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Remote Sensing
Volume 17
Issue 16
10.3390/rs17162757
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States

by
Eugênia Kelly Luciano Batista
1,*,
Andrew T. Hudak
2,
Jeff W. Atkins
3,
Eben North Broadbent
4,
Kody Melissa Brock
4,
Michael J. Campbell
5,
Nuria Sánchez-López
2,
Monique Bohora Schlickmann
4,
Francisco Mauro
6,
Andres Susaeta
7,
Eric Rowell
8,
Caio Hamamura
9,
Ana Paula Dalla Corte
10,
Inga La Puma
2,
Russell A. Parsons
11,
Benjamin C. Bright
2,
Jason Vogel
4,
Inacio Thomaz Bueno
4,
Gabriel Maximo da Silva
12,
Carine Klauberg
4,
Jinyi Xia
4,
Jessie F. Eastburn
5,
Kleydson Diego Rocha
4 and
Carlos Alberto Silva
4add Show full author list remove Hide full author list
1
College of Natural Resources, University of Idaho, Moscow, ID 83844, USA
2
USDA Forest Service, Rocky Mountain Research Station, Moscow, ID 83843, USA
3
USDA Forest Service, Southern Research Station, New Ellenton, SC 29809, USA
4
School of Forest, Fisheries, & Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA
5
School of Environment, Society & Sustainability, University of Utah, Salt Lake City, UT 84112, USA
6
Instituto Universitario de Investigación en Gestión Forestal Sostenible, University of Valladolid, 42004 Soria, Spain
7
Department of Forest Engineering, Resources & Management, Oregon State University, Corvallis, OR 97331, USA
8
Department of Atmospheric Science, Desert Research Institute, Reno, NV 89512, USA
9
Federal Institute of Education, Science and Technology of São Paulo, São Paulo 01109-010, SP, Brazil
10
Department of Forest Science, Federal University of Paraná, Curitiba 80060-000, PR, Brazil
11
USDA Forest Service, Fire Sciences Lab, Missoula, MT 59808, USA
12
National Institute for Space Research, São José dos Campos 12227-010, SP, Brazil
 
add Show full affiliation list
 
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*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(16), 2757; https://doi.org/10.3390/rs17162757
Submission received: 4 July 2025 / Revised: 6 August 2025 / Accepted: 7 August 2025 / Published: 8 August 2025
(This article belongs to the Section Forest Remote Sensing)
Versions Notes

Abstract

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R2, RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure.
Keywords: LiDAR; modeling; random forest; fire management; biomass; vegetation structure LiDAR; modeling; random forest; fire management; biomass; vegetation structure

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MDPI and ACS Style

Batista, E.K.L.; Hudak, A.T.; Atkins, J.W.; Broadbent, E.N.; Brock, K.M.; Campbell, M.J.; Sánchez-López, N.; Schlickmann, M.B.; Mauro, F.; Susaeta, A.; et al. Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States. Remote Sens. 2025, 17, 2757. https://doi.org/10.3390/rs17162757

AMA Style

Batista EKL, Hudak AT, Atkins JW, Broadbent EN, Brock KM, Campbell MJ, Sánchez-López N, Schlickmann MB, Mauro F, Susaeta A, et al. Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States. Remote Sensing. 2025; 17(16):2757. https://doi.org/10.3390/rs17162757

Chicago/Turabian Style

Batista, Eugênia Kelly Luciano, Andrew T. Hudak, Jeff W. Atkins, Eben North Broadbent, Kody Melissa Brock, Michael J. Campbell, Nuria Sánchez-López, Monique Bohora Schlickmann, Francisco Mauro, Andres Susaeta, and et al. 2025. "Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States" Remote Sensing 17, no. 16: 2757. https://doi.org/10.3390/rs17162757

APA Style

Batista, E. K. L., Hudak, A. T., Atkins, J. W., Broadbent, E. N., Brock, K. M., Campbell, M. J., Sánchez-López, N., Schlickmann, M. B., Mauro, F., Susaeta, A., Rowell, E., Hamamura, C., Dalla Corte, A. P., La Puma, I., Parsons, R. A., Bright, B. C., Vogel, J., Bueno, I. T., Silva, G. M. d., ... Silva, C. A. (2025). Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States. Remote Sensing, 17(16), 2757. https://doi.org/10.3390/rs17162757

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