## 2 Materials
### PlanetScope Satellite Imagery and Preprocessing
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & \\ \hline \multirow{4}{*}{Spectral Band} & Coastal Blue (431–452 nm) & Yellow (600–620 nm) \\  & Blue (465–515 nm) & Red (650–680 nm) \\  & Green I (513–549 nm) & Red-Edge (697–713 nm) \\  & Green (547–583 nm) & Near Infrared (845–885 nm) \\ \hline \multirow{4}{*}{Date} & 6 April 2022 & 4 June 2022 \\  & 30 April 2022 & 18 June 2022 \\  & 15 May 2022 & 2 July 2022 \\ \hline Spatial resolution & \multicolumn{2}{c}{3 m} \\ \hline Data product & \multicolumn{2}{c}{Ortho Tile Analytic Product Level 3A} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The main characteristics of the PSB.SD sensor and dates used in this research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **PS** & **Bands Used** & **Reference** \\ \hline NDVI & & \(\begin{array}{c}\text{B8}\ -\ \text{B6}\\ \text{B8}\ +\ \text{B6}\\ \text{B8}\ -\ \text{B4}\\ \text{B8}\ +\ \text{B4}\\ \text{B8}\ -\ \text{B6}\\ \text{B8}\ +\ \text{6.0}\ \text{B6}\\ \text{B8}\ -\ \text{B6}\\ \text{B8}\ +\ \text{6.0}\ \text{B6}\\ \text{B8}\ -\ \text{B6}\\ \text{B8}\ +\ \text{B6}\\ \text{B8}\ +\ \text{B6}\\ \text{B4}\ +\ \text{B6}\\ \end{array}\) & [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the vegetation indices calculated from the PlanetScope (PS) imagery. This research also employed the gray level co-occurrence matrix (GLCM) in order to incorporate texture information in the mapping of allergenic tree species in urban green spaces. The GLCM function, introduced by [PERSON] et al. [43], analyzes the texture of an image by determining the frequency of specific value-pair combinations and spatial relationships among pixels in the image. This function is useful for extracting important textural information from an image while reducing the number of correlated features [44]. Each spectral band was segmented individually, and multiple textural variables were extracted (using the GLCM measures). However, after using multiple feature selection (FS) methods (Section 3.2) with the above-mentioned GLCM features, only three GLCM features were chosen in a final classification, namely, GLCM mean, variance, and correlation [45]. These texture features were calculated (Equations (1)-(3)) as follows [46; 47]:  
\[\text{GLCM\,Mean}\ =\ \sum_{\text{i}\,=\,2}^{\text{N}\text{g}}\text{i}\text{P}_{ \text{x}}\ +\ \text{y}^{(\text{i})} \tag{1}\]  
\[\text{GLCM\,Variance}\ =\ \sum_{\text{i}\,\sum_{\text{j}}}(\text{i}\ -\ \mu)^{2}\ \text{P}(\text{i},\text{j}) \tag{2}\]  
\[\text{GLCM\,Correlation}\ =\ \sum_{\text{ij}\,=\,0}^{\text{N}-1}\text{P}_{\text{ij}} \Big{[}(\text{i}\ -\mu_{\text{i}})(\text{i}\ -\mu_{\text{j}})/\sqrt{(\sigma_{\text{i}}^{2})(\sigma_{\text{j}}^{2})}\Big{]} \tag{3}\]  
where \(\text{P}_{\text{ij}}\) are the probability values occurring in adjacent within a defined window or neighborhood in the original image, and i is the column label and j is the row label of the GLCM [47]. \(\text{P}_{\text{x}}(\text{i})\) is equal to the i-th entry in the GLCM matrix retrieved by the row sums of \(\text{P}_{\text{ij}}\).