We evaluated the three-dimensional (3D) structure, composition, and biomass of understory fuels common to prescribed burn programs in pine-dominated forests of the southeastern and western United States. Traditional fuel characterization in these systems has been limited to two-dimensional representations, including biomass, height, and cover estimates per unit area. The objective of this study was to compare close-range photogrammetry with terrestrial lidar scanning (TLS), validated by destructive sampling to inform gridded 3D maps of live and dead understory fuels. We developed and analyzed a large dataset of calibrated plots using co-located terrestrial lidar scanning (TLS), close-range photogrammetry, and destructive biomass sampling conducted at 18 field sites across the southeastern and western US. Field sites represented vegetation commonly burned in prescribed fire programs, including southeastern mesic flatwood forests, southeastern loblolly-sweetgum forests, western ponderosa pine and mixed conifer forests, and western grasslands. Scanning methods were compared with metrics derived from fine-scale volumetric fuels sampling to determine the most effective method for mapping fuels in 3D. TLS and SfM-based point cloud metrics were variable in their accuracy dependent on vegetation type and levels of occlusion in understory vegetation, and neither was effective at predicting fuels within the lowest sampled stratum (0–10 cm). Modeled relationships between photogrammetry and TLS-based metrics and fuel biomass and bulk density can be used to produce unit-scale mapping for prescribed burn programs that are informed by the 3D structure and composition of understory fuels. However, point-cloud metrics may be limited in their utility in dense fuel complexes with high levels of occlusion. This work supports advances in the characterization and mapping of wildland fuel beds for use in physics-based models of fire behavior and effects that rely on gridded, 3D inputs.

Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support. Using our integrated, co-located methods, we detail methods used to produce hierarchically scaled datasets of the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). The study demonstrates that 3D fuel characterizations can provide consistently scaled and labeled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and SfM photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.

Background Quantifying and predicting wildland fire behavior is crucial for fire management, ecological research and mitigating wildfire impacts. Rate of spread (ROS), fireline intensity (FI) and fire radiative power (FRP) are key fire behavior metrics.Aims This study leverages uncrewed aircraft systems (UASs) equipped with thermal infrared (TIR) sensors and machine learning models to quantify and predict fire behavior.Methods Using repeat-pass UAS-based TIR imagery, we derived high-resolution FRP, FI and ROS estimates and trained artificial neural network (ANN) and random forest (RF) models to predict ROS.Key results This approach predicted ROS with low error (mean absolute errors (MAEs) below 0.04 m s-1, root mean squared errors (RMSEs) below 0.06 m s-1 and R2 values above 0.90) in short-term predictions for a single prescribed grassland fire, while maintaining computational efficiency.Conclusions Both ANN and RF models performed well, but RF performed better, with less training data, lower propensity for overfitting and less sensitivity to spatial autocorrelation.Implications Although currently demonstrated as a proof of concept at a single site with a specific fuel type and short-term prediction horizon, our integrated methodology shows research and development potential for supporting data-driven wildfire management strategies aimed at mitigating fire impacts, optimizing resource allocation and improving firefighter safety.

Wildland fire is known to play an important role in aerosolizing microbes sourced from vegetation and soil communities, but little is known about their spatiotemporal patterns across environmental gradients in smoke plumes. During prescribed fires in Kansas tallgrass prairies, simultaneous deployment of three uncrewed aerial vehicles (UAVs) equipped with bioaerosol sampling and environmental sensor payloads enabled simultaneous measurements ( 100 m intervals) across smoke plumes to better understand patterns of aerosolized microbes, relationships to their environment and sources, and their transport. Using data collected from nine smoke plume transects, we made comparisons to samples collected from ambient air to evaluate hypotheses about microbial composition and diversity as the smoke advected with prevailing winds. As distance from the flaming front increased, our data indicated changes in the composition of smoke-borne microbial assemblages that correlate to plume environmental gradients. The detectable contribution of terrestrial communities as sources of smoke bioaerosols decreased with deposition, smoke aging, and dilution with background air. Microbial composition of the smoke also differed based on the historical fire regimes of the burn units compared. Our results also demonstrate that “hazardous” Air Quality Index levels, based on particulate matter concentrations, corresponded to a fourfold increase in bacterial taxa richness, relating a novel microbial component to the metric most widely used to communicate with the public about smoke hazards. Grassland fires demonstrated an attenuated smoke signal as smoke aged, traveled, and diffused, but across the 300 m distance measured, the plume and its biological assemblage remained distinct from background air. Smoke assemblages were driven by fluctuation in relative humidity (RH), particulate matter (PM) concentration, and differences among the vegetation and soils of the source communities as influenced by historical fire frequency in the tallgrass prairie sites. The distinction between smoke and background air microbial composition and diversity was not as pronounced as in previous studies of higher intensity fires consuming greater fuel loads, yet richness and diversity increased with deteriorating air quality indices. This relationship shows that fire behavior producing more PM emits and transports a wider variety of terrestrial microbes from tallgrass prairies.

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels such as the 'Scott and Burgan 40' using the as-received Forest Inventory and Analysis (FIA) field survey plot data obtained from the USDA Forest Service. However, this basic approach yielded relatively poor results due to the inadequate amount of training data. Pseudo-labeled and fully synthetic datasets were developed using generative AI approaches to address the limitations of ground truth data availability. These synthetic datasets were used for augmenting the FIA data from California to enhance the robustness and coverage of model training. The use of an ensemble of methods including deep learning neural networks, decision trees, and gradient boosting offered a fuel mapping accuracy of nearly 80\%. Through extensive experimentation and evaluation, the effectiveness of the proposed approach was validated for regions of the 2021 Dixie and Caldor fires. Comparative analyses against high-resolution data from the National Agriculture Imagery Program (NAIP) and timber harvest maps affirmed the robustness and reliability of the proposed approach, which is capable of near-real-time fuel mapping.

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.

Understanding fire behavior is a crucial step in wildfire risk assessment and management. Accurate and near real-time knowledge of the spatio-temporal characteristics of fuels is critical for analyzing pre-fire risk mitigation and managing active-fire emergency response. Geospatial modeling and land cover mapping using remote sensing combined with artificial intelligence techniques can provide fuel information at regional scales with high accuracy and resolution, as evidenced by the extensive recent work in the literature that appeared with increasing volume in the open literature. This paper provides a comprehensive survey of the state-of-the-art in wildfire fuel mapping, focusing on the research frontier of fire fuel models, fuel mapping methods, remote sensing data sources, existing datasets/reference maps, and applicable artificial intelligence techniques. The main findings highlight the increasing research on fire fuel mapping worldwide, with a considerable emphasis on multispectral imagery and the Random Forest classifier for its efficacy with limited data. The majority of these studies concentrate on relatively limited geographical scales spanning a small variety of fuel types, thus leaving a gap in regional and national-scale mapping. Further, this review focuses on identifying the major challenges in wildfire fuel mapping and viable solutions as they relate to (i) ground truth data scarcity, (ii) mapping understory vegetation, (iii) temporal latency, and (iv) lack of uncertainty-aware models. Lastly, this paper identifies potential AI-driven solutions that promise a significant leap in fuel mapping and discusses the latest developments and potential future trends in AI-based fuel mapping applications.

Current wildfire management systems lack integrated virtual environments that combine historical data with immersive digital representations, hindering deep analysis and effective decision making. This paper introduces FIRETWIN, a cyber-physical Digital Twin (DT) designed to bridge complex ecological data and operationally relevant, high-fidelity visualizations for actionable incident response. FIRETWIN generates a dynamic 3D virtual globe that visualizes evolving fire behavior in real time, driven by output from physics-based fire models. The system supports multimodal perspectives, including satellite and drone viewpoints comparable to NOAA GOES-18 imagery - enabling comprehensive scenario analysis. Users interact with the environment to assess current fire conditions, anticipate progression, and evaluate available resources. Leveraging Google Maps, Unreal Engine, and pre-generated outputs from the CAWFE coupled weather-wildland fire model, we reconstruct the spread of the 2014 King Fire in California Eldorado National Forest. Procedural forest generation and particle-level fire control enable a level of realism and interactivity not possible in field training.

Background Researchers have developed technologies for fine-scale characterization of fuels via laser scanning and fine-scale measurement of surface fire behavior via terrestrial long-wave infrared (LWIR) imaging. Few studies have compared these technologies for their ability to estimate fuel consumption. Aims Here we compare fuel consumption estimated from point cloud data with fuel consumption estimated from LWIR imagery collected during prescribed burns of pine woodlands in the southeastern United States. Methods We adapted existing methods to estimate and map pre- and post-fire fuels and fuel consumption across several prescribed burn units. We related mapped estimates of fuel consumption to coincident estimates of fuel consumption based on energy release calculations derived from LWIR imaging. Key results Fuel consumption estimated from point cloud data was positively and significantly related to LWIR-derived consumption estimates at LWIR plots (R2 = 0.72, n = 14). Conclusions We demonstrate a methodology for mapping fuel consumption from laser scanning data that provides consumption estimates comparable to those of LWIR imagery. Implications Our findings highlight the relative importance of both surface and understory fuels to fire effects in fire-dependent pine woodlands of the southeastern United States, and the need for more research examining relationships between LWIR imagery and combusted fuels.

Deadwood is a critical component of forest ecosystems, storing nutrients for plants and serving as a carbon store and emission source. Climate change influences forest ecosystem dynamics with the potential for deadwood to emit carbon more rapidly due to accelerated decay and increased wildfires and increased inputs via mass forest mortality and disturbance events. To objectively inform our understanding of wildfires and associated carbon emissions, this study estimates the carbon content of dead fine woody debris (FWD) using multimodal data, such as Landsat-8 multispectral imagery, Sentinel-1 (C-band) and PALSAR (L-band) synthetic aperture radar (SAR) imagery, and terrain features to estimate the FWD of less than 0.25 in (1 h), 0.25-1 in (10 h), and 1-3 in (100 h). This data fusion provides spectral information to assess vegetation health that correlates with deadwood, as well as penetrability from SAR, resulting in structural information and biomass sensitivity. An ensemble machine learning (ML) model was trained using measurements from the Forest Inventory and Analysis (FIA) Database. A feature importance analysis was also performed to investigate the importance of input features to the model's performance. A super learner regression (SLR) model composed of 9 base learners, including an ElasticNet model as meta-learner, was proposed and achieved the R-2 values of 0.75, 0.72, and 0.62 to estimate 1-, 10-, and 100-h FWD, respectively. The validated model was then used to estimate deadwood carbon in the 2021 Dixie Fire region of California, demonstrating the effectiveness of our approach, emphasizing the value of multimodal data for real-time FWD carbon stock estimation.

The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature estimates, a valuable improvement over non-radiometric data that requires irradiance measurements to be converted into visible images using RGB color palettes. Despite its benefits, this technology has been underutilized largely due to a lack of available data for researchers. This study addresses this gap by introducing methods for collecting and processing synchronized visual spectrum and radiometric thermal imagery using UAVs at prescribed fires. The included imagery processing pipeline drastically simplifies and partially automates each step from data collection to neural network input. Further, we present the FLAME 3 dataset, the first comprehensive collection of side-by-side visual spectrum and radiometric thermal imagery of wildland fires. Building on our previous FLAME 1 and FLAME 2 datasets, FLAME 3 includes radiometric thermal Tag Image File Format (TIFFs) and nadir thermal plots, providing a new data type and collection method. This dataset aims to spur a new generation of machine learning models utilizing radiometric thermal imagery, potentially trivializing tasks such as aerial wildfire detection, segmentation, and assessment. A single-burn subset of FLAME 3 for computer vision applications is available on Kaggle with the full 6 burn set available to readers upon request.

Accurate fuel condition assessment is crucial for predicting fire behavior, enhancing operational decision support, and improving overall fire management. Our approach utilizes diverse data sources, such as Landsat-8 optical imagery, Sentinel-1 (C-band) SAR imagery, PALSAR (L-band) SAR imagery, and terrain features, to estimate time-lag fuel loadings (1 hour, 10 hours, and 100 hours). Optical data mainly captures the characteristics of leaf and forest canopy, while SAR data is more sensitive to forest vertical structures due to its strong penetrability. An ensemble model was trained on the Forest Inventory and Analysis (FIA) plots and spectral indices. Followed by, feature importance analysis and the inclusion of polynomial features were undertaken. The ensemble strategy, involving neural networks, decision trees, gradient boosting, and ensemble methods, achieved R 2 values of 0.72, 0.70, and 0.60 for 1-hour, 10-hour, and 100-hour fuel loads. Extensive experimentation in the 2021 Dixie Fire incident validates the effectiveness of our approach, emphasizing the value of leveraging multimodal data and ensemble machine learning models for real-time fuel load estimation.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Background Prescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. Aims We developed methods for processing uncrewed aircraft systems (UAS) imagery into spatially explicit pyrometrics, including measurements of fuel consumption, rate of spread, and residence time to quantitatively measure three prescribed fires. Methods We collected infrared (IR) imagery continuously (0.2 Hz) over prescribed burns and one experimental calibration burn, capturing fire progression and combustion for multiple hours. Key results Pyrometrics were successfully extracted from UAS-IR imagery with sufficient spatiotemporal resolution to effectively measure and differentiate between fires. UAS-IR fuel consumption correlated with weight-based measurements of 10 1-m2 experimental burn plots, validating our approach to estimating consumption with a cost-effective UAS-IR sensor (R2 = 0.99; RMSE = 0.38 kg m-2). Conclusions Our findings demonstrate UAS-IR pyrometrics are an accurate approach to monitoring fire behaviour and effects, such as measurements of consumption. Prescribed fire is a fine-scale process; a ground sampling distance of <2.3 m2 is recommended. Additional research is needed to validate other derived measurements. Implications Refined fire monitoring coupled with refined objectives will be pivotal in informing fire management of best practices, justifying the use of prescribed fire and providing quantitative feedback in an uncertain environment.

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

Electromagnetic radiation at 1550 nm is highly absorbed by water and offers a novel way to collect fuel moisture data, along with 3D structures of wildland fuels/vegetation, using lidar. Two terrestrial laser scanning (TLS) units (FARO s350 (phase shift, PS) and RIEGL vz-2000 (time of flight, TOF)) were assessed in a series of laboratory experiments to determine if lidar can be used to estimate the moisture content of dead forest litter. Samples consisted of two control materials, the angle and position of which could be manipulated (pine boards and cheesecloth), and four single-species forest litter types (Douglas-fir needles, ponderosa pine needles, longleaf pine needles, and southern red oak leaves). Sixteen sample trays of each material were soaked overnight, then allowed to air dry with scanning taking place at 1 h, 2 h, 4 h, 8 h, 12 h, and then in 12 h increments until the samples reached equilibrium moisture content with the ambient relative humidity. The samples were then oven-dried for a final scanning and weighing. The spectral reflectance values of each material were also recorded over the same drying intervals using a field spectrometer. There was a strong correlation between the intensity and standard deviation of intensity per sample tray and the moisture content of the dead leaf litter. A multiple linear regression model with a break at 100% gravimetric moisture content produced the best model with R2 values as high as 0.97. This strong relationship was observed with both the TOF and PS lidar units. At fuel moisture contents greater than 100% gravimetric water content, the correlation between the pulse intensity values recorded by both scanners and the fuel moisture content was the strongest. The relationship deteriorated with distance, with the TOF scanner maintaining a stronger relationship at distance than the PS scanner. Our results demonstrate that lidar can be used to detect and quantify fuel moisture across a range of forest litter types. Based on our findings, lidar may be used to quantify fuel moisture levels in near real-time and could be used to create spatial maps of wildland fuel moisture content.

Chapter 4 Wildland Fuel Characterization Across Space and Time Susan J. Prichard, Susan J. Prichard School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USASearch for more papers by this authorEric Rowell, Eric Rowell Tall Timbers Research Station, Tallahassee, Florida, USASearch for more papers by this authorRobert E. Keane, Robert E. Keane Rocky Mountain Research Station, United States Forest Service, Missoula, Montana, USASearch for more papers by this authorAndrew T. Hudak, Andrew T. Hudak Forestry Sciences Laboratory, Rocky Mountain Research Station, United States Forest Service, Moscow, Idaho, USASearch for more papers by this authorDuncan Lutes, Duncan Lutes Rocky Mountain Research Station, United States Forest Service, Missoula, Montana, USASearch for more papers by this authorE. Louise Loudermilk, E. Louise Loudermilk Southern Research Station, United States Forest Service, Athens, Georgia, USASearch for more papers by this author Susan J. Prichard, Susan J. Prichard School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USASearch for more papers by this authorEric Rowell, Eric Rowell Tall Timbers Research Station, Tallahassee, Florida, USASearch for more papers by this authorRobert E. Keane, Robert E. Keane Rocky Mountain Research Station, United States Forest Service, Missoula, Montana, USASearch for more papers by this authorAndrew T. Hudak, Andrew T. Hudak Forestry Sciences Laboratory, Rocky Mountain Research Station, United States Forest Service, Moscow, Idaho, USASearch for more papers by this authorDuncan Lutes, Duncan Lutes Rocky Mountain Research Station, United States Forest Service, Missoula, Montana, USASearch for more papers by this authorE. Louise Loudermilk, E. Louise Loudermilk Southern Research Station, United States Forest Service, Athens, Georgia, USASearch for more papers by this author Book Editor(s):Tatiana V. Loboda, Tatiana V. LobodaSearch for more papers by this authorNancy H. F. French, Nancy H. F. FrenchSearch for more papers by this authorRobin C. Puett, Robin C. PuettSearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/9781119757030.ch4Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Wildland fuels are foundational to smoke prediction and generally contribute to high uncertainty in smoke production and dispersion modeling. Until recently, wildland fuels and fuel consumption have been mapped using traditional methods to estimate the cover, height, and biomass (kg m –2 ). Over the past decade, significant progress has been made in describing and quantifying fuels more accurately with 3D characterization and quantification across large spatial scales. This chapter introduces wildland fuels and approaches to mapping them at scales relevant to smoke management, smoke impacts to communities, and future research needs. Topics covered include (1) an introduction to wildland fuels, (2) traditional approaches to mapping wildland fuels. (3) next-generation approaches to wildland fuel mapping, and (4) research and development needs. Because source characterization of wildland fuels is critical to predicting smoke impacts, reviewing how to measure and map wildland fuel biomass and consumption provides important context for fire and fuels managers, smoke scientists, and policy makers. 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Lidar (light detection and ranging) has been used for mapping fuel loads in Longleaf Pine (Pinus palustris Mill.) forests ecosystems. However, there are sources of bias and uncertainty associated with estimating crown-bulk density (CBD) from either Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) data. Therefore, the aim of this study was to assess the utility of ALS and TLS systems and their combination (ALS+TLS) in predicting CBD in a longleaf pine forest ecosystem in Florida. In the field, tree attributes, such as tree height (HT), crown width (CW), crown base height (CBH) and diameter at breast height (DBH) in three plots of ~ 0.19 ha were measured and CBD (kg/m 3 ) was calculated. Individual trees were detected from ALS, TLS and ALS+TLS, and lidar-derived crown-level metrics were computed for CBD modeling. The results show that CBD can be accurately predicted from ALS, TLS and ALS+TLS. However, the ALS + TLS improved CBD prediction accuracy only slightly. Given that ALS+TLS fusion is less practical and more expensive, our comparison suggests that either ALS or TLS measurements are still reasonable for CBD prediction and their usefulness is justified.

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.