## 3 Results
### Tree Canopy Delineation  
The evaluation of the design by calculating precision_del, recall_del and f1_del was performed for the crowns correctly identified (IoU > 0.5) in each model and the results are shown in Table 4. These metrics are calculated for each tree and, to evaluate the overall performance, the mean and standard deviation of every one were computed. The RGB model showed an average precision_del of 93.5%, a recall_del of 91.7%, and a F1 of 92.3% among a set of 2086 crowns. The RGBC model showed a positive variation in relation to precision_del, with 96.1%, and a negative variation related to recall_del, of 87.8%, and F1, with 91.6%, in a total of 2108 crowns. Visually (Figure 10), it is possible to see that a significant difference between the two results is the coverage of the trace from the inferred masks. In the RGB model, the crown masks generally covered the entire region comprised visually by the tree canopy, while in the RGBC model there is a slight retreat from its limits. This is evidenced by the high value of precision_del associated with the low value of recall_del. A possible justification for this result is the difference in the nature of the information obtained from the RGB bands and by the CHM, because while the RGB image with very high spatial resolution is able to represent the extremities of the canopies by the reflectance from even the most extreme leaves, the generation and the accuracy of the CHM is limited by the Structure from Motion process and the filtering performed in the DSM to generate the DTM. The IoU average was calculated for all the inferred crowns presenting an intersection with some reference and only for those with values above 0.5 (TP), the results are shown in Table 5. In all cases, the RGB model showed higher average values of IoU than the RGBC, with an average of 0.862 considering all crowns and 0.867 only for those with IoU > 0.5. The RGBC model presented 0.847 and 0.848 for all values, as well as for those higher than 0.5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **RGB Model** & **RGBC Model** \\ \hline Precision\_del & 0.935 \(\pm\) 0.082 & 0.961 \(\pm\) 0.051 \\ Recall\_del & 0.917 \(\pm\) 0.058 & 0.878 \(\pm\) 0.067 \\ F1-Score\_del & 0.923 \(\pm\) 0.060 & 0.916 \(\pm\) 0.046 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics results of delineation process. Figure 10: Examples of the difference in canopy delineation between models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **All** & **>0.5** \\ \hline RGB & 0.862 \(\pm\) 0.081 & 0.867 \(\pm\) 0.061 \\ RGBC & 0.847 \(\pm\) 0.070 & 0.848 \(\pm\) 0.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean IoU values of the correctly identified crowns. On the other hand, analyzing the histograms of the two models (Figure 11), one observes that the RGB model presents a maximum IoU value lower than the RGBC one (0.969 and 0.997) and inferred a greater number of crowns that, despite containing an intersection with the respective reference, had a lower IoU than the minimum necessary to be considered a TP (0 < IoU < 0.5). Furthermore, the RGB model resulted in a total of 96.72% (2037) of the crowns with an IoU above 0.7, while the RGBC model reached 97.58% (2060), indicating that, although both models present high fidelity in relation to the ground truth, the RGBC performed slightly better in the delimitation of the traces that agree most with the reference. To determine the relationship of the area and perimeter of each resulting mask with the respective reference, a linear regression analysis was performed and the results are shown in Figures 12 and 13. According to Figure 12, both models achieved results with an expressive correlation between the areas of the corresponding pairs (predicted and reference), and the RGB model has slightly better results than the RGBC. The coefficient of determination (R\({}^{2}\)) of the RGB model is 0.973 and 0.967 for the RGBC model.