Fuel Load Estimates Research Articles

Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review

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.

Estimating fuel load for wildfire risk assessment at regional scales using earth observation data: A case study in Southwestern Australia

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires.

Incorporating burn heterogeneity with fuel load estimates may improve fire behaviour predictions in south-east Australian eucalypt forest

Background Simulations of fire spread are vital for operational fire management and strategic risk planning. Aims To quantify burn heterogeneity effects on post-fire fuel loads, and test whether modifying fuel load estimates based on the fire severity and patchiness of the last fire improves the accuracy of simulations of subsequent fires. Methods We (1) measured fine fuels in eucalypt forests in south-eastern Australia following fires of differing severity; (2) modified post-fire fuel accumulation estimates based on our results; and (3) ran different fire simulations for a case-study area which was subject to a planned hazard reduction burn followed by a wildfire shortly thereafter. Key results Increasing fire severity resulted in increased reduction in bark fuels. In contrast, surface and elevated fuels were reduced by similar amounts following both low-moderate and high-extreme fire severity. Accounting for burn heterogeneity, and fire severity effects on bark, improved the accuracy of fire spread for a case study fire. Conclusions Integration of burn heterogeneity into post-burn fuel load estimates may substantially improve fire behaviour predictions. Implications Without accounting for burn heterogeneity, patchy burns of low severity may mean that risk estimations are incorrect. This has implications for evaluating the cost-effectiveness of planned burn programmes.

Branching out: species-specific canopy architecture limits live crown fuel consumption in Intermountain West USA conifers

BackgroundAccurate estimates of available live crown fuel loads are critical for understanding potential wildland fire behavior. Existing crown fire behavior models assume that available crown fuels are limited to all tree foliage and half of the fine branches less than 6 mm in diameter (1 h fuel). They also assume that this relationship is independent of the branchwood moisture content. Despite their widespread use, these assumptions have never been tested, and our understanding of the physiochemical properties that govern live crown flammability and consumption remains limited. To test these assumptions, we sampled branches from 11 common Intermountain West USA conifers and determined the corrected available fuel estimates using physiochemical measurements, diameter subsize class distributions, and a bench-scale consumption experiment. Additional branches were air-dried to explore interaction between moisture content and consumption. Corrected available live crown fuel was compared to existing models across species and then used to determine potential differences in crown fire energy release.ResultsAcross the 11 common conifers, distinct patterns of sub 1 h fuel distributions were strong predictors of whether the existing available live crown fuel models overestimated, approximately correctly estimated, or underestimated available live fuel. Fine branchwood distributions generally fell into three archetypes: fine skewed, normally distributed, and coarse skewed. Based on our corrected estimates, existing models overestimated the potential canopy energy by 34% for an average-sized western larch and underestimated it by 18.8% for western hemlock. The critical fine branchwood consumption diameter varied with species and moisture content. Larger proportions of fine branches were consumed as the branchwood dried, and nearly all the 1 h fuel was consumed when the branches were completely dry.ConclusionsThese results suggest that available live canopy fuel load estimates should consider species and moisture content to accurately assess and map fuel loads across landscapes. This work has implications for forest and fire management in conifer-dominated forests throughout western North America, and in other similar forests worldwide.

Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 × 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R2 values ranged from 0.63 to 0.78, while the R2 values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R2 values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations.

Fuel load and flight range estimation of migrating passerines in the western part of the Carpathian Basin during the autumn migration

Estimating fuel load and potential flight ranges of migrant passerines are basic issues in understanding bird migration strategies. Thirteen sub-Saharan and three pre-Saharan migrant passerine species were analysed in this study. The birds were captured at the Tömörd Bird Ringing Station in the western part of the Carpathian Basin. A general linear model with body mass as the dependent variable and fat score, muscle score and wing length as independent variables were used to estimate lean body mass (body mass without fuel deposits) and fuel load. In ten of the species studied, models considering interactions between factors fit the data better than the main-effect models. Body mass was positively correlated with the fat score in all species, with muscle score in ten species and wing length in 14 species. During autumn, fuel load tended to be larger in the sub-Saharan migrants, especially in four species which pass over the Mediterranean Sea, Common Nightingale (Luscinia megarhynchos), Icterine Warbler (Hippolais icterina), Garden Warbler (Sylvia borin) and Barred Warbler (Curruca nisoria). Nine sub-Saharan migrants, Marsh Warbler (Acrocephalus palustris), Sedge Warbler (A. schoenobaenus), Eurasian Reed Warbler (A. scirpaceus), European Pied Flycatcher (Ficedula hypoleuca), Spotted Flycatcher (Muscicapa striata), Wood Warbler (Phylloscopus sibilatrix), Willow Warbler (Ph. trochilus), Common Whitethroat (C. communis) and Lesser Whitethroat (C. curruca) had estimated flight ranges similar (<1300 km) to two pre-Saharans, European Robin (Erithacus rubecula) and Eurasian Blackap (S. atricapilla). The three short-distance migrants, including the Common Chiffchaff (Ph. collybita) with the shortest distance, had sufficient fuel load to reach their southern European wintering sites without needing to refuel at stopover sites.

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

Forest foliage fuel load estimation from multi-sensor spatiotemporal features

• Spatiotemporal features of spaceborne SAR and optical data were combined first time. • The addition of spatiotemporal features greatly improves the estimation of FFL. • Temporal features seem more important than spatial features. • Both spatial and temporal changes in FFL can indicate forest fire risk. Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R 2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R 2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R 2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

Calibration of disk pasture meter to estimate a fire fuel load (aboveground plant biomass) for fire management in the northeastern steppe of Mongolia

Grassland fires are the most common disturbance in Mongolia's east. Fire risk management in Mongolia's eastern grasslands should include fuel load estimates (aboveground plant biomass) to prevent fire damage. The disc pasture meter (DPM) is a common, rapid, and nondestructive method for estimating aboveground plant biomass in grasslands. Calibration is needed for the DPM application in a given area due to regional variations in climate and plant diversity, as well as site-specific environmental factors. We calibrated a DPM for Mongolia's north-eastern grasslands bordering Russia, classified as a very high-risk region for grassland fire. The calibration was carried out at twelve different sites, and double sampling (DPM reading and plant biomass harvesting) was done at 108 points. The mean of the total dry plant biomass of the sites was 11.27 centner ha-1 (±1.93 SE). The sites differed in total dry plant biomass and its composition. It indicates that the sites are needed different fire managements. Although we developed three linear regression models for DPM readings (100, 200, and 300) to estimate an optimal sampling effort, the model with 300 DPM models had the highest determination coefficient (0.82). Therefore, we suggest the model (y=3.18x+3.1) with the 300 DPM readings for further application.

Trends in forest fuel accumulation in pine forests of Kyiv Polissya in Ukraine

Abstract At present, forest fire research is becoming especially relevant in Ukraine. This study examines patterns of forest fuel accumulation in pine (Pinus sylvestris L.) stands that grow in different soil conditions with different pine stand structure. To estimate the load of forest fuel of different fractions, a combined methodology was used: the weighing method and the FIREMON (fuel load estimation) method. It was found that increase in surface forest fuel loads is not directly proportional to forest stands’ age. Fractional size distribution, capacity and loads of forest fuel depend on several factors, among which the greatest role is played by forestry characteristics of the pine stand. It was determined that in the forest site conditions of type C (fairly rich soils) in Kyiv Polissya, the share of forest litter compared to pine stands that grow in poor soil conditions (A) is smaller, ranging from 41% to 76% of the total forest fuel load. The mass proportion of the duff layer varies from 15% in young forest stands to 43% in mature stands. It was established that changes in forest fuel fractions for 1, 10, 100 and 1000 hours varied insignificantly with age rate. The share of substratum woody debris of 10 and 100 hours was insignificant and depended more on the forestry treatment regime on these sites. The mass proportion of coarse woody debris (1000 hours) was also insignificant, varying from 0% to 5.9% of the total load of surface fuel.

Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1-h fuel load using standard fuel parameters or site-specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi-source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel-1), optical (Sentinel-2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1-h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1-h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management.

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.

Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning

Forest fuel loads and structural characteristics strongly affect fire behavior, regulating the rate of spread, fireline intensity, and flame length. Accurate fuel characterization, including disaggregation of the fuel load by size classes, is therefore essential to obtain reliable predictions from fire behavior simulators and to support decision-making in fuel management and fire hazard prediction. A total of 55 sample plots of four of the main non-tree covered shrub communities in NW Spain were non-destructively sampled to estimate litter depth and shrub cover and height for species. Fuel loads were estimated from species-specific equations. Moreover, a single terrestrial laser scanning (TLS) scan was collected in each sample plot and features related to the vertical and horizontal distribution of the cloud points were calculated. Two alternative approaches for estimating size-disaggregated fuel loads and live/dead fractions from TLS data were compared: (i) a two-steps indirect estimation approach (IE) based on fitting three equations to estimate shrub height and cover and litter depth from TLS data and then use those estimates as inputs of the existing species-specific fuel load equations by size fractions based on these three variables; and (ii) a direct estimation approach (DE), consisting of fitting seven equations, one for each fuel fraction, to relate the fuel load estimates to TLS data. Overall, the direct approach produced more balanced goodness-of-fit statistics for the seven fractions considered jointly, suggesting that it performed better than the indirect approach, with equations explaining more than 80% of the observed variability for all species and fractions, except the litter loads.

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.

A Digitized Fuel Load Surveying Methodology Using Machine Vision

Fuel load is a crucial parameter for evaluating design fires for buildings. However, the availability of fuel load data is currently hindered by the lack of an efficient on-site data collection method. This research develops a new methodology for fuel load surveys that can facilitate the collection, storage, and analysis of fuel load data for a variety of building occupancies. The new survey method harnesses recent developments in mobile electronic devices, cloud storage, and machine vision to efficiently complete fuel load surveys in buildings. A four-step method is developed comprising digital inventory, data organization, item matching through computer vision, and fuel load estimation. The method is completed through an interactive electronic surveying form accessible on any mobile device with an internet connection, such as a tablet. The application allows taking digital measurements and pictures of the room and content, which are stored, and later searched through retail search engines using image recognition. Automatic matching of the picture with an online catalogue item gives access to further information about this item which are then used to evaluate the item fuel load, using a table of materials calorific values coded in the application. A wide adoption of this method could provide benefits by progressively populating such digital fuel load database. The results can then be used to provide design guidelines for fuel load density in codes and standards, for application in performance-based design of structures in fire.

Application of a Digitized Fuel Load Surveying Methodology to Office Buildings

This paper discusses application of a new methodology that facilitates fuel load surveys in buildings. The methodology consists of four steps comprising digital inventory, data organization, item matching through computer vision, and fuel load estimation, and is programmed into a digitized surveying application. The present paper applies the methodology to three office buildings and provides the results of surveyed fuel load density. A total office area of 1720 m2 was surveyed consisting of 34 closed offices and 161 cubicles within 12 large open plan office spaces. Compartment areas range from 8 m2 to 87 m2 for closed offices and 24 m2 to 345 m2 for open plan offices. The measured fuel load density for movable content had a mean of 1115 MJ/m2 with a standard deviation of 614 MJ/m2. When including the fixed content, the measured total fuel load density had a mean of 1486 MJ/m2 with a standard deviation of 726 MJ/m2. These values are considerably larger than values found in older surveys and most code provisions. The surveyed rooms had large quantities of paper, which amounted to 54% of the movable fuel load on average. Based on these results, and findings from other recent surveys, it is recommended to collect additional data. The work has established the foundation toward a fully automatized method, relying on an electronic form and a structured database of recorded information. A wide adoption of this method could populate an extensive fuel load database which can then be used to provide design guidelines for fuel load density in codes and standards, for application in performance-based design.

Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa

African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s−1). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean r = 0.96) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m−2. In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean r = 0.76). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg−1 needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.

Biomass Estimations of Invasives Yaupon, Chinese Privet and Chinese Tallow in East Texas Hardwood and Pine Ecosystems

Forest understory fuels can have profound effects on fire behavior and crown fire initiation. Accurate fire behavior prediction in understory fuels is an essential component for estimating fire intensity and severity during wildfire and prescribed fire events. This study focused on estimating temporal and seasonal changes in fuel loading parameters associated with the expansion of invasive yaupon (Ilex vomitoria), Chinese privet (Ligustrum sinense), and Chinese tallow (Triadica sebifera) in East Texas pine and hardwood ecosystems. Fuel loading data of invasive species infested sites indicated significant increases in understory biomass when compared to 1988 estimates, suggesting a clear need to revise regional fuel models. Multiple and simple regression biomass prediction equations were developed for all three-invasive species to facilitate fuel load estimates. These improved prediction equations will enhance fire management efforts as well as invasive species mitigation efforts in east Texas.