Comparing terrestrial and mobile laser scanning approaches for multi-layer fuel load prediction in the Western United States
| Authors: | Eugenia Kelly Luciano Batista, Andrew T. Hudak, Jeff W. Atkins, Eben North Broadbent, Kody Melissa Brock, Michael J. Campbell, Nuria Sanchez-Lopez, Monique Bohora Schlickmann, Francisco Mauro, Andres Susaeta, Eric Rowell, Caio Hamamura, Ana Paula Dalla Corte, Inga La Puma, Russell A. Parsons, Benjamin C. Bright, Jason Vogel, Inacio Thomaz Bueno, Gabriel Maximo da Silva, Carine Klaubers, Jinyi Xia, Jessie F. Eastburn, Kleydson Diego Rocha, Carlos Alberto Silva |
| Year: | 2025 |
| Type: | Scientific Journal |
| Station: | Rocky Mountain Research Station |
| DOI: | https://doi.org/10.3390/rs17162757 |
| Source: | Remote Sensing |
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.