# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 1 Introduction  
The introduction of modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, to the areas of forestry and road infrastructure management is a significant step toward the efficient and safe use of resources to protect infrastructure from potential threats. The use of laser scanning, unlike classical photogrammetry, has a fundamental advantage. This method is not dependent on natural sunlight, which means that the analysis of these data is not disturbed by shadows or surrounding objects. Also, a key advantage is the fact that laser pulses can penetrate through small gaps in tree crowns, enabling the collection of information on the ground structure [1]. Today, advanced methods of terrain elevation data acquisition are used, including digital photogrammetry, satellite remote sensing techniques, LiDAR, and InSAR technology. Digital elevation data acquisition processes are characterized by varying efficiency, cost, time, and precision [2; 3; 4; 5]. Elevation models provide the basis for an accurate understanding of landforms and land cover characteristics [6; 7]. The Digital Terrain Model (DTM) and Digital Surface Model (DSM) are common numerical elevation models. The Canopy Height Model (CHM), which is synonymous with a normalized digital surface model (nDSM) for forest areas, is often discussed in the literature on forest areas [8; 9; 10; 11]. CHM models are used to perform analyses to extract information about a stand of trees. They are oftenused in forestry to determine the quantities that characterize a stand, which is important for describing tree growth [12], planning silvicultural activities [13], and inventorying timber volume and stand biomass [12; 14; 15]. The precision of a canopy model is affected by many factors, including the density of the point cloud, the interpolation algorithm used, and the resolution of the DTM and DSM models from which the CHM model is created. The accuracy of tree canopy mapping mainly depends on the resolution [16], canopy cover [17], and tree height [18]. The larger the pixel size of the raster, the more general the information about the object. Assessing the heights of individual trees and determining their crown shapes based on field measurements is costly and labor-intensive, and is usually realized only in small study areas. With advances in airborne laser scanning technology, it is possible to accurately and systematically map the crowns of individual trees and determine their heights over large areas by studying the spatial structure of tree crowns [19; 20]. It is possible to determine stand characteristics using laser scanners. These data make it possible to detect individual trees and determine their characteristics, such as height, position, or crown size [21; 22]. The method involves detecting local maxima in a normalized digital surface model, which are then identified as tree tops. The entire process begins with the application of a low-pass filter on the nDSM to smooth the surface, reduce noise, and extract more general features. The application of the filter reduces the number of detected extremes [23]. After the filtering process, the algorithm identifies local maxima using a circular window. When detecting extremes, the window moves across the image to identify values higher than those in their neighborhood and consider them as the height of the tree [24]. The detected maximum is marked as the initial region around which the tree crown grows [23]. In the Polish road system, one of the key aspects of safety is the problem of trees falling on road infrastructure, which results in local hazards, leading to accidents or obstructions on the route. Trees growing near roads, without adequate safety zones, pose an exceptional risk to road users. The General Directorate of National Roads and Highways in Poland (GDDKiA) is responsible for assessing trees, placing particular emphasis on safety aspects and determining condition and aesthetics.