Stereo vision from space to track tree fall incident

Abstract Forests are essential for regulating the climate, enhancement of air quality, and the preservation of biodiversity. However, tree falls pose significant risks to infrastructure, particularly powerlines, leading to widespread blackouts and substantial damage. Traditional methods for monitoring tree fall risks, such as field surveys, are often costly, time-consuming, and lack real-time capabilities. While airborne Light Detection and Ranging (LiDAR) provides precise data for monitoring tree fall risks, it still faces challenges related to frequency of data acquisition and high costs. In response to the European Space Agency's call for more cost-effective monitoring approaches, this study investigates the potential of using very high-resolution optical satellite data, specifically from Pléiades satellite imagery, for assessing tree fall risks to powerlines. Key forest structure metrics such as canopy complexity using the Rumple Index, canopy height, as well as distance to powerlines were analyzed across four study sites in Finland and Switzerland. Sites with simpler canopy structures exhibited stronger correlations between stereo and LiDAR height measurements ( R 2 values up to 0.64). Stereo-based measurements can overall provide acceptable accuracy (ca. 96.57%) in detecting trees compared with LiDAR data. The results demonstrated that the Rumple Index can identify areas with simpler canopy structures, where stereo-based height measurements yield high accuracy. These findings suggest the potential of hybrid approaches that integrate both stereo imagery and airborne LiDAR data, tailored to site-specific characteristics, for accurate risk assessments. This study contributes to the ongoing efforts in developing an understanding of vegetation management along powerlines, to inform decision-makers in their endeavors to identify and mitigate risks associated with tree falls.