# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 4 Conclusions  
Data acquired using laser scanning technology constitute a reliable source of information. Based on them, a great deal of detailed spatial analysis can be carried out using a Geographic Information System (GIS). This combination makes it possible to perform advanced topographic analysis, terrain modeling, object identification, and monitoring of environmental changes. Using LiDAR data with GIS, it is possible to effectively conduct comprehensive research and effectively manage geographic space.  
The study uses OPALS software and the Pycrown package for tree detection. Recognizing trees in high-density areas posed a significant challenge to the algorithms, often leading to misidentification of tree tops. The next steps involved analyzing the detected tops in the context of a wind model that identifies potential windthrow locations based on direction. The study showed that the most common wind was a western wind, with gusts exceeding 20 m/s. The southeast wind was the most dangerous, as it was the strongest, but it occurred singularly during the two years analyzed and represents an anomaly. The inclusion of this anomaly in the study significantly increased the number of potential windthrows.  
The conducted research shows that an important stage in analyzing the risk of tree fall is the selection of data processing methods and considering additional atmospheric factors in the analysis, such as wind direction and strength. The OPALS method outperformed others in terms of tree detection, achieving an accuracy level of 74%.  
The presented results clearly show that for the selected section of the S6 highway in Poland, there is a real danger of a tree falling on the road lane. Taking the results from OPLAS as the best depending on the selected variant, it is concluded that between 140 and 577 trees pose a real danger of falling on the S6 road in high winds. This is contrary to what the Directorate of National Roads and Highways claims, and these results also show the wrong attitude to the presented danger for about 84% of drivers. This study is valuable information for the GDDKiA in terms of the dangers of windthrows of trees growing along the S6 road, but it is also useful for application reasons, as this procedure can be applied to any highway where there is uncertainty about the existing dangers.  
In further research, the authors will focus on improving the effectiveness of tree detection using orthophotos and deep learning. The use of additional data and machine learning in particular should improve tree identification in forest areas with high tree density.  
Conceptualization, T.K.; methodology, T.K. and D.W.; software, T.K. and D.W.; validation, T.K., D.W. and G.S.; formal analysis, T.K., M.S.-K., M.S. and B.C.; investigation, T.K., D.W., G.S., M.S.-K., M.S. and B.C.; writing--original draft preparation, T.K., D.W. and M.S.-K.; writing--review and editing, T.K., D.W., G.S., M.S.-K., M.S. and B.C.; visualization, D.W.; supervision, T.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.