Live Fuel Moisture Content Research Articles

Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

Continental-scale prediction of live fuel moisture content using soil moisture information

Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM0-35cm. The annual cycles of LFMC and SM0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications.

Live fuel moisture content time series in Catalonia since 1998

Key messageWe present a structured and curated database covering 21 years of LFMC measurements in the Catalan region, along with an associated R package to manage updates and facilitate quality processing and visualisation. The data set provides valuable information to study plant responses to drought and improve fire danger prediction. Dataset access is athttps://doi.org/10.5281/zenodo.4675335, and associated metadata are available athttps://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/583fdbae-3200-4fa7-877c-54df0e6c5542.

Evaluation of microwave soil moisture data for monitoring live fuel moisture content (LFMC) over the coterminous United States

Live fuel moisture content (LFMC), which is the ratio of water in the fresh biomass to the dry biomass, is a key variable that affects wildfire behaviour. Previous studies have assessed soil moisture as a predictor of LFMC over small areas with limited data, but a comprehensive evaluation at sub-continental scale is still lacking, and the explanatory utility has not been evaluated under different aridity conditions. In this study, the utility was evaluated using microwave soil moisture data from the ESA ECV_SM product from 1979 to 2018 and LFMC data from over 1000 sites in the coterminous United States. A time-lagged robust linear regression model was adopted, and the results were compared with analysis from in situ soil moisture measurements at adjacent sites. The results suggested that at most sites the LFMC correlates best with soil moisture within 60 days prior to LFMC sampling, and that the correlation is lower in areas with complex terrain. LFMC can be estimated from soil moisture with a mean RMSE of around 20%. The correlation between LFMC and soil moisture is significant (p<0.01) in most regions, and is mostly stable in different years. The major fuel types with a high response to soil moisture include pine, redcedar, sagebrush, oak, manzanita, chamise, mesquite and juniper, depending on the region. The LFMC ~ soil moisture correlation varies with the aridity condition, and soil moisture has a higher explanatory utility on LFMC under dry conditions. An analysis using SMAP Level-4 product indicated that the surface and root-zone soil moisture perform similarly in LFMC estimation. This study suggests that microwave soil moisture data contain sufficient information on LFMC, and may serve as a reference for the development of more sophisticated LFMC estimation methods.

Modelling chamise fuel moisture content across California: a machine learning approach

Live fuel moisture content plays a significant and complex role in wildfire propagation. However, in situ historical and near real-time live fuel moisture measurements are temporally and spatially sparse within wildfire-prone regions. Routine bi-weekly sampling intervals are sometimes exceeded if the weather is unfavourable and/or field personnel are unavailable. To fill these spatial and temporal gaps, we have developed a daily gridded chamise (Adenostoma fasciculatum) live fuel moisture product that can be used, in conjunction with other predictors, to assess current and historical wildfire danger/behaviour. Chamise observations for 52 new- and 41 old-growth California sites from the National Fuel Moisture Database were statistically related to dynamically downscaled high-resolution weather predictors using a random forest machine learning model. This model captures reasonably well the temporal and spatial variability of chamise live fuel moisture content within California. Compared with observations, model-predicted live fuel moisture values have an overall R2, root mean squared error (RMSE) and bias of 0.79, 15.34% and 0.26%, respectively, for new growth and 0.63, 8.81% and 0.11% for old growth. Given the success of the model, we have begun to use it to produce daily forecasts of chamise live fuel moisture content for California utilities.

Quantitative assessment of grassland foliar moisture parameters as an inference on rangeland condition in the mesic rangelands of southern Africa

ABSTRACT Understanding Grass Water Content (GWC) is critical in assessing rangelands health as well as utilization rates and patterns. Spatial explicit information on grassland ecology, particularly in southern Africa remains rudimentary due to the lack of affordable data primary sources and robust user-friendly data processing algorithms. These are required to enable efficient and routine monitoring and assessment of GWC to inform policy and management. The Sentinel 2 Multispectral Instrument (MSI) in conjunction with environmental variables acquired at moderately high resolution, provide an attractive primary data source for determining grassland water content at landscape scale. This study, therefore, evaluated the utility of Sentinel 2 MSI data (e.g. Red-Edge (RE) and the Shortwave Infrared (SWIR) bands and environmental variables) in estimating GWC, herein referred to as Canopy Water Content (CWC), Dry Moisture Content (DMC), Canopy Storage Capacity (CSC), and Live Fuel Moisture Content (LFMC) in the Mesic rangelands of southern Africa. The results illustrated that both the RE and SWIR bands were critical in characterizing GWC variables in rangelands. Further, the best models for estimating CWC, DMC, CSC and LMFC yielded a relative root-mean-square error (rRMSE) of 20.8%, 19.5%, 9.3% and 18.5%, respectively. The most optimal variables for characterizing these plant selected variables included RE and SWIR bands centred at 740 nm (B6), 783 nm (B7), 1610 nm (B11) and 2190 nm (B12) as well as their derivatives in concert with environmental variables such as Topographic Wetness Index, maximum curvature and aspect. These findings are critical towards developing a robust, efficient and spatially explicit monitoring framework for southern African rangelands.

Spatial and temporal dynamics of live fuel moisture content in eastern Mediterranean woodlands are driven by an interaction between climate and community structure

The initiation and spread of wildfires depend, among others, on the live fuel moisture content (LFMC) in plants. Whereas the intrinsic and environmental drivers of LFMC in individual plants are fairly well understood, less is known about ecological interactions potentially affecting LFMC in diverse plant communities. Here, we asked how the interaction between climate and species diversity affects the spatial and temporal dynamics of LFMC in Mediterranean plant communities. We sampled LFMC in six plant communities located along a steep precipitation gradient in four monthly intervals and analysed how LFMC was affected by the structure of the community as well by site and climate conditions. LFMC varied greatly among species, and climate and species richness had an interactive effect on LFMC. On average, species richness had a strong negative effect on LFMC in sites with high rainfall, whereas in drier sites, richness had a small positive effect. Yet the effect of richness on LFMC varied across species, with some exhibiting a positive effect of richness on their LFMC. Individuals surrounded by more heterospecifics tended to have lower LFMC compared with ones surrounded by conspecifics. Our results highlight the interactive roles of climate and community characteristics on LFMC dynamics in eastern Mediterranean ecosystems.

Needle Senescence Affects Fire Behavior in Aleppo Pine (Pinus halepensis Mill.) Stands: A Simulation Study

Research Highlights: Pre-programmed cell death in old Aleppo pine needles leads to low moisture contents in the forest canopy in July, the time when fire activity nears its peak in the Western Mediterranean Basin. Here, we show, for the first time, that such needle senescence may increase fire behavior and thus is a potential mechanism explaining why the bulk of the annual burned area in the region occurs in early summer. Background and Objectives: The brunt of the fire season in the Western Mediterranean Basin occurs at the beginning of July, when live fuel moisture content is near its maximum. Here, we test whether a potential explanation to this conundrum lies in Aleppo pine needle senescence, a result of pre-programmed cell death in 3-years-old needles, which typically occurs in the weeks preceding the peak in the burned area. Our objective was to simulate the effects of needle senescence on fire behavior. Materials and Methods: We simulated the effects of needle senescence on canopy moisture and structure. Fire behavior was simulated across different phenological scenarios and for two highly contrasting Aleppo pine stand structures, a forest, and a shrubland. Wildfire behavior simulations were done with BehavePlus6 across a wide range of wind speeds and of dead fine surface fuel moistures. Results: The transition from surface to passive crown fire occurred at lower wind speeds under simulated needle senescence in the forest and in the shrubland. Transitions to active crown fire only occurred in the shrubland under needle senescence. Maximum fire intensity and severity were always recorded in the needle senescence scenario. Conclusions: Aleppo pine needle senescence may enhance the probability of crown fire development at the onset of the fire season, and it could partly explain the concentration of fire activity in early July in the Western Mediterranean Basin.

Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning

Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, dead FMC measurements are provided by a relatively sparse network of remote automatic weather stations (RAWS), while live FMC is relatively infrequently measured manually. We developed a high resolution, gridded, real-time FMC data sets that did not previously exist for assimilation into operational wildland fire prediction systems based on ML. We used surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Moderate Resolution Imaging Spectrometer Terra and Aqua reflectances are used to predict the live and dead FMC measured by the Wildland Fire Assessment System and RAWS). We evaluate multiple machine learning methods including multiple linear regression, random forests (RFs), gradient boosted regression and artificial neural networks. The models are trained to learn the relationships between the satellite reflectances, surface weather and soil moisture observations and FMC. After training on data corresponding to the temporally and spatially nearest grid points to the irregularly spaced surface FMC observations, the machine learning models could be applied to all grid cells for a gridded product over the Conterminous United States (CONUS). The results show generally that the rule-based approaches have the lowest errors likely due to the sharp decision boundaries among the predictors, and the RF approach that utilizes bagging to avoid over-fitting has the lowest error on the test dataset. The errors are typically between 25%−33% the typical variability of the FMC data, which indicate the skill of the RF in estimating the FMC based on satellite data and surface characteristics. The FMC gridded product based on the RF runs operationally daily over CONUS and can be assimilated into WRF-Fire for more accurate wildland fire spread predictions.

Linking Forest Flammability and Plant Vulnerability to Drought

Globally, fire regimes are being altered by changing climatic conditions. New fire regimes have the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Despite the co-occurrence of forest fires with drought, current approaches to modelling flammability largely overlook the large body of research into plant vulnerability to drought. Here, we outline the mechanisms through which plant responses to drought may affect forest flammability, specifically fuel moisture and the ratio of dead to live fuels. We present a framework for modelling live fuel moisture content (moisture content of foliage and twigs) from soil water content and plant traits, including rooting patterns and leaf traits such as the turgor loss point, osmotic potential, elasticity and leaf mass per area. We also present evidence that physiological drought stress may contribute to previously observed fuel moisture thresholds in south-eastern Australia. Of particular relevance is leaf cavitation and subsequent shedding, which transforms live fuels into dead fuels, which are drier, and thus easier to ignite. We suggest that capitalising on drought research to inform wildfire research presents a major opportunity to develop new insights into wildfires, and new predictive models of seasonal fuel dynamics.

Effects of firewood harvesting intensity on biodiversity and ecosystem services in shrublands of northern Patagonia

BackgroundForest management has historically focused on provisioning of goods (e.g. timber, biomass), but there is an increasing interest to manage forests also to maintain biodiversity and to provide other ecosystem services (ES).MethodsWe evaluated the effects of firewood harvesting intensity on biodiversity and different ES in three contrasting shrubland sites in northern Patagonia (Argentina). At each site, four harvesting treatments, representing various levels of harvest intensity, were randomly assigned to eight permanent sample plots of 31.5 m × 45 m during 2013–2014.ResultsWe found that the effects of increasing harvesting intensity on plant diversity changed from negative to positive (and from nonlinear to more linear responses) with increasing site productivity. Harvesting intensity showed contrasting effects on variables related to fire protection ecosystem service, since it reduced fuel amount (potentially reducing fire spread) but also reduced live fuel moisture content (potentially increasing flammability) at the three sites. Two variables related to soil formation and protection ES, leaf litter cover and aerial soil cover, decreased with harvesting intensity at the three sites.ConclusionsWe conclude that shrubland management for firewood production may enhance biodiversity without compromising certain important ES. The intensity of harvesting should be determined according to site conditions and forecasted impacts on biodiversity, fire and soil formation and protection.

Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R2 between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R2 = 0.49) than the best empirical model fitted with MCD43A4 images (R2 = 0.75). R2 between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R2 from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (&lt;80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.

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

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.

SAR-enhanced mapping of live fuel moisture content

Assessing wildfire risk presents several challenges due to uncertainty in fuel flammability and ignition potential. Live fuel moisture content (LFMC) - the mass of water per unit dry biomass in vegetation - exerts a direct control on fuel ignitability, fuel availability and fire spread, and is thus an important parameter in assessing wildfire risk. Current estimates of LFMC from optical remote sensing or meteorological indices are insufficient to accurately map LFMC at landscape scale. In this study, we present a physics-assisted recurrent neural network model for mapping LFMC every 15 days at 250 m resolution over the western US using microwave backscatter (from Sentinel-1) and optical reflectance (from Landsat-8). The model was physics-assisted by adding handcrafted inputs representing radiative transfer processes and related parameters. Field data from the National Fuel Moisture Database was used to train the deep learning model. The model had a satisfactory overall predictability of R2 = 0.63, RMSE = 25.0% and bias = 1.9% when cross-validated at 125 sites. The model was substantially better at predicting site-means (R2 = 0.71 and RMSE = 14.3%) as compared to site-anomalies (R2 = 0.55 and RMSE = 21.3%). Of the 6 land cover types tested, the model was most accurate in predicting LFMC in shrublands (R2 = 0.69), while closed broadleaf deciduous sites had the least accuracy (R2 = 0.49). Incorporating microwave backscatter as a model input significantly enhanced performance (R2 increasing from 0.44 to 0.63, and RMSE decreasing from 31.8% to 25.0%). Microwave backscatter therefore presents a complementary source of information to optical remote sensing metrics for LFMC mapping. The high resolution dynamic LFMC maps presented here may assist in improved wildfire risk characterization and also have potential applications in quantifying plant drought stress at large scales.

Utilizing Vegetation and Environmental New Micro Spacecraft (VENµS) Data to Estimate Live Fuel Moisture Content in Israel's Mediterranean Ecosystems

Increasing fire activity in Mediterranean ecosystems necessitates the development of new methods to quantify fire risk. Fire risk is strongly affected by live fuel moisture content (LFMC) in plants. Unfortunately, LFMC is time-consuming to measure in-situ . Remote sensing is a promising alternative to field sampling of LFMC, but existing approaches utilize sensors with high spatial resolution but infrequent revisit times, or frequent acquisition at coarse spatial grains. We developed and evaluated LFMC models for Israel's Mediterranean ecosystems using Vegetation and Environmental New Micro Spacecraft (VENµS), a satellite which was developed specifically for monitoring Mediterranean vegetation. We combined vegetation indices derived from VENµS with ancillary data to build statistical models of LFMC in six study sites located along a steep rainfall gradient. Out of the five vegetation indices we tested, only red-edge position was a significant predictor of LFMC, though its effect depended on tree cover. A model including red-edge position, tree cover, year-day, and slope-aspect explained 32.5% of the variation in LFMC. The moderate predictive power of this model was higher than expected given that VENµS does not have the shortwave infrared (SWIR) bands which are typically used to detect water content in plants. A comparison with six vegetation indices derived from Sentinel 2 data revealed that VENµS’ data explained considerably more variation in LFMC, even though some Sentinel 2 VI's are based on SWIR bands. Our results suggest that VENµS data, combined with ancillary data, may provide a rough estimate of LFMC in Israel's Mediterranean regions and as such might be suitable for preliminary monitoring purposes.

Protecting the Wildland-Urban Interface in California: Greenbelts vs Thinning for Wildfire Threats to Homes

This study utilized native chaparral and sage scrub shrubs planted in lightly irrigated greenbelts around homes to evaluate the impact on live fuel moisture content (LFMC) and predicted fire behavior. As to be expected LFMC varied markedly throughout the year being over 100% in winter in all species and treatments that included adjacent thinned native shrublands and untreated control shrublands. However, in the summer and fall there were marked differences between treatments. For most species lightly irrigated plants had the highest LFMC in the summer and fall, followed by thinned treatments and controls. These differences in moisture content coupled with structural differences in the vegetation contributed to expected differences in flame length and rate of spread. Lightly irrigated native shrubs planted around homes can reduce fire hazard while possessing other desirable features of utilizing native vegetation.

Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements

Operational forest fire danger rating systems rely on the recent evolution of meteorological variables to estimate dead fuel condition. Further combining the latter with meteorological and environmental variables, they predict fire occurrence and spread. In this study we retrieved live fuel condition from MODIS multispectral measurements in the near infrared and shortwave infrared. Next, we combined these retrievals with an extensive dataset on actual forest fires in Campania (13,595 km2), Italy, to determine how live fuel condition affects the probability distribution functions of fire characteristics. Accordingly, the specific objective of this study was to develop and evaluate a new approach to estimate the probability distribution functions of fire burned area, duration and rate of spread as a function of the Perpendicular Moisture Index (PMI), whose value decreases with decreasing live fuel moisture content (LFMC). To this purpose, available fire data was intersected with MODIS 8-day composited reflectance data so to associate each fire event with the corresponding pre-fire PMI observation. Fires were then grouped in ten decile bins of PMI, and the conditional probability distribution functions of burned area, fire duration and rate of spread were determined in each bin. Distributions of burned area and rate of spread vary across PMI decile bins, while no significant difference was observed for fire duration. Further testing this result with a likelihood ratio test confirmed that PMI is a covariate of burned area and rate of spread, but not of fire duration. We defined an extreme event as a fire whose burned area (respectively rate of spread) exceeds the 95th percentile of the frequency distribution of all observed fire events. The probability distribution functions in the ten decile bins of PMI were combined to obtain a conditional probability distribution function, which was then used to predict the probability of extreme fires, as defined. It was found that the probability of extreme events steadily increases with decreasing PMI. Overall, at the end of the dry season the probability of extreme events is about the double than at the beginning. These results may be used to produce frequently (e.g. daily) updated maps of the probability of extreme events given a PMI map retrieved from e.g. MODIS reflectance data.

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.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Development and participatory evaluation of fireline intensity and flame property models for managed burns on Calluna-dominated heathlands

BackgroundPrescribed burning plays an important role in the management of many ecosystems and can also be used to mitigate landscape-scale fire risk. Safe and effective application of prescribed fire requires that managers have a robust understanding of potential fire behavior in order to decide on the appropriate tools and tactics for any burning operation. Shrubland ecosystems, including heaths and moors, are known to exhibit intense fire behavior under marginal burning conditions under which fire would not be expected to spread in other vegetation types. This makes developing fire behavior predictions for such systems important. Traditional managed burning is widely used as a tool in Calluna vulgaris (L.) Hull-dominated heath and moorland landscapes in northwest Europe, but in some regions, especially the United Kingdom, there is significant debate over fire use. Despite the controversy, there is general agreement on the need to (1) understand relationships between fuel structure and potential fire behavior, and (2) improve burning practice to optimize potential trade-offs between different ecosystem services. Our aim was to provide knowledge to improve management practice by developing models of potential fireline intensity and flame length. We conducted 27 burns in three developmental stages of Calluna with different stand structures and estimated fireline intensity, flame length, flame height, and flame angle. Flame properties were assessed using photographs and visual observation. We evaluated our models using a participatory research approach for which conservation and land managers submitted basic observations on fire behavior and fire weather for their burns.ResultsFireline intensity and flame height increased significantly across age-related Calluna phases. Regression modeling revealed that fireline intensity could be adequately estimated by a combination of fuel height and wind speed, with taller fuels and higher wind speeds related to more intense fires. Predictions were, however, improved by accounting for live fuel moisture content. Flame length and height were modeled as a function of fireline intensity using standard approaches, but adequately performing models for flame angle could not be established. Evaluation data provided by land managers was noisy, but their qualitative assessments of fire behavior and estimates of flame length were significantly correlated with predictions from our models.ConclusionsFire intensities and flame properties seen in northern Calluna heathlands are similar to those encountered in shrublands associated with climates and fuels more commonly perceived as representing high fire danger. The results demonstrated that our models perform tolerably well although there is substantial uncertainty in their predictions. The models were used to develop a fire behavior nomogram that can provide an indication of potential fireline intensity and flame length prior to commencing a burn.