结合星载激光和多光谱影像的城市树种分类

Urban Tree Species Classification Combining Spaceborne LiDAR and Multispectral Imagery

摘要:

城市树木种类是影响城市森林固碳能力和维持生态系统稳定的重要因素，但城市树木空间分布广泛、所处环境复杂，目前缺少适用的树种分类模型，因此尝试将星载激光引入树种分类。综合考虑植被冠层结构、水平光谱与空间环境特征，并通过特征空间分析定量度量各参数贡献以构建最优特征集合，最后利用支持向量机（SVM）算法建立结合星载激光与光学影像的城市树种分类模型。上海市内4个代表性区域树种分类实验结果表明，所构建的融合模型准确性较高，Kappa系数达到0.82，总体分类精度为87.04%。星载激光能够在城市树种分类中发挥重要作用，其表征的植被三维结构特征与空间环境特征一同对城市树种分类做出了突出贡献。

Abstract:

The urban tree species are an important factor affecting the ability of carbon sequestration by urban forest and the maintenance of ecosystem stability. However， due to the wide spatial distribution and complex environment of urban trees， there is a lack of tree species classification models applicable to cities. In this paper， the spaceborne LiDAR is introduced into tree species classification. Considering the vegetation canopy structure， horizontal spectra and spatial environment characteristics， the optimal feature set is constructed by quantitatively measuring the contribution of each parameter through feature space analysis. Finally， an urban tree species classification model combining spaceborne LiDAR and optical images is established using support vector machine （SVM） algorithm. Four representative regions in Shanghai are selected for validation， and the results show that the proposed fusion model has a high accuracy with the Kappa coefficient reaching 0.82 and the overall classification accuracy of 87.04%. The spaceborne LiDAR plays an important role in the urban tree species classification， and its retrieved 3D structural variables of vegetation together with spatial environmental characteristics play a major contribution to urban tree species classification.