Cities are facing too many challenges. Urban trees are essential as they provide services in terms of air pollution mitigation, freshness, biodiversity, and citizens’ well-being. Accurate data on location, species, and structural characteristics are essential for quantifying tree benefits. However, the cost of measuring thousands of individual trees through field campaigns can be prohibitive and reliable information on domestic gardens is lacking due to difficulties in acquiring systematic data. The main objective of this study was to investigate the suitability of very-high resolution satellite imagery, e.g., WorldView-2, for detecting, delineating, and classifying the dominant plant species in both public and private urban areas. The characterization of urban trees is difficult due to the complexity of the urban environment (buildings, shadows, open courtyards)the diversity of species and the spatial proximity between trees. To overcome these constraints, a canopy-based classification was developed with the selection of new relevant spectral and texture-based features for each tree species. Four spectral bands (blue, green, yellow, red) and four texture features (i.e., energy, entropy, inverse difference moment, Haralick correlation) were found to be the most efficient attributes for canopy-based classification from WV-2 images. Then, a classification of vegetation types, by using a Random Forest classifier, and ground validation were performed. In the two study areas, Aix-en-Provence (France) and Florence (Italy), 22 and 20 dominant species were identified and classified with an overall accuracy of 84% and 83%, respectively. The highest classification accuracy was obtained for Pinus spp. and Platanus acerifolia in Aix-en-Provence, and for Celtis australis and Cupressus sempervirens in Florence. The lowest classification accuracy was obtained for Quercus spp. in Aix-en-Provence, and Magnolia grandiflora in Florence.

城市面临太多挑战。城市树木是必不可少的，因为它们在缓解空气污染、清新、生物多样性和公民福祉方面提供服务。关于位置、物种和结构特征的准确数据对于量化树木效益至关重要。然而，通过实地活动测量数千棵树木的成本可能高得令人望而却步，而且由于难以获取系统数据，因此缺乏关于家庭花园的可靠信息。本研究的主要目的是调查超高分辨率卫星图像（例如 WorldView-2）是否适用于检测、描绘和分类公共和私人城市地区的主要植物物种。由于城市环境的复杂性（建筑物、阴影、开放式庭院）物种的多样性和树木之间的空间接近度。为了克服这些限制，开发了基于树冠的分类，为每个树种选择了新的相关光谱和基于纹理的特征。发现四个光谱带（蓝色、绿色、黄色、红色）和四个纹理特征（即能量、熵、逆差矩、Haralick 相关性）是 WV-2 图像中基于冠层的分类的最有效属性。然后，使用随机森林分类器对植被类型进行分类，并进行地面验证。在普罗旺斯地区艾克斯（法国）和佛罗伦萨（意大利）两个研究区，分别识别和分类了 22 种和 20 种优势种，总体准确率分别为 84% 和 83%。获得了最高的分类精度松属 和普罗旺斯地区艾克斯的Platanus acerifolia ，以及佛罗伦萨的Celtis australis和Cupressus sempervirens 。Quercus spp的分类精度最低。在普罗旺斯地区艾克斯和佛罗伦萨的Magnolia grandiflora。