Assessing the effects of burn severity on post-fire tree structures using the fused drone and mobile laser scanning point clouds

Abstract

Wildfires burn heterogeneously across the landscape and create complex forest structures. Quantifying the structural changes in post-fire forests is critical to evaluating wildfire impacts and providing insights into burn severities. To advance the understanding of burn severities at a fine scale, forest structural attributes at the individual tree level need to be examined. The advent of drone laser scanning (DLS) and mobile laser scanning (MLS) has enabled the acquisition of high-density point clouds to resolve fine structures of individual trees. Yet, few studies have used DLS and MLS data jointly to examine their combined capability to describe post-fire forest structures. To assess the impacts of the 2017 Elephant Hill wildfire in British Columbia, Canada, we scanned trees that experienced a range of burn severities 2 years post-fire using both DLS and MLS. After fusing the DLS and MLS data, we reconstructed quantitative structure models to compute 14 post-fire biometric, volumetric, and crown attributes. At the individual tree level, our data suggest that smaller pre-fire trees tend to experience higher levels of crown scorch than larger pre-fire trees. Among trees with similar pre-fire sizes, those within mature stands (age class: > 50 years) had lower levels of crown scorch than those within young stands (age class: 15—50 years). Among pre-fire small- and medium-diameter trees, those experiencing high crown scorch had smaller post-fire crowns with unevenly distributed branches compared to unburned trees. In contrast, pre-fire large-diameter trees were more resistant to crown scorch. At the plot level, low-severity fires had minor effects, moderate-severity fires mostly decreased tree height, and high-severity fires significantly reduced diameter at breast height, height, and biomass. Our exploratory factor analyses further revealed that stands dominated by trees with large crown sizes and relatively wide spacing could burn less severely than stands characterized by regenerating trees with high crown fuel density and continuity. Overall, our results demonstrate that fused DLS-MLS point clouds can be effective in quantifying post-fire tree structures, which facilitates foresters to develop site-specific management plans. The findings imply that the management of crown fuel abundance and configuration could be vital to controlling burn severities.

Revealing Three-Dimensional Variations in Fuel Structures in Subtropical Forests through Backpack Laser Scanning

“…Due to the higher surface fuel density and lower upper crown density, Chinese fir and mixed coniferous-broadleaved forests may have a higher probability of surface fires, consistent with previous research indicating that these stands possess highly flammable loads [58]. Furthermore, the ladder fuel density in Chinese fir and mixed coniferous-broadleaved forests is also higher, potentially making crown fires more likely [15,69,70]. This study emphasizes the need for pruning excess branches and increasing CBH in fuel management in Chinese subtropical forests to reduce contact with surface fuels [71,72].…”

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

“…Similar data fusion studies also predicted canopy fuel variables, such as canopy fuel load (kg/ m 2 ), and surface fuel layers (including coarse woody debris biomass) with adjusted R 2 ranging between 0.55-0.94 [166]. At the ITA scale, post-fire changes in DBH and biomass can be estimated by fusing MLS data with ULS/ALS, where the below-canopy measurements are enabled by the MLS data [162]. However, a fusion of ALS and TLS data for ITA metrics was recently documented to offer no particular advantage over either sensor used alone [169].…”

“…6) Other: 9.1% of the papers [148][149][150][151][152][153][154][155][156][157][158][159][160] include a variety of applications, such as mapping the pigment distribution and quantifying taxonomic, functional, and phylogenetic diversity, tree age estimation etc. 7) Fuel load: 6.3% of the papers [161][162][163][164][165][166][167][168][169] include applications that deal with fuel load and forest fire modeling.…”

“…The raw datasets can be fused using ground control points (GCPs) or by identifying similar features in the datasets [74,100] using the same coordinate system acquired through GNSS or total stations. Other studies [26,112,162] used manual co-registration by identifying similar features such as the tallest tree, trees with large crowns, or tree locations. These features were used to guide the manual shifting process and to correctly co-register the two datasets.…”

Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

“…Although methods have been developed to use TLS as an operational fuel hazard observation technology [104], wall-to-wall assessments over large areas are not feasible. In this sense, future research in fire severity behavior should address the use of mobile laser scanning (MLS), a new remote sensing technique that has not much been considered in forestry applications [105] and wildfire science [106].…”