Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Background: Characterization of physical fuel distributions across heterogeneous landscapes is needed to understand fire behavior, account for smoke emissions, and manage for ecosystem resilience. Remote sensing measurements at various scales inform fuel maps for improved fire and smoke models. Airborne lidar that directly senses variation in vegetation height and density has proven to be especially useful for landscape-scale fuel load and consumption mapping. Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. We estimated consumption across the 2019 Castle and Ikes Fires by subtracting 2020 fuel load maps from 2019 fuel load maps and examined the relationship between mapped surface fuels and years since fire, as recorded in the Monitoring Trends in Burn Severity (MTBS) database.Results: R-squared correlations between predicted and ground-observed fuels were 50, 39, 59, and 48% for available canopy fuel, 1- to 1000-h fuels, litter and duff, and total surface fuel (sum of 1- to 1000-h, litter and duff fuels), respectively. Lidar metrics describing overstory distribution and density, understory density, Landsat fire history metrics, and elevation were important predictors. Mapped surface fuel loads were positively and nonlinearly related to time since fire, with asymptotes to stable fuel loads at 10–15 years post fire. Surface fuel consumption averaged 16.1 and 14.0 Mg ha^− 1for the Castle and Ikes Fires, respectively, and was positively correlated with the differenced Normalized Burn Ratio (dNBR). We estimated surface fuel consumption to be 125.3 ± 54.6 Gg for the Castle Fire and 27.6 ± 12.0 Gg for the portion of the Ikes Fire (42%) where pre- and post-fire airborne lidar were available.Conclusions: We demonstrated and reinforced that canopy and surface fuels can be predicted and mapped with moderate accuracy using airborne lidar data. Landsat-derived fire history helped account for spatial and temporal variation in surface fuel loads and allowed us to describe temporal trends in surface fuel loads. Our fuel load and consumption maps and methods have utility for land managers and researchers who need landscape-wide estimates of fuel loads and emissions. Fuel load maps based on active remote sensing can be used to inform fuel management decisions and assess fuel structure goals, thereby promoting ecosystem resilience. Multitemporal lidar-based consumption estimates can inform emissions estimates and provide independent validation of conventional fire emission inventories. Our methods also provide a remote sensing framework that could be applied in other areas where airborne lidar is available for quantifying relationships between fuels and time since fire across landscapes.

Remote sensing products provide a vital understanding of wildfire effects across a landscape, but detection and delineation of low- and mixed-severity fire remain difficult. Although data provided by the Monitoring Trends in Burn Severity (MTBS) project are frequently used to assess severity in the United States, alternative indices can offer improvement in the measurement of low-severity fire effects and would be beneficial for future product development and adoption. This research note evaluated one such alternative, the Mid-Infrared Bi-Spectral Index (MIRBI), which was developed in savannah ecosystems to isolate spectral changes caused by burning and reduce noise from other factors. MIRBI, differenced MIRBI (dMIRBI) and burn severity indices used by MTBS were assessed for spectral optimality at distinguishing severity and the ability to differentiate between unburned and burned canopy in a conifer forest. The MIRBI indices were better at isolating changes caused by burning and demonstrated higher spectral separability, particularly at low severity. These findings suggest that MIRBI indices can provide an enhanced alternative or complement to current MTBS products in high-canopy-cover forests for applications such as discernment of fire perimeters and unburned islands, as well as identification of low-severity fire effects.

Background: There is an ongoing need for improved understanding of wildfire plume dynamics. Aims: To improve process-level understanding of wildfire plume dynamics including strong (>10 m s−1) fire-generated winds and pyrocumulus (pyroCu) development. Methods: Ka-band Doppler radar and two Doppler lidars were used to quantify plume dynamics during a high-intensity prescribed fire and airborne laser scanning (ALS) to quantify the fuel consumption. Key results: We document the development of a strongly rotating (>10 m s−1) pyroCu-topped plume reaching 10 km. Plume rotation develops during merging of discrete plume elements and is characterised by inflow and rotational winds an order of magnitude stronger than the ambient flow. Deep pyroCu is initiated after a sequence of plume-deepening events that push the plume top above its condensation level. The pyroCu exhibits a strong central updraft (~35 m s−1) flanked by mechanically and evaporative forced downdrafts. The downdrafts do not reach the surface and have no impact on fire behaviour. ALS data show plume development is linked to large fuel consumption (~20 kg m−2). Conclusions: Interactions between discrete plume elements contributed to plume rotation and large fuel consumption led to strong updrafts triggering deep pyroCu. Implications: These results identify conditions conducive to strong plume rotation and deep pyroCu initiation.