Post-fire Analysis Research Articles

Behavior of cross-shaped stiffened concrete-filled steel tubular stub columns after fire exposure

The cross-shaped stiffened concrete-filled steel tubular stub column is constructed by incorporating steel reinforcement ribs into the traditional cross-shaped design, which helps delay the buckling of the steel tube and enhances its restraining effect on the concrete. However, existing standards and research proposed design methods for estimating the post-fire bearing capacity of steel reinforced concrete stub columns are primarily applicable to the ordinary steel tubes with conventional thickness and sections. Therefore, it is necessary to investigate the axial compressive properties of high-strength concrete stub columns with irregular thin-walled steel tubes after exposure to fire, which can contribute to their potential future application. Effects of various experimental parameters, such as heating time, width-thickness ratio, and longitudinal spacing of stiffeners were investigated. In total one specimen subjected to ambient temperature and eight cross-shaped stiffened concrete-filled steel tubular stub columns subjected to fire conditions were prepared for measuring temperature-time curves of specimens during ISO-834 standard fire, load-displacement curves, load-transverse, and longitudinal strain curves of specimens under axial compression loading. The failure modes of the specimens were recorded, and the influence of each parameter on the post-fire mechanical properties of the specimens was analyzed based on the experimental results. ABAQUS software was utilized to develop a model for post-fire analysis, which was validated by comparing it with experimental data. After validation, the model was used to analyze the underlying mechanism, and a comprehensive parametric study of members was conducted. Based on the results of the parametric study and the model developed for calculating bearing capacity of the cross-shaped stiffened concrete-filled steel tube stub column at ambient temperature, a simplified equation accounting for the effects of elevated temperature was proposed to predict the residual bearing capacity of the cross-shaped stiffened concrete-filled steel tube stub column after fire exposure. The average error between the simplified formula and the finite element simulation results is 0.925, and the mean square error is 0.085.

Fire-Induced Damage and Postfire Analysis of the Compression Properties of Cross-Laminated Timber

Fire-Induced Damage and Postfire Analysis of the Compression Properties of Cross-Laminated Timber

Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data

Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre-fire risk assessments to inform mitigation efforts. This study aimed to investigate the relationship between predictions from pre-fire risk assessments and outcomes observed through post-fire burn severity analyses. In this study, forest fire risk was assessed through the Fuzzy Analytical Hierarchy Process (FAHP), in which fire-oriented factors were used as input. The degree of burn was determined by the Random Forest method using 11,519 training points and 400 test points on Sentinel-2 satellite images under three different classes. According to the results obtained from 266 selected test points located within the forest, all primary factors put forth increased high burn severity. Climate, in particular, emerged as the most significant factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, accounting for 67%. This was followed by topography with 50% accuracy at a high fire intensity. In the risk assessment based on the FAHP method, climate was assigned the highest weight value among the other factors (32.2%), followed by topography (27%). To evaluate the results more comprehensively, both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in the Case 1 area, moderate burn severity had the highest in the Case 2 area. During the days of the fire, the direction of spreading was obtained from the MODIS images. In this way, the fire severity was also interpreted depending on the direction of fire progression. Through an analysis of various case studies and literature, this research underlines both the inherent strengths and limitations of predicting forest fire behavior-based pre-fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Post Fire Dynamic Analysis of Steel Buildings Subject to Wind Loadings in South of Iraq

Post-fire analysis of a six-story square steel building under the action of dynamic wind forces in the south of Iraq is presented. The nonlinear time-history analysis using direct integration method is accomplished by SAP 2000 V16 program while geometric nonlinear parameters are included. The post fire deformations values and their configurations along building are based on available literature that related to post fire deformations of steel buildings at 550oCwith reducing of yield stress and modulus of elasticity) by 10% due to fire. In the present study, two post fire scenarios are considered, with wind speed of 42 m/s, according to Iraqi meteorological and IQS.301 standards. The aim of this study is to investigate the post-fire dynamic analysis of a multi-story steel moment-resisting building subjected to dynamic along-wind loads. Variations of base shear, base moment, drift ratio, and displacement are considered for discussion and comparison. Its concluded that the presence of deformation in building after fire and the reduction in yield stress and modulus of elasticity increased the base shear, base moment and drift ratio as average by about 12.5, 20& 64%respectively under the effect wind load. The fire deformations may be critical and the structural decision for the building safety should be done via structural analysis of the building taking into consideration different parameters related to post fire effects.

Prediction of post-fire seismic performance of reinforced concrete frames

Due to the importance of evaluating the seismic behavior of structures after a fire, a numerical study was conducted on RC frames using the detailed finite element method. The simulation process consists of two separate steps. The first step is heat transfer analysis to estimate heat distribution in the various elements of RC frames, and the second step is evaluating the post-fire seismic performance of the frames using thermal–mechanical analysis. The test specimens used to verify the proposed FE model include two RC frames with different beam-column bending capacity ratios, which are in two different conditions, including the specimens at ambient temperature as control specimens and fired specimens inside of the furnace chamber. In the heat transfer analysis step, the sensitivity of responses to two effective parameters, including the moisture content and the emissivity of the concrete surface has been investigated. In the post-fire analysis, among the various models presented for the post-fire behavior of concrete, three stress–strain models were selected for cyclic analysis of RC frames and the results were compared with the test data. Also, the bond-slip behavior of the concrete-steel interface has been simulated using the equivalent springs technique. It was seen from the comparison of the numerical results with the test data, that the proposed model accurately estimates the resistance and the dissipated energy of frames in each cycle. In addition, parametric studies showed that every 30 min increase in the fire duration based on the ISO 834 standard curve causes a 10% decrease in post-fire seismic resistance compared to the control specimen.

Post-fire assessment of heating temperatures experienced by concrete using short video imaging, hyperspectral imaging and laser-induced breakdown spectroscopy

This study develops rapid post-fire analysis methods to predict the heating temperature to which the concrete was exposed. Short video imaging (SVI), hyperspectral imaging (HSI) and laser-induced breakdown spectroscopy (LIBS) were used for the first time to obtain spectra of concrete after high-temperature exposure (100–800 °C). The differences in colour levels and spectroscopic signals due to varying temperatures were observed. To handle the complex relationship between spectra and temperatures, machine learning models were used to extract meaningful information from spectra for the quantification of temperature. Furthermore, domain knowledge related to concrete composition and LIBS signal was integrated into machine learning to improve quantification performance. The highest coefficients of determination for prediction achieved based on SVI, HSI and LIBS measurements were 91.4, 94.8 and 98.6, respectively. These results demonstrate that SVI, HSI and LIBS can be fast and viable methods for assessing the fire temperature that the concrete has experienced.

Post-Fire Analysis of Thermally Sprayed Coatings: Evaluating Microstructure, Mechanical Integrity, and Corrosion Behavior

This paper examines the impact of fire on the microstructural, mechanical, and corrosion behavior of wire-arc-sprayed zinc, aluminum, and Zn-Al pseudo-alloy coatings. Steel plates coated with these materials were subjected to temperatures in increments of 100 °C, starting from 300 °C and progressing until failure. Microstructural characterization, microhardness, abrasion resistance, and electrochemical impedance studies were performed on the post-fire coatings. The findings from this study show that heat had a positive impact on the performance of zinc and Zn-Al pseudo-alloy coatings when they were exposed to temperatures of up to 400 °C, while aluminum coatings maintain their performance up to 600 °C. However, above these temperatures, the effectiveness of coatings was observed to decline, due to increased high-temperature oxidation, and porosity, in addition to decreased microhardness, abrasion resistance, and corrosion protection performance. Based on the findings from this study, appropriately sealed thermal-spray-coated steel components can be reused after exposure to fire up to a specific temperature depending on the coating material.

Application of laser-induced breakdown spectroscopy and chemometrics for rapid identification of fire-retardant/resistant coatings from fire residues

The use of fire-retardant/resistant coatings (FRC) is an effective and important method to delay the spread of fire or to protect the intact structure against fire. Due to the high demand for FRC in end-user industries such as construction and transportation, how to efficiently determine the identity of FRC has become a critical but difficult task in fire protection and investigation. This paper presents the application of laser-induced breakdown spectroscopy (LIBS) and chemometrics for rapid post-fire analysis of FRC. We attempt to identify four types of FRC from fire residues, identify FRC based on heating devices, and predict the heating temperatures of FRC. FRC residues were prepared as pressed pellets and then scanned by an integrated LIBS system in the ultraviolet–visible, visible and near-infrared ranges. The obtained spectra were pre-processed to select an appropriate number of variables, and the relationship between the spectral intensity and the categorical information was modelled using partial least squares (PLS). The class separability was demonstrated by visualisation in a low-dimensional space through principal component analysis (PCA) projection. Furthermore, important spectral lines that contribute to the classification and regression models were identified and explained. For the identification of FRC types and heating devices, all samples were accurately classified in repeated out-of-sample tests. For the prediction of heating temperature (°C), the R2 and RMSE were 0.96 and 20.11, respectively. These results coupled with the simplicity of the experimental processes indicate that LIBS is a promising candidate for rapid and on-site FRC analysis in fire protection and investigation.

Applications of Artificial Intelligence in Fire Safety of Agricultural Structures

Artificial intelligence applications in fire safety of agricultural structures have practical economic and technological benefits on commercial agriculture. The FAO estimates that wildfires result in at least USD 1 billion in agriculture-related losses due to the destruction of livestock pasture, destruction of agricultural buildings, premature death of farm animals, and general disruption of agricultural activities. Even though artificial neural networks (ANNs), genetic algorithms (GAs), probabilistic neural networks (PNNs), and adaptive neurofuzzy inference systems (ANFISs), among others, have proven useful in fire prevention, their application is limited in real farm environments. Most farms rely on traditional/non-technology-based methods of fire prevention. The case for AI in agricultural fire prevention is grounded on the accuracy and reliability of computer simulations in smoke movement analysis, risk assessment, and postfire analysis. In addition, such technologies can be coupled with next-generation fire-retardant materials such as intumescent coatings with a polymer binder, blowing agent, carbon donor, and acid donor. Future prospects for AI in agriculture transcend basic fire safety to encompass Society 5.0, energy systems in smart cities, UAV monitoring, Agriculture 4.0, and decentralized energy. However, critical challenges must be overcome, including the health and safety aspects, cost, and reliability. In brief, AI offers unlimited potential in the prevention of fire hazards in farms, but the existing body of knowledge is inadequate.

Post-fire analysis and numerical modeling of a fire-damaged concrete bridge

Extreme fire hazard is not a typical design consideration for highway bridges because of the low probability of occurrence. However, historically, fire events caused higher numbers of bridge collapse than earthquakes on the nation’s highways, showing the criticality of fires on highway bridges. Despite this fact, the during and post-fire performance of highway bridges is still not well-understood. The current study evaluated the post-fire performance of a fire-damaged concrete bridge in Irving, Texas, through theoretical modeling and in-situ testing. The bridge sustained significant damage when a fuel tanker crashed and caught fire in May 2005, and was then repaired and strengthened with carbon fiber reinforced polymer laminates. A three-tiered calibrated numerical modeling scheme involving computational fluid dynamics-based fire model, heat transfer and stress analysis was developed. The accuracy of the scheme was validated through static bridge load testing. It was found that the modulus of elasticity of the fire damaged section of the bridge was reduced by more than 50%. The numerical model represented the bridge fire with reasonable accuracy. The model thermal profiles showed good correlation with actual post-fire observations. The concrete modulus of elasticity decreased by 10% and 75% for the girder and the deck, respectively. The actual strain and displacements from the load testing closely matched those from the calibrated model.

Comparación multitemporal de áreas quemadas en un bosque seco tropical utilizando el índice de área quemada (IAQ)

Los análisis post incendio, como el índice de área quemada (IAQ), permiten discriminar áreas afectadas y severidad de los incendios forestales. En este estudio se comparó según la frecuencia de incendios, el comportamiento anual del IAQ en la región de bosque seco tropical del Área de Conservación Guanacaste, Costa Rica entre los años 1997 al 2019. Los resultados evidencian que las áreas frecuentemente quemadas por incendios forestales muestran un comportamiento diferenciado en el IAQ a lo largo de todos los años. Mostrando una relación directa entre la frecuencia de incendios forestales y los valores de IAQ. Donde a mayor frecuencia de incendios forestales, los valores de IAQ son mayores. Por otro lado, las áreas que han sido poco afectadas o no han sido afectadas del todo mostraron valores bajos de IAQ. Estos resultados podrían validar la hipótesis de que después de dos a tres incendios, el banco de semillas es completamente destruido. Esto debido a que la composición y estructura de los bosques secos tropicales cambia considerablemente después de múltiples incendios.

Spatial pattern analysis of post-fire damages in the Menderes District of Turkey

Forest fires, whether caused naturally or by human activity can have disastrous effects on the environment. Turkey, located in the Mediterranean climate zone, experiences hundreds of forest fires every year. Over the past two decades, these fires have destroyed approximately 308000 ha of forest area, threatening the sustainability of its ecosystem. This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1, 2017, by using pre- and post-fire Sentinel 2 (10 m and 20 m) and Landsat 8 (30 m) satellite images, MODIS and VIIRS fire radiative power (FRP) data (1000 m and 375 m, respectively), and reference data obtained from a field study. Hence, image processing techniques integrated with the Geographic Information System (GIS) database were applied to a satellite image data set to monitor, analyze, and map the effects of the forest fire. The results show that the land surface temperature (LST) of the burned forest area increased from 1 to 11°C. A high correlation (R = 0.81) between LST and burn severity was also determined. The burned areas were calculated using two different classification methods, and their accuracy was compared with the reference data. According to the accuracy assessment, the Sentinel (10 m) image classification gave the best result (96.43% for Maximum Likelihood, and 99.56% for Support Vector Machine). The relationship between topographical/forest parameters, burn severity and disturbance index was evaluated for spatial pattern distribution. According to the results, the areas having canopy closure between 71%–100% and slope above 35% had the highest burn incidence. As a final step, a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the post-fire analysis. A high correlation was found between FRP-slope, and FRP-burn severity (0.96 and 0.88, respectively).

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

Jack pine (pinus banksiana) forests are unique ecosystems controlled by wildfire. Understanding the traits of revegetation after wildfire is important for sustainable forest management, as these forests not only provide economic resources, but also are home to specialized species, like the Kirtland Warbler (Setophaga kirtlandii). Individual tree detection of jack pine saplings after fire events can provide information about an environment’s recovery. Traditional satellite and manned aerial sensors lack the flexibility and spatial resolution required for identifying saplings in early post-fire analysis. Here we evaluated the use of unmanned aerial systems and geographic object-based image analysis for jack pine sapling identification in a region burned during the 2012 Duck Lake Fire in the Upper Peninsula of Michigan. Results of this study indicate that sapling identification accuracies can top 90%, and that accuracy improves with the inclusion of red and near infrared spectral bands. Results also indicated that late season imagery performed best when discriminating between young (<5 years) jack pines and herbaceous ground cover in these environments.

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Abstract. Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site.

Contribution to the development of colorimetry as a method for the assessment of fire-damaged concrete

This study presents a new method of using colorimetry for post-fire assessment of concrete. Changes in concrete colour with temperature are related to chemical changes of cement and aggregates. Two concretes were studied: an ordinary concrete (OC) and a high-performance concrete (HPC). The difference between both concretes was the water–cement ratio. They were made with the same cement and the same riverbed aggregates. Small samples were heated with different target temperatures: 100, 200 ... 1000 °C. After each cooling period, a picture was taken with a flatbed scanner. Two types of colour image decomposition were then used: RGB (red, green and blue) and HSI (hue, saturation and intensity) to analyse the evolution of the colour of the concrete with temperature. The evolution of red, green and blue components with temperature was analysed and two parameters of colorimetric analysis in order to predict the maximum temperature reached by concrete during fire were proposed. As the results have shown, the blue colour histogram gradually widens at its base as the temperature increases. Parameter of colorimetric analysis 1 is the measurement of this width. Parameter of colorimetric analysis 2 is associated with the appearance of very light colours beyond 800 °C. Thus, the number of pixels with intensity of red or green or blue higher than 225 increases very significantly between 800 and 900 C. Parameter of colorimetric analysis 3 is the average hue of the image (in the HSI colour space). It was found that parameter of colorimetric analysis 3 decreases significantly with temperature. This corresponds to the reddening of concrete with temperature due to the oxidation of mineral containing iron. To validate the use of these parameters of colorimetric analysis for the post-fire analysis of concrete, small concrete slabs were heated. They were positioned in place of the oven door, thus providing a thermal gradient through their thickness. Images taken during the cooling process were analysed using the proposed parameters of colorimetric analysis.

Post-fire analysis of pre-fire mapping of fire-risk: A recent case study from Mt. Carmel (Israel)

The recent devastating wildfire on Mt. Carmel provided a unique opportunity to evaluate a fire-risk map constructed for the region, published two years ago in this journal. This largest forest fire in the history of Israel, occurred during December 2010, covering 2180 ha, burning more than half-million trees and causing the loss of life of 45 people. A study of fire risk in this area was conducted between 2007 and 2009 utilizing a combination of Monte Carlo simulation of spatial spread of fire ignition with fire behavior model (FARSITE). The fire risk map produced in 2009 is assessed here with reference to the area burnt during December 2010. The results showed that most of burnt areas corresponded to high risk levels in the risk map. According to a null model, the five lower risk levels taken together would have corresponded to 50% of the burnt area, while in fact they were presented in only 5.6% of the area. In contrast, the three highest risk levels, for which the null model expectation would be a representation of 30%, were represented in 87% of the area. Comparing the fire risk map against the map of the real recent fire provided support to the general approach, and strengthened the confidence of our fire risk model.

Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines

Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objective of the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM using diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.

Infrared characterization of fine-scale variability in behavior of boreal forest fires

Spatial and temporal variability in forest fire behavior, caused by differences in microsites, fuel types and condition, topography, and other factors across even relatively small areas, has been poorly characterized in most previous studies. Past characterization of forest fires has often been limited by monitoring techniques that relied on timing systems in coarse-resolution sampling grids. We report documentation and analysis of fire behavior for several experimental fires using a camcorder-sized infrared camera mounted in a helicopter hovering over the target fires. These fires were conducted as part of the Russian FIRE BEAR Project in boreal Pinus sylvestris L. forests of central Siberia. Final results provide quantitative information on fire front location, rates of spread, temperatures, and total radiation energy (kW/m2) observed during the fires at resolutions from 2.5 to 1.0 m across experimental burn plots ranging from 2.3 to 4.0 ha. Further postfire analysis using GIS produced a detailed spatial and temporal quantification of fireline intensity (kW/m) across the plot area. This type of infrared monitoring and analysis helps to support clearer assessment of relationships between fire behavior and ecological impacts. Such data permit accurate fire behavior estimates at various temporal and spatial scales rather than using an overall plot average. This method allows the sample size to be quite large, so that statistical analysis of the fire behavior data can provide an associated level of confidence.

Nonlinear pre-fire and post-fire analysis of steel frames

A numerical procedure based on the plastic hinge concept for study of the structural behaviour of steel framed structures exposed to fire is described. Most previous research on fire analysis considered the structural performance due to rising temperature. When strain reversal occurs during the cooling phase, the stress–strain curve is different. The plastic deformation is incorporated into the stress–strain curve to model the strain reversal effect in which unloading under elastic behaviour is allowed. This unloading response is traced by the incremental–iterative Newton–Raphson method. The mechanical properties of the steel member in the present fire analysis follows both Eurocode 3 Part 1.2 and BS5950 Part 8, which implicitly allow for thermal creep deformation. This paper presents an efficient fire analysis procedure for predicting thermal and cooling effects on an isolated element and a multi-storey frame. Several numerical and experimental examples related to structural behaviour in cooling phase are studied and compared with results obtained by other researchers. The proposed method is effective in the fire safety design and analysis of a building in a real fire scenario. The scope of investigation is of great significance since a large number of rescuers would normally enter a fire site as soon as the fire is extinguished and during the cooling phase, so a structural collapse can be catastrophic.

The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires

Using pre- and post-fire satellite imagery from SPOT2, we examined the fire severity and intensity of the Christmas 2001 wildfires in the greater Sydney Basin, Australia. We computed a Normalised Difference Vegetation Index (NDVI) from the two satellite images captured before (November 2001) and after (January 2002) the wildfires, then subtracted the later from the former to produce a difference image (NDVIdiff) which was subsequently classified into six fire severity classes (unburnt, low, moderate, high, very high and extreme severity). We then tested the fire severity classification on 342 sample sites within the 225 000ha fire affected area using a qualitative visual assessment guide. We found that the NDVIdiff classification produced an accuracy of at least 88% (K hat = 0.86), with the greatest discrepancy being between the low and moderate classification. Knowledge of rate of spread over some of the affected area, coupled with a complete knowledge of fuel loads, was used to retrospectively model fire intensity, which in areas of extreme fire intensity, produced heat energy levels exceeding 70 000 kW m–1. Importantly, we found no positive effect of topography on fire severity, in fact finding an inverse relationship between slope and fire severity and no effect due to aspect. Further analysis showed that flat to moderate slopes less than 18° across all aspects suffered the greatest vegetal destruction, and there was no relationship between north-westerly aspects and fire severity. We also introduce a relatively simple method for estimating fuel load biomass using a combination of satellite image and rapid field assessment. We found 79% accuracy for this method based on 125 sample sites. It is postulated that this type of analysis can greatly improve our understanding of the spatial impact of fire, how natural areas within the fire ground were impacted, and how remote sensing and GIS technologies can be efficiently used in fire management planning and post-fire analysis.