# Automated Tree Detection Using Image Processing and Multisource Data
## 5 Numerical Experiments
### Results  
First, we analyzed and compared the percentage of the area covered by vegetation. However, we did not differentiate based on height information (see the first two columns on the left in Table 3). We can see that the vegetation area varies across different datasets, with the lowest value being 12.6% (the sixth row), and the highest value being 77.9% (the last row). Next, we incorporated height information to further differentiate the vegetation data separating trees (vegetation with a height difference from sea level over 15 m) from the remaining vegetation (like meadows). These data can be seen in the three columns on the right in Table 3. The most important observation here is the \"High veg.\" column, which represents trees, and the \"Other veg.\" column, which represents the remaining vegetation. Notably, the tree areas, which are crucial to our study, constitute only a small portion of the area initially marked as \"Vegetation\" (the first column in Table 3). Thus, it can be assumed that the actual area of high vegetation around the city is much smaller, as indicated only by the NDVI data. Figure 8: Magnification of the second analyzed area. Figure 7: Magnification of the first analyzed area. Figure 9: Potential improvement using the modified edge-detection algorithm. Next, we analyzed the percentage of objects other than vegetation in the available datasets. This information is presented in Figure 10, where we indicate the coverage of the area by various objects. Notably, the area covered by buildings (green dots in the figure) was relatively small in all cases, accounting for less than 10% of the area. There was considerable variation in road coverage, with three datasets covering only around 5% of the area, the second dataset including around 30% of roads, and the sixth dataset including over 70% of roads. The most important aspect of this analysis is the distinction between high vegetation (trees) and low vegetation (for example, meadows). Several datasets showed substantial differences between these two categories, while two datasets did not include any trees at all. Thus, it can be observed that the proposed approach is well suited for estimating the area coverage of various types of samples (trees, roads, or meadows). This information could be used directly to estimate areas where the coverage of high vegetation is expected to increase. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & \multicolumn{2}{c}{**NDVI Data**} & \multicolumn{2}{c}{**NDVI and.asc Data**} \\ \hline  & **Vegetation** & **Other** & **High Veg. ** & **Other Veg. ** & **Other** \\ \hline
6.127.29.03.1 & 60.5\% & 39.5\% & 27.7\% & 32.8\% & 39.5\% \\
6.130.29.24.4 & 28.2\% & 71.8\% & 2.4\% & 25.8\% & 71.8\  
Ultimately, we were interested in the percentage of samples initially classified as vegetation (based on NDVI files) that, when height information from.asc data was considered, were not classified as trees. At the same time, we investigated the difference between two areas: one labeled as vegetation only and the other with a height greater than 15 m. Both results can be seen in Table 4. Two overlapping metrics (the Jaccard index and the conformity index) were used to estimate the difference between the two areas: the vegetation area based on NDVI data and the combination of NDVI data and height information. For the Jaccard index, the best value would be 1--indicating that all vegetation identified by the NDVI data is high vegetation--while a value of 0 would indicate the opposite--none of the identified areas are trees. We can see that only in selected cases did trees cover more than 20% of the entire vegetation area, while the three remaining datasets showed relatively small coverage of trees. These results are confirmed by the CC index, which indicated that the large number of samples labeled as vegetation were not trees (lower values suggest a small number of trees in the area).