Search Results (9)

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest > extreme gradient boosting > support vector machine > stepwise linear regression > k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings. Full article

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

The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R²) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R² value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China. Full article

Landscape fires are substantial sources of (greenhouse) gases and aerosols. Fires in savanna landscapes represent more than half of global fire carbon emissions. Quantifying emissions from fires relies on accurate burned area, fuel load and burning efficiency data. Of these, fuel load remains the source of the largest uncertainty. In this study, we used high spatial resolution images from an Unmanned Aircraft System (UAS) mounted multispectral camera, in combination with meteorological data from the ERA-5 land dataset, to model instantaneous pre-fire above-ground biomass. We constrained our model with ground measurements taken in two locations in savanna-dominated regions in Southern Africa, one low-rainfall region (660 mm year${}^{-1}$) in the North-West District (Ngamiland), Botswana, and one high-rainfall region (940 mm year${}^{-1}$) in Niassa Province (northern Mozambique). We found that for fine surface fuel classes (live grass and dead plant litter), the model was able to reproduce measured Above-Ground Biomass (AGB) (R${}^2$ of 0.91 and 0.77 for live grass and total fine fuel, respectively) across both low and high rainfall areas. The model was less successful in representing other classes, e.g., woody debris, but in the regions considered, these are less relevant to biomass burning and make smaller contributions to total AGB. Full article

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. Full article

There is concern that forest management activities such as chemical thinning may increase hazardous fuel loading and therefore increase risk of stand-replacing wildfire. Chemical thinning, often accomplished by frill treatment of unwanted trees, leaves trees standing dead for a time before they fall and become surface fuels. In coastal northern California, frill treatment is used as a forest rehabilitation treatment that removes tanoak (Notholithocarpus densiflorus) to release merchantable conifers from excessive competition. We studied fuel bed depth and fuel loading after frill treatment of tanoak along a 16-year chronosequence that substituted space for time. The total depth of fuel bed was separated into woody fuels, litter, and duff. The height of each layer was variable and greatest on average in post-treatment year 5 after treated tanoak had begun to break apart and fall. Initially, the evergreen tanoak trees retained their foliage for at least a year after treatment. Five years after treatment, many tanoak had fallen and transitioned to become fine- and coarse woody debris. After 11 years, the larger pieces of down wood were mostly classified as rotten. After 16 years, the fuel loading appeared roughly equivalent to pre-treatment levels, however we did not explicitly test for differences due to potential confounding between time and multiple factors such as inter-annual climate variations and site attributes. Nevertheless, our data provide some insight into changes in surface fuel characteristics due to rehabilitation treatments. These data can be used as inputs for fire behavior modeling to generate indicative predictions of fire effects such as fire severity and how these change over time since treatment. Full article

Fire regimes are ultimately controlled by wildland fuel dynamics over space and time; spatial distributions of fuel influence the size, spread, and intensity of individual fires, while the temporal distribution of fuel deposition influences fire’s frequency and controls fire size. These “shifting fuel mosaics” are both a cause and a consequence of fire regimes. This paper synthesizes results from two major fuel dynamics studies that described the spatial and temporal variability of canopy and surface wildland fuel characteristics found in US northern Rocky Mountain forests. Eight major surface fuel components—four downed dead woody fuel size classes (1, 10, 100, 1000 h), duff, litter, shrub, and herb—and three canopy fuel characteristics—loading, bulk density and cover—were studied. Properties of these fuel types were sampled on nested plots located within sampling grids to describe their variability across spatiotemporal scales. Important findings were that fuel component loadings were highly variable (two to three times the mean), and this variability increased with the size of fuel particles. The spatial variability of loadings also varied by spatial scale with fine fuels (duff, litter, 1 h, 10 h) varying at scales of 1 to 5 m; coarse fuels at 10 to 150 m, and canopy fuels at 100 to 600 m. Fine fuels are more uniformly distributed over both time and space and decayed quickly, while large fuels are rare on the landscape but have a high residence time. Full article

Fire severity varies with forest composition and structure, reflecting micrometeorology and the fuel complex, but their respective influences are difficult to untangle from observation alone. We quantify the differences in fire weather between different forest types and the resulting differences in modeled fire behavior. Collection of in-stand weather data proceeded during two summer periods in three adjacent stands in northern Portugal, respectively Pinus pinaster (PP), Betula alba (BA), and Chamaecyparis lawsoniana (CL). Air temperature, relative humidity and wind speed varied respectively as CL < PP < BA, PP < CL < BA, and CL < BA < PP. Differences between PP and the other types were greatest during the warmest and driest hours of the day in a sequence of 10 days with high fire danger. Estimates of daytime moisture content of fine dead fuels and fire behavior characteristics for this period, respectively, from Behave and BehavePlus, indicate a CL < BA < PP gradient in fire potential. High stand density in CL and BA ensured lower wind speed and higher fuel moisture content than in PP, limiting the likelihood of an extreme fire environment. However, regression tree analysis revealed that the fire behavior distinction between the three forest types was primarily a function of the surface fuel complex, and more so during extreme fire weather conditions. Full article

Fire is nearly ubiquitous in the terrestrial biosphere, with profound effects on earth surface carbon storage, climate, and forest functions. Fuel quality is an important parameter determining forest fire behavior, which differs among both tree species and organs. Fuel quality is not static: when dead plant material decomposes, its structural, chemical, and water dynamic properties change, with implications for fuel flammability. However, the interactions between decomposition and flammability are poorly understood. This study aimed to determine decomposition’s effects on fuel quality and how this directly and indirectly affects wood flammability. We did controlled experiments on water dynamics and fire using twigs of four temperate tree species. We found considerable direct and indirect effects of decomposition on twig flammability, particularly on ignitability and burning time, which are important variables for fire spread. More decomposed twigs ignite and burn faster at given water content. Moreover, decomposed twigs dry out faster than fresh twigs, which make them flammable sooner when drying out after rain. Decomposed fine woody litters may promote horizontal fire spread as ground fuels and act as a fuel ladder when staying attached to trees. Our results add an important, previously poorly studied dynamic to our understanding of forest fire spread. Full article