# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 2 Materials and Methods
### Winds Model  
To increase the effectiveness of the prediction of potential hazards, a detailed analysis was carried out using data from the Institute of Meteorology and Water Management for four different stations (Szczecin, Koszalin, Kolobrzeg, and Smolsko) on wind direction and gusts in the study area. From the acquired data, moments were selected when wind gust speeds exceeded 20 m/s. These values of wind gust speeds were extracted because winds reaching values above 20 m/s are classified as a meteorological hazard (Table 2). \begin{table}
\begin{tabular}{l l l l} \hline
**Level** & **Danger** & **Average 10 Min Wind Speed** & **Wind Gust Speed** & **Description of Levels** \\ \hline  & & & Hurricane-force winds—cause destruction of entire buildings and flat-rooted structures, tear sections of industrial power lines and break their support structures, hinder vehicle travel, and uproot trees with their roots, causing windfall. \\ \hline
2 & HIGH RISK & 72 km/h–90 km/h (20 m/s–25 m/s) & 90 km/h–115 km/s (25 m/s–32 m/s) & Strong gate—the wind can cause significant damage to buildings, break and uproot shallow-rooted trees, sway power line cables, and during settling or freezing rain, it can snap them due to overload. \\ \hline
1 & MODERATE RISK & 54 km/h–72 km/h (15 m/s–20 m/s) & 72 km/h–90 km/s (20 m/s–25 m/s) & Gale—the wind overturns wooden fences, billboards, and road signs, can cause damage to buildings, tears off individual roof tiles, and breaks large tree branches. During snowfall, it causes snowdrifts and snowstorms. \\ \hline
0 & NORMAL STATE & No forecast of strong winds. \\ \hline \end{tabular}
\end{table} 
Table 2: Thresholds of high wind threat, determined for forecast maps. Figure 6: Relationship between height of a tree and distance to nearest point on road. The first parameter analyzed was wind gusts, which represent sudden increases in wind speed that exceed the average wind speed by at least 5 m/s. These instantaneous increases in speed are characterized by a short duration, not exceeding 2 min. The second important parameter of the analysis was the direction of the wind gust, defining the origin of the direction from which the wind blows. These are given descriptively according to the eight-directional wind rose as follows [39; 40]:  
* Northern (N, 360\({}^{\circ}\)): od 338\({}^{\circ}\) do 22\({}^{\circ}\);
* Northeastern (NE, 45\({}^{\circ}\)): od 23\({}^{\circ}\) do 67\({}^{\circ}\);
* Eastern (E, 90\({}^{\circ}\)): od 68\({}^{\circ}\) do 112\({}^{\circ}\);
* Southeastern (SE, 135\({}^{\circ}\)): od 113\({}^{\circ}\) do 157\({}^{\circ}\);
* Southern (S, 180\({}^{\circ}\)): od 158\({}^{\circ}\) do 202\({}^{\circ}\);
* Southwestern (SW, 225\({}^{\circ}\)): od 203\({}^{\circ}\) do 247\({}^{\circ}\);
* Western (W, 270\({}^{\circ}\)): od 248\({}^{\circ}\) do 292\({}^{\circ}\);
* Northwestern (NW, 315\({}^{\circ}\)): od 293\({}^{\circ}\) do 337\({}^{\circ}\). **3. Results and Discussion**  
The study used four tree detection strategies (Table 3):  
1. OPALS with resolution of 0.25 m;
2. PyCrown with a resolution of 0.25 m and 3 \(\times\) 3 median filter;
3. PyCrown with a resolution of 0.25 m and 5 \(\times\) 5 median filter;
4. PyCrown with a resolution of 1 m and 5 \(\times\) 5 median filter. For the PyCrown library, the number of detected trees is higher when using a small window size. At the same resolution, using a 3 \(\times\) 3 window size resulted in the detection of 11,667 potential tree tops. In contrast, using a 5 \(\times\) 5 filter almost halved the number of detected trees, as 6244 trees were detected. This reduction was due to the merging of trees and branches due to over-smoothing of the nDSM, as well as lower sensitivity to fine features [19]. The use of the forTreeDetection module from OPALS allowed for the identification of 9305 trees. To check the correctness of the detected trees, the detection was manually verified. A random selection of 500 trees was made and the correctness of their detection by the algorithms was determined.