Fuel Load Research Articles (Page 1)

Fire exclusion and encroachment by shade-tolerant, often fire-sensitive and/or opportunistic tree species threaten dominance of fire-dependent, open pine (Pinus spp.) forests throughout the southeastern U.S., largely due to encroaching species’ traits that reduce flammability. To better understand fuels within fire-dependent pine ecosystems undergoing encroachment, we measured loads of varying fuel types (herbaceous, shrub, leaf litter, duff, woody debris) in 96 plots with little to no recent management across a pine dominance gradient at five sites in east-central Alabama, USA in 2022. Across pine-, mixedwoods-, and hardwood-dominated plots, we found similar mean basal area (36.5 – 40.2 m2 ha-1), canopy cover (> 90%), and total fuel loads (20 – 22 Mg ha-1). Encroaching species had lower basal area and density in pine-dominated plots, but their mean leaf litter fuel loads were similar among plot types (0.9 – 1.5 Mg ha-1). However, encroaching species’ relative contribution to leaf litter fuelbeds declined with increasing pine basal area due to a significant linear increase in pine leaf litter fuel loads. Overall, leaf litter was the only fuel to co-vary with pine dominance, suggesting fuel homogenization with encroachment. Future studies experimentally manipulating leaf litter fuels could determine thresholds where encroaching species’ fire-suppressing leaf litter negates pine’s fire-promoting effects.

Abstract Equatorial Southeast Asia (ESEA) is crucial to global climate dynamics, particularly during El Niño events, which greatly enhance biomass burning activities and lead to significant declines in air quality. The fire activity within this region is intricately linked to the phases of the El Niño-Southern Oscillation (ENSO) and the prevailing regional monsoon patterns, both of which dictate the frequency and intensity of biomass burning events. However, there is a concerning absence of comprehensive inventories detailing these emissions in ESEA. This data shortfall limits our understanding of the full impact of biomass burning on local and global scales, underscoring the pressing need for enhanced emissions inventory initiatives in the region. This study seeks to evaluate biomass burning emissions specifically for the non-El Niño years of 2013 and 2021 through a bottom-up approach. To analyze land cover distribution and identify burned areas throughout ESEA, we utilized remote sensing data from MODIS alongside geospatial analysis tools. Emission estimates were derived by multiplying the burned land area (in km2) by combustion factors (CF), fuel loading (FL), and emission factors (EF) sourced from existing literature. Our findings illustrate a stark contrast in total emissions, with 2013 generating a significantly higher total of 7,289,220.68 Mg compared to 1,536,779.55 Mg in 2021. Both years exhibited a bi-modal emission pattern, reflective of the equatorial precipitation regime, which produces two distinct dry seasons. The primary emission species identified were carbon dioxide (CO2), followed by carbon monoxide (CO) and non-methane volatile organic compounds (NMVOCs), with shrublands and evergreen forests acting as significant contributors. Notably, Sumatra and Kalimantan emerged as key emission hotspots in this analysis.

Impact of Fuel Loading Models on Fuel Cycle Dynamic Simulations

Potential fire behavior varied significantly among tree species, directly influencing forest fire intensity and spread. To quantify these differences and evaluate species-specific fuel traits for fire management applications, this study conducted field surveys and sample collection in the Jin Dian Yuanbaoshan Forest Area, Kunming, Yunnan Province. Using a combustion bed experiment, we simulated the burning behavior of Acacia dealbata, Alnus nepalensis, and Pinus armandii under windless conditions, recording ignition time, extinction time, flame height, spread rate, and calculating fire intensity. Comparative analysis revealed: (1) Fire intensity ranking: P. armandii needles > A. dealbata leaves > P. armandii branches > A. nepalensis leaves > P. armandii bark > A. dealbata branches > A. nepalensis branches > A. dealbata bark > A. nepalensis bark; (2) The biological firebreaks composed of A. nepalensis and A. dealbata in Yuanbaoshan exhibited effective flame-retardant performance; (3) Coarse woody fuels negatively affected prescribed burning intensity and effectiveness. By quantifying fire behavior differences among these species, this study provides scientific support for fuel management and fire-resistant species selection in central Yunnan, while offering practical guidance for prescribed burning strategies in the Jin Dian Yuanbaoshan Forest Area.

Impact of Thinning Strategy, Surface Fuel Loading and Burning Conditions on Fuel Treatment Efficacy in Ponderosa Pine Dominated Forests of the Southern Rocky Mountains

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.

Abstract We aimed to assess how peatland drainage altered the spatiotemporal variability in forest cover, aboveground biomass, and tree productivity and how these changes related to the spatial variability in peat burn severity. We studied a black spruce and birch dominated boreal peatland in Parkland County, Alberta, Canada, which was drained in 1987 and burned in 2021. Using remote sensing techniques (historical imagery and LiDAR), we determined that forest cover increased by 180% following drainage and aboveground tree biomass decreased from 26.1 kg m−2 adjacent to the nearest drainage ditch to 2.8 kg m−2 95 m away from the nearest ditch. Field surveys and a LiDAR‐based analysis were conducted to measure the spatial variability in peat burn severity. Drained peatland margins experienced the greatest peat burn severity with a mean depth of burn of 26.9 ± 12.6 cm (34.0 ± 10.1 kg C m−2) compared to natural middles at 15.3 ± 6.2 cm (8.3 ± 2.1 kg C m−2), where peat burn severity increased with proximity to ditches and greater aboveground biomass. We present a conceptual model outlining the increases in aboveground and peat fuel loads following drainage and suggest that the area around a ditch that is impacted by drainage, which is commonly assumed to be 30 m, likely increases through time in forested peatlands due to the afforestation feedback. Drained peatlands represent a severe fire risk for communities and fire management agencies. Peatland restoration should be integrated into fuel management strategies to reduce the risk that drained peatlands pose.

Forests play a pivotal role in global carbon sequestration, biodiversity conservation, and climate change mitigation [...]

Fuel loading after steep slope salvage logging in the southern Rocky Mountains

Timing and Frequency of Goat Grazing Affect Invasive Species Cover and Fuel Loads in Southern California1

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels.

In Maldives, the demand for Marine Gas Oil (MGO) are on the increase due to expansion of number of vessels on voyage and the need for reliable and safe storage facilities designed as per API 650 is on more consideration .It has found that traditional design and development of these facilities to augment the existing utilities has led to some off sets with the API 650 In this work attempt has been made to design a storage tank farm of holding a 5000 x4 MT of MGO in a single dikes enclosure that has been appropriately designed in line with the codes and standard. Material selection was done with the requirements of the recent editions of API 650, ASME B&PV Code, ASTM etc. and some adequate design method has been chosen in line with the structural stability aspect. Since one tank is having capacity of 5000MT, the 20,000MT of MGO storage utility has been designed including loading/unloading piping section as well. It was found that, the nominal diameter is 21 m without space constraint, height is 18 m, number of course is 10, and height of each course is 1.8m. Also, the thickness of each course of tank shell is in the order of 14mm, to 6mm starting from bottom. The bottom and annular plate thickness are 10mm. Carbon steel A36M/A36 material was selected for the design. The overall weight of the tank is 6055 MT, which is found to be stable with an anchorage. The structural stability was followed by the ASCE 7-02 and related studies. Fuel loading and offloading through pump house and a Conventional Buoy Moring (CBM) Station has also given in the layout. The Civil foundation was not covered in this publication with the stability checks. Finally, the Control and Instrumentation aspect has also presented in the P&ID of the system under consideration. Therefore, this attempt is an integration of basic aspects of tank farm.

(1) Objective: To improve forest fire prevention, this study provides a reference for forest fire risk assessment in Sichuan Province. (2) Methods: This research focuses on various forest vegetation types in Sichuan Province. Given data from 6848 sample plots, five machine learning models—random forest, extreme gradient boosting (XGBoost), k-nearest neighbors, support vector machine, and stacking ensemble (Stacking)—were employed. Bayesian optimization was utilized for hyperparameter tuning, resulting in machine learning models for predicting forest fuel loads (FLs) across five different vegetation types. (3) Results: The FL model incorporates not only vegetation characteristics but also site conditions and climate data. Feature importance analysis indicated that structural factors (e.g., canopy closure, diameter at breast height, and tree height) dominated in cold broadleaf, subtropical broadleaf, and subtropical mixed forests, while climate factors (e.g., mean annual temperature and temperature seasonality) were more influential in cold coniferous and subtropical coniferous forests. Machine learning-based FL models outperform the multiple stepwise regression model in both fitting ability and prediction accuracy. The XGBoost model performed best for cold coniferous, cold broadleaf, subtropical broadleaf, and subtropical mixed forests, with coefficient of determination (R2) values of 0.79, 0.85, 0.81, and 0.83, respectively. The Stacking model excelled in subtropical coniferous forests, achieving an R2 value of 0.82. (4) Conclusions: This study establishes a theoretical foundation for predicting forest fuel capacity in Sichuan Province. It is recommended that the XGBoost model be applied to predict fuel loads (FLs) in cold coniferous forests, cold broadleaf forests, subtropical broadleaf forests, and subtropical mixed forests, while the Stacking model is suggested for predicting FLs in subtropical coniferous forests. Furthermore, this research offers theoretical support for forest fuel management, forest fire risk assessment, and forest fire prevention and control in Sichuan Province.

Results of Numerical Simulation of Measurements of Scram Worth Performed during the Start-Up Phase of the Second and Third Fuel Loads of the Novovoronezh NPP Unit 6

Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a “Variable Selection Using Random Forests” (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R2 = 0.32, RMSE = 0.53 Mg/ha for litter and R2 = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management.

Digitized fuel load survey in commercial and university office buildings for fire safety assessment

In western North America, quaking aspen stands ( Populus tremuloides Michx.) have predominantly been described as low flammability, “fireproof” forests, but the specific relationship between aspen stand composition, fuel characteristics, and potential fire behavior is not fully understood. We investigated surface and canopy fuel characteristics in 80 aspen stands in Utah, U.S., that spanned gradients of tree species composition from aspen to conifer dominance and stand development from early to late stages. We quantified fuel type and load, measured fuel moisture content in representative stands across two summer seasons, and modeled flame lengths in each stand. Fuel type and load varied greatly across stands, though late development, conifer-dominated stands had significantly higher (∼2–5 times) fine dead woody and litter load and significantly lower (∼2–5 times) live understory herbaceous load compared to pure aspen stands. Fuel moisture content did not vary by stand type. Modeled flame lengths were lowest in pure aspen stands, and flame lengths increased linearly with decreasing aspen composition, suggesting that potential surface fire behavior increases as a seral aspen stand progresses through succession to conifer dominance.

Wildfire pressure involves today to implement silvicultural practices that provide a good compromise between reducing fire risk and maintaining ecological functioning. Thinning reduces tree density and low branches, but results in the deposition of a considerable biomass of woody debris on the ground (up to 4800 g m2 in this study). They can be eliminated by prescribed burning, but this raises questions about the fire intensity that can be generated and the impact on soil fauna. We undertook the monitoring of a thinning and prescribed burning operation, separated and combined, in November 2020, in a Pinus laricio stand prone to fire risk, located in Bavella, Corsica. Fuel load was determined, and temperature measurements in the soil were performed using K‐type thermocouples. Soil arthropod populations were monitored using pitfall traps, in particular Collembola, Acari, Aranae, and Coleoptera. The combination of thinning and burning resulted in a fire intensity of 75.8 versus 8.4 kW m−1 for burning alone. Maximum temperature rise measured at −2 cm below the surface was less than 5°C for both treatments. The combination of thinning and burning did not result in higher fire intensity at ground level than burning alone, and the soil showed high insulation capacity. Most of the woody debris that burned was small‐diameter, and large‐diameter debris remained unconsumed. This burning, performed during a period of low biological activity, had no effect on soil arthropods, and the presence of large debris may have provided refuge areas. Collembola group was the faster to recover, and were followed by cohorts of predators in summer, especially Acari. Our results suggest that a combination of burning and thinning in autumn may be beneficial for fire prevention. However, the decomposition of woody debris in relation to fire risk, and the occurrence of pests after these treatments need to be monitored.

This paper aimed to provide technical support for fuel management by exploring different strengths of fuel removal on the physical and chemical properties and flammability of Betula platyphylla forests in the wildland–urban interface. After investigating the northeastern region during the forest fire prevention period in May 2023, a typical WUI area was selected, and three different treatment strengths, combined with a control, were set up to carry out indoor and outdoor experiments for 27 weeks. Compared with previous studies, this study mainly investigated and analyzed the dynamic changes in the physical and chemical properties and fuel flammability after different intensities of treatments on a time scale. By processing and analyzing the data, the following results were obtained. Significant differences existed in the fuel loading of different time-lag fuels over time (p < 0.05). The ash and ignition point of 1 h time-lag fuel after different treatment intensities generally increased first and then decreased, and the higher heat value and ash-free calorific value generally decreased first and then increased. The physical and chemical properties of 10 h and 100 h time-lag fuel fluctuated with time, but the overall change was insignificant. The indicator that had the greatest impact on the combustion comprehensive score for different time-lag fuels was fuel loading. The change in the flammability of dead surface fuel with time varied significantly, and different treatment intensities effectively reduced the fuel’s flammability. The reduction effects, presented in descending order, were as follows: medium-strength treatment > low-strength treatment > high-strength treatment > control check. In conclusion, different treatment intensities have significant effects on the flammability of the fuel, and the medium-strength treatment has the best effect. Considering the ecological and economic benefits, adopting the medium-strength treatment for the WUI to regulate the fuel is recommended.

Evaluating the feasibility of machine learning algorithms for combustion regime classification in biodiesel-fueled homogeneous charge compression ignition engines