resolution Geoeye-1 multispectral imagery and LiDAR point clouds allow for improved classification of urban tree species using object-based and support vector machine classification (SVM). Five common urban trees are classified: Acer platanoides; Acer platanoides ‘Schwedleri’; Picea pungens; Gleditsia triacanthos; and Tilia cordata. Numerous features are used for classification: index derived from imagery reflectance; texture of imagery; LiDAR height and intensity; and a LiDAR-generated normalized digital surface model. Classification is performed to evaluate the contribution of individual features, groups of features, and the combination of features from both imagery and LiDAR data. Classification results in an overall accuracy of 85.08% when features from both data sources are combined, compared with 77.73% when using only LiDAR features, and 71.85% when using only imagery features.

中文翻译：

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从高分辨率多光谱图像和 LiDAR 数据导出的特征评估，用于树种的基于对象的支持向量机分类

摘要 遥感可以在了解城市森林的构成方面发挥关键作用。本研究分析了高分辨率 Geoeye-1 多光谱图像和 LiDAR 点云如何使用基于对象和支持向量机分类 (SVM) 改进城市树种分类。五种常见的城市树木被分类： Acer platanoides; Acer platanoides 'Schwedleri'; 云杉; 皂角；和椴树。许多特征用于分类：从影像反射率导出的指数；图像的纹理；LiDAR 高度和强度；和 LiDAR 生成的归一化数字表面模型。执行分类以评估来自图像和 LiDAR 数据的单个特征、特征组以及特征组合的贡献。