Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland
Year: 2025
Type: article
Abstract: The southeastern United States (US) is known for its highly productive forests, but they are under intense threat from increasing climate-induced windstorms like hurricanes and tornadoes. This study explored the effectiveness of unmanned aerial vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) to detect, classify, and map windstorm damage in ten pine-dominated forest stands (10–20 acres each). Three classification techniques, Random Forest (RF), Maximum Likelihood (ML), and Decision Tree (DT), were tested on two datasets: RGB imagery integrated with LiDAR-derived Canopy Height Model (CHM) and without LiDAR-CHM. Using LiDAR-CHM integrated datasets, RF achieved an average Overall Accuracy (OA) of 94.52% and a kappa coefficient (k) of 0.92, followed by ML (average OA = 89.52% and k = 0.85), and DT (average OA = 81.78% and k = 0.75). The results showed that RF consistently outperformed ML and DT in classification accuracy across all sites. Without LiDAR-CHM, the performance of all classifiers significantly declined, underscoring the importance of structural data in distinguishing among the classification categories (downed trees, standing trees, ground, and water). These findings highlight the role of UAV-derived LiDAR-CHM in improving classification accuracy for assessing the impact of windstorm damage on forest stands.
Source: Drones
Authors: Dipika Badal, Richard Cristan, Lana L. Narine, Sanjiv Kumar, Arjun Rijal, Manisha Parajuli
Institutions: Auburn University, Kentucky State University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415820418