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Volume 9
Issue 11
10.3390/drones9110756
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland

by
Dipika Badal
1,
Richard Cristan
1,*,
Lana L. Narine
1,
Sanjiv Kumar
1,
Arjun Rijal
1 and
Manisha Parajuli
2
1
College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA
2
College of Agriculture, Health, and Natural Resources, Kentucky State University, Frankfort, KY 40601, USA
*
Author to whom correspondence should be addressed.
Drones 2025, 9(11), 756; https://doi.org/10.3390/drones9110756 (registering DOI)
Submission received: 27 August 2025 / Revised: 15 October 2025 / Accepted: 23 October 2025 / Published: 31 October 2025
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
Versions Notes

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.
Keywords: UAVs; hurricane; tornado; CHM; classification algorithms; random forest UAVs; hurricane; tornado; CHM; classification algorithms; random forest

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MDPI and ACS Style

Badal, D.; Cristan, R.; Narine, L.L.; Kumar, S.; Rijal, A.; Parajuli, M. Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland. Drones 2025, 9, 756. https://doi.org/10.3390/drones9110756

AMA Style

Badal D, Cristan R, Narine LL, Kumar S, Rijal A, Parajuli M. Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland. Drones. 2025; 9(11):756. https://doi.org/10.3390/drones9110756

Chicago/Turabian Style

Badal, Dipika, Richard Cristan, Lana L. Narine, Sanjiv Kumar, Arjun Rijal, and Manisha Parajuli. 2025. "Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland" Drones 9, no. 11: 756. https://doi.org/10.3390/drones9110756

APA Style

Badal, D., Cristan, R., Narine, L. L., Kumar, S., Rijal, A., & Parajuli, M. (2025). Effectiveness of Unmanned Aerial Vehicle-Based LiDAR for Assessing the Impact of Catastrophic Windstorm Events on Timberland. Drones, 9(11), 756. https://doi.org/10.3390/drones9110756

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