scholarly journals Fuel moisture content enhances nonadditive effects of plant mixtures on flammability and fire behavior

2015 ◽  
Vol 5 (17) ◽  
pp. 3830-3841 ◽  
Author(s):  
Luke G. Blauw ◽  
Niki Wensink ◽  
Lisette Bakker ◽  
Richard S. P. Logtestijn ◽  
Rien Aerts ◽  
...  
Keyword(s):  
Moisture Content ◽  
Fire Behavior ◽  
Fuel Moisture ◽  
Fuel Moisture Content ◽  
Nonadditive Effects

scholarly journals ESTIMATING FINE DEAD FUEL MOISTURE CONTENT UNDER EQUATORIAL CLIMATE CONDITIONS

FLORESTA ◽  
2021 ◽  
Vol 51 (3) ◽  
pp. 696
Author(s):  
Benjamin Leonardo Alves White ◽  
Maria Flaviane Almeida Silva
Keyword(s):  
Moisture Content ◽  
Fire Behavior ◽  
Great Influence ◽  
Fuel Moisture ◽  
Statistical Parameters ◽  
Fire Weather Index ◽  
Ignition Probability ◽  
Climate Conditions ◽  
Equatorial Climate ◽  
Fuel Moisture Content

The measurement of the fine dead fuel moisture content (FDFMC) is extremely important for forest fire prevention and suppression activities, as it has a great influence on the ignition probability and fire behavior. The Fine Fuel Moisture Code (FFMC) from the Fire Weather Index (FWI), is one of the most used models to estimate the FDFMC. Nevertheless, studies that assess the efficiency of this model in Brazil or in low latitude regions are rare. The present study aimed to evaluate the efficiency of the FFMC in an equatorial climate area and to develop a new model capable of estimating the FDFMC with greater precision. For this purpose, 861 random samples of fine dead fuel had their moisture content determined through oven drying. The obtained values were compared with those estimated by the FFMC and correlated with meteorological parameters to build a regression model. The results obtained show that the FDFMC was overestimated by the FFMC. The independent variables with the greatest influence on the FDFMC were, in decreasing order of significance: air relative humidity, air temperature, amount of rainfall in the last 24 hours and number of days without rainfall. The developed model presented good statistical parameters (r2 = 0.86; p <0.0001; RMSE = 0.22) and can be used, in areas with similar characteristics of the study area, to estimate the daily fire risk and to determine ideal conditions for prescribed burns.

scholarly journals Why is the Effect of Live Fuel Moisture Content on Fire Rate of Spread Underestimated in Field Experiments in Shrublands?

2018 ◽  
Author(s):  
Francois Pimont ◽  
Julien Ruffault ◽  
Nicolas Martin ◽  
Jean-Luc Dupuy
Keyword(s):  
Moisture Content ◽  
Field Experiments ◽  
Fire Behavior ◽  
Fire Season ◽  
Special Focus ◽  
Fuel Moisture ◽  
Power Functions ◽  
Random Measurement Error ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

Live fuel moisture content (LFMC) influences fire activity at landscape scale and fire behavior in laboratory experiments. However, field evidences linking LFMC to fire behavior are very limited despite numerous field experiments. In the present study, we reanalyze a shrubland fire dataset with a special focus on LFMC to explain this counterintuitive outcome. We found that this controversy might result from three reasons. First, the range of experimental LFMC &nbsp;data was too moist to reveal significant effect with the widespread exponential or power functions. Indeed, LFMC exhibited a strong effect below 100%, but marginal above this threshold, contrary to these functions. Second, we found that the LFMC significance was unlikely when the size of the dataset was smaller than 40. Finally, a complementary analysis suggested that 10 to 15% of random measurement error in variables could lead to an underestimation by 30&nbsp;% of the LFMC effect. The effect of LFMC in field experiments is thus stronger than previously reported in the range prevailing during the actual French fire season and in accordance with observations at different scales. This highlights the need to improve our understanding of the relationship between LFMC and fire behavior to refine fire danger predictions.

scholarly journals A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models

2020 ◽  
Vol 12 (11) ◽  
pp. 1714
Author(s):  
Mariano García ◽  
David Riaño ◽  
Marta Yebra ◽  
Javier Salas ◽  
Adrián Cardil ◽  
...  
Keyword(s):  
Time Series ◽  
Moisture Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Behavior ◽  
Burned Area ◽  
Fuel Moisture ◽  
Individual Site ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

Assessing the probability of sustained flaming in masticated fuel beds

2015 ◽  
Vol 45 (1) ◽  
pp. 68-77 ◽  
Author(s):  
T.J. Schiks ◽  
B.M. Wotton
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Fire Behavior ◽  
Fire Risk ◽  
Field Testing ◽  
Behavior Modeling ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Weather Index ◽  
Fuel Moisture Content

Mechanical mastication is increasingly used as a fuel management treatment to reduce fire risk at the wildland–urban interface, although ignition and fire behaviour in these novel fuel beds are poorly understood. We investigated the influence of observed fuel moisture content, wind speed, and firebrand size on the probability of sustained flaming of masticated fuel beds under both laboratory and field settings. Logistic regression techniques were applied to assess the probability of sustained flaming in both datasets. Models for the field were also developed using estimated moisture from three sets of weather-based models: (i) the hourly Fine Fuel Moisture Code (FFMC) from the Canadian Forest Fire Weather Index System, (ii) the National Fire Danger Rating System (NFDRS) moisture estimates for 1 h and 10 h fuels, and (iii) a masticated surface fuel moisture model (MAST). In both laboratory and field testing, the likelihood of a successful ignition increased with decreasing moisture content and increasing wind speed; the effect of firebrand size was only apparent in laboratory testing. The FFMC, NFDRS, and MAST predictions had somewhat reduced discriminative power relative to direct moisture in predicting the probability of sustained flaming based on our field observations. Our results speak to the disparity between the fire behaviour modeling that occurs in the laboratory and the fire behavior modeling that occurs in the field, as the methodology permitted comparison of predictions from sustained flaming models that were developed for one experimental setting and applied to the other.

scholarly journals Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5475 ◽  
Author(s):  
Juan Villacrés ◽  
Tito Arevalo-Ramirez ◽  
Andrés Fuentes ◽  
Pedro Reszka ◽  
Fernando Auat Cheein
Keyword(s):  
Moisture Content ◽  
Water Content ◽  
Eucalyptus Globulus ◽  
Fire Behavior ◽  
Risk Assessments ◽  
Moisture Loss ◽  
Spectral Signature ◽  
Fuel Moisture ◽  
Fuel Moisture Content ◽  
Different Levels

Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata—commonly found in Mediterranean WUI areas—which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave—associated with the water content—is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

2021 ◽  
Author(s):  
Chunquan Fan ◽  
Binbin He ◽  
Peng Kong ◽  
Hao Xu ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Fuel Moisture Content

scholarly journals Evaluating the Effects of Projected Climate Change on Forest Fuel Moisture Content

2014 ◽  
Vol 37 ◽  
pp. 65-69
Author(s):  
Kellen Nelson ◽  
Daniel Tinker
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Stand Structure ◽  
Canopy Cover ◽  
Fire Season ◽  
Climate Change Scenarios ◽  
Fuel Moisture ◽  
Mature Forest ◽  
Forest Fuel ◽  
Fuel Moisture Content

Understanding how live and dead forest fuel moisture content (FMC) varies with seasonal weather and stand structure will improve researchers’ and forest managers’ ability to predict the cumulative effects of weather on fuel drying during the fire season and help identify acute conditions that foster wildfire ignition and high rates of fire spread. No studies have investigated the efficacy of predicting FMC using mechanistic water budget models at daily time scales through the fire season nor have they investigated how FMC may vary across space. This study addresses these gaps by (1) validating a novel mechanistic live FMC model and (2) applying this model with an existing dead FMC model at three forest sites using five climate change scenarios to characterize how FMC changes through time and across space. Sites include post-fire 24-year old forest, mature forest with high canopy cover, and mature forest affected by the mountain pine beetle with moderate canopy cover. Climate scenarios include central tendency, warm/dry, warm/wet, hot/dry, and hot/wet.

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