Fuel Moisture Research Articles

Flowering phenology indicates plant flammability in a dominant shrub species

Abstract Changes in plant phenology provide vital information on climatic shifts, species interactions, and ecological processes. By quantifying plant phenology, we reveal patterns of how plants are integrating environmental conditions and we use this information to inform conservation, policy, and management. In wildfire-prone regions of the world and especially California, a critical ecological process and management issue is wildfire disturbance. To gauge wildfire risk in these regions, scientists and managers monitor live fuel moisture (LFM), a proxy for plant flammability. We investigated the relationship between plant phenology and LFM for the widespread shrub species Adenostoma fasciculatum from two counties in Southern California over 6 years and analyzed the data in the context of LFM thresholds of wildfire risk. We found a correlation between transitions in reproductive phenology and LFM for A. fasciculatum. We also correlated reproductive phenology from the National Phenology Network with nearby LFM measurements from the National Fuel Moisture Database and found a similar relationship that spans the state of California. With simple observations of reproductive phenology, on-the-ground measurements of a dominant shrub species allow for rapid assessment of plant flammability with the potential for large spatial coverage. This study reveals how plant phenological patterns can inform us about ecological processes and potentially contribute to management decisions.

Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China

Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.

Assessment of resource-use strategy of some common woody plant species grown under semiarid climatic condition of Quetta Balochistan-Pakistan

The aim of this study was to evaluate the resource use strategy of some woody plants in semi-arid climatic condition of Quetta, Balochistan Pakistan. The plant sample were collected from eleven selected different woody plant species namely; Fraxinus xanthoxyloides, Robinia pseudoacacia, Ficus carica, Morus alba, Morus nigra, Ailanthus altissima, Melia azedarach, Pinus halepensis. Malus domestica, Prunus armeniaca and Tamarix sp. during three seasons spring, summer and autumn, in 2017-18. The resources-use strategy of the selected plant species was determined by commonly used indicators such as Leaf Relative Water Content (LRWC), Leaf Dry Matter Content (LDMC) and Live Fine Fuel Moisture Content (LFFMC). Data regarding annual average RWC, LDMC and LFFMC in all the investigated plant was found in ranged between 25.12-73.89 %, 0.21-0.40 mg/g and 58.0-221.0 mg/g, respectively. The maximum annual average RWC, LDMC and LFFMC was reported in Ficus carica, Pinus halepensis and Melia azedarach, while the minimum was found in Alianthus altissima, Fraxinus xanthoxyloides and Alianthus altissima, respectively. Among seasons; the highest RWC was found during spring which followed by summer and minimum was originated in autumn season. The higher LDMC was recorded in autumn season as compared to summer and spring, suggesting a more efficient conservation of nutrients and the maximum LFFM contents were found in autumn followed by summer and lowest was in spring. Keywords: Balochistan; Plant species; Relative water content; Live fine fuel moisture content; Dry matter content http://dx.doi.org/10.19045/bspab.2019.80127

THE POTENTIAL OF DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING IN 3D CANOPY FUEL MOISTURE CONTENT MAPPING

Abstract. Terrestrial laser scanning (TLS) instruments have been widely utilized in measuring vegetation canopy structural parameters, being capable of providing high density point clouds. However, less attention has been paid to using TLS intensity data in estimating vegetation biochemical attributes, and calculating water status metrics, that can help in early detection of vegetation stress and risk of wildfire. Water status metrics, such as the leaf Equivalent Water Thickness (EWT) and the Fuel Moisture Content (FMC), are being commonly estimated from optical remote sensing data. However, such estimates mainly reflect the water status of canopy top and ignore the vertical heterogeneity of water content distribution within the canopy. The estimates are also affected by canopy structure and understory reflectance. Such limitations can potentially be addressed using TLS intensity data, as observations are performed in three dimensions (3D). This study therefore investigated the potential of using dual-wavelength TLS intensity data to estimate FMC in 3D. The calculated Normalized Difference Index (NDI) of 808 nm near infrared and 1550 nm shortwave infrared wavelengths was found to be correlated to FMC at leaf level for four different tree species. The correlation was moderate, and the relationships were not consistent between species. NDI was subsequently used to estimate FMC at canopy level in seven trees in a small tree plot with an average error < 5 %. The 3D estimates of FMC revealed vertical heterogeneity in all trees measured, which varied between species and also between trees from the same species.

ESTIMACIÓN PRÁCTICA DE LA VARIABILIDAD DE LA HUMEDAD DE LOS COMBUSTIBLES BAJO DIFERENTES ECOSISTEMAS FORESTALES


 
 
 Para el uso de sistemas de simulación de comportamiento del fuego es necesario conocer, entre otros factores, el contenido de humedad de los combustibles forestales, ya que este influye en el precalentamiento, la ignición, la velocidad de propagación, la intensidad de la línea de fuego y en la producción de humo. Sin embargo, su cálculo es complicado, porque se deben considerar los cambios en las condiciones ambientales. Lo cual hace difícil su estimación rápida y precisa para fines operativos, o de toma de decisiones en el manejo del fuego. Por lo que el objetivo del presente trabajo fue la generación de modelos prácticos para el cálculo de los contenidos de humedad de combustibles forestales de 1-hora y de 10-horas. Estos basados en variaciones de humedad relativa y temperatura, que se monitorearon al aplicar quemas controladas en cuatro ecosistemas forestales (pino-encino, matorral-pino, pino y pastizal-pino). Los resultados sugieren que la humedad relativa es la variable que mejor estima el contenido de humedad de los combustibles (R2 = 0.955 para combustibles de 1-hora y R2 = 0.965 para combustibles de 10 horas). La información generada es útil para diversos propósitos, como la definición de áreas de riesgo y peligro de incendio, estimaciones del comportamiento del fuego, planeación de la implementación de quemas controladas, etc. Finalmente, se sugiere realizar estudios para evaluar las variaciones temporales del contenido de humedad de combustibles forestales.
 
 

Shifting States, Altered Fates: Divergent Fuel Moisture Responses after High Frequency Wildfire in an Obligate Seeder Eucalypt Forest

High frequency wildfires can shift the structure and composition of obligate seeder forests and initiate replacement with alternative vegetation states. In some forests, the alternative stable state is drier and more easily burned by subsequent fires, driving a positive feedback that promotes further wildfire and perpetuates alternative stable states. Mountain Ash (Eucalyptus regnans (F.Muell.)) forests are highly valued for their biodiversity, water, timber and carbon. Fires are a natural part of the lifecycle of these forests, but too frequent fires can eliminate Mountain Ash and trigger a transition to lower stature, non-eucalypt forests which are dominated by understorey species. This study sought to better understand the fuel moisture dynamics of alternative stable states resulting from high frequency wildfires. A vegetation mosaic in the Central Highlands, Victoria created a unique opportunity to measure fuel moisture in adjacent forest stands that differed in overstorey species composition and time since fire. Specifically, we measured fuel moisture and microclimate at two eucalypt sites (9 and 79 years old) and three non-eucalypt sites (two 9 year old and one 79 year old). Fuel availability, defined here as the number of days surface fuels were below 16% and dry enough to ignite and sustain fire, was calculated to estimate flammability. Fuel availability differed between sites, particularly as a function of time since fire, with recently burnt sites available to burn more often (4–17 versus 0–3 days). There were differences in fuel availability between non-eucalypt sites of the same age, suggesting that high frequency fire does not always lead to the same vegetation condition or outcome for fuel availability. This indicates there is potential for both positive and negative flammability feedbacks following state transition depending on the composition of the non-eucalypt state. This is the first study to provide empirical insight into the fuel moisture dynamics of alternative stable states in Mountain Ash forests.

Effects of Fuel Moisture Content on Emissions from a Rocket-Elbow Cookstove.

Exposure to air pollution from solid-fuel cookstoves is a leading risk factor for premature death; however, the effect of fuel moisture content on air pollutant emissions from solid-fuel cookstoves remains poorly constrained. The objective of this work was to characterize emissions from a rocket-elbow cookstove burning wood at three different moisture levels (5%, 15%, and 25% on a dry mass basis). Emissions of carbon dioxide (CO2), carbon monoxide (CO), methane, fine particulate matter (PM2.5), PM2.5 elemental carbon (EC), PM2.5 organic carbon, formaldehyde, acetaldehyde, benzene, toluene, ethylbenzene, and xylenes were measured. Emission factors (EFs; g·MJdelivered-1) for all pollutants, except CO2 and EC, increased with increasing fuel moisture content: CO EFs increased by 84%, PM2.5 EFs increased by 149%, formaldehyde EFs increased by 216%, and benzene EFs increased by 82%. Both modified combustion efficiency and the temperature at the combustion chamber exit decreased with increasing fuel moisture, suggesting that the energy required to vaporize water in the fuel led to lower temperatures in the combustion chamber and lower gas-phase oxidation rates. These results illustrate that changes in fuel equilibrium moisture content could cause EFs for pollutants such as PM2.5 to vary by a factor of 2 or more across different geographic regions.

Impacts of Bed Material Activation and Fuel Moisture Content on the Gasification Rate of Biomass Char in a Fluidized Bed

The use of certain bed materials has been Activated found to increase the steam gasification rate of biomass char. olivine The present work investigates how this phenomenon is influenced by different parameters (e.g., temperature, fuel type, and fuel moisture content), using a laboratory-scale bubbling fluidized bed gasifier. Silica sand, fresh olivine, and activated olivine were employed as bed materials, and three biomass fuels (wood chips, wood pellets, and forest residue pellets) were considered. Switching the bed material from silica sand to activated olivine resulted in a significant increase in the char gasification rate for all three fuels, with further increases noted as the fuel particle size was decreased. The observed effect was strongest (up to 4-fold) during the initial conversion phase (char gasification degrees < 20%) when the temperature was relatively low (<= 800 degrees C). The moisture content of the wood chips (0-40%) had no significant effect on the char gasification rate.

Nitrogen-Bearing Emissions From Burning Corn Straw in a Fixed-Bed Reactor: Effects of Fuel Moisture, Torrefaction, and Air Flowrate

Combustion-generated emissions of acid gases, such as nitrogen-bearing species, constitute environmental pollutants and some are subjected to environmental regulations. Assessment of such emissions is important to decide what systems need to be put in place for their control. This applies to both conventional fossil fuels and for alternative environmentally friendlier fuels, such as renewable biomass. This research investigated the emissions of nitrogen-bearing gases, which evolve from combustion of biomass (corn straw) in a fixed bed furnace, as a function of specific air flowrate (m˙air) through the bed and of moisture content of the fuel. The effect of torrefaction of corn straw on the combustion-generated nitrogen bearing emissions was also examined. The predominant nitrogen-bearing species in the combustion effluents were hydrogen cyanide (HCN), nitrogen oxide (NO), and ammonia (NH3). Increasing m˙air through the bed, to enhance the combustion rate, increased the emissions of HCN, NO, and NH3. As the m˙air through the bed increased by a factor of 5, the amounts of HCN, NO, and NH3 gases increased by factors of 3–4. As the moisture content of the biomass was reduced by drying, the combustion-generated emissions of NO increased mildly, whereas those of both NH3 and HCN decreased. Furthermore, the combustion-generated emissions of NO and NH3 from torrefied biomass were found to be higher than those from raw biomass. In contrast, the combustion-generated emissions of HCN from torrefied biomass were found to be lower than those generated from raw biomass.

Evaluation of Gridded Precipitation Data and Interpolation Methods for Forest Fire Danger Rating in Alberta, Canada

AbstractThe Canadian Forest Fire Weather Index System is the primary measurement of wildfire danger in Canada. Interpolating daily precipitation, one of the inputs for the Fire Weather Index System is a key challenge in areas without sufficient weather stations. This work evaluates the performance of gridded precipitation from the Canadian Precipitation Analysis (CaPA) System and six interpolation methods to achieve the best fire danger rating in Alberta, Canada. Results show that the CaPA System has only average performance due to limited radar coverage (10%) in the forested region; however, using the CaPA System as a covariate with regression kriging generates significantly better precipitation estimates. Ordinary kriging, regression kriging with elevation as a covariate, and the thin‐plate smoothed spline are methods with similar performance. Fuel moisture codes of the Fire Weather Index System respond differently to precipitation amounts due to differences in their time constants for drying. Fine fuels with a short drying time (Fine Fuel Moisture Code) are best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. Compacted organic fuels with longer drying times (Duff Moisture Code and Drought Code) are best estimated by regression kriging with CaPA because it better predicts significant precipitation events. The dense fire weather station network in our study area (~3.0 stations/10,000 km2) allows us to perform a sensitivity analysis, and we find that a threshold of &gt;0.5 stations/10,000 km2 is needed for regression kriging with CaPA to become appreciably better than the CaPA System.

Live Fuel Moisture Content: The ‘Pea Under the Mattress’ of Fire Spread Rate Modeling?

Currently, there is a dispute on whether live fuel moisture content (FMC) should be accounted for when predicting a real-world fire-spread rate (RoS). The laboratory and field data results are conflicting: laboratory trials show a significant effect of live FMC on RoS, which has not been convincingly detected in the field. It has been suggested that the lack of influence of live FMC on RoS might arise from differences in the ignition of dead and live fuels: flammability trials using live leaves subjected to high heat fluxes (80–140 kW m−2) show that ignition occurs before all of the moisture is vaporized. We analyze evidence from recent studies, and hypothesize that differences in the ignition mechanisms between dead and live fuels do not preclude the use of overall fine FMC for attaining acceptable RoS predictions. We refer to a simple theory that consists of two connected hypotheses to explain why the effect of live FMC on field fires RoS has remained elusive so far: H1, live tree foliage FMC remains fairly constant over the year; and H2, the seasonal variation of live shrubs’ FMC correlates with the average dead FMC. As a result, the effect of live FMC is not easily detected by statistical analysis.

Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system.

Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.

Influence of Fuel Moisture Content, Packing Ratio and Wind Velocity on the Ignition Probability of Fuel Beds Composed of Mongolian Oak Leaves via Cigarette Butts

Cigarette butts are an important human firebrand and account for a significant amount of man-made fires. To better address forest fires caused by cigarette butts, the influencing factors governing the ignition probability of cigarette butts can be used to establish a prediction model. This study obtains the influencing factors of the ignition probability of cigarette butts in order to establish a prediction model by constructing fuel beds composed of Mongolian oak leaves with varied fuel moisture content and packing ratios. A total of 2520 ignition experiments were then conducted by dropping cigarette butts on the fuel beds to test the burning probability of the fuels under varied wind speeds. Moisture content, wind speed, and their interaction significantly influenced ignition probability. In the absence of wind, the ignition probability is zero. The maximum moisture content of Mongolian oak leaves that could be ignited by cigarette butts was 15%. A logistic model and self-built model for predicting the ignition probability were established using these results; the mean absolute error values for the two models were 2.71% and 1.13%, respectively, and the prediction error of the self-built model was lower than that of the logistic model. This is important research to mitigate the threat of forest fires due to cigarette butts given the frequent occurrence of these events.

How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems

Live Fuel Moisture Content (LFMC) is a critical variable affecting fire ignition, behavior and severity in many ecosystems. Although the use of meteorological drought indices as proxies for LFMC is a straightforward and widespread approach, it is largely unknown whether it can provide reliable estimates of LFMC, either for local or spatial applications. We address this issue by evaluating the capacity of drought indices to predict LFMC quantitative variations and critical values. LFMC observations used for reference were measured on six different Mediterranean shrub species for 15 years in 20 different sites in Southern France. Six drought indices were evaluated: the Duff Moisture Code (DMC) and Drought Code (DC) of the Canadian Forest Fire Weather Index System, the Keetch-Byram Drought Index (KBDI), the Nesterov Index (NI) and the Relative Water Content (RWC) of the soil derived from a forest water balance model for low (80 mm) and high (160 mm) field capacities. The species were classified in two groups according to their seasonal variability: high and low responding species. We found large differences in the capacity of drought indices to predict LFMC, with indices that simulate long-term drought dynamics (DC, RWC and KBDI) generally performing better than others (NI and DMC). Once calibrated at stand scale, drought indices showed a good potential for predicting LFMC of high responding species, although large variations between sites were observed. In contrast, spatial predictability was limited with a RMSE and R2 on the order of 20% and 0.3, respectively (for high responding species). Our results suggest that drought indices should therefore be used with caution for spatial applications in wildfire research and operational fire management. Because they can explicitly consider environmental (soil, climate) and biological (species traits related to dehydration) factors, mechanistic indices have a great potential to improve LFMC predictions.

Modeling surface fuels moisture content in Pinus brutia stands

Fuel moisture content is an important variable for forest fires because it affects fuel ignition and fire behavior. In order to accurately predict fuel ignition potential, fuel moisture content must be assessed by evaluating fire spread, fireline intensity and fuel consumption. Our objective here is to model moisture content of surface fuels in normally stocked Calabrian pine (Pinus brutia Ten.) stands in relation to weather conditions, namely temperature, relative humidity, and wind speed in the Mugla province of Turkey. All surface fuels were categorized according to diameter classes and fuel types. Six fuel categories were defined: these were 0–0.3, 0.3–0.6, and 0.6–1 cm diameter classes, and cone, surface litter, and duff. Plastic containers 15 × 20 cm in size with 1 × 1 mm mesh size were used. Samples were taken from 09:00 to 19:00 h and weighed every 2 h with 0.01 g precision for 10 days in August. At the end of the study, samples were taken to the laboratory, oven-dried at 105 °C for 24 h and weighed to obtain fuel-moisture contents. Weather measurements were taken from a fully automated weather station set up at the study site prior to the study. Correlation and regression analyses were carried out and models were developed to predict fuel moisture contents for desorption and adsorption phase for each fuel type categories. Practical fuel moisture prediction models were developed for dry period. Models were developed that performed well with reasonable accuracy, explaining up to 92 and 95.6% of the variability in fuel-moisture contents for desorption and adsorption phases, respectively. Validation of the models were conducted using an independent data set and known fuel moisture prediction models. The predictive power of the models was satisfactory with mean absolute error values being 1.48 and 1.02 for desorption and adsorption as compared to the 2.05 and 1.60 values for the Van Wagner’s hourly litter moisture content prediction model. Results obtained in this study will be invaluable for fire management planning and modeling.

A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing

Fuel Moisture Content (FMC) is one of the primary drivers affecting fuel flammability that lead to fires. Satellite observations well-grounded with field data over the highly climatologically and ecologically diverse Australian region served to estimate FMC and flammability for the first time at a continental-scale. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 360 observations at 32 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland, and forest) close to those obtained elsewhere (r2 = 0.58, RMSE = 40%) but without site-specific calibration. Logistic regression models were developed to generate a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest. The models obtained an “Area Under the Curve” calculated from the Receiver Operating Characteristic plot method of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.

MODELOS MATEMÁTICOS DE PREVISÃO DO TEOR DE UMIDADE DOS MATERIAIS COMBUSTÍVEIS FLORESTAIS FINOS E MORTOS

O presente artigo busca descrever, por meio de uma revisão da literatura, os principais modelos matemáticos existentes para estimar o teor de umidade dos materiais combustíveis florestais finos e mortos, ou seja, os materiais da classe de 1-h de timelag, com base em variáveis meteorológicas. A determinação desses valores compreende uma importante informação para o delineamento de ações de prevenção e combate a incêndios florestais, e de realização de queimadas controladas, já que respondem pela probabilidade de ignição e comportamento do fogo. Com base na análise realizada, percebe-se que o Fine Fuel Moisture Code (FFMC), um dos componentes do Fire Weather Index (FWI) canadense, constitui o modelo de previsão do teor de umidade mais utilizado no mundo. Porém, considerando-se que trabalhos na literatura relatam limitações e imprecisão tanto no FFMC quanto nos demais modelos analisados nesse artigo, é essencial a validação dos mesmos antes de serem utilizados de forma operacional. Em função da pequena quantidade de estudos envolvendo essa temática no Brasil, recomenda-se a validação ou desenvolvimento de novos modelos, a fim de se aprimorar os programas de prevenção e de delineamento de risco de incêndios florestais em nível nacional.

Nondestructive Estimation of Standing Crop and Fuel Moisture Content in Tallgrass Prairie

Accurate estimation of standing crop and herbaceous fuel moisture content (FMC) are important for grazing management and wildfire preparedness. Destructive sampling techniques have been used to accurately estimate standing crop and FMC, but those techniques are laborious and time consuming. Existing nondestructive methods for estimating standing crop in tallgrass prairie have limitations, and few studies have examined nondestructive estimation techniques for FMC in this environment. Therefore, our objective was to develop robust models for nondestructive estimation of standing crop and FMC in tallgrass prairie. We calibrated and validated stepwise multiple linear regression (SMLR) and artificial neural network (ANN) models for standing crop and FMC using data collected in tallgrass prairies near Stillwater, Oklahoma. Day of year (DOY), canopy height (CH), Normalized Difference Vegetation Index (NDVI), and percent reflectance in five wavelength bands were candidate input variables for the models. The study spanned two growing seasons and nine patches located within three pastures under patch burn management, and the resulting data set with >3 000 observations was split randomly with 85% for model calibration and 15% withheld for validation. Standing crop ranged from 0 to 852 g m−2, and FMC ranged from 0% to 204%. With DOY, CH, and NDVI as predictors, the SMLR model for standing crop produced a root mean squared error (RMSE) of 119 g m−2 on the validation data, while the RMSE of the corresponding ANN model was 116 g m−2. With the same predictors, the SMLR model for FMC produced an RMSE of 26.7% compared with 23.8% for the corresponding ANN model. Thus, the ANN models provided better prediction accuracy but at the cost of added computational complexity. Given the large variability in the underlying datasets, the models developed here may prove useful for nondestructive estimation of standing crop and FMC in other similar grassland environments.

Seasonal Patterns and Drivers of Ashe Juniper Foliar Live Fuel Moisture and Relevance to Fire Planning

Foliar live fuel moisture (LFM)—the weight of water in living plant foliage expressed as a percentage of dry weight—typically affects fire behavior in live wildland fuels. In juniper communities, juniper LFM is important for planning prescribed burns and wildfire response but can be time consuming to obtain regularly. Also, there has been little analysis of the ways in which juniper LFM varies seasonally or is affected by weather conditions, soil moisture, or other variables such as drought index. Using an eight-year dataset of Ashe juniper (Juniperus ashei J. Buchholz) LFM observations from four sites in central Texas, USA, we found that the interannual variability of Ashe juniper LFM differs among seasons. Throughout the eight-year sample period, winter LFM fluctuated within a narrow range between about 80% and 120% and was weakly related to Keetch-Byram Drought Index (KBDI). During summer, LFM fluctuated widely and was more strongly related to KBDI. KBDI below 315 was positively related to LFM, while KBDI above 526 was strongly negatively related to LFM. For this region, we offer a KBDI of about 500 as a threshold for fire planning, above which the potential for critically low Ashe juniper LFM becomes more likely.