## 1 Introduction
The findings suggest that variations in the diversity and abundance of allergenic tree species in urban green spaces may be responsible for the trends observed in the dynamics and behavior of airborne pollen from these species. However, using remote sensing data to accurately identify individual allergenic tree species is difficult due to their small area and patchiness in urban green spaces [12]. To address this challenge, a key objective of this research will be to utilize high-resolution satellite imagery for mapping allergenic tree species in urban areas, since the mapping of individual trees becomes more feasible from a resolution of 3 m or higher [13]. In the past, mapping urban green areas usually employed field measurements [7], which are time-consuming, labor-intensive, and ineffective since these areas change rapidly over time. Currently, remote sensing (RS) technology has proven to be an efficient method for the classification of urban green spaces [14]. Depending on the spatial resolution of the RS data used and the extent of the study area investigated, low-resolution imagery (MODIS; [15; 16]), with moderate resolution (Landsat, Sentinel-2; [17; 18; 19]), and with the advancement of the satellite sensors with very high-resolution (WorldView; [20; 21]), has been utilized to acquire rapid and accurate information over green areas. However, the temporal frequency of the above-mentioned satellite imagery may not be sufficient to capture the variations in green spaces. In addition, the spatial resolution may not be detailed enough to capture variations in urban green volumes accurately [18]. To overcome these issues, PlanetScope (PS) satellite imagery [22] offers significant benefits over previous RS data due to its daily temporal resolution and 3 m spatial resolution, providing an effective solution to overcome existing issues. Hence, PS data has been successfully used, for example, in mapping rubber plantations [23], snow-covered areas in forested mountain ecosystems [24], mapping of lava flows in barren regions [25], and land-cover classification in urban areas [20]. Furthermore, in 2019, PlanetScope announced the general commercial availability of the next generation of PS Monitoring product, which includes eight spectral bands, instead of previously offering four bands [26]. Therefore, the performance of the eight-band PS imagery is investigated in this research for tree species in a highly urbanized area. Image classification methods (e.g., unsupervised, supervised) are usually used to map urban green areas. Mostly used unsupervised classification methods include ISODATA, or k-means clustering [21; 27; 28], whereas the most important supervised classification methods for urban green area mapping are random forest (RF), support vector machine (SVM), and neural networks (NN). [PERSON] et al. [29] used an RF classifier and an object-oriented approach for tree mapping in urban areas. The tests conducted using RF showed that this classifier is highly effective in identifying wooded vegetation in terms of user's and producer's accuracy. Furthermore, [PERSON] et al. [30] proposed a method that utilizes neural networks and crowdsourced geospatial big data to automatically map urban green spaces (UGS). This method increased the user's accuracy in UGS mapping using Sentinel-2 imagery with 10 m spatial resolution. Deep learning (DL) methods such as convolutional neural networks (CNN) have been widely utilized for image classification in recent years. [PERSON] et al. [14] presented a DL method for UGS mapping based on the phenological features using very high-resolution satellite imagery (i.e., GaoFen-2). The results indicate that the above-mentioned method can efficiently solve the misclassification problem of evergreen and deciduous trees. Furthermore, the integration of multiple sources and multi-spectral RS datasets leads to the creation of large-scale datasets for classification.