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

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.

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Publication Details

Journal
Remote Sensing
Published
2025-08-08
DOI
https://doi.org/10.3390/rs17162757
Citations
1
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
1.11

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article

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

Andres Susaeta, Benjamin C. Bright, Eugênia Kelly Luciano Batista, Ana Paula Dalla Côrte et al.
1 citations
Remote Sensing
Fire effects on ecosystems
1.11
article

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

Andres Susaeta, Benjamin C. Bright, Eugênia Kelly Luciano Batista, Ana Paula Dalla Côrte, Eben N. Broadbent, Nuria Sánchez‐López, Jason G. Vogel, Michael J. Campbell, Inácio Thomaz Bueno, Jessie F. Eastburn, Carine Klauberg, Monique Bohora Schlickmann, Francisco Mauro, Caio Hamamura, Jinyi Xia, Gabriel M. da Silva, Kleydson Diego Rocha, Andrew Thomas Hudak, Jeff W. Atkins, Eric Rowell, Russell A. Parsons, Inga P. La Puma, Carlos Alberto Silva, Kathryn A. Brock
article en
1 citations

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.

Remote SensingVol. 17(16)
Federal Institute of São Paulo (BR), Universidad de Valladolid (ES), Oregon State University (US), US Forest Service (US), University of Idaho (US), University of Utah (US), University of Florida (US), Southern Research Station (US), Rocky Mountain Research Station (US), Universidade Federal do Paraná (BR), Desert Research Institute (US), Instituto Nacional de Pesquisas Espaciais (BR)
Joint Fire Science Program, Universidad de Valladolid
Openalex Percentile: Top 21%
Fire effects on ecosystems
1.11
DOIOpen Access PDFOpenAlex
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