# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 2 Materials and Methods
### Winds Model
Based on this, the OPALS software (Figure 7) was found to perform best in this analysis, with an accuracy of 74% (Table 3). When the PyCrown algorithm was used, many trees were missed, or when larger trees were present, clusters of points were formed in a single crown. The accuracy of PyCrown detection methods for a raster resolution of 0.25 m and a used filter of 5 \(\times\) 5 m was 61%; for a filter of 3 \(\times\) 3 m, it was 53%; and the worst detection, with only 4% accuracy, was achieved by the PyCrown method, with a raster resolution of 1 m and a 5 \(\times\) 5 filter. [PERSON] et al. [22] tested individual tree detection (ITD) methods developed based on ALS data. They used Random Forest (RF), Support Vector Machine, and k-Nearest Neighbor to develop a method to correct the results of ITD algorithms to identify individual trees more reliably. The RF algorithm gave the best results. It was possible to identify under-segmentation and over-segmentation: Overall accuracy = 87% and Kappa coefficient = 0.79. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **OPALS** & **PyCrown** & **PyCrown** & **PyCrown** \\ \hline Raster resolution [m] & 0.25 & 0.25 & 0.25 & 1 \\ \hline Filter used & Adjusted automatically & 3 \(\times\) 3 & 5 \(\times\) 5 & 5 \(\times\) 5 \\ \hline Number of trees detected & 9 305 & 11,667 & 6244 & 366 \\ \hline Detection efficiency & 74\% & 53\% & 61\% & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection of trees. During the verification of the entire area, it was noted that points were detected that were erroneously located on bridges or fences (Figure 8), at a distance of about 12 m from the road. Figure 8: Example of incorrectly identified tree tops. Figure 7: Sample summary of all tree detection methods. Three different analyses were conducted for each of the three tree detection methods, taking into account different wind directions:  
* Direction determined based on median;
* Direction selected for maximum gust;
* Direction determined by the entire range selected from the histogram. An example of the results of the analysis examining the relationship between the trees' locations and the road route is shown in Figure 11.  
Figure 11: Example results. (**A**) Direction determined by median, (**B**) range of most common directions, (**C**) direction of maximum gusts. Figure 10: Histogram of wind directions. Wherever a point was located on a road route, it was considered a potential hazard site where a tree could fall. Potential hazards were selected, and the results are shown in Table 6. After the algorithms identified the trees, manual validation of the correctness of the detection of these trees was carried out, and their effectiveness was again determined. OPALS correctly identified threats in all three strategies with an efficiency of more than 80%. PyCrown showed an effectiveness of about 60% with a 3 \(\times\) 3 filter mask and about 80% with a 5 \(\times\) 5 filter mask. Unfortunately, PyCrown, with a 5 \(\times\) 5 filter mask, detected far fewer trees for the filter size used, resulting in low usability. The topic of the threat of falling dead trees, not close to roads, but on trails and paths, was explored by [PERSON] et al. [41]. In the research, remote sensing scenes from PlanetScope and LiDAR data (airborne laser scanning, density 4 points/sqm) were used to detect dead-standing trees, which are a threat on hiking trails. Providing security should include removing diseased and dead trees that could threaten the safety of individuals. The PlanetScope (Planet) satellite images were characterized by high spatial (GSD 3.125 m), spectral (4 spectral bands: R, G, B, and NIR), and radiometric (16-bit) resolution. Imageries, as well as LiDAR data (nDSM), were analyzed using Geographic Object-Based Image Analysis (GEOBIA) in eCognition Developer (TRIMBLE) software. The project aimed to develop a map of changes in the structure and health of forest areas adjacent to hiking trails using an innovative, semi-automatic GEOBIA method of image analysis.